A new strategy for identifying polysialylated proteins reveals they are secreted from cancer cells as soluble proteins and as part of extracellular vesicles
Bibliographic record
Abstract
Abstract Polysialic acid (polySia) is a long homopolymer consisting of α2,8-linked sialic acid with tightly regulated expression in humans. In healthy adults, it occurs on cell surface glycoproteins in neuronal, reproductive, and immune tissues; however, it is aberrantly present in many cancers and its overexpression correlates with significantly increased metastasis and poor prognosis. Prompted by the observation that the MCF-7 breast cancer cell line contains only intracellular polySia, we investigated the secretion of polySia from MCF-7 cells. PolySia was found predominantly on soluble proteins in MCF-7 conditioned media, but also on extracellular vesicles (EVs), secreted from the cells. Since MCF-7 cells do not express known polysialylated proteins, we developed a robust method for purifying polysialylated proteins that uses a metabolic labelling strategy to introduce a bioorthogonal functionality into polySia. Using this method we identified three previously unknown polysialylated proteins, and found that two of these proteins - AGR2 and QSOX2 – were secreted from MCF-7 cells. We confirmed that QSOX2 found in EV-depleted MCF-7 cell conditioned media was polysialylated. Herein we report the secretion of polysialic acid on both soluble and EV-associated proteins from MCF-7 cancer cells and introduce a new method to efficiently identify polysialylated proteins. These findings have exciting implications for understanding the roles of polySia in cancer progression and metastasis and for identifying new cancer biomarkers.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".