Evidence map›Paper›PMID 40692205›Full record

ArticlePlant biotechnology journal2026

Structural Design and Immunogenicity of a Novel Self-Adjuvanting Mucosal Vaccine Candidate for SARS-CoV-2 Expressed in Plants.

Mi-Young Kim, Andy Cano Tran, Ju Kim, Humblenoble Stembridge Ayuk, Adam Sparrow, Lorenzo Bossi, Megan Brown, Emil Joseph Vergara, Kathrin Göritzer, Elisabetta Groppelli and 4 more

Abstract read
In one paragraph

Article in Plant biotechnology journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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

14 authors.

Mi-Young KimDepartment of Molecular Biology and the Institute for Molecular Biology and Genetics, Jeonbuk National University, Jeonju, Republic of Korea.
Andy Cano TranInstitute for Infection and Immunity, City St George's University of London, London, UK.
Ju KimDepartment of Molecular Biology and the Institute for Molecular Biology and Genetics, Jeonbuk National University, Jeonju, Republic of Korea.
Humblenoble Stembridge AyukUniversité Claude Bernard Lyon 1, Villeurbanne, France.
Adam SparrowInstitute for Infection and Immunity, City St George's University of London, London, UK.
Lorenzo BossiImmunXperts SA, A Q2 Solutions Company, Charleroi, Belgium.
Megan BrownInstitute for Infection and Immunity, City St George's University of London, London, UK.
Emil Joseph VergaraInstitute for Infection and Immunity, City St George's University of London, London, UK.
Kathrin GöritzerInstitute for Infection and Immunity, City St George's University of London, London, UK.
Elisabetta GroppelliInstitute for Infection and Immunity, City St George's University of London, London, UK.
Tae-Ho KwonGencellbiotech Inc., Wanju, Republic of Korea.
Julian K C MaInstitute for Infection and Immunity, City St George's University of London, London, UK.ORCID https://orcid.org/0000-0003-0767-0042
Yong-Suk JangDepartment of Molecular Biology and the Institute for Molecular Biology and Genetics, Jeonbuk National University, Jeonju, Republic of Korea.
Rajko ReljicInstitute for Infection and Immunity, City St George's University of London, London, UK.ORCID https://orcid.org/0000-0003-4351-8355

Funding

EC H2020 Maria Sklodowska ITN 860325MRC-KHIDI UK-Korea partnership award MC-PC-18069National Research Foundation (NRF) of Korea 2020K1A4A7A02095058National Research Foundation (NRF) of Korea RS-2024-00442345St George's Concept Development Award
6 · The paper itself

Abstract

Mucosal vaccination for COVID-19 to boost preexisting though insufficient systemic and local/mucosal immunity remains an attractive prospect but there are currently no licensed mucosal vaccines against this infection. Here, using a plant expression system, we developed a novel mucosal vaccine platform for respiratory viruses and demonstrated its application in the context of SARS-CoV-2 infection. In addition to the antigen itself, the PCF (Platform CTB-Fc) vaccine candidate incorporates two molecular adjuvants, the IgG-Fc antibody fragment and the nontoxic cholera toxin B subunit (CTB), with the first targeting the vaccine to IgG receptors on antigen-presenting cells, and the second providing local adjuvanticity by targeting cellular gangliosides in the mucosae. We demonstrated that this vaccine candidate is highly immunogenic in mice, inducing virus-neutralising systemic and mucosal antibodies as well as tissue resident memory T cells in the lungs. We also demonstrated that SRBD-PCF is recognised by immune cells from exposed or vaccinated individuals, and that circulating antibodies also bind to the antigen within the vaccine, forming immune complexes (IC). Finally, with a view of respiratory delivery, we demonstrated that the vaccine can be aerosolised without loss of material or biological activity, and that it is noncytotoxic and nonhaemolytic to human cells. Furthermore, we demonstrate that the plant expression system represents a suitable platform to produce these complex, multifunctional macromolecules capable of simultaneously binding to multiple targets. Our data strongly support the case for a safe, self-adjuvanting mucosal COVID-19 vaccine development, as means to boosting both systemic and mucosal immunity.

Indexed as

COVID-19COVID-19 VaccinesSARS-CoV-2Adjuvants, ImmunologicAnimalsAntibodies, ViralCholera ToxinFemaleHumansImmunity, MucosalMiceMice, Inbred BALB CNicotianaAdjuvants, ImmunologicAntibodies, ViralCholera ToxinCOVID-19 VaccinesAPCs targetingcholera toxin B subunit (CT‐B)immunoglobulin Fcmacro molecular adjuvantmucosal vaccine platformSARS‐CoV‐2

Identifiers

PMID40692205
PMCPMC12854891

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.