Publication date: May 24, 2025
Post-COVID syndrome affects 10-35 % of COVID-19 patients, and up to 85 % of hospitalized individuals, underscoring the need for early identification of high-risk cases. We hypothesized that the proteomic profile of serum small extracellular vesicles (sEVs) obtained during acute SARS-CoV-2 infection could predict post-COVID syndrome. Serum samples from 59 patients, stratified as asymptomatic, moderate, or severe, were analyzed. sEVs were isolated, characterized by electron microscopy, nanoparticle tracking, and flow cytometry, then profiled via LC-MS. Classification models integrating comorbidities, acute symptoms, and sEV proteomics distinguished the three groups, with sEV data outperforming conventional measures. Of 620 identified proteins, 30 showed significant differences between symptomatic and asymptomatic patients, including 12 linked to complement activation. ELISA confirmed LC-MS results that serum sEVs of post-COVID patients had altered C1 inhibitor, C3, and C5 levels. These results suggest that sEV-based proteomics can enable earlier detection and more targeted follow-up for individuals at risk of post-COVID syndrome.
| Concepts | Keywords |
|---|---|
| Immunol | Biomarkers |
| Models | Complement system |
| Nanoparticle | Extracellular vesicles |
| Post-COVID syndrome | |
| Proteomics |
Semantics
| Type | Source | Name |
|---|---|---|
| disease | MESH | syndrome |
| disease | MESH | COVID-19 |
| pathway | REACTOME | SARS-CoV-2 Infection |
| drug | DRUGBANK | Human C1-esterase inhibitor |