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      HITS-CLIP Analysis Uncovers a Link between the Kaposi’s Sarcoma-Associated Herpesvirus ORF57 Protein and Host Pre-mRNA Metabolism

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          Abstract

          The Kaposi’s sarcoma associated herpesvirus (KSHV) is an oncogenic virus that causes Kaposi’s sarcoma, primary effusion lymphoma (PEL), and some forms of multicentric Castleman’s disease. The KSHV ORF57 protein is a conserved posttranscriptional regulator of gene expression that is essential for virus replication. ORF57 is multifunctional, but most of its activities are directly linked to its ability to bind RNA. We globally identified virus and host RNAs bound by ORF57 during lytic reactivation in PEL cells using high-throughput sequencing of RNA isolated by cross-linking immunoprecipitation (HITS-CLIP). As expected, ORF57-bound RNA fragments mapped throughout the KSHV genome, including the known ORF57 ligand PAN RNA. In agreement with previously published ChIP results, we observed that ORF57 bound RNAs near the oriLyt regions of the genome. Examination of the host RNA fragments revealed that a subset of the ORF57-bound RNAs was derived from transcript 5´ ends. The position of these 5´-bound fragments correlated closely with the 5´-most exon-intron junction of the pre-mRNA. We selected four candidates (BTG1, EGR1, ZFP36, and TNFSF9) and analyzed their pre-mRNA and mRNA levels during lytic phase. Analysis of both steady-state and newly made RNAs revealed that these candidate ORF57-bound pre-mRNAs persisted for longer periods of time throughout infection than control RNAs, consistent with a role for ORF57 in pre-mRNA metabolism. In addition, exogenous expression of ORF57 was sufficient to increase the pre-mRNA levels and, in one case, the mRNA levels of the putative ORF57 targets. These results demonstrate that ORF57 interacts with specific host pre-mRNAs during lytic reactivation and alters their processing, likely by stabilizing pre-mRNAs. These data suggest that ORF57 is involved in modulating host gene expression in addition to KSHV gene expression during lytic reactivation.

          Author Summary

          During viral replication, the oncogenic Kaposi’s sarcoma-associated herpesvirus (KSHV) modulates both host and viral gene expression. KSHV ORF57 is a multifunctional posttranscriptional regulator that is essential for viral replication and stabilizes viral RNAs. Previous studies demonstrated that ORF57 RNA-binding is essential for its activity, but the full spectrum of ORF57 targets are unknown. Here we employed a high-throughput analysis to identify RNA fragments bound by ORF57 during lytic reactivation. As expected, we found targets that mapped to the viral genome, and we further uncovered novel host targets, a subset of which had ORF57 bound near their 5´ ends. Further examination of this subset demonstrated that ORF57 bound preferentially at the 5´-most exon-intron boundary. ORF57 affected the pre-mRNA abundance from these genes, most likely by stabilizing otherwise unstable inefficiently spliced pre-mRNAs. In at least one case, this stabilization led to increases in mRNA expression of the host gene. We suggest that KSHV employs the same mechanism to stabilize intronless viral RNAs and cellular unspliced pre-mRNAs to modulate viral and host gene expression during lytic reactivation.

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          Most cited references 84

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          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.
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            MEME Suite: tools for motif discovery and searching

            The MEME Suite web server provides a unified portal for online discovery and analysis of sequence motifs representing features such as DNA binding sites and protein interaction domains. The popular MEME motif discovery algorithm is now complemented by the GLAM2 algorithm which allows discovery of motifs containing gaps. Three sequence scanning algorithms—MAST, FIMO and GLAM2SCAN—allow scanning numerous DNA and protein sequence databases for motifs discovered by MEME and GLAM2. Transcription factor motifs (including those discovered using MEME) can be compared with motifs in many popular motif databases using the motif database scanning algorithm Tomtom. Transcription factor motifs can be further analyzed for putative function by association with Gene Ontology (GO) terms using the motif-GO term association tool GOMO. MEME output now contains sequence LOGOS for each discovered motif, as well as buttons to allow motifs to be conveniently submitted to the sequence and motif database scanning algorithms (MAST, FIMO and Tomtom), or to GOMO, for further analysis. GLAM2 output similarly contains buttons for further analysis using GLAM2SCAN and for rerunning GLAM2 with different parameters. All of the motif-based tools are now implemented as web services via Opal. Source code, binaries and a web server are freely available for noncommercial use at http://meme.nbcr.net.
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              Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration

              Data visualization is an essential component of genomic data analysis. However, the size and diversity of the data sets produced by today’s sequencing and array-based profiling methods present major challenges to visualization tools. The Integrative Genomics Viewer (IGV) is a high-performance viewer that efficiently handles large heterogeneous data sets, while providing a smooth and intuitive user experience at all levels of genome resolution. A key characteristic of IGV is its focus on the integrative nature of genomic studies, with support for both array-based and next-generation sequencing data, and the integration of clinical and phenotypic data. Although IGV is often used to view genomic data from public sources, its primary emphasis is to support researchers who wish to visualize and explore their own data sets or those from colleagues. To that end, IGV supports flexible loading of local and remote data sets, and is optimized to provide high-performance data visualization and exploration on standard desktop systems. IGV is freely available for download from http://www.broadinstitute.org/igv, under a GNU LGPL open-source license.
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                Author and article information

                Affiliations
                [1 ]Department of Microbiology, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
                [2 ]Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
                University of California, Berkeley, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: NKC ES. Performed the experiments: ES OVH. Analyzed the data: YX TW NKC ES. Wrote the paper: NKC ES TW. Conceived, designed, and implemented the bioinformatics analysis pipeline: YX TW.

                ‡ These authors contributed equally to this work.

                Contributors
                Role: Editor
                Journal
                PLoS Pathog
                PLoS Pathog
                plos
                plospath
                PLoS Pathogens
                Public Library of Science (San Francisco, CA USA )
                1553-7366
                1553-7374
                24 February 2015
                February 2015
                : 11
                : 2
                25710169 4339584 10.1371/journal.ppat.1004652 PPATHOGENS-D-14-02251

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

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                Figures: 9, Tables: 0, Pages: 35
                Product
                Funding
                This work was funded by the National Institutes of Health: AI081710 to NKC and 5R01CA152301 to YX. Funding was also provided by the Cancer Prevention and Research Institute of Texas: RP101251 to YX. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Custom metadata
                All relevant data are within the paper and its Supporting Information files except for the raw sequencing data which is available from NIH GEO database under the accession number GSE64413.

                Infectious disease & Microbiology

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