Pulmonary Surfactant Protein-Hitchhiking Inhalable Vaccines Augment Mucosal and Systemic Antiviral Immunity
Bibliographic record
Abstract
Mucosal immunity is vital to provide effective protection against respiratory virus infections. However, the effective delivery of vaccine antigen to the respiratory mucosa is challenging because of the natural mucosal barrier. Here, we describe an approach exploiting the natural pulmonary surfactant (PS)-associated protein as a chaperone for transportation across the pulmonary mucosal barrier to enhance the lung resident memory T (T RM ) cells, long-lived antibody response, and secretory immunoglobulin A (SIgA) generation via vaccination. Pulmonary immunization with an inhalable albumin-templated Mn nanoadjuvant (iMnNA)-formulated mucosal vaccine (MnVac) candidate promoted the in situ, local PS protein corona formation on iMnNA through binding to the albumin, thereby increasing the vaccine accumulation in pulmonary parenchyma and the antigen uptake by antigen-presenting cells (APCs). When formulated with a SARS-CoV-2 receptor-binding domain (RBD) dimer, this inhalable RBD-MnVac induced at least 3-fold higher and persistent (up to ∼240 days) RBD-specific antibody responses and higher frequencies in long-lived plasma cells (LLPC) in the bone marrow even at half antigen dose of the intramuscular immunization. The MnVac enhanced the systemic and local mucosal immune responses through activation of the stimulator of interferon genes (STING) pathway in the lung. Additionally, the heterosubtypic RBD dimer and influenza subunit MnVac extended the breadth of the protective antibody response against a number of viral variants. Overall, these findings support the use of iMnNA as a promising mucosal adjuvant candidate for fighting respiratory infectious diseases and future pandemics.
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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 | 0.001 | 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) | 0.000 | 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".