Blog
About

11
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Genome-wide analysis of the transcriptional response to porcine reproductive and respiratory syndrome virus infection at the maternal/fetal interface and in the fetus

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) infection of pregnant pigs can result in congenital infection and ultimately fetal death. Little is known about immune responses to infection at the maternal-fetal interface and in the fetus itself, or the molecular events behind virus transmission and disease progression in the fetus. To investigate these processes, RNA-sequencing of two sites, uterine endothelium with adherent placental tissue and fetal thymus, was performed 21 days post-challenge on four groups of fetuses selected from a large PRRSV challenge experiment of pregnant gilts: control (CON), uninfected (UNINF), infected (INF), and meconium-stained (MEC) (n = 12/group). Transcriptional analyses consisted of multiple contrasts between groups using two approaches: differential gene expression analysis and weighted gene co-expression network analysis (WGCNA). Biological functions, pathways, and regulators enriched for differentially expressed genes or module members were identified through functional annotation analyses. Expression data were validated by reverse transcription quantitative polymerase chain reaction (RTqPCR) carried out for 16 genes of interest.

          Results

          The immune response to infection in endometrium was mainly adaptive in nature, with the most upregulated genes functioning in either humoral or cell-mediated immunity. In contrast, the expression profile of infected fetal thymus revealed a predominantly innate immune response to infection, featuring the upregulation of genes regulated by type I interferon and pro-inflammatory cytokines. Fetal infection was associated with an increase in viral load coupled with a reduction in T cell signaling in the endometrium that could be due to PRRSV-controlled apoptosis of uninfected bystander cells. There was also evidence for a reduction in TWIST1 activity, a transcription factor involved in placental implantation and maturation, which could facilitate virus transmission or fetal pathology through dysregulation of placental function. Finally, results suggested that events within the fetus could also drive fetal pathology. Thymus samples of meconium-stained fetuses exhibited an increase in the expression of pro-inflammatory cytokine and granulocyte genes previously implicated in swine infectious disease pathology.

          Conclusions

          This study identified major differences in the response to PRRSV infection in the uterine endometrium and fetus at the gene expression level, and provides insight into the molecular basis of virus transmission and disease progression.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12864-016-2720-4) contains supplementary material, which is available to authorized users.

          Related collections

          Most cited references 65

          • Record: found
          • Abstract: found
          • Article: not found

          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

           K Livak,  T Schmittgen (2001)
          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            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
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found

              TopHat: discovering splice junctions with RNA-Seq

              Motivation: A new protocol for sequencing the messenger RNA in a cell, known as RNA-Seq, generates millions of short sequence fragments in a single run. These fragments, or ‘reads’, can be used to measure levels of gene expression and to identify novel splice variants of genes. However, current software for aligning RNA-Seq data to a genome relies on known splice junctions and cannot identify novel ones. TopHat is an efficient read-mapping algorithm designed to align reads from an RNA-Seq experiment to a reference genome without relying on known splice sites. Results: We mapped the RNA-Seq reads from a recent mammalian RNA-Seq experiment and recovered more than 72% of the splice junctions reported by the annotation-based software from that study, along with nearly 20 000 previously unreported junctions. The TopHat pipeline is much faster than previous systems, mapping nearly 2.2 million reads per CPU hour, which is sufficient to process an entire RNA-Seq experiment in less than a day on a standard desktop computer. We describe several challenges unique to ab initio splice site discovery from RNA-Seq reads that will require further algorithm development. Availability: TopHat is free, open-source software available from http://tophat.cbcb.umd.edu Contact: cole@cs.umd.edu Supplementary information: Supplementary data are available at Bioinformatics online.
                Bookmark

                Author and article information

                Affiliations
                [ ]Department of Agricultural, Food, and Nutritional Science, University of Alberta, Edmonton, AB Canada
                [ ]Department for Farm Animals and Veterinary Public Health, University Clinic for Swine, University of Veterinary Medicine, Vienna, Austria
                [ ]Department of Large Animal Clinical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, SK Canada
                [ ]Animal Parasitic Diseases Laboratory, Beltsville Agricultural Research Center, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, MD USA
                [ ]Key Lab of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
                Contributors
                jamiewilkinson@hotmail.com
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                20 May 2016
                20 May 2016
                2016
                : 17
                2720
                10.1186/s12864-016-2720-4
                4875603
                27207143
                © Wilkinson et al. 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.

                Funding
                Funded by: Government of Saskatchewan
                Award ID: 20110001
                Funded by: Genome Canada
                Award ID: Application of Genomics to Improve Swine Health and Welfare
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2016

                Genetics

                transcriptome, rna-sequencing, pig, fetus, prrsv, gene

                Comments

                Comment on this article