Document Type : Original Article
Authors
1 Department of Fisheries, Faculty of Animal Sciences and Fisheries, Sari Agricultural and Natural Resources University, Sari, Iran
2 Department of Animal Science, Faculty of Agriculture, University of Jiroft, Jiroft, Iran
3 Department of Cell and Molecular Biology, Faculty of Science, University of Andishesazan, Neka, Iran
4 Department of Food Hygiene and Public Health, Faculty of Veterinary Medicine, Shahid Bahonar University of Kerman, Kerman, Iran
Abstract
Viral hemorrhagic septicemia virus (VHSV) is a rhabdovirus reported to cause economic loss in fish farms. Because of the lack of adequate preventative treatments, the identification of multipath genes involved in VHS infection might be an alternative to explore the possibility of using drugs for the seasonal prevention of this fish disease. We propose labeling a category of drug molecules by further classification and interpretation of the Drug Gene Interaction Database using gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment scores. The study investigated disease networks of up-and down-regulated genes to find those with high interaction as substantial genes in pathways among the different disease networks. We prioritized these genes based on their relationship to those associated with VHS infection in the context of human protein-protein interaction networks and disease pathways. Among the 29 genes as potential drug targets, nine were selected as promising druggable genes (ERBB2, FGFR3, ITGA2B, MAP2K1, NGF, NTRK1, PDGFRA, SCN2B, and SERPINC1). PDGFRA is the most important druggable up-and down-regulated gene and is considered an important gene in the IMATINIB pathway. This study findings indicate a promising approach for drug target prediction for VHS treatment, which might be useful for disease therapeutics.
Keywords
- Smail DA. Viral hemorrhagic septicemia. In: Woo PTK, Bruno DW (Eds). Fish diseases and disorders, vol. 3: Viral, bacterial and fungal infections. New York, USA: CABI Publishing 1999; 123-147.
- Yasutake WT. Fish viral diseases: clinical, histopathological, and comparative aspects. In: The pathology of fishes. University of Wisconsin Press1975;247-271.
- Renault T, Novoa B. Viruses infecting bivalve molluscs. Aqua Living Resour 2004;17:397-409.
- Schütze H, Mundt E, Mettenleiter TC. Complete genomic sequence of viral hemorrhagic septicemia virus, a fish rhabdovirus. Virus Genes 1999; 19(1):59-65.
- Novoa B, Romero A, Mulero V, et al. Zebrafish (Danio rerio) as a model for the study of vaccination against viral hemorrhagic septicemia virus (VHSV). Vaccine 2006 ;24(31-32):5806-5816.
- Sudhagar A, Kumar G, El-Matbouli M. Transcriptome analysis based on RNA-Seq in understanding pathogenic mechanisms of diseases and the immune system of fish: A comprehensive review. Int JMol Sci 2018;19(1):245. doi: 10.3390/ijms19010245.
- Mizuarai S, Yamanaka K, Itadani H, et al. Discovery of gene expression-based pharmacodynamics biomarker for a p53 context-specific anti-tumor drug Wee1 inhibitor. Mol Cancer 2009;8:34. doi: 10.1186/1476-4598-8-34.
- Liebler DC, Guengerich FP. Elucidating mechanisms of drug-induced toxicity. Nat Rev Drug Discov 2005; 4(5):410-420.
- Taylor IW, Linding R, Warde-Farley D, et al. Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat Biotechnol 2009; 27(2):199-204.
- Bastos LFS, Coelho MM. Drug repositioning: playing dirty to kill pain. CNS Drugs 2014;28(1):45-61.
- Gardner TS, di Bernardo D, Lorenz D, et al. Inferring genetic networks and identifying compound mode of action via expression profiling. Science 2003; 301(5629):102-105.
- DiMasi JA, Hansen RW, Grabowski HG. The price of innovation: new estimates of drug development costs. J Health Econ 2003;22(2):151-185.
- Lamb J, Crawford ED, Peck D, et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 2006;313(5795):1929-1935.
- Pujol A, Mosca R, Farrés J, et al. Unveiling the role of network and systems biology in drug discovery. Trends Pharmacol Sci 2010;31(3):115-123.
- Yap YL, Zhang XW, Smith D, et al. Molecular gene expression signature patterns for gastric cancer diagnosis. Comput Biol Chem 2007;31(4):275-287.
- Hu G, Agarwal P. Human disease-drug network based on genomic expression profiles. PloS ONE 2009;4(8):e6536. doi:10.1371/journal.pone.0006536.
- Iorio F, Bosotti R, Scacheri E, et al. Discovery of drug mode of action and drug repositioning from trans-criptional responses. Proc Natl Acad Sci USA 2010;107(33):14621-14626.
- Estepa A, Coll J. Innate multigene family memories are implicated in the viral-survivor zebrafish phenotype. PloS ONE 2015;10(8):e0135483. doi:10.1371/journal. pone.0135483.
- Ritchie, ME, Phipson, B, Wu, D, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015; 43(7): e47. doi: 10.1093/nar/gkv007.
- Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000;28(1):27-30.
- Glaab E, Baudot A., Krasnogor N, et al. EnrichNet: network-based gene set enrichment analysis. Bioinformatics, 2012; 28(18): i451-i457.
- Jensen LJ, Kuhn M, Stark M, et al. STRING 8-a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res 2009;37(Database issue):D412-D416.
- Winterhalter C, Widera P, Krasnogor N. JEPETTO: a cytoscape plugin for gene set enrichment and topological analysis based on interaction networks. Bioinformatics 2014;30(7):1029-1030.
- Fruzangohar M, Ebrahimie E, Adelson DL. A novel hypothesis-unbiased method for Gene Ontology enrichment based on transcriptome data. PloS ONE 2017;12(2):e0170486.doi:10.1371/journal.pone.01.7486.g004.
- Griffith M, Griffith OL, Coffman AC, et al. DGIdb: mining the druggable genome. Nat Methods 2013; 10(12): 1209-1210.
- Chinchilla B, Encinas P, Estepa A, et al. Transcriptome analysis of rainbow trout in response to non-virion (NV) protein of viral hemorrhagic septicemia virus (VHSV). Appl Microbiol Biotechnol 2015;99(4):1827-1843.
- Encinas P, Garcia-Valtanen P, Chinchilla B, et al. Identification of multipath genes differentially expressed in pathway-targeted microarrays in zebrafish infected and surviving spring viremia carp virus (SVCV) suggest preventive drug candidates. PLoS ONE 2013;8(9):e73553. doi:10.1371/journal. pone.0073553.
- Encinas P, Rodriguez-Milla MA, Novoa B, et al. Zebrafish fin immune responses during high mortality infections with viral hemorrhagic septicemia rhabdo-virus. A proteomic and trans-criptomic approach. Bmc Genomics 2010;11:518. doi:10.1186/1471-2164-11-518.
- Cho HK, Kim J, Moon JY, et al. Microarray analysis of gene expression in olive flounder liver infected with viral hemorrhagic septicemia virus (VHSV), Fish Shellfish Immun 2016;49: 66-78
- Jørgensen HBH, Sørensen P, Cooper GA, et al. General and family-specific gene expression responses to viral hemorrhagic septicemia virus infection in rainbow trout (Oncorhynchus mykiss). Mol Immunol. 2011; 48(8):1046-1058.
- National Center for Biotechnology Information. Pub Chem Database; NCBI GeneID=5156, https://pubchem. ncbi.nlm.nih.gov/target/gene/5156. Accessed Feb 18, 2021.
- Wishart D, Feunang YD, Guo ACet al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res D1074-D1082.
)