Quantitative proteomics and biological activity of extracellular vesicles engineered to express SARS‐CoV‐2 spike protein
Bibliographic record
Abstract
SARS-CoV-2 viral infection led to the devastating COVID-19 pandemic, where illness stemmed from interactions between virions and recipient host cells resulting in multi-layered pathological consequences. The role of the infection portal is now understood to be the cellular angiotensin converting enzyme-2 (ACE2) receptor, which binds to viral spike (S) protein initiating virion internalisation process. Since SARS-CoV-2 virions bear some resemblance to endogenously produced small extracellular vesicles (sEVs) we reasoned that EVs engineered to express S protein (viral mimics) may interfere with viral infection. Here, we report generation of HEK293T cells producing sEVs enriched for transmembrane S-protein tagged with green fluorescent protein (S/GFP). Strikingly, S protein drove the GFP tag to the membrane of sEVs, while GFP alone was not efficiently included in the sEV cargo. High-throughput quantitative proteomics revealed that S/GFP sEVs contained over 1000 proteins including canonical components of the exosomal pathway such as ALIX, syntenin-1, and tetraspanins (CD81, CD9), but depleted for calnexin and cytochrome c. We found that 84 sEV proteins were significantly altered by the presence of S/GFP. S protein expressing EVs efficiently adhered to target cells in an ACE2-dependent manner, but they were poorly internalised. Importantly, prolonged administration of S/GFP EV to K18-hACE2 mice provided a significant protection against SARS-CoV-2 infection. Thus, the generation of sEV containing S protein can be considered as a novel therapeutic approach in reducing the transmission of SARS-CoV-2.
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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) | 0.000 | 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".