A long-term stable cold-chain-friendly HIV mRNA vaccine encoding multi-epitope viral protease cleavage site immunogens inducing immunogen-specific protective T cell immunity
Bibliographic record
Abstract
The lack of success in clinical trials for HIV vaccines highlights the need to explore novel strategies for vaccine development. Research on highly exposed seronegative (HESN) HIV-resistant Kenyan female sex workers revealed naturally protective immunity is correlated with a focused immune response mediated by virus-specific CD8 T cells. Further studies indicated that the immune response is unconventionally focused on highly conserved sequences around HIV viral protease cleavage sites (VPCS). Thus, taking an unconventional approach to HIV vaccine development, we designed lipid nanoparticles loaded with mRNA that encodes multi-epitopes of VPCS (MEVPCS-mRNA LNP), a strategic design to boost antigen presentation by dendritic cells, promoting effective cellular immunity. Furthermore, we developed a novel cold-chain compatible mRNA LNP formulation, ensuring long-term stability and compatibility with cold-chain storage/transport, widening accessibility of mRNA LNP vaccine in low-income countries. The in-vivo mouse study demonstrated that the vaccinated group generated VPCS-specific CD8 memory T cells, both systemically and at mucosal sites of viral entry. The MEVPCS-mRNA LNP vaccine-induced CD8 T cell immunity closely resembled that of the HESN group and displayed a polyfunctional profile. Notably, it induced minimal to no activation of CD4 T cells. This proof-of-concept study underscores the potential of the MEVPCS-mRNA LNP vaccine in eliciting CD8 T cell memory specific to the highly conserved multiple VPCS, consequently having a broad coverage in human populations and limiting viral escape mutation. The MEVPCS-mRNA LNP vaccine holds promise as a candidate for an effective prophylactic HIV 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) | 0.000 | 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 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".