Determining Adenosine Deaminase 1 (ADA-1) Impact on Immune Memory and Durability as a Molecular Adjuvant in a SARS-CoV-2 DNA Vaccine Formulation
Bibliographic record
Abstract
Abstract The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), responsible for coronavirus disease of 2019 (COVID-19), has infected millions of people causing a global pandemic. SARS-CoV-2 vaccines candidates have demonstrated acute immunogenicity and protection however, it has yet to be demonstrated whether natural or vaccine induced immunity against SARS-CoV-2 induces long term, protective immunity. In this study we sought to understand if adenosine deaminase (ADA), as a molecular adjuvant, can enhance immune memory and durability in the context of a SARS-CoV-2 DNA vaccine. Mice were immunized with a plasmid encoding for SARS-CoV-2 spike alone or in combination with plasmid encoded ADA. Subsequent B and T cell responses were measured until d60pi. In mice co-immunized with ADA, there were increased concentrations of spike receptor binding domain (RBD)-specific IgG in the sera which were found to bind RBD at an increased affinity as well as exhibit increased neutralization capability against SARS-CoV-2 pseudotyped viruses. Additionally, ADA co-immunized mice exhibited increased frequency of RBD specific memory B cells. In regard to T cell responses, mice co-immunized with ADA exhibited increased spike-specific IFN-γ, TNF-a and IL-2 as measured by flow cytometry and ELISpot. The ADA-enhanced anti-spike antibody durability over time was associated with increased frequencies of T follicular helper cells (TFH). Preliminary analysis supports that co-immunization with pADA impacts viral load in a SARS-CoV-2 infection model. These data suggest that ADA enhances immune memory and durability and supports further study with translational focus for enhancement of vaccines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".