Mining the Proteome of Human Ovarian Cancer Extracellular Vesicles Using Thermolysin Proteolysis
Bibliographic record
Abstract
Abstract This study investigates the utility of Thermolysin as a proteolytic enzyme to enhance the depth and coverage of proteomic analysis in ovarian cancer (OC) extracellular vesicles (EVs). EVs were isolated from OC cell lines and ascites fluid samples from women diagnosed with high-grade serous carcinoma. Proteins were digested using Thermolysin and Trypsin/LysC, followed by label-free data-dependent acquisition (DDA) and data-independent acquisition (DIA) mass spectrometry. The proteolytic efficiency, sequence coverage, peptide complexity, and proteomic depth were compared between Thermolysin and Trypsin/LysC digests. In silico analyses predicted theoretical benchmarks of these parameters using a core set of 22 proteins, and Gene set enrichment analyses (GSEA) highlighted the biological relevance of proteins identified throughout the study. Thermolysin digestion significantly increased the complexity or of peptide pools compared to Trypsin/LysC leading to limited peptide and protein identification, albeit total sequence coverage was increased through complementation to tryptic peptides. In both cell line and ascites EVs, Thermolysin identified unique proteins not detected by Trypsin/LysC that are known drivers of metastatic solid cancers, such as Ly6E. Offline strong cation exchange (SCX) fractionation improved proteomic depth and sequence coverage obtained with Thermolysin to generate a spectral library for DIA. Our DIA analysis of Thermolysin digests revealed the presence of NODAL in OC ascites EVs, a protein associated with poor clinical prognosis, which was not detected in Trypsin/LysC digests. The importance of NODAL was cross-validated in TGCA-OV and AOCS datasets by clinical cohorts by assessing RNA levels in solid tumors or ascites fluid, respectively. Collectively, we demonstrate that Thermolysin complements traditional enzymes like Trypsin/LysC to provide a more comprehensive proteomic landscape for biomarker discovery.
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 | 0.001 | 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) | 0.000 | 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".