Mucosal chemokine adjuvant supports heterologous protection against Delta and Omicron VOCs in the context of a SARS-CoV-2 wild-type DNA vaccine
Bibliographic record
Abstract
Abstract Emerging variants of concern (VOCs) and waning vaccine-induced immunity challenge SARS-CoV-2 control. Directing vaccine-induced humoral and cell-mediated responses to mucosal surfaces could enhance vaccine efficacy and potentially reduce transmission. We immunized mice with DNA plasmids expressing a wild-type SARS-CoV-2 spike protein (pS) alone, or with the DNA-encoded mucosal chemokine adjuvant cutaneous T cell-attracting chemokine (pCTACK). Mice co-immunized with pS and pCTACK exhibited increased spike specific IFNy T cell responses compared to those receiving the pS alone. While both groups showed similar levels of spike receptor binding domain (RBD)-specific IgG in their serum, in the mucosa, co-immunization with pCTACK enhanced RBD-specific IgA, and cecal extracts from pCTACK co-immunized animals neutralized pseudotyped viruses. When ACE2 transgenic mice were immunized, rested for 3 months, and challenged with a heterologous SARS-CoV-2 Delta variant, co-immunization with pCTACK supported 100% survival while immunization with wild-type pS antigen alone supported 60% survival. Enhanced protection from morbidity was also observed as 100% of pCTACK co-immunized animals did not exhibit infection-induced weight loss. When animals were immunized similarly and challenged with Omicron BA.2, pCTACK co-delivery resulted insignificantly decreased viral loads compared to pS-only immunization. These studies demonstrate that mucosal chemokine adjuvants can direct vaccine-induced responses to specific immunological sites with significant impact on heterologous challenge. These data have significant implications for the design of improved vaccines targeting SARS-CoV-2. This work was supported by NIH NCI award T32 CA09171 (ENG) and NIH NIAID award T32-AI-055400 (EMP) and The Wistar Institute coronavirus discovery fund with additional support from CEPI/Inovio Pharmaceuticals.
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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".