Bioinformatics analysis to design a multi-epitope mRNA vaccine against S. agalactiae exploiting pathogenic proteins.

Autor: Barazesh, Mahdi, Abbasi, Maryam, Mohammadi, Mohsen, Nasiri, Mohammad naser, Rezaei, Faranak, Mohammadi, Shiva, Kavousipour, Soudabeh
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Zdroj: Scientific Reports; 11/16/2024, Vol. 14 Issue 1, p1-23, 23p
Abstrakt: Antibiotic resistance in bacterial pathogen infections is a growing global issue that occurs due to their adaptation to changing environmental conditions. Therefore, producing an efficient vaccine as an alternative approach can improve the immune system, eradicate related pathogens, and overcome this growing problem. Streptococcus agalactiae belongs to group B Streptococcus (GBS). Colonization of GBS during pregnancy is a significant risk factor for infants and young children. S. agalactiae infected population exhibits resistance to beta-lactams, including penicillin and the second-line antibiotics erythromycin and clindamycin. On the other hand, there are currently no commercial vaccines against this pathogen. Vaccination of pregnant women is a highly effective method to protect newborns and infants from S. agalactiae infection, and it has been identified as an urgent demand by the World Health Organization. This study employed various immunoinformatic tools to develop an effective vaccine that could trigger both humoral and cell-mediated immunity and prevent disease. For this purpose, three conserved antigenic proteins of the main pathogenic strains of S. agalactiae were utilized to predict CTL, HTL, and B-cell epitopes for producing an mRNA vaccine against different strains of S. agalactiae. The selected epitopes were fused using proper linkers. The Resuscitation promoting factor E (RpfE) sequence was incorporated in the designed vaccine construct as an adjuvant to boost its immune response. Different physicochemical characteristics of the final designed vaccine, modeling of the three-dimensional structure, molecular docking, molecular dynamics simulation, and immunological response simulation were screened following vaccine administration in an in vivo model. Computational immune simulation data identified that IgG1, IgM, INF γ, IL-2, T helper, and B-cell populations increased significantly after vaccination. These findings suggested that the vaccine candidate may provide good protection against S. agalactiae infection. However, experimental and animal model studies are required for additional validation and implementation in human vaccination programs. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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