Blog
About

  • Record: found
  • Abstract: found
  • Article: found
Is Open Access

Transcriptomics Sequencing Provides Insights into Understanding the Mechanism of Grass Carp Reovirus Infection

Read this article at

Bookmark
      There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

      Abstract

      Grass carp is an important aquaculture fish species in China that is affected by severe diseases, especially haemorrhagic disease caused by grass carp reovirus (GCRV). However, the mechanisms of GCRV invasion and infection remain to be elucidated. In the present study, Ctenopharyngodon idellus kidney (CIK) cells were infected with GCRV, harvested at 0, 8, 24, and 72 h post infection, respectively, and then subjected to transcriptomics sequencing. Each sample yielded more than 6 Gb of clean data and 40 million clean reads. To better understand GCRV infection, the process was divided into three phases: the early (0–8 h post infection), middle (8–24 h post infection), and late (24–72 h) stages of infection. A total of 76 (35 up-regulated, 41 down-regulated), 553 (463 up-regulated, 90 down-regulated), and 284 (150 up-regulated, 134 down-regulated) differently expressed genes (DEGs) were identified during the early, middle, and late stages of infection, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that DEGs were mainly involved in carbohydrate biosynthesis, transport, and endocytosis in the early stage, phagocytosis and lysosome pathways were mainly enriched in the middle stage, and programmed cell death, apoptosis, and inflammation were largely associated with the late stage. These results suggest GCRV infection is a gradual process involving adsorption on the cell surface, followed by endocytosis into cells, transport by lysosomes, and eventually resulted in cell necrosis and/or apoptosis. Our findings provide insight into the mechanisms of grass carp reovirus infection.

      Related collections

      Most cited references 44

      • Record: found
      • Abstract: found
      • Article: not found

      Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

       K Livak,  T Schmittgen (2001)
      The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
        Bookmark
        • Record: found
        • Abstract: found
        • Article: not found

        Mapping and quantifying mammalian transcriptomes by RNA-Seq.

        We have mapped and quantified mouse transcriptomes by deeply sequencing them and recording how frequently each gene is represented in the sequence sample (RNA-Seq). This provides a digital measure of the presence and prevalence of transcripts from known and previously unknown genes. We report reference measurements composed of 41-52 million mapped 25-base-pair reads for poly(A)-selected RNA from adult mouse brain, liver and skeletal muscle tissues. We used RNA standards to quantify transcript prevalence and to test the linear range of transcript detection, which spanned five orders of magnitude. Although >90% of uniquely mapped reads fell within known exons, the remaining data suggest new and revised gene models, including changed or additional promoters, exons and 3' untranscribed regions, as well as new candidate microRNA precursors. RNA splice events, which are not readily measured by standard gene expression microarray or serial analysis of gene expression methods, were detected directly by mapping splice-crossing sequence reads. We observed 1.45 x 10(5) distinct splices, and alternative splices were prominent, with 3,500 different genes expressing one or more alternate internal splices.
          Bookmark
          • Record: found
          • Abstract: found
          • Article: not found

          Differential expression analysis for sequence count data

          High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.
            Bookmark

            Author and article information

            Affiliations
            [1 ]State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China; chengeng@ 123456ihb.ac.cn (G.C.); helibowudi@ 123456ihb.ac.cn (L.H.); luolifei145@ 123456163.com (L.L.); huangrong@ 123456ihb.ac.cn (R.H.); liaolj@ 123456ihb.ac.cn (L.L.); liyongming8080@ 123456sohu.com (Y.L.); zyzhu@ 123456ihb.ac.cn (Z.Z.)
            [2 ]University of Chinese Academy of Sciences, Beijing 101408, China
            Author notes
            [* ]Correspondence: wangyp@ 123456ihb.ac.cn ; Tel.: +86-27-6878-0081
            Journal
            Int J Mol Sci
            Int J Mol Sci
            ijms
            International Journal of Molecular Sciences
            MDPI
            1422-0067
            06 February 2018
            February 2018
            : 19
            : 2
            29415502
            5855710
            10.3390/ijms19020488
            ijms-19-00488
            © 2018 by the authors.

            Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

            Categories
            Article

            Molecular biology

            grass carp reovirus, grass carp, transcriptomics sequencing, phagosome

            Comments

            Comment on this article