Modifying the glycosylation profile of SARS-CoV-2 spike-based subunit vaccines alters focusing of the humoral immune response in a mouse model
Bibliographic record
Abstract
BACKGROUND: Protein subunit vaccines have a strong track record of efficacy and safety and have been widely applied for prevention of a variety of infectious diseases. The impacts of post-translational modifications of vaccine antigens are often overlooked, despite the fact that they can vary significantly depending on the expression hosts (e.g., bacteria, yeast, plant, insect or mammalian cells) and the culture conditions used for their manufacturing. METHODS: Using SARS-CoV-2 spike trimers as model antigens, we sought to evaluate the immunological impact of modulating their state of glycosylation. Spike proteins rich in complex-type (CT), high-mannose (HM) or paucimannose (PM) N-linked glycans were produced using Chinese Hamster Ovary (CHO) cells (cultured with or without the mannosidase inhibitor kifunensine) or insect cells. RESULTS: Here we show that when these antigens are adjuvanted with liposomes composed of sulfated lactosyl archaeol (SLA), all glycoforms are highly immunogenic and induce abundant spike-specific serum IgG and IFN-γ producing T-cells within female C57BL/6 mice. The spike antigen with CT glycans induces a significantly more potent neutralizing immune response, which directly correlates to more abundant receptor binding domain (RBD)-specific IgG when comparing to the antigen with HM glycans. This observation remains true whether the spike is resistin- or T4 foldon-trimerized, indicating that the glycosylation effect is not trimerization domain-specific. Spike with PM glycans induces remarkably low titers of neutralizing antibodies and RBD-specific IgG. CONCLUSIONS: The results highlight the significant impacts of a vaccine's antigen glycosylation profile in directing the immune response, which should be an important consideration for designing efficient protein-based vaccines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 teacher head, 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".