Computational Identification of Essential Enzymes as Potential Drug Targets in Shigella flexneri Pathogenesis Using Metabolic Pathway Analysis and Epitope Mapping
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Shigella flexneri is a facultative intracellular pathogen that causes bacillary dysentery in humans. Infection with S. flexneri can result in more than a million deaths yearly and most of the victims are children in developing countries. Therefore, identifying novel and unique drug targets against this pathogen is instrumental to overcome the problem of drug resistance to the antibiotics given to patients as the current therapy. In this study, a comparative analysis of the metabolic pathways of the host and pathogen was performed to identify this pathogen’s essential enzymes for the survival and propose potential drug targets. First, we extracted the metabolic pathways of the host, Homo sapiens, and pathogen, S. flexneri, from the KEGG database. Next, we manually compared the pathways to categorize those that were exclusive to the pathogen. Further, all enzymes for the 26 unique pathways were extracted and submitted to the Geptop tool to identify essential enzymes for further screening in determining the feasibility of the therapeutic targets that were predicted and analyzed using PPI network analysis, subcellular localization, druggability testing, gene ontology and epitope mapping. Using these various criteria, we narrowed it down to prioritize 5 novel drug targets against S. flexneri and one vaccine drug targets against all strains of Shigella. Hence, we suggest the identified enzymes as the best putative drug targets for the effective treatment of S. flexneri.
Abstract
Shigella flexneri is a facultative intracellular pathogen that causes bacillary dysentery in humans. Infection with S. flexneri can result in more than a million deaths yearly and most of the victims are children in developing countries. Therefore, identifying novel and unique drug targets against this pathogen is instrumental to overcome the problem of drug resistance to the antibiotics given to patients as the current therapy. In this study, a comparative analysis of the metabolic pathways of the host and pathogen was performed to identify this pathogen’s essential enzymes for the survival and propose potential drug targets. First, we extracted the metabolic pathways of the host, Homo sapiens, and pathogen, S. flexneri, from the KEGG database. Next, we manually compared the pathways to categorize those that were exclusive to the pathogen. Further, all enzymes for the 26 unique pathways were extracted and submitted to the Geptop tool to identify essential enzymes for further screening in determining the feasibility of the therapeutic targets that were predicted and analyzed using PPI network analysis, subcellular localization, druggability testing, gene ontology and epitope mapping. Using these various criteria, we narrowed it down to prioritize 5 novel drug targets against S. flexneri and one vaccine drug targets against all strains of Shigella. Hence, we suggest the identified enzymes as the best putative drug targets for the effective treatment of S. flexneri.
참고문헌 (30)
- Lanata CF, "Global causes of diarrheal disease mortality in children <5years of age : a systematic review", PLoS One, 8, pp.e72788~ (2013)
- Mandal J, "The recent trends of Shigellosis : a JIPMER perspective", J. Clin. Diagn. Res, 6, pp.1474~1477 (2012)
- von Seidlein L, "A multicentre study of Shigella diarrhoea in six Asian countries : disease burden, clinical manifestations, and microbiology", PLoS Med, 3, pp.e353~ (2006)
- Naheed A, "Fluoroquinolone-resistant Shigella dysenteriae type 1 in northeastern Bangladesh", Lancet Infect. Dis, 4, pp.607~608 (2004)
- Sivapalasingam S, "High prevalence of antimicrobial resistance among Shigella isolates in the United States tested by the National Antimicrobial Resistance Monitoring System from 1999 to 2002", Antimicrob. Agents Chemother, 50, pp.49~54 (2006)
- Dutta D, "Emergence of multidrug-resistant Shigella dysenteriae type 1 causing sporadic outbreak in and around Kolkata, India", J. Health Popul. Nutr, 21, pp.79~80 (2003)
- Uddin R, "Core proteomic analysis of unique metabolic pathways of Salmonella enterica for the identification of potential drug targets", PLoS One, 11, pp.e0146796~ (2016)
- Hema K, "Identification of putative drug targets and vaccine candidates for pathogens causing atherosclerosis", Biochem. Anal. Biochem, 4, pp.1~ (2015)
- Vetrivel U, "A novel in silico approach to identify potential therapeutic targets in human bacterial pathogens", Hugo J, 5, pp.25~34 (2011)
- Hasan MA, "Identification of putative drug targets in Vancomycin-resistant Staphylococcus aureus(VRSA)using computer aided protein data analysis", Gene, 575, pp.132~143 (2016)
- Munikumar M, "In silico identification of common putative drug targets among the pathogens of bacterial meningitis", Biochem. Anal. Biochem, 1, pp.123~ (2012)
- Kanehisa M, "The KEGG databases at GenomeNet", Nucleic Acids Res, 30, pp.42~46 (2002)
- Wei W, "Geptop : a gene essentiality prediction tool for sequenced bacterial genomes based on orthology and phylogeny", PLoS One, 8, pp.e72343~ (2013)
- Altschul SF, "Basic local alignment search tool", J. Mol. Biol, 215, pp.403~410 (1990)
- Szklarczyk D, "STRING v10 : protein-protein interaction networks, integrated over the tree of life", Nucleic Acids Res, 43(Database), pp.D447~D452 (2015)
- Cline MS, "Integration of biological networks and gene expression data using Cytoscape", Nat. Protoc, 2, pp.2366~2382 (2007)
- Assenov Y, "Computing topological parameters of biological networks", Bioinformatics, 24, pp.282~284 (2008)
- Bader GD, "An automated method for finding molecular complexes in large protein interaction networks", BMC Bioinformatics, 4, pp.2~ (2003)
- Yu NY, "PSORTb 3. 0 : improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes", Bioinformatics, 26, pp.1608~1615 (2010)
- Krogh A, "Predicting transmembrane protein topology with a hidden Markov model : application to complete genomes", J. Mol. Biol, 305, pp.567~580 (2001)
- Sonnhammer ELL, "A hidden Markov model for predicting transmembrane helices in protein sequences", Proceedings of the Sixth International Conference on Intelligent Systems for Molecular Biology, pp.175~182 (1998)
- Wishart DS, "DrugBank 5. 0 : a major update to the DrugBank database for 2018", Nucleic Acids Res, 46(D1), pp.D1074~D1082 (2018)
- Yang H, "Therapeutic target database update 2016 : enriched resource for bench to clinical drug target and targeted pathway information", Nucleic Acids Res, 44(D1), pp.D1069~D1074 (2016)
- Huang da W, "Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources", Nat. Protoc, 4, pp.44~57 (2009)
- Huang da W, "Bioinformatics enrichment tools : paths toward the comprehensive functional analysis of large gene lists", Nucleic Acids Res, 37, pp.1~13 (2009)
- Saha S, "Prediction methods for B-cell epitopes", Methods Mol. Biol, 409, pp.387~394 (2007)
- Glas M, "The soluble periplasmic domains of Escherichia coli cell division proteins FtsQ", FtsB, J. Biol. Chem, pp.2015, 290[DOI]
- Buddelmeijer N, "A complex of the Escherichia coli cell division proteins FtsL, FtsB and FtsQ forms independently of its localization to the septal region", Mol. Microbiol, 52, pp.1315~1327 (2004)
- Den Blaauwen T, "Bacterial cell division proteins as antibiotic targets", Bioor. Chem, 55, pp.27~38 (2014)
- Lock RL, "Cell-division inhibitors : new insights for future antibiotics", Nat. Rev. Drug Discov, 7, pp.324~338 (2008)