Lysine Acetylome of Breast Cancer-Derived Small Extracellular Vesicles Reveals Specific Acetylation Patterns for Metabolic Enzymes
Bibliographic record
Abstract
Cancer-derived small extracellular vesicles have been proposed as promising potential biomarkers for diagnosis and prognosis of breast cancer (BC). We performed a proteomic study of lysine acetylation of breast cancer-derived small extracellular vesicles (sEVs) to understand the potential role of the aberrant acetylated proteins in the biology of invasive ductal carcinoma and triple-negative BC. Three cell lines were used as models for this study: MCF10A (non-metastatic), MCF7 (estrogen and progesterone receptor-positive, metastatic) and MDA-MB-231 (triple-negative, highly metastatic). For a comprehensive protein acetylation analysis of the sEVs derived from each cell line, acetylated peptides were enriched using the anti-acetyl-lysine antibody, followed by LC-MS/MS analysis. In total, there were 118 lysine-acetylated peptides, of which 22, 58 and 82 have been identified in MCF10A, MCF7 and MDA-MB-231 cell lines, respectively. These acetylated peptides were mapped to 60 distinct proteins and mainly identified proteins involved in metabolic pathways. Among the acetylated proteins identified in cancer-derived sEVs from MCF7 and MDA-MB-231 cell lines are proteins associated with the glycolysis pathway, annexins and histones. Five acetylated enzymes from the glycolytic pathway, present only in cancer-derived sEVs, were validated. These include aldolase (ALDOA), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), phosphoglycerate kinase (PGK1), enolase (ENO) and pyruvate kinase M1/2 (PKM). For three of these enzymes (ALDOA, PGK1 and ENO) the specific enzymatic activity was significantly higher in MDA-MB-231 when compared with MCF10A-derived sEVs. This study reveals that sEVs contain acetylated glycolytic metabolic enzymes that could be interesting potential candidates for early BC diagnostics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".