Optimized Production of Virus-like Particles in a High-CHO-Cell-Density Transient Gene Expression System for Foot-and-Mouth Disease Vaccine Development
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Foot-and-mouth disease virus (FMDV) poses a continuous threat to livestock health and agricultural economies. Current vaccines require high biosafety standards and are costly to produce. While novel vaccine technologies have been explored, most fail to meet industrial scalability, cost-efficiency, or multiserotype flexibility required for effective FMD control. This study aimed to evaluate the feasibility of using a high-cell density transient gene expression (TGE) system in CHO cells for the production of FMDV virus-like particles (VLPs) as a recombinant vaccine platform. METHODS: VLP expression was optimized by adjusting cDNA and polyethyleneimine (PEI) concentrations. Expression yields were compared at 24 and 48 h post-transfection to determine optimal harvest timing. We further tested the system's capacity to express different serotypes and chimeric constructs, incorporating VP1 sequences from various FMDV strains. Immunogenicity was evaluated in swine using VLPs from the A2001 Argentina strain as a model. RESULTS: titer 1.43) and liquid-phase blocking ELISA (LPBE, titer 2.20) at 28 days post-vaccination (dpv). Titers remained above protective thresholds up to 60 dpv with a single dose. A booster at 28 dpv further elevated titers to levels comparable to those induced by the inactivated vaccine. CONCLUSIONS: Our results demonstrate the feasibility of using CHO cell-based TGE for producing immunogenic FMDV VLPs. This platform shows promise for scalable, cost-effective, and biosafe development of recombinant FMD vaccines.
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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 | 0.001 | 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 | 0.001 | 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".