Mixed lipopeptide-based mucosal vaccine elicits a long-term bone marrow memory response that is potentially cross-reactive against a broad-spectrum of coronaviruses in mice
Bibliographic record
Abstract
Introduction SARS-CoV-2 is continuing to prevail as an endemic virus, and therefore, we need a next-generation vaccine that prevents SARS-CoV-2 infections, broadly protects against multiple CoVs, and induces long-term local and systemic immunity. To address that need, we have designed a mixed lipopeptide-based pan-coronavirus (LP Mix ) vaccine based on T and B cell epitopes derived from highly conserved and functional regions of the SARS-CoV-2 spike (S), nucleocapsid (N), and membrane (M) proteins. Methods Male C57BL/6 mice (n=5 per group) were immunized intranasally twice, 14 days apart, with the LP Mix vaccine candidates, which consisted of seven lipopeptides (LP1–LP7), with or without HKCC (heat-killed Caulobacter crescentus ), a novel mucosal adjuvant. At 2.5 weeks, 2 months, and 7 months post-immunization, lung, spleen, bone marrow, and bronchoalveolar lavage (BAL) samples were collected for immunological analyses. Additionally, blood samples were collected monthly to monitor antibody titers. Results We demonstrate that intranasal immunizations of mice with LP Mix induced a long-lasting systemic IgM/IgG, and mucosal IgA response against a broad-spectrum of CoVs, showing clinically significant levels of neutralizing antibody titers. Splenocytes and bone marrow cells, derived from LP Mix immunized mice, demonstrated a robust proliferation response against vaccine antigens (P 1-7 ), which were maintained up to 2 months and 7 months, after LP Mix immunizations, respectively. Moreover, antigen-specific B cells and memory CD4 + /CD8 + T cells were long-lived and maintained up to 7 months after LP Mix immunizations, in the lungs, spleen and bone marrow. The addition of HKCC (heat-killed Caulobacter crescentus ), a novel mucosal adjuvant, promoted the longevity of memory CD4 + /CD8 + T cell and B cell responses. Discussion Overall, our study demonstrates that a mucosal lipopeptide-based vaccine targeting conserved SARS-CoV-2 epitopes elicits durable, long-lasting immune responses against a broad range of coronaviruses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".