Cationic Lipid/DNA Complex-Adjuvanted Influenza A Virus Vaccination Induces Robust Cross-Protective Immunity
Bibliographic record
Abstract
Influenza A virus is a negative-strand segmented RNA virus in which antigenically distinct viral subtypes are defined by the hemagglutinin (HA) and neuraminidase (NA) major viral surface proteins. An ideal inactivated vaccine for influenza A virus would induce not only highly robust strain-specific humoral and T-cell immune responses but also cross-protective immunity in which an immune response to antigens from a particular viral subtype (e.g., H3N2) would protect against other viral subtypes (e.g., H1N1). Cross-protective immunity would help limit outbreaks from newly emerging antigenically novel strains. Here, we show in mice that the addition of cationic lipid/noncoding DNA complexes (CLDC) as adjuvant to whole inactivated influenza A virus vaccine induces significantly more robust adaptive immune responses both in quantity and quality than aluminum hydroxide (alum), which is currently the most widely used adjuvant in clinical human vaccination. CLDC-adjuvanted vaccine induced higher total influenza virus-specific IgG, particularly for the IgG2a/c subclass. Higher levels of multicytokine-producing influenza virus-specific CD4 and CD8 T cells were induced by CLDC-adjuvanted vaccine than with alum-adjuvanted vaccine. Importantly, CLDC-adjuvanted vaccine provided significant cross-protection from either a sublethal or lethal influenza A viral challenge with a different subtype than that used for vaccination. This superior cross-protection afforded by the CLDC adjuvant required CD8 T-cell recognition of viral peptides presented by classical major histocompatibility complex class I proteins. Together, these results suggest that CLDC has particular promise for vaccine strategies in which T cells play an important role and may offer new opportunities for more effective control of human influenza epidemics and pandemics by inactivated influenza virus vaccine.
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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) | 0.001 | 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 | 0.001 | 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".