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      Revealing the characteristics of ZIKV infection through tissue-specific transcriptome sequencing analysis

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          Abstract

          Background

          Recently, Zika virus (ZIKV) re-emerged in India and was potentially associated with microcephaly. However, the molecular mechanisms underlying ZIKV pathogenesis remain to be explored.

          Results

          Herein, we performed a comprehensive RNA-sequencing analysis on ZIKV-infected JEG-3, U-251 MG, and HK-2 cells versus corresponding uninfected controls. Combined with a series of functional analyses, including gene annotation, pathway enrichment, and protein–protein interaction (PPI) network analysis, we defined the molecular characteristics induced by ZIKV infection in different tissues and invasion time points. Data showed that ZIKV infection and replication in each susceptible organ commonly stimulated interferon production and down-regulated metabolic-related processes. Also, tissue-specific immune responses or biological processes (BPs) were induced after ZIKV infection, including GnRH signaling pathway in JEG-3 cells, MAPK signaling pathway in U-251 MG cells, and PPAR signaling pathway in HK-2 cells. Of note, ZIKV infection induced delayed antiviral interferon responses in the placenta-derived cell lines, which potentially explains the molecular mechanism by which ZIKV replicates rapidly in the placenta and subsequential vertical transmission occurs.

          Conclusions

          Together, these data may provide a systemic insight into the pathogenesis of ZIKV infection in distinct human tissue-derived cell lines, which is likely to help develop prophylactic and therapeutic strategies against ZIKV infection.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12864-022-08919-5.

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          Most cited references89

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          KEGG: kyoto encyclopedia of genes and genomes.

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          KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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            KEGG: new perspectives on genomes, pathways, diseases and drugs

            KEGG (http://www.kegg.jp/ or http://www.genome.jp/kegg/) is an encyclopedia of genes and genomes. Assigning functional meanings to genes and genomes both at the molecular and higher levels is the primary objective of the KEGG database project. Molecular-level functions are stored in the KO (KEGG Orthology) database, where each KO is defined as a functional ortholog of genes and proteins. Higher-level functions are represented by networks of molecular interactions, reactions and relations in the forms of KEGG pathway maps, BRITE hierarchies and KEGG modules. In the past the KO database was developed for the purpose of defining nodes of molecular networks, but now the content has been expanded and the quality improved irrespective of whether or not the KOs appear in the three molecular network databases. The newly introduced addendum category of the GENES database is a collection of individual proteins whose functions are experimentally characterized and from which an increasing number of KOs are defined. Furthermore, the DISEASE and DRUG databases have been improved by systematic analysis of drug labels for better integration of diseases and drugs with the KEGG molecular networks. KEGG is moving towards becoming a comprehensive knowledge base for both functional interpretation and practical application of genomic information.
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              Toward understanding the origin and evolution of cellular organisms

              In this era of high‐throughput biology, bioinformatics has become a major discipline for making sense out of large‐scale datasets. Bioinformatics is usually considered as a practical field developing databases and software tools for supporting other fields, rather than a fundamental scientific discipline for uncovering principles of biology. The KEGG resource that we have been developing is a reference knowledge base for biological interpretation of genome sequences and other high‐throughput data. It is now one of the most utilized biological databases because of its practical values. For me personally, KEGG is a step toward understanding the origin and evolution of cellular organisms.
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                Author and article information

                Contributors
                ty_qiu@126.com
                zhangxiaoyan@fudan.edu.cn
                xujianqing@fudan.edu.cn
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                8 October 2022
                8 October 2022
                2022
                : 23
                : 697
                Affiliations
                [1 ]GRID grid.11841.3d, ISNI 0000 0004 0619 8943, Institutes of Biomedical Sciences, , Shanghai Medical College, Fudan University, ; Shanghai, 200032 China
                [2 ]GRID grid.470110.3, ISNI 0000 0004 1770 0943, Shanghai Public Health Clinical Center, Fudan University, ; Shanghai, 201508 China
                [3 ]GRID grid.413087.9, ISNI 0000 0004 1755 3939, Department of Immunotherapy and Shanghai Key Laboratory of Organ Transplantation, , Zhongshan Hospital, Fudan University, ; Shanghai, 200032 People’s Republic of China
                Article
                8919
                10.1186/s12864-022-08919-5
                9546753
                2c912b40-ec67-44ac-90ac-0499235d6907
                © The Author(s) 2022

                Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 18 June 2022
                : 26 September 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 31900483
                Award ID: 81901598
                Award ID: 82071788
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Genetics
                zika virus,tissue-specific,transcriptome sequencing analysis,host immune responses,type i interferon

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