Tuning the immune response: sulfated archaeal glycolipid archaeosomes as an effective vaccine adjuvant for induction of humoral and cell-mediated immunity towards the SARS-CoV-2 Omicron variant of concern
Bibliographic record
Abstract
Liposomes composed of sulfated lactosyl archaeol (SLA) have been shown to be a safe and effective vaccine adjuvant with a multitude of antigens in preclinical studies. In particular, SLA-adjuvanted SARS-CoV-2 subunit vaccines based on trimeric spike protein antigens were shown to be immunogenic and efficacious in mice and hamsters. With the continued emergence of SARS-CoV-2 variants, we sought to evaluate next-generation vaccine formulations with an updated antigenic identity. This was of particular interest for the widespread Omicron variant, given the abundance of mutations and structural changes observed within its spike protein compared to other variants. An updated version of our resistin-trimerized SmT1 corresponding to the B.1.1.529 variant was successfully generated in our Chinese Hamster Ovary (CHO) cell-based antigen production platform and characterized, revealing some differences in protein profile and ACE2 binding affinity as compared to reference strain-based SmT1. We next evaluated this Omicron-based spike antigen for its immunogenicity and ability to generate robust antigen-specific immune responses when paired with SLA liposomes or AddaS03 (a mimetic of the AS03 oil-in-water emulsion adjuvant system found in commercialized SARS-CoV-2 protein vaccines). Immunization of mice with vaccine formulations containing this updated antigen with either adjuvant stimulated neutralizing antibody responses favouring Omicron over the reference strain. Cell-mediated responses, which play an important role in the neutralization of intracellular infections, were induced to a much higher degree with the SLA adjuvant relative to the AddaS03-adjuvanted formulations. As such, updated vaccines that are better capable of targeting towards SARS-CoV-2 variants can be generated through an optimized combination of antigen and adjuvant components.
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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) | 0.000 | 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".