Mining the Proteome of Human Ovarian Cancer Extracellular Vesicles Using Thermolysin Proteolysis

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.