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      Deciphering Transcriptome and Complex Alternative Splicing Transcripts in Mammary Gland Tissues from Cows Naturally Infected with Staphylococcus aureus Mastitis

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          Abstract

          Alternative splicing (AS) contributes to the complexity of the mammalian proteome and plays an important role in diseases, including infectious diseases. The differential AS patterns of these transcript sequences between the healthy (HS3A) and mastitic (HS8A) cows naturally infected by Staphylococcus aureus were compared to understand the molecular mechanisms underlying mastitis resistance and susceptibility. In this study, using the Illumina paired-end RNA sequencing method, 1352 differentially expressed genes (DEGs) with higher than twofold changes were found in the HS3A and HS8A mammary gland tissues. Gene ontology and KEGG pathway analyses revealed that the cytokine–cytokine receptor interaction pathway is the most significantly enriched pathway. Approximately 16k annotated unigenes were respectively identified in two libraries, based on the bovine Bos taurus UMD3.1 sequence assembly and search. A total of 52.62% and 51.24% annotated unigenes were alternatively spliced in term of exon skipping, intron retention, alternative 5′ splicing and alternative 3ʹ splicing. Additionally, 1,317 AS unigenes were HS3A-specific, whereas 1,093 AS unigenes were HS8A-specific. Some immune-related genes, such as ITGB6, MYD88, ADA, ACKR1, and TNFRSF1B, and their potential relationships with mastitis were highlighted. From Chromosome 2, 4, 6, 7, 10, 13, 14, 17, and 20, 3.66% (HS3A) and 5.4% (HS8A) novel transcripts, which harbor known quantitative trait locus associated with clinical mastitis, were identified. Many DEGs in the healthy and mastitic mammary glands are involved in immune, defense, and inflammation responses. These DEGs, which exhibit diverse and specific splicing patterns and events, can endow dairy cattle with the potential complex genetic resistance against mastitis.

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          Most cited references35

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          Identification of novel transcripts in annotated genomes using RNA-Seq.

          We describe a new 'reference annotation based transcript assembly' problem for RNA-Seq data that involves assembling novel transcripts in the context of an existing annotation. This problem arises in the analysis of expression in model organisms, where it is desirable to leverage existing annotations for discovering novel transcripts. We present an algorithm for reference annotation-based transcript assembly and show how it can be used to rapidly investigate novel transcripts revealed by RNA-Seq in comparison with a reference annotation. The methods described in this article are implemented in the Cufflinks suite of software for RNA-Seq, freely available from http://bio.math.berkeley.edu/cufflinks. The software is released under the BOOST license. cole@broadinstitute.org; lpachter@math.berkeley.edu Supplementary data are available at Bioinformatics online.
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            Negative regulation of TLR4 via targeting of the proinflammatory tumor suppressor PDCD4 by the microRNA miR-21.

            The tumor suppressor PDCD4 is a proinflammatory protein that promotes activation of the transcription factor NF-kappaB and suppresses interleukin 10 (IL-10). Here we found that mice deficient in PDCD4 were protected from lipopolysaccharide (LPS)-induced death. The induction of NF-kappaB and IL-6 by LPS required PDCD4, whereas LPS enhanced IL-10 induction in cells lacking PDCD4. Treatment of human peripheral blood mononuclear cells with LPS resulted in lower PDCD4 expression, which was due to induction of the microRNA miR-21 via the adaptor MyD88 and NF-kappaB. Transfection of cells with a miR-21 precursor blocked NF-kappaB activity and promoted IL-10 production in response to LPS, whereas transfection with antisense oligonucleotides to miR-21 or targeted protection of the miR-21 site in Pdcd4 mRNA had the opposite effect. Thus, miR-21 regulates PDCD4 expression after LPS stimulation.
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              Adaptation genomics: the next generation.

              Understanding the genetics of how organisms adapt to changing environments is a fundamental topic in modern evolutionary ecology. The field is currently progressing rapidly because of advances in genomics technologies, especially DNA sequencing. The aim of this review is to first briefly summarise how next generation sequencing (NGS) has transformed our ability to identify the genes underpinning adaptation. We then demonstrate how the application of these genomic tools to ecological model species means that we can start addressing some of the questions that have puzzled ecological geneticists for decades such as: How many genes are involved in adaptation? What types of genetic variation are responsible for adaptation? Does adaptation utilise pre-existing genetic variation or does it require new mutations to arise following an environmental change? Copyright © 2010 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                26 July 2016
                2016
                : 11
                : 7
                : e0159719
                Affiliations
                [001]Dairy Cattle Research Center, Shandong Academy of Agricultural Sciences, Jinan, Shandong, P.R. China
                Wageningen UR Livestock Research, NETHERLANDS
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: JMH. Performed the experiments: XGW ZHJ MHH. Analyzed the data: XGW JMH. Contributed reagents/materials/analysis tools: QJ CHY YZ YS RLL CFW JFZ. Wrote the paper: XGW JMH.

                Article
                PONE-D-16-07579
                10.1371/journal.pone.0159719
                4961362
                27459697
                ff912b29-7508-4794-8258-c47bad7d471b
                © 2016 Wang et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 22 February 2016
                : 6 July 2016
                Page count
                Figures: 3, Tables: 3, Pages: 16
                Funding
                Funded by: Youth Talents Training Program of Shandong Academy of Agricultural Sciences
                Award ID: SAAS-YTTP-2014
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 31371255; 31271328; 31401049
                Award Recipient :
                Funded by: Cow Innovation Team of the Shandong Province Modern Agricultural Industry Technology System
                Award ID: SDAIT-12-011-02
                Award Recipient :
                This study was supported by grants from Youth Talents Training Program of Shandong Academy of Agricultural Sciences (SAAS-YTTP-2014), the National Natural Science Foundation of China (31371255; 31271328; 31401049), and the Cow Innovation Team of the Shandong Province Modern Agricultural Industry Technology System (SDAIT-12-011-02). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Medicine and Health Sciences
                Women's Health
                Maternal Health
                Mastitis
                Biology and Life Sciences
                Immunology
                Immune Response
                Medicine and Health Sciences
                Immunology
                Immune Response
                Biology and Life Sciences
                Zoology
                Animal Diseases
                Bovine Mastitis
                Biology and Life Sciences
                Computational Biology
                Genome Complexity
                Introns
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Complexity
                Introns
                Biology and Life Sciences
                Immunology
                Immune Response
                Inflammation
                Medicine and Health Sciences
                Immunology
                Immune Response
                Inflammation
                Medicine and Health Sciences
                Diagnostic Medicine
                Signs and Symptoms
                Inflammation
                Medicine and Health Sciences
                Pathology and Laboratory Medicine
                Signs and Symptoms
                Inflammation
                Biology and Life Sciences
                Anatomy
                Reproductive System
                Breast Tissue
                Mammary Glands
                Medicine and Health Sciences
                Anatomy
                Reproductive System
                Breast Tissue
                Mammary Glands
                Biology and Life Sciences
                Anatomy
                Exocrine Glands
                Mammary Glands
                Medicine and Health Sciences
                Anatomy
                Exocrine Glands
                Mammary Glands
                Biology and life sciences
                Genetics
                Gene expression
                RNA processing
                Alternative Splicing
                Biology and life sciences
                Biochemistry
                Nucleic acids
                RNA
                RNA processing
                Alternative Splicing
                Biology and Life Sciences
                Genetics
                Gene Expression
                Custom metadata
                Data were deposited in the National Center for Biotechnology Information (NCBI) SRA database (SRX1447227) and within the paper and supporting information.

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