Quantitative proteomics identifies novel PIAS1 substrates involved in cell migration and motility
Bibliographic record
Abstract
ABSTRACT The Protein Inhibitor of Activated STAT 1 (PIAS1) is an E3 SUMO ligase that plays important roles in various cellular pathways, including STAT signaling, p53 pathway, and the steroid hormone signaling pathway. PIAS1 can SUMOylate PML (at Lys-65 and Lys-160) and PML-RARα promoting their ubiquitin-mediated degradation. Increasing evidence shows that PIAS1 is overexpressed in various human malignancies, including prostate and lung cancers. To understand the mechanism of action of PIAS1, we developed a quantitative SUMO proteomic approach to identify potential substrates of PIAS1 in a system-wide manner. Our analyses enabled the profiling of 983 SUMO sites on 544 proteins, of which 204 SUMO sites on 123 proteins were identified as putative PIAS1 substrates. These substrates are involved in different cellular processes, such as transcriptional regulation, DNA binding and cytoskeleton dynamics. Further functional studies on Vimentin (VIM), a type III intermediate filament protein involved in cytoskeleton organization and cell motility, revealed that PIAS1 exerts its effects on cell migration and cell invasion through the SUMOylation of VIM at Lys-439 and Lys-445 residues. VIM SUMOylation was necessary for its dynamic disassembly, and cells expressing a non-SUMOylatable VIM mutant showed reduced levels of proliferation and migration. Our approach not only provides a novel strategy for the identification of E3 SUMO ligase substrates, but also yields valuable biological insights into the unsuspected role of PIAS1 and VIM SUMOylation on cell motility.
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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 | 0.001 | 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".