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Record W7118121008 · doi:10.18103/mra.v13i12.7143

Preparedness for Future Pandemics Using a Highly Effective Recombinant Vesicular Stomatitis Virus-Based Vaccine Platform Technology: Strategies for Developing Superior Vaccines

2025· article· W7118121008 on OpenAlexaff
Gyoung Ki m, Kunyu Wu, C. Yong Kang

Bibliographic record

VenueMedical Research Archives · 2025
Typearticle
Language
FieldMedicine
TopicVirology and Viral Diseases
Canadian institutionsWestern University
Fundersnot available
KeywordsVesicular stomatitis virusGlycoproteinSignal peptideRecombinant DNAViral envelopeRhabdoviridaeVector (molecular biology)VirusMutant

Abstract

fetched live from OpenAlex

We have developed highly effective, avirulent vesicular stomatitis virus (VSV) vectors to create potent recombinant viral vector-based vaccines. These vaccines induce both humoral and cellular immune responses. For developing a safe and effective viral vector vaccine, we chose VSV because of its broad host range and efficient replication. To enhance the safety of the rVSV vector, we introduced mutations in the M gene to attenuate it, since the VSV M protein is responsible for VSV-induced pathogenesis. The combined mutations G21E, M51R, and L111F (GML) in the M protein of the Indiana serotype of VSV (VSVInd-GML), along with mutations G22E, M48R, and M51R (GMM), and G22E, M48R, M51R, and L110F (GMML) in the M protein of VSV New Jersey serotype (VSVNJ), were used to generate VSVNJ-GMM and VSVNJ-GMML, respectively. The rVSVInd-GML, rVSVNJ-GMM, and rVSVNJ-GMML exhibited reduced cytopathic effects in vitro and across various animal species. Animals injected with up to 5 billion live M gene mutant VSV showed no significant adverse effects, whereas only 1,000 wild-type VSV were enough to kill mice within four days. All future pandemics are likely caused by airborne enveloped RNA viruses that feature surface glycoproteins on their virions. An effective signal peptide at the N-terminus of all glycoproteins is crucial for efficient synthesis, proper processing, glycosylation, intracellular transport, and secretion. Therefore, we replaced the natural signal peptides of viral glycoproteins with a highly efficient signal peptide from honeybee melittin, known for its effectiveness in glycoprotein biosynthesis and intracellular transport. Additionally, we attached the transmembrane domain and cytoplasmic tail of VSV G protein (Gtc) to the C-terminus of the target glycoprotein to enhance its incorporation into pseudotype virions. To prevent vector immunity in booster immunization, we utilised two different serotypes of VSV, along with pseudotype virions carrying the VSV G protein and the glycoproteins of the target virus. These pseudovirions will bind to the VSV receptor, low-density lipoprotein (LDL) receptor, and the receptors of the target surface glycoprotein to initiate infection. The M gene mutants of VSVInd and VSVNJ vectors, which carry surface glycoprotein genes from target viruses, stimulate strong humoral and cellular immune responses and protect animals from challenges with wild-type viruses. These M gene mutant vectors are ideal for developing vaccines to fight future pandemics. This article explains how to develop an effective vaccine for future pandemics caused by enveloped RNA viruses.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.844
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.405
Teacher spread0.367 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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