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Detalle de: Suppression subtraction hybridization (SSH) and macroarray techniques reveal differential gene expression profiles in brain of sea bream infected with nodavirus

Title Suppression subtraction hybridization (SSH) and macroarray techniques reveal differential gene expression profiles in brain of sea bream infected with nodavirus
Abstract

Despite of the impact that viruses have on aquatic organisms, relatively little is known on how fish fight against these infections. In this work, the brain gene expression pattern of sea bream (Sparus aurata) in response to nodavirus infection was investigated. We used the suppression subtractive hybridization (SSH) method to generate a subtracted cDNA library enriched with gene transcripts differentially expressed after 1 day post-infection. Some of the ESTs from the infected tissues fell in gene categories related to stress and immune responses. For the reverse library (ESTs expressed in controls compared with infected tissues) the most abundant transcripts were of ribosomal and mitochondrial nature. Several ESTs potentially induced by virus exposure were selected for in vivo expression studies. We observed a clear difference in expression between infected and control samples for two candidate genes, ubiquitin conjugating enzyme 7 interacting protein, which seems to play an important role in apoptosis and the interferon induced protein with helicase C domain 1 (mda-5) that contributes to apoptosis and regulates the type I IFN production, a key molecule of the antiviral innate response in most organisms.

Apa Reference Dios, S., Poisa-Beiro, L., Figueras, A. & Novoa, B. (2007). Suppression subtraction hybridization (SSH) and macroarray techniques reveal differential gene expression profiles in brain of sea bream infected with nodavirus. Molecular Immunology, 44(9), 2195-2204. 10.1016/j.molimm.2006.11.017
Doi 10.1016/j.molimm.2006.11.017
Publication Date Mar 2007
Publication Type Article
Researcher Antonio Figueras
Ontology Transcriptomics, Expression Profiling, Marine Systems
Research Groups
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