Adenosine deaminase augments SARS-CoV-2 specific cellular and humoral responses in aged mouse models of immunization and challenge
Bibliographic record
Abstract
Despite numerous clinically available vaccines and therapeutics, aged patients remain at increased risk for COVID-19 morbidity. Furthermore, various patient populations, including the aged can have suboptimal responses to SARS-CoV-2 vaccine antigens. Here, we characterized vaccine-induced responses to SARS-CoV-2 synthetic DNA vaccine antigens in aged mice. Aged mice exhibited altered cellular responses, including decreased IFNγ secretion and increased TNFα and IL-4 secretion suggestive of TH2-skewed responses. Aged mice exhibited decreased total binding and neutralizing antibodies in their serum but significantly increased TH2-type antigen-specific IgG1 antibody compared to their young counterparts. Strategies to enhance vaccine-induced immune responses are important, especially in aged patient populations. We observed that co-immunization with plasmid-encoded adenosine deaminase (pADA)enhanced immune responses in young animals. Ageing is associated with decreases in ADA function and expression. Here, we report that co-immunization with pADA enhanced IFNγ secretion while decreasing TNFα and IL-4 secretion. pADA expanded the breadth and affinity SARS-CoV-2 spike-specific antibodies while supporting TH1-type humoral responses in aged mice. scRNAseq analysis of aged lymph nodes revealed that pADA co-immunization supported a TH1 gene profile and decreased FoxP3 gene expression. Upon challenge, pADA co-immunization decreased viral loads in aged mice. These data support the use of mice as a model for age-associated decreased vaccine immunogenicity and infection-mediated morbidity and mortality in the context of SARS-CoV-2 vaccines and provide support for the use of adenosine deaminase as a molecular adjuvant in immune-challenged populations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".