4.6 Article

De novo whole transcriptome profiling of Edwardsiella tarda isolated from infected fish (Labeo catla)

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GENE
卷 701, 期 -, 页码 152-160

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ELSEVIER
DOI: 10.1016/j.gene.2019.03.028

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Edwardsiella tarda; Immune response; Protein-protein interaction; Transcriptome; Gene ontology

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Edwardsiella tarda belongs to the genera of Gram negative bacterium mainly associated with edwardsiellosis, the most commonly found infectious fish disease throughout the globe. E. tarda is also a widespread pathogen which cause infections such as cellulitis or gas gangrene and generalized infections in humans. To control the escalating infection of E. trada on various species, it is essential to decoded the mysterious mechanism behind the bacterial infection at transcript level. In this present study, we carry out a de novo E. tarda Whole transcriptome sequencing, isolated from infected fish intestine using SOLiD sequencing platform. RNA-Seq data analysis was performed using various bioinformatics pipelines. Protein-protein interaction study for pathway enrichment and gene ontology study were executed for further investigation. Assembly statistics for E. tarda dataset showed that the number of transcript contigs was 9657 out of which 6749 were GO annotated whereas 1528 were not assigned any GO terms. GO analysis showed that the expressed genes were enhanced with molecular function, cellular component and biological process. A KEGG enrichment study showed that pathway's that are directly linked with immune diseases like Rheumatoid arthritis (0.2%), Tuberculosis (0.3%) Endocytosis (0.6%) was considerably enriched. Protein-protein interaction study showed that most of the expressed proteins were involved in metabolic pathways, flagellar assembly, Propanoate metabolism, Microbial metabolism in diverse environments, Butanoate metabolism and Carbon. The present study provides novel E. tarda transcriptome sequence data, allowing us to identify biologically significant genes and their functional relationship with fish diseases, and will be useful in recognize the reliable therapeutic targets in near feature.

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