Sevgi Kalkanlı Taş1, Duygu Kırkık2, Kübra Öztürk2, Alpaslan Tanoğlu3. 1. Department of Immunology, Health Sciences University School of Medicine, İstanbul, Turkey. 2. Department of Medical Biology, Health Sciences University School of Medicine, İstanbul, Turkey. 3. Department of Gastroenterology, Sultan Abdulhamit Training and Research Hospital, İstanbul, Turkey.
Abstract
BACKGROUND/AIMS: Helicobacter pylori is classified as a gram-negative bacteria and can cause significant diseases, including gastric cancer, mucosa-associated lymphoid tumor, peptic ulcer, and chronic gastritis. Recent studies have shown that some autoimmune diseases are also associated with H. pylori. In the past decades, polymorphisms of certain genes of H. pylori, mechanisms and strains of H. pylori, and new therapeutic approaches have continued to be defined. Bioinformatic tools continue to be used in drug design and vaccine design. This study aimed to investigate the cag pathogenicity island (cagPAI) of H. pylori using an in silico approach, which could contribute to vaccine studies. MATERIALS AND METHODS: The pathogenicity island of H. pylori was obtained from GenBank and analyzed with ClustalW software. Structures of cag Virb11 (Hp0525) and an inhibitory protein (Hp1451) were obtained, and codon optimization and secondary and tertiary structure prediction for the cagPAI of H. pylori were analyzed using Garnier-Osguthorpe-Rabson IV secondary structure prediction method and self-optimized prediction method with alignment software. The BcePred prediction server was used to distinguish linear B-cell epitopes, and prediction of T-cell was obtained with NetCTL and MHCPred. RESULTS: According to the physicochemical parameters, the cagPAI of H. pylori was analyzed and found to be stable, and 2 B-cell epitopes of cagPAI of H. pylori and 2 T-cell epitopes of cagPAI were found in this study. CONCLUSION: B- and T-cell epitopes that we have identified can induce both humoral and cellular immune responses. Thus, these epitopes have a potential for vaccine studies. Consequently, this in silico analysis should be combined with other pieces of evidence, including experimental data, to assign function.
BACKGROUND/AIMS: Helicobacter pylori is classified as a gram-negative bacteria and can cause significant diseases, including gastric cancer, mucosa-associated lymphoid tumor, peptic ulcer, and chronic gastritis. Recent studies have shown that some autoimmune diseases are also associated with H. pylori. In the past decades, polymorphisms of certain genes of H. pylori, mechanisms and strains of H. pylori, and new therapeutic approaches have continued to be defined. Bioinformatic tools continue to be used in drug design and vaccine design. This study aimed to investigate the cag pathogenicity island (cagPAI) of H. pylori using an in silico approach, which could contribute to vaccine studies. MATERIALS AND METHODS: The pathogenicity island of H. pylori was obtained from GenBank and analyzed with ClustalW software. Structures of cag Virb11 (Hp0525) and an inhibitory protein (Hp1451) were obtained, and codon optimization and secondary and tertiary structure prediction for the cagPAI of H. pylori were analyzed using Garnier-Osguthorpe-Rabson IV secondary structure prediction method and self-optimized prediction method with alignment software. The BcePred prediction server was used to distinguish linear B-cell epitopes, and prediction of T-cell was obtained with NetCTL and MHCPred. RESULTS: According to the physicochemical parameters, the cagPAI of H. pylori was analyzed and found to be stable, and 2 B-cell epitopes of cagPAI of H. pylori and 2 T-cell epitopes of cagPAI were found in this study. CONCLUSION: B- and T-cell epitopes that we have identified can induce both humoral and cellular immune responses. Thus, these epitopes have a potential for vaccine studies. Consequently, this in silico analysis should be combined with other pieces of evidence, including experimental data, to assign function.
Authors: Marielena Vogel Saivish; Gabriela de Lima Menezes; Vivaldo Gomes da Costa; Gislaine Celestino Dutra da Silva; Rafael Elias Marques; Maurício Lacerda Nogueira; Roosevelt Alves Da Silva Journal: Int J Mol Sci Date: 2022-07-12 Impact factor: 6.208