Lipid nanoparticle encapsulation enhances the immunogenicty of a SARS-CoV-2 Delta variant-based DNA vaccine and enables protection against heterologous omicron subvariants
Bibliographic record
Abstract
Abstract mRNA vaccines were instrumental in limiting the extent of the COVID-19 pandemic. Despite being presented as an alternative to mRNA vaccines, DNA vaccines have not achieved similar levels of clinical success. We evaluated the immunogenicity and protective efficacy of a CD40L-adjuvanted Spike DNA vaccine based on the B.1.617.2 (Delta) variant when encapsulated in lipid nanoparticle formulations, created using DLin-KC2-DMA and SM-102 ionizable lipids. Syrian hamsters were vaccinated twice intramuscularly with unformulated or LNP-encapsulated DNA vaccines before being challenged with homologous or heterologous BA.5 (Omicron) strains of SARS-CoV-2. LNP encapsulation enhanced immune responses following one and two doses, increasing binding and neutralizing antibody titers. LNP encapsulation also enabled the neutralization of multiple omicron sub variants. LNP encapsulation also provided greater protection from challenge with both Delta and Omicron strains of SARS-CoV-2, reducing weight loss and suppressing viral replication in the upper and lower respiratory tract. This reduced viral burden coincided with the prevention of lung pathology. These results highlight the increased effectiveness of DNA vaccines afforded using similar delivery vehicles as those found in commercial mRNA vaccine formulations. Further studies are required to fully understand the safety and tolerability of this approach, as well as to directly compare the DNA formulations with mRNA 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 | 0.000 | 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) | 0.001 | 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".