Lipid nanoparticle encapsulation of a Delta spike-CD40L DNA vaccine improves effectiveness against Omicron challenge in Syrian hamsters
Bibliographic record
Abstract
The effectiveness of mRNA vaccines largely depends on their lipid nanoparticle (LNP) component. Herein, we investigate the effectiveness of DLin-KC2-DMA (KC2) and SM-102-based LNPs for the intramuscular delivery of a plasmid encoding B.1.617.2 (Delta) spike fused with CD40 ligand. LNP encapsulation of this CD40L-adjuvanted DNA vaccine with either LNP formulation drastically enhanced antibody responses, enabling neutralization of heterologous Omicron variants. The DNA-LNP formulations provided excellent protection from homologous challenge, reducing viral replication, and preventing histopathological changes in the pulmonary tissues. Moreover, the DNA-LNP vaccines maintained a high level of protection against heterologous Omicron BA.5 challenge despite a reduced neutralizing response. In addition, we observed that DNA-LNP vaccination led to the pulmonary downregulation of interferon signaling, interleukin-12 signaling, and macrophage response pathways following SARS-CoV-2 challenge, shedding some light on the mechanisms underlying the prevention of pulmonary injury. These results highlight the potential combination of molecular adjuvants with LNP-based vaccine delivery to induce greater and broader immune responses capable of preventing inflammatory damage and protecting against emerging variants. These findings could be informative for the future design of both DNA and 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".