Co-immunization with adenosine deaminase rescues age-associated impairment of SARS-COV-2 synDNA vaccine-induced responses
Bibliographic record
Abstract
Abstract SARS-CoV-2 is responsible for a global pandemic claiming over 2 million lives and infecting over 100 million people. Elderly patients display increased COVID-19 morbidity and mortality. Vaccine candidates have been studied and deployed in the clinic. However, age-associated immune deficits are known to cause sub-optimal responses, and the longevity of vaccine-induced responses is under investigation. Germinal center follicular helper T cells (TFH) promote affinity maturation of B cell receptors and memory B cell differentiation and are defined by expression of adenosine deaminase-1 (ADA-1). We investigated the adjuvant properties of plasmid-encoded adenosine deaminase (pADA) in the context of a SARS-CoV-2 spike synDNA antigens. Young and aged mice were immunized with plasmid-encoded spike (pS) alone or co-immunized with pS and pADA and cellular and humoral responses were evaluated. When immunized with pS alone, aged mice had decreased spike-binding IgG and neutralization titers. However, young and aged animals co-immunized with pADA had similar humoral responses. We observed similar trends in serum antibody affinity as measured by SPR. pADA co-immunization also rescued age-associated decreases in spike-specific IFNy secretion in the spleens and lungs of co-immunized aged mice as measured by ELISpot. Finally, preliminary data analysis indicates that co-immunization with pADA significantly impacts viral load in a model of SARS-CoV-2 infection. These data suggest that pADA enhances antigen-specific cellular and humoral immunity in aged mice and supports further study of this molecule as an immunoadjuvant for vaccines targeting elderly 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) | 0.000 | 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 | 0.001 |
| 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".