DNA Vaccine-Encoded Flagellin Can Be Used as an Adjuvant Scaffold to Augment HIV-1 gp41 Membrane Proximal External Region Immunogenicity
Bibliographic record
Abstract
Flagellin’s potential as a vaccine adjuvant has been increasingly explored over the last three decades. Monomeric flagellin proteins are the only known agonists of Toll-like receptor 5 (TLR5). This interaction evokes a pro-inflammatory state that impacts upon both innate and adaptive immunity. While pathogen associated molecular patterns (PAMPs) like flagellin have been used as stand-alone adjuvants that are co-delivered with antigen, some investigators have demonstrated a distinct advantage to incorporating antigen epitopes within the structure of flagellin itself. This approach has been particularly effective in enhancing humoral immune responses. We sought to use flagellin as both scaffold and adjuvant for HIV gp41 with the aim of eliciting antibodies to the membrane proximal external region (MPER). Accordingly, we devised a straightforward step-wise approach to select flagellin-antigen fusion proteins for gene-based vaccine development. Using plasmid DNA vector-based expression in mammalian cells, we demonstrate robust expression of codon-optimized full length and hypervariable region-deleted constructs of Salmonella enterica subsp. enterica serovar Typhi flagellin (FliC). An HIV gp41 derived sequence including the MPER (gp41607–683) was incorporated into various positions of these constructs and the expressed fusion proteins were screened for effective secretion, TLR5 agonist activity and adequate MPER antigenicity. We show that incorporation of gp41607–683 into a FliC-based scaffold significantly augments gp41607–683 immunogenicity in a TLR5 dependent manner and elicits modest MPER-specific humoral responses in a mouse model.
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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".