Mammalian cells-based platforms for the generation of SARS-CoV-2 virus-like particles
Bibliographic record
Abstract
Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of COVID-19. Though many COVID-19 vaccines have been developed, most of them are delivered via intramuscular injection and thus confer relatively weak mucosal immunity against the natural infection. Virus-Like Particles (VLPs) are self-assembled nanostructures composed of key viral structural proteins, that mimic the wild-type virus structure but are non-infectious and non-replicating due to the lack of viral genetic material. In this study, we efficiently generated SARS-CoV-2 VLPs by co-expressing the four SARS-CoV-2 structural proteins, specifically the membrane (M), small envelope (E), spike (S) and nucleocapsid (N) proteins. We show that these proteins are essential and sufficient for the efficient formation and release of SARS-CoV-2 VLPs. Moreover, we used lentiviral vectors to generate human cell lines that stably produce VLPs. Because VLPs can bind to the virus natural receptors, hence leading to entry into cells and viral antigen presentation, this platform could be used to develop novel vaccine candidates that are delivered intranasally. Highlights Identification of protein requirements for SARS-CoV-2 VLP production by transient transfection Lentiviral transduction to create cells stably producing SARS-CoV-2 VLPs Isolation of cell clones for the production of SARS-CoV-2 VLPs New putative platforms for vaccine development
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".