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      Bioinformatic analysis reveals the expression of unique transcriptomic signatures in Zika virus infected human neural stem cells

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          Abstract

          Background

          The single-stranded RNA Flavivirus, Zika virus (ZIKV), has recently re-emerged and spread rapidly across the western hemisphere’s equatorial countries, primarily through Aedes mosquito transmission. While symptoms in adult infections appear to be self-limiting and mild, severe birth defects, such as microcephaly, have been linked to infection during early pregnancy. Recently, Tang et al. (Cell Stem Cell 2016, doi: 10.1016/j.stem.2016.02.016) demonstrated that ZIKV efficiently infects induced pluripotent stem cell (iPSC) derived human neural progenitor cells (hNPCs), resulting in cell cycle abnormalities and apoptosis. Consequently, hNPCs are a suggested ZIKV target.

          Methods

          We analyzed the transcriptomic sequencing (RNA-seq) data (GEO: GSE78711) of ZIKV (Strain: MR766) infected hNPCs. For comparison to the ZIKV-infected hNPCs, the expression data from hNPCs infected with human cytomegalovirus (CMV) (Strain: AD169) was used (GEO: GSE35295). Utilizing a combination of Gene Ontology, database of human diseases, and pathway analysis, we generated a putative systemic model of infection supported by known molecular pathways of other highly related viruses.

          Results

          We analyzed RNA-sequencing data for transcript expression alterations in ZIKV-infected hNPCs, and then compared them to expression patterns of iPSC-derived hNPCs infected with CMV, a virus that can also induce severe congenital neurological defects in developing fetuses. We demonstrate for the first time that many of cellular pathways correlate with clinical pathologies following ZIKV infection such as microcephaly, congenital nervous system disorders and epilepsy. Furthermore, ZIKV activates several inflammatory signals within infected hNPCs that are implicated in innate and acquired immune responses, while CMV-infected hNPCs showed limited representation of these pathways. Moreover, several genes related to pathogen responses are significantly upregulated upon ZIKV infection, but not perturbed in CMV-infected hNPCs.

          Conclusion

          The presented study is the first to report enrichment of numerous pro-inflammatory pathways in ZIKV-infected hNPCs, indicating that hNPCs are capable of signaling through canonical pro-inflammatory pathways following viral infection. By defining gene expression profiles, new factors in the pathogenesis of ZIKV were identified which could help develop new therapeutic strategies.

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          Most cited references19

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            FunRich: An open access standalone functional enrichment and interaction network analysis tool.

            As high-throughput techniques including proteomics become more accessible to individual laboratories, there is an urgent need for a user-friendly bioinformatics analysis system. Here, we describe FunRich, an open access, standalone functional enrichment and network analysis tool. FunRich is designed to be used by biologists with minimal or no support from computational and database experts. Using FunRich, users can perform functional enrichment analysis on background databases that are integrated from heterogeneous genomic and proteomic resources (>1.5 million annotations). Besides default human specific FunRich database, users can download data from the UniProt database, which currently supports 20 different taxonomies against which enrichment analysis can be performed. Moreover, the users can build their own custom databases and perform the enrichment analysis irrespective of organism. In addition to proteomics datasets, the custom database allows for the tool to be used for genomics, lipidomics and metabolomics datasets. Thus, FunRich allows for complete database customization and thereby permits for the tool to be exploited as a skeleton for enrichment analysis irrespective of the data type or organism used. FunRich (http://www.funrich.org) is user-friendly and provides graphical representation (Venn, pie charts, bar graphs, column, heatmap and doughnuts) of the data with customizable font, scale and color (publication quality).
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              R: A Language and environmental for statistical computing

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                Author and article information

                Contributors
                alyssa.rolfe@med.fsu.edu
                dale.bosco@med.fsu.edu
                jingying.wang0618@gmail.com
                richard.nowakowski@med.fsu.edu
                jqfan@princeton.edu
                850 645 2013 , yi.ren@med.fsu.edu
                Journal
                Cell Biosci
                Cell Biosci
                Cell & Bioscience
                BioMed Central (London )
                2045-3701
                10 June 2016
                10 June 2016
                2016
                : 6
                : 42
                Affiliations
                [ ]Department of Biomedical Sciences, College of Medicine, Florida State University, 1115 West Street, Tallahassee, FL 32306 USA
                [ ]Statistical Laboratory, Princeton University, Princeton, NJ 08540 USA
                Author information
                http://orcid.org/0000-0002-0176-493X
                Article
                110
                10.1186/s13578-016-0110-x
                4902960
                27293547
                305868c2-9469-42c9-a470-f091e237f03e
                © The Author(s) 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 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.

                History
                : 13 May 2016
                : 3 June 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01GM100474
                Award ID: R01GM072611
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2016

                Cell biology
                Cell biology

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