Adenosine deaminase augments SARS-CoV-2 specific cellular and humoral responses in aged mouse models of immunization and challenge
Bibliographic record
Abstract
Abstract Despite numerous clinically available vaccines and therapeutics, aged patients remain at increased risk for COVID-19 morbidity and mortality. Furthermore, the aged patient population has suboptimal responses to SARS-CoV-2 vaccine antigens. Here, we characterized vaccine-induced responses to SARS-CoV-2 synthetic DNA vaccine antigens in aged mouse models. Aged mice had altered cellular responses, including decreased IFNγ secretion and increased TNFα secretion as compared to young mice. Aged mice had significantly decreased binding and neutralizing antibodies in their serum compared to their young counterparts. Co-immunization with the plasmid-encoded molecular adjuvant adenosine deaminase-1 (pADA) enhanced cellular responses and expanded the breadth and affinity of humoral responses in aged mice. pADA co-delivery also altered the gene expression profiles of lymph node lymphocytes in aged animals. scRNAseq analysis of aged lymph nodes revealed that pADA co-immunization supported a strong TH1 gene profile and decreased FoxP3 gene expression. Upon challenge pADA co-immunization decreased viral loads and age-associated morbidity and mortality in mouse-adapted and ACE2 transgenic SARS-CoV-2 challenge models. These data support the use of aged mice as a model for age-associated decreases in vaccine immunogenicity and increased in infection-mediated morbidity and mortality in the context of SARS-CoV-2. These studies provide further support for the use of adenosine deaminase as a molecular adjuvant in the elderly. 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) | 0.001 | 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) | 0.002 | 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".