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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".