PV-10 enhances immune responses in hepatitis B vaccination through STING pathway
Bibliographic record
Abstract
Despite the efficacy of current hepatitis B vaccines, approximately 10% of immunocompetent individuals remain non-responsive, underscoring the urgent need for novel adjuvants to enhance vaccine-induced immunity. In this study, we investigated PV-10, a 10% Rose Bengal solution, for its potential to activate the stimulator of interferon genes (STING) pathway and enhance both innate and adaptive immune responses. Through molecular docking, we demonstrated that Rose Bengal binds to the ligand-binding domain of STING with an affinity of −7.1 kcal/mol (−7.0 to −9.0 kcal/mol for moderate binding), promoting dimer stabilization via hydrophobic and hydrogen-bonding interactions. In the human acute monocytic leukemia cell line THP-1, treatment with PV-10 induced the phosphorylation of key downstream signaling proteins, including TBK1, IRF3, and NF-κB p65, and uniquely generated a high molecular weight STING band indicative of dimer formation. Cytokine profiling revealed a time-dependent increase in pro-inflammatory cytokines and chemokines following PV-10 treatment. Furthermore, in an in vitro model, dendritic cells were pulsed with hepatitis B surface antigen (HBsAg)-derived peptides, HBV-1 (TVELLSFLPSDFFPSV, extended HBsAg epitope) and HBV-2 (FLPSDFFPSV, minimal cytotoxic T lymphocyte epitope) and then the pulsed DCs were used to prime CD8+ T-cells. HBsAg–primed CD8+ T-cells exhibited significantly enhanced IFN-γ secretion when co-cultured with HBsAg-positive hepatoma cells in the presence of PV-10 compared to vehicle-treated controls. These findings indicate that PV-10 functions as a potent STING agonist, stabilizing STING dimerization, and eliciting an immune microenvironment conducive to robust antigen presentation and T-cell activation, thereby demonstrating its potential as a novel adjuvant for improving hepatitis B vaccine efficacy, particularly in vaccine non-responders.
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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 | 0.001 |
| 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".