Mechanistic insight into the induction of cellular immune responses by encapsulated and admixed archaeosome-based vaccine formulations
Bibliographic record
Abstract
Archaeosomes are liposomes formulated using total polar lipids (TPLs) or semi-synthetic glycolipids derived from archaea. Conventional archaeosomes with entrapped antigen exhibit robust adjuvant activity as demonstrated by increased antigen-specific humoral and cell-mediated responses and enhanced protective immunity in various murine infection and cancer models. However, antigen entrapment efficiency can vary greatly resulting in antigen loss during formulation and variable antigen:lipid ratios. In order to circumvent this, we recently developed an admixed archaeosome formulation composed of a single semi-synthetic archaeal lipid (SLA, sulfated lactosylarchaeol) which can induce similarly robust adjuvant activity as an encapsulated formulation. Herein, we evaluate and compare the mechanisms involved in the induction of early innate and antigen-specific responses by both admixed (Adm) and encapsulated (Enc) SLA archaeosomes. We demonstrate that both archaeosome formulations result in increased immune cell infiltration, enhanced antigen retention at injection site and increased antigen uptake by antigen-presenting cells and other immune cell types, including neutrophils and monocytes following intramuscular injection to mice using ovalbumin as a model antigen. In vitro studies demonstrate SLA in either formulation is preferentially taken up by macrophages. Although the encapsulated formulation was better able to induce antigen-specific CD8+ T cell activation by dendritic cells in vitro, both encapsulated and admixed formulations gave equivalently enhanced protection from tumor challenge when tested in vivo using a B16-OVA melanoma model. Despite some differences in the immunostimulatory profile relative to the SLA (Enc) formulation, SLA (Adm) induces strong in vivo immunogenicity and efficacy, while offering an ease of formulation.
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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) | 0.001 | 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".