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      Macrophages from Susceptible and Resistant Chicken Lines have Different Transcriptomes following Marek’s Disease Virus Infection

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          Abstract

          Despite successful control by vaccination, Marek’s disease (MD) has continued evolving to greater virulence over recent years. To control MD, selection and breeding of MD-resistant chickens might be a suitable option. MHC-congenic inbred chicken lines, 6 1 and 7 2, are highly resistant and susceptible to MD, respectively, but the cellular and genetic basis for these phenotypes is unknown. Marek’s disease virus (MDV) infects macrophages, B-cells, and activated T-cells in vivo. This study investigates the cellular basis of resistance to MD in vitro with the hypothesis that resistance is determined by cells active during the innate immune response. Chicken bone marrow-derived macrophages from lines 6 1 and 7 2 were infected with MDV in vitro. Flow cytometry showed that a higher percentage of macrophages were infected in line 7 2 than in line 6 1. A transcriptomic study followed by in silico functional analysis of differentially expressed genes was then carried out between the two lines pre- and post-infection. Analysis supports the hypothesis that macrophages from susceptible and resistant chicken lines display a marked difference in their transcriptome following MDV infection. Resistance to infection, differential activation of biological pathways, and suppression of oncogenic potential are among host defense strategies identified in macrophages from resistant chickens.

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          We derive a quantile-adjusted conditional maximum likelihood estimator for the dispersion parameter of the negative binomial distribution and compare its performance, in terms of bias, to various other methods. Our estimation scheme outperforms all other methods in very small samples, typical of those from serial analysis of gene expression studies, the motivating data for this study. The impact of dispersion estimation on hypothesis testing is studied. We derive an "exact" test that outperforms the standard approximate asymptotic tests.
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            Moderated statistical tests for assessing differences in tag abundance.

            Digital gene expression (DGE) technologies measure gene expression by counting sequence tags. They are sensitive technologies for measuring gene expression on a genomic scale, without the need for prior knowledge of the genome sequence. As the cost of sequencing DNA decreases, the number of DGE datasets is expected to grow dramatically. Various tests of differential expression have been proposed for replicated DGE data using binomial, Poisson, negative binomial or pseudo-likelihood (PL) models for the counts, but none of the these are usable when the number of replicates is very small. We develop tests using the negative binomial distribution to model overdispersion relative to the Poisson, and use conditional weighted likelihood to moderate the level of overdispersion across genes. Not only is our strategy applicable even with the smallest number of libraries, but it also proves to be more powerful than previous strategies when more libraries are available. The methodology is equally applicable to other counting technologies, such as proteomic spectral counts. An R package can be accessed from http://bioinf.wehi.edu.au/resources/
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              FastQC: A Quality Control Tool for Higher Throughput Sequence Data

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                Author and article information

                Journal
                Genes (Basel)
                Genes (Basel)
                genes
                Genes
                MDPI
                2073-4425
                22 January 2019
                February 2019
                : 10
                : 2
                : 74
                Affiliations
                [1 ]The Roslin Institute and R(D)SVS, University of Edinburgh, Easter Bush, Midlothian EH25 9RG, UK; pcb23m@ 123456yahoo.com (P.C.); richard.kuo@ 123456roslin.ed.ac.uk (R.K.); lonneke.vervelde@ 123456roslin.ed.ac.uk (L.V.); bernadette.dutia@ 123456roslin.ed.ac.uk (B.M.D.)
                [2 ]Chittagong Veterinary and Animal Sciences University, Khulshi, Chittagong 4225, Bangladesh
                Author notes
                [* ]Correspondence: jacqueline.smith@ 123456roslin.ed.ac.uk ; Tel.: +44-(0)131-6519155
                [†]

                Deceased.

                Author information
                https://orcid.org/0000-0002-5673-9619
                https://orcid.org/0000-0003-2241-1743
                https://orcid.org/0000-0002-2813-7872
                Article
                genes-10-00074
                10.3390/genes10020074
                6409778
                30678299
                30710a63-81f1-47a6-8c95-1a9ccaa1eb23
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 13 November 2018
                : 21 January 2019
                Categories
                Article

                chickens,marek’s disease virus,disease resistance,macrophages,rna-seq

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