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      RNA-Seq Whole Transcriptome Analysis of Bovine Mammary Epithelial Cells in Response to Intracellular Staphylococcus aureus

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          Abstract

          Staphylococcus aureus ( S. aureus), a common mastitis pathogen widespread in the natural environment of dairy farms, is capable of invading mammary epithelial cells making treatment difficult. However, the mechanism of the response of bovine mammary epithelial cell to S. aureus invasion remains elusive. In this study, transcriptomic analysis and bioinformatics tools were applied to explore the differentially expressed RNAs in bovine mammary epithelial cells (bMECs) between the control and S. aureus-treated group. A total of 259 differentially expressed mRNAs (DEmRNAs), 27 differentially expressed microRNAs (DEmiRNAs), and 21 differentially expressed long non-coding RNAs (DElncRNAs) were found. These RNAs mainly enrich the inflammatory response, immune response, endocytosis, and cytokine-cytokine receptor interaction. qRT-PCR was used to analyze the quality of the RNA-seq results. In particular, to the defense mechanism of bovine mammary epithelial cells against intracellular S. aureus, the PPAR signaling pathway and the genes (ACOX2, CROT, and NUDT12) were found to be up-regulated to promote the production of peroxisomes and ROS, DRAM1 expression was also up-regulated to facilitate the activation of autophagy, indicating that the above mechanisms were involved in the elimination of intracellular S. aureus in bovine mammary epithelial cells.

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          Most cited references76

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

            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).
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              clusterProfiler: an R package for comparing biological themes among gene clusters.

              Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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                Author and article information

                Contributors
                Journal
                Front Vet Sci
                Front Vet Sci
                Front. Vet. Sci.
                Frontiers in Veterinary Science
                Frontiers Media S.A.
                2297-1769
                07 December 2020
                2020
                : 7
                : 642
                Affiliations
                [1] 1College of Veterinary Medicine, Shandong Agricultural University , Tai‘an, China
                [2] 2Research Center for Animal Disease Control Engineering, Shandong Agricultural University , Tai‘an, China
                [3] 3China Animal Health and Epidemiology Center , Qingdao, China
                [4] 4College of Veterinary Medicine, China Agricultural University , Beijing, China
                Author notes

                Edited by: Jasim Muhammad Uddin, Bangladesh Agricultural University, Bangladesh

                Reviewed by: Md. Aminul Islam, Tohoku University, Japan; Viswas K. Nagaleekar, Indian Veterinary Research Institute (IVRI), India

                *Correspondence: Yongxia Liu liuyongxia@ 123456sdau.edu.cn

                This article was submitted to Veterinary Infectious Diseases, a section of the journal Frontiers in Veterinary Science

                †These authors have contributed equally to this work

                Article
                10.3389/fvets.2020.00642
                7793973
                33681316
                72afd9ba-93d2-41b4-bcae-f480623850c9
                Copyright © 2020 Wang, Su, Yu, Geng, Li, Wang, Zhang, Liu, Liu and Han.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 03 June 2020
                : 07 August 2020
                Page count
                Figures: 10, Tables: 3, Equations: 0, References: 77, Pages: 14, Words: 7187
                Categories
                Veterinary Science
                Original Research

                staphylococcus aureus,bovine mastitis,bovine mammary epithelial cells,transcriptome,microrna,lncrna 3

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