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. 2024 Nov 21:15:1456078.
doi: 10.3389/fimmu.2024.1456078. eCollection 2024.

Immunoinformatic design of a multivalent vaccine against Brucella abortus and its evaluation in a murine model using a DNA prime-protein boost strategy

Affiliations

Immunoinformatic design of a multivalent vaccine against Brucella abortus and its evaluation in a murine model using a DNA prime-protein boost strategy

Raúl E Molina et al. Front Immunol. .

Abstract

Introduction: The development of effective vaccines against Brucella abortus is critical due to its significant impact on human and animal health. The objective of this study was to design and evaluate in silico and in vivo a multivalent vaccine based on the immunogenic potential of three selected open reading frames (ORFs) of Brucella.

Methods: The designed construct, named S22, was analyzed in silico to evaluate its physicochemical properties, antigenicity, allergenicity and toxicity. This construct was modeled and subjected to molecular dynamics analysis. Additionally, the antigenicity and protection induced by this construct was evaluated through In vivo assays immunizing BALB/c mice with protein (S22), DNA (pVS22) and combining both vaccine formats using a prime boost immunization strategy.

Results: All bioinformatics analyses showed safe and high quality structural features, revealing favorable interactions between S22 and the TLR4/MD2 complex. Moreover, results from in vivo assays indicated that the S22 protein induced robust levels of IgG1 and IgG2a, suggesting a balanced Th1 and Th2 immune response. The DNA construct (pVS22) elicited primarily a Th1 response, whereas the use of a prime boost strategy, which combines both formats resulted in a balanced immune response with significant induction of lymphoproliferation and elevated.

Discussion: Although our assays did not demonstrate the induction of a substantial protective response against B. abortus, this construct was capable of inducing immunogenicity. This study highlights the utility of in silico design for predicting and optimizing candidate vaccines and underscores the potential of using strategies such as prime boost, which incorporate antigens of different biological nature to modulate the immune response, while balancing parameters such as stability of the antigens and the cost of production.

Keywords: immunogenicity; immunoinformatic; interferon gamma; molecular dynamics; multivalent vaccines; prime-boost strategy; protection.

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Conflict of interest statement

The authors declare that the research was conducted without any commercial or financial relationships that could potentially create a conflict of interest.

Figures

Figure 1
Figure 1
Schematic representation of the immunization and challenge protocol. Each group received three doses of their respective vaccines or PBS in a final volume of 100 µL. In addition, a positive control group was included, which was immunized once intraperitoneally (i.p.) with 5 x 10^8 CFU of B. abortus RB51. After 45 days from the last immunization, mice in each group were challenged with 1 x 10^4 CFU of B. abortus 2308. 15 days after challenge, the mice were euthanized and spleen were collected to evaluate the protection of each immunization strategy. Routes of administration: i.m. (intramuscular), i.d. (intradermal), s.c. (subcutaneous) and i.p. (intraperitoneal).
Figure 2
Figure 2
(A) Aminoacidic sequence of the designed protein and schematic representation of the proposed model. (B) Refined 3D model of the designed vaccine obtained from the IntFold7 server and refined by the GalaxyRefine server. Blue protein SOD, red protein ZnMP, and in green, the SH3-like domain protein. (C) Ramachandran plot of the refined model indicates that 98% of the residues are in favored regions and 99.8% of residues are in allowed regions. (D) ProSA-web overall model quality plot displaying the model Z-score of -9.85. (E) ProSA-web local quality model showing average energies calculations in 40 residues window. (F) ERRAT plot showing an overall quality factor of 97.554. Yellow bars represent regions with an error rate between 95% and 99%.
Figure 3
Figure 3
(A) Molecular docking complex representation of S22 protein vaccine and TLR4 complex. Interacting residues between the S22 protein and TLR4/lymphocyte antigen 96 complex are highlighted. (B–E) MD simulation trajectory-based graphs for analysis of structural stability. Graphs generated by GROMACS at different stages of MD simulations of the designed construct.
Figure 4
Figure 4
(A) SDS PAGE and Western blot analysis of the purified protein. Lane 1: Protein molecular weight marker. Lane 2: S22 protein SDS PAGE electrophoresis band. Lane 3: Protein molecular weight marker. Lane 4: Immunodetection of 6xHis Tag of S22 protein by Western blot. (B) Agarose gel electrophoresis of the pVS22 plasmid. Lane 1: DNA molecular weight marker. Lane 2: Linearized pVS22 plasmid. Lane 3: Products of the PstI and BamHI restriction enzymes double digestion of pVS22 plasmid. (C) Serum IgG1 and IgG2a antibody titers measured by ELISA at days 0, 15, 30, and 45 post-immunizations. Results are expressed as mean ± SD of log10 of the last reciprocal serum dilution value above the cut-off. (D) Lymphoproliferative response of splenocytes after in vitro stimulation with 0, 0.2, 1, or 5 μg/ml of recombinant S22 protein, and 0, 4, or 20 μg /ml of B. abortus 2308 total proteins (crude Brucella proteins). Results are shown as mean ± SD of 3H-thymidine incorporation from cells (CPM, counts per minute). (E) Cytokine levels of IFN-γ, IL-4, and TNF-α quantified by sandwich ELISA from the supernatant of in vitro stimulated splenocytes with 0, 0.2, 1, or 5 μg /ml of recombinant S22 protein. Results are expressed as mean ± SD of cytokine concentration. * P < 0.05; ** P < 0.01; *** P < 0.001 and **** P < 0.0001.

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