Evidence map›Paper›PMID 40841725›Full record

ArticleScientific reports2025

Design of a multi-Epitope mRNA vaccine against Brucella type IV secretion system using reverse vaccinology and immunogenicity approaches.

Jia-Rui Luo, Xin-Xin Qi, Ting-Ting Tian, Kai-Yu Shang, Hui-Dong Shi, Chuang Li, Zheng-Long Chai, Jian-Bing Ding, Yue-Jie Zhu, Feng-Bo Zhang

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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

10 authors.

Jia-Rui LuoDepartment of Clinical Laboratory, The First Affiliated Hospital of Xinjiang Medical University, No. 393, Xinyi Road, Urumqi, 830011, Xinjiang, China.
Xin-Xin QiDepartment of Clinical Laboratory, The First Affiliated Hospital of Xinjiang Medical University, No. 393, Xinyi Road, Urumqi, 830011, Xinjiang, China.
Ting-Ting TianDepartment of Clinical Laboratory, The First Affiliated Hospital of Xinjiang Medical University, No. 393, Xinyi Road, Urumqi, 830011, Xinjiang, China.
Kai-Yu ShangDepartment of Clinical Laboratory, The First Affiliated Hospital of Xinjiang Medical University, No. 393, Xinyi Road, Urumqi, 830011, Xinjiang, China.
Hui-Dong ShiDepartment of Clinical Laboratory, The First Affiliated Hospital of Xinjiang Medical University, No. 393, Xinyi Road, Urumqi, 830011, Xinjiang, China.
Chuang LiDepartment of Clinical Laboratory, The First Affiliated Hospital of Xinjiang Medical University, No. 393, Xinyi Road, Urumqi, 830011, Xinjiang, China.
Zheng-Long ChaiDepartment of Clinical Laboratory, The First Affiliated Hospital of Xinjiang Medical University, No. 393, Xinyi Road, Urumqi, 830011, Xinjiang, China.
Jian-Bing DingState Key Laboratory of Pathogenesis, Prevention, Treatment of Central Asian High Incidence Diseases, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Yue-Jie ZhuReproductive Medicine Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, China. 115127149@qq.com.
Feng-Bo ZhangDepartment of Clinical Laboratory, The First Affiliated Hospital of Xinjiang Medical University, No. 393, Xinyi Road, Urumqi, 830011, Xinjiang, China. zfb131@163.com.

Funding

National Natural Science Foundation of China and regional science foundation projects 82360394National Natural Science Foundation of China and regional science foundation projects 82460399Science and Technology Department of Xinjiang Uygur Autonomous Region, Outstanding Youth Project 2022D01E69Youth Science and Technology Top Talent Program 2022TSYCCX0112
6 · The paper itself

Abstract

Brucellosis is a zoonotic disease caused by bacteria of the Brucella genus. This study investigates the development of a multi-epitope mRNA vaccine aimed at combating Brucella infections. The approach involves selecting the architecture of the pathogenic type IV secretion system (T4SS) of Brucella using bioinformatics tools and reverse vaccinology methodologies. The candidate proteins chosen for this vaccine include VirB2, VirB3, and VirB6, along with the effector protein BtpA. Key findings include robust antigenicity scores for the candidate proteins (VirB2:0.5685; VirB3:0.5329;VirB6:0.5054;BtpA:0.5575).A variety of tools were essential for identifying potential epitopes suitable for incorporation into the mRNA vaccine, as well as for utilizing appropriate linkers for docking. Molecular dynamics simulations confirmed stable interactions between the vaccine and TLR4, with a docking score of - 350.15. Furthermore, computer cloning methods were employed alongside simulated gel electrophoresis to ensure a comprehensive evaluation of the vaccine's properties. The results of this investigation indicate that the newly formulated mRNA vaccine effectively stimulates an immune response, presenting innovative strategies for prevention and management. Although comprehensive evaluations of vaccine design have been conducted using bioinformatics tools and molecular simulations, certain limitations still exist, primarily manifested in the reliance on computational predictions without validation from wet lab experiments. Future research must further verify the accuracy of computational results through laboratory experiments.

Indexed as

BrucellaBrucella VaccineBrucellosisEpitopesType IV Secretion SystemsBacterial ProteinsComputational BiologyHumansImmunogenicity, VaccineMolecular Docking SimulationMolecular Dynamics SimulationRNA, MessengerToll-Like Receptor 4VaccinologyBacterial ProteinsBrucella VaccineEpitopesRNA, MessengerToll-Like Receptor 4Type IV Secretion SystemsBioinformaticsBrucellosisMRNA vaccineMulti-EpitopeType IV secretion system (T4SS)

Identifiers

PMID40841725
PMCPMC12370932

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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.