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      Transcriptional Response of Ovine Lung to Infection with Jaagsiekte Sheep Retrovirus

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          Abstract

          Ovine pulmonary adenocarcinoma is a chronic respiratory disease of sheep caused by jaagsiekte sheep retrovirus (JSRV). OPA is a significant economic problem for sheep farmers in many countries and is a valuable animal model for some forms of human lung cancer. Here, we examined the changes in host gene expression that occur in the lung in response to JSRV infection. We identified a large number of genes with altered expression in infected lung, including factors with roles in cancer and immune system function. We also compared the data from OPA to previously published data from human lung adenocarcinoma and found a large degree of overlap in the genes that were dysregulated. The results of this study provide exciting new avenues for future studies of OPA and may have comparative relevance for understanding human lung cancer.

          ABSTRACT

          Jaagsiekte sheep retrovirus (JSRV) is the etiologic agent of ovine pulmonary adenocarcinoma (OPA), a neoplastic lung disease of sheep. OPA is an important economic and welfare issue for sheep farmers and a valuable naturally occurring animal model for human lung adenocarcinoma. Here, we used RNA sequencing to study the transcriptional response of ovine lung tissue to infection by JSRV. We identified 1,971 ovine genes differentially expressed in JSRV-infected lung compared to noninfected lung, including many genes with roles in carcinogenesis and immunomodulation. The differential expression of selected genes was confirmed using immunohistochemistry and reverse transcription-quantitative PCR. A key finding was the activation of anterior gradient 2, yes-associated protein 1, and amphiregulin in OPA tumor cells, indicating a role for this oncogenic pathway in OPA. In addition, there was differential expression of genes related to innate immunity, including genes encoding cytokines, chemokines, and complement system proteins. In contrast, there was little evidence for the upregulation of genes involved in T-cell immunity. Many genes related to macrophage function were also differentially expressed, reflecting the increased abundance of these cells in OPA-affected lung tissue. Comparison of the genes differentially regulated in OPA with the transcriptional changes occurring in human lung cancer revealed important similarities and differences between OPA and human lung adenocarcinoma. This study provides valuable new information on the pathogenesis of OPA and strengthens the use of this naturally occurring animal model for human lung adenocarcinoma.

          IMPORTANCE Ovine pulmonary adenocarcinoma is a chronic respiratory disease of sheep caused by jaagsiekte sheep retrovirus (JSRV). OPA is a significant economic problem for sheep farmers in many countries and is a valuable animal model for some forms of human lung cancer. Here, we examined the changes in host gene expression that occur in the lung in response to JSRV infection. We identified a large number of genes with altered expression in infected lung, including factors with roles in cancer and immune system function. We also compared the data from OPA to previously published data from human lung adenocarcinoma and found a large degree of overlap in the genes that were dysregulated. The results of this study provide exciting new avenues for future studies of OPA and may have comparative relevance for understanding human lung cancer.

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          Most cited references 81

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          Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

          DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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              HTSeq—a Python framework to work with high-throughput sequencing data

              Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de
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                Author and article information

                Contributors
                Role: Editor
                Journal
                J Virol
                J. Virol
                jvi
                jvi
                JVI
                Journal of Virology
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                0022-538X
                1098-5514
                21 August 2019
                15 October 2019
                1 November 2019
                15 October 2019
                : 93
                : 21
                Affiliations
                [a ]Moredun Research Institute, Edinburgh, United Kingdom
                [b ]Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom
                Ulm University Medical Center
                Author notes
                Address correspondence to David J. Griffiths, david.griffiths@ 123456moredun.ac.uk .
                [*]

                Present address: Anna Eleonora Karagianni, Roslin Institute and Royal (Dick) School of Veterinary Studies, Easter Bush, Midlothian, United Kingdom; Henny M. Martineau, Royal Veterinary College, Hatfield, Hertfordshire, United Kingdom.

                Citation Karagianni AE, Vasoya D, Finlayson J, Martineau HM, Wood AR, Cousens C, Dagleish MP, Watson M, Griffiths DJ. 2019. Transcriptional response of ovine lung to infection with jaagsiekte sheep retrovirus. J Virol 93:e00876-19. https://doi.org/10.1128/JVI.00876-19.

                Article
                00876-19
                10.1128/JVI.00876-19
                6803282
                31434729
                Copyright © 2019 Karagianni et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                Page count
                supplementary-material: 6, Figures: 9, Tables: 3, Equations: 0, References: 81, Pages: 21, Words: 11957
                Product
                Funding
                Funded by: Scottish Government, https://doi.org/10.13039/100012095;
                Award Recipient : Award Recipient : Award Recipient : Award Recipient : Award Recipient :
                Funded by: UK Research and Innovation | Biotechnology and Biological Sciences Research Council (BBSRC), https://doi.org/10.13039/501100000268;
                Award ID: BB/L009129/1
                Award Recipient : Award Recipient : Award Recipient : Award Recipient :
                Funded by: UK Research and Innovation | Biotechnology and Biological Sciences Research Council (BBSRC), https://doi.org/10.13039/501100000268;
                Award ID: BB/L008505/1
                Award Recipient : Award Recipient :
                Categories
                Pathogenesis and Immunity
                Custom metadata
                November 2019

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