Dynamic Phosphoproteomics Uncovers Signaling Pathways Modulated by Anti-oncogenic Sphingolipid Analogs
Bibliographic record
Abstract
The anti-neoplastic sphingolipid analog SH-BC-893 starves cancer cells to death by down-regulating cell surface nutrient transporters and blocking lysosomal trafficking events. These effects are mediated by the activation of protein phosphatase 2A (PP2A). To identify putative PP2A substrates, we used quantitative phosphoproteomics to profile the temporal changes in protein phosphorylation in FL5.12 cells following incubation with SH-BC-893 or the specific PP2A inhibitor LB-100. These analyses enabled the profiling of more than 15,000 phosphorylation sites, of which 958 sites on 644 proteins were dynamically regulated. We identified 114 putative PP2A substrates including several nutrient transporter proteins, GTPase regulators ( e.g. Agap2, Git1), and proteins associated with actin cytoskeletal remodeling ( e.g. Vim, Pxn). To identify SH-BC-893-induced cell signaling events that disrupt lysosomal trafficking, we compared phosphorylation profiles in cells treated with SH-BC-893 or C2-ceramide, a non-vacuolating sphingolipid that does not impair lysosomal fusion. These analyses combined with functional assays uncovered the differential regulation of Akt and Gsk3b by SH-BC-893 (vacuolating) and C2-ceramide (non-vacuolating). Dynamic phosphoproteomics of cells treated with compounds affecting PP2A activity thus enabled the correlation of cell signaling with phenotypes to rationalize their mode of action.
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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".