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      Gene expression profiles alteration after infection of virus, bacteria, and parasite in the Olive flounder ( Paralichthys olivaceus )

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          Abstract

          Olive flounder ( Paralichthys olivaceus) is one of economically valuable fish species in the East Asia. In comparison with its economic importance, available genomic information of the olive flounder is very limited. The mass mortality caused by variety of pathogens (virus, bacteria and parasites) is main problem in aquaculture industry, including in olive flounder culture. In this study, we carried out transcriptome analysis using the olive flounder gill tissues after infection of three types of pathogens (Virus; Viral hemorrhagic septicemia virus, Bacteria; Streptococcus parauberis, and Parasite; Miamiensis avidus), respectively. As a result, we identified total 12,415 differentially expressed genes (DEG) from viral infection, 1,754 from bacterial infection, and 795 from parasite infection, respectively. To investigate the effects of pathogenic infection on immune response, we analyzed Gene ontology (GO) enrichment analysis with DEGs and sorted immune-related GO terms per three pathogen groups. Especially, we verified various GO terms, and genes in these terms showed down-regulated expression pattern. In addition, we identified 67 common genes (10 up-regulated and 57 down-regulated) present in three pathogen infection groups. Our goals are to provide plenty of genomic knowledge about olive flounder transcripts for further research and report genes, which were changed in their expression after specific pathogen infection.

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          Anti-immunology: evasion of the host immune system by bacterial and viral pathogens.

          Multicellular organisms possess very sophisticated defense mechanisms that are designed to effectively counter the continual microbial insult of the environment within the vertebrate host. However, successful microbial pathogens have in turn evolved complex and efficient methods to overcome innate and adaptive immune mechanisms, which can result in disease or chronic infections. Although the various virulence strategies used by viral and bacterial pathogens are numerous, there are several general mechanisms that are used to subvert and exploit immune systems that are shared between these diverse microbial pathogens. The success of each pathogen is directly dependant on its ability to mount an effective anti-immune response within the infected host, which can ultimately result in acute disease, chronic infection, or pathogen clearance. In this review, we highlight and compare some of the many molecular mechanisms that bacterial and viral pathogens use to evade host immune defenses.
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            Interactions between extracellular matrix and growth factors in wound healing.

            Dynamic interactions between growth factors and extracellular matrix (ECM) are integral to wound healing. These interactions take several forms that may be categorized as direct or indirect. The ECM can directly bind to and release certain growth factors (e.g., heparan sulfate binding to fibroblast growth factor-2), which may serve to sequester and protect growth factors from degradation, and/or enhance their activity. Indirect interactions include binding of cells to ECM via integrins, which enables cells to respond to growth factors (e.g., integrin binding is necessary for vascular endothelial growth factor-induced angiogenesis) and can induce growth factor expression (adherence of monocytes to ECM stimulates synthesis of platelet-derived growth factor). Additionally, matrikines, or subcomponents of ECM molecules, can bind to cell surface receptors in the cytokine, chemokine, or growth factor families and stimulate cellular activities (e.g., tenascin-C and laminin bind to epidermal growth factor receptors, which enhances fibroblast migration). Growth factors such as transforming growth factor-beta also regulate the ECM by increasing the production of ECM components or enhancing synthesis of matrix degrading enzymes. Thus, the interactions between growth factors and ECM are bidirectional. This review explores these interactions, discusses how they are altered in difficult to heal or chronic wounds, and briefly considers treatment implications.
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              TCC: an R package for comparing tag count data with robust normalization strategies

              Background Differential expression analysis based on “next-generation” sequencing technologies is a fundamental means of studying RNA expression. We recently developed a multi-step normalization method (called TbT) for two-group RNA-seq data with replicates and demonstrated that the statistical methods available in four R packages (edgeR, DESeq, baySeq, and NBPSeq) together with TbT can produce a well-ranked gene list in which true differentially expressed genes (DEGs) are top-ranked and non-DEGs are bottom ranked. However, the advantages of the current TbT method come at the cost of a huge computation time. Moreover, the R packages did not have normalization methods based on such a multi-step strategy. Results TCC (an acronym for Tag Count Comparison) is an R package that provides a series of functions for differential expression analysis of tag count data. The package incorporates multi-step normalization methods, whose strategy is to remove potential DEGs before performing the data normalization. The normalization function based on this DEG elimination strategy (DEGES) includes (i) the original TbT method based on DEGES for two-group data with or without replicates, (ii) much faster methods for two-group data with or without replicates, and (iii) methods for multi-group comparison. TCC provides a simple unified interface to perform such analyses with combinations of functions provided by edgeR, DESeq, and baySeq. Additionally, a function for generating simulation data under various conditions and alternative DEGES procedures consisting of functions in the existing packages are provided. Bioinformatics scientists can use TCC to evaluate their methods, and biologists familiar with other R packages can easily learn what is done in TCC. Conclusion DEGES in TCC is essential for accurate normalization of tag count data, especially when up- and down-regulated DEGs in one of the samples are extremely biased in their number. TCC is useful for analyzing tag count data in various scenarios ranging from unbiased to extremely biased differential expression. TCC is available at http://www.iu.a.u-tokyo.ac.jp/~kadota/TCC/ and will appear in Bioconductor (http://bioconductor.org/) from ver. 2.13.
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                Author and article information

                Contributors
                khs307@pusan.ac.kr
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                24 December 2018
                24 December 2018
                2018
                : 8
                : 18065
                Affiliations
                [1 ]ISNI 0000 0001 0719 8572, GRID grid.262229.f, Department of Biological Sciences, College of Natural Sciences, , Pusan National University, ; Busan, 46241 Republic of Korea
                [2 ]ISNI 0000 0001 0719 8572, GRID grid.262229.f, Institute of Systems Biology, , Pusan National University, ; Busan, 46241 Republic of Korea
                [3 ]ISNI 0000 0004 0470 5905, GRID grid.31501.36, Center for Convergence Approaches in Drug Development (CCADD), , Graduate School of Convergence Science and Technology, Seoul National University, ; Suwon, 16229 Republic of Korea
                [4 ]ISNI 0000 0001 0719 8572, GRID grid.262229.f, Department of Chemistry, Center for Proteome Biophysics and Chemistry Institute for Functional Materials, , Pusan National University, ; Busan, 46241 Republic of Korea
                [5 ]ISNI 0000 0001 0719 8994, GRID grid.412576.3, Department of Aquatic Life Medicine, , Pukyong National University, ; Busan, 48513 Republic of Korea
                [6 ]Biotechnology Research Division, National Fisheries Research and Development Institute, 216 Gijanghaean-ro, Gijang-eup, Gijang-gun, Busan, 46083 Republic of Korea
                [7 ]Theragen ETEX Bio Institute, Suwon, 16229 Republic of Korea
                [8 ]ISNI 0000 0004 0532 9454, GRID grid.411144.5, Department of Parasitology and Genetics, , Kosin University College of Medicine, ; Busan, 49267 Korea
                [9 ]ISNI 0000 0001 0310 3978, GRID grid.412050.2, Department of Biochemistry, College of Oriental Medicine, , Dongeui University, ; Busan, 47227 Korea
                [10 ]ISNI 0000 0001 0661 1492, GRID grid.256681.e, Department of Marine Biology and Aquaculture, , College of Marine Science, Gyeongsang National University, ; Tongyeong, 53064 Korea
                Author information
                http://orcid.org/0000-0002-6963-2685
                Article
                36342
                10.1038/s41598-018-36342-y
                6305387
                30584247
                ab90e1ad-b63f-4446-8b5b-7ff9371e80a0
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 23 August 2017
                : 14 November 2018
                Funding
                Funded by: This research was a part of the project titled “Omics based on fishery disease control technology development and industrialization (20150242),” funded by the Ministry of Oceans and Fisheries, Korea.
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