Evidence map›Paper›PMID 41070969›Full record

ArticleJournal of virology2025

Biomimetic nanovaccines with self-adjuvant effects induced broad-spectrum neutralizing antibodies against SARS-CoV-2 infection in rodents.

Weiqi Wang, Pengye Du, Yongkun Zhao, Yuan Liang, Cheng Zhang, Hongjie Zhang, Xianzhu Xia, Bo Liu, Pengpeng Lei, Feihu Yan

Abstract read
In one paragraph

Article in Journal of virology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Weiqi Wang *Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, Jilin, China.ORCID 0009-0003-4366-1554
Pengye Du *University of Science and Technology of China, Hefei, Anhui, China.
Yongkun ZhaoChangchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, Jilin, China.
Yuan LiangUniversity of Science and Technology of China, Hefei, Anhui, China.
Cheng ZhangChangchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, Jilin, China.
Hongjie ZhangUniversity of Science and Technology of China, Hefei, Anhui, China.
Xianzhu XiaChangchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, Jilin, China.
Bo LiuDepartment of Microorganism Engineering, Beijing Institute of Biotechnology, Beijing, China.ORCID 0009-0001-7900-8907
Pengpeng LeiUniversity of Science and Technology of China, Hefei, Anhui, China.ORCID 0000-0002-7091-5199
Feihu YanChangchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Changchun, Jilin, China.ORCID 0000-0002-2808-5486

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The emergence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants seriously threatens the efficacy of current coronavirus disease 2019 (COVID-19) vaccines. Therefore, there is an urgent need to develop next-generation vaccine platforms capable of counteracting current and prospective viral variants. In this study, we employed synthetic biology technology to develop self-adjuvant-effect biomimetic nanovaccines with broad-spectrum capabilities for the prevention of SARS-CoV-2 infection. The biomimetic nanovaccines prepared by loading the SARS-CoV-2 RBD protein into dendritic mesoporous organosilicon nanoparticles (DMOSN) significantly promote the recruitment of dendritic cells (DCs) to secondary lymphoid organs, thereby initiating antibody-dependent humoral immune responses mediated by follicle helper T (Tfh) cells, germinal center (GC) B cells, and plasma cells. Moreover, DMOSN@RBD induced not only potent humoral immunity but also Th2-biased cellular immunity along with robust Th1-type cellular immune responses, which are pivotal in restricting SARS-CoV-2 infection. Our work provides a simple and environmentally friendly strategy for synthesizing nanovaccines with significant immunostimulatory potential, offering novel insights for the future development of durable and effective antiviral broad-spectrum nanovaccines.IMPORTANCEThe persistent evolution of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) underscores the critical need to continually assess vaccine immunogenicity and protective efficacy against emerging variants in preclinical animal models. Our study demonstrates that biomimetic nanoparticle vaccines elicit more durable antibody responses and enhanced T cell responses compared to conventional aluminum hydroxide-adjuvanted formulations. Notably, RBD antigen-decorated dendritic mesoporous organosilica nanoparticles (DMOSN@RBD) exhibit broad-spectrum neutralization potential against multiple SARS-CoV-2 variants of concern (VOCs). These findings establish engineered mesoporous silica nanoparticles as a potent immunostimulatory platform capable of simultaneously enhancing both humoral and cellular immunity in subunit vaccine design, particularly through the induction of robust T cell responses typically challenging to achieve with protein-based vaccines.

Indexed as

Antibodies, NeutralizingBroadly Neutralizing AntibodiesCOVID-19COVID-19 VaccinesNanoparticlesSARS-CoV-2Adjuvants, ImmunologicAnimalsAntibodies, ViralBiomimetic MaterialsDendritic CellsFemaleHumansImmunity, HumoralMiceMice, Inbred BALB CAdjuvants, ImmunologicAntibodies, NeutralizingAntibodies, ViralBroadly Neutralizing AntibodiesCOVID-19 VaccinesNanovaccinesSpike Glycoprotein, Coronavirusspike protein, SARS-CoV-2biomimetic nanovaccinesbroad-spectrumcellular immunitycross-neutralizing antibodySARS-CoV-2

Identifiers

PMID41070969
PMCPMC12645919

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.