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      Experimental challenge with bovine respiratory syncytial virus in dairy calves: bronchial lymph node transcriptome response

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          Abstract

          Bovine Respiratory Disease (BRD) is the leading cause of mortality in calves. The objective of this study was to examine the response of the host’s bronchial lymph node transcriptome to Bovine Respiratory Syncytial Virus (BRSV) in a controlled viral challenge. Holstein-Friesian calves were either inoculated with virus (10 3.5 TCID 50/ml × 15 ml) (n = 12) or mock challenged with phosphate buffered saline (n = 6). Clinical signs were scored daily and blood was collected for haematology counts, until euthanasia at day 7 post-challenge. RNA was extracted and sequenced (75 bp paired-end) from bronchial lymph nodes. Sequence reads were aligned to the UMD3.1 bovine reference genome and differential gene expression analysis was performed using EdgeR. There was a clear separation between BRSV challenged and control calves based on gene expression changes, despite an observed mild clinical manifestation of the disease. Therefore, measuring host gene expression levels may be beneficial for the diagnosis of subclinical BRD. There were 934 differentially expressed genes (DEG) (p < 0.05, FDR <0.1, fold change >2) between the BRSV challenged and control calves. Over-represented gene ontology terms, pathways and molecular functions, among the DEG, were associated with immune responses. The top enriched pathways included interferon signaling, granzyme B signaling and pathogen pattern recognition receptors, which are responsible for the cytotoxic responses necessary to eliminate the virus.

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          Most cited references55

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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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            KEGG: kyoto encyclopedia of genes and genomes.

            M Kanehisa (2000)
            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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              Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

              Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.
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                Author and article information

                Contributors
                Sinead.Waters@Teagasc.ie
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                14 October 2019
                14 October 2019
                2019
                : 9
                : 14736
                Affiliations
                [1 ]ISNI 0000 0001 1512 9569, GRID grid.6435.4, Animal and Bioscience Research Department, , Animal & Grassland Research and Innovation Centre, Teagasc, ; Grange, Co. Meath Ireland
                [2 ]Veterinary Sciences Division, Agri-Food and Biosciences Institute, Stormont, Belfast, Northern Ireland
                [3 ]ISNI 0000 0001 2162 3504, GRID grid.134936.a, Division of Animal Sciences, , University of Missouri, ; Columbia, MO USA
                Author information
                http://orcid.org/0000-0001-9844-1573
                http://orcid.org/0000-0003-2114-9958
                http://orcid.org/0000-0002-6997-7245
                Article
                51094
                10.1038/s41598-019-51094-z
                6791843
                31611566
                559c602d-fa56-434e-a8e6-0a02f0456591
                © The Author(s) 2019

                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
                : 28 June 2019
                : 19 September 2019
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                © The Author(s) 2019

                Uncategorized
                transcriptomics,viral infection
                Uncategorized
                transcriptomics, viral infection

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