Identification of the Novel Candidate Genes and Variants in Boar Liver Tissues with Divergent Skatole Levels Using RNA Deep Sequencing

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Abstract

Boar taint is the unpleasant odour of meat derived from non-castrated male pigs, caused by the accumulation of androstenone and skatole in fat. Skatole is a tryptophan metabolite produced by intestinal bacteria in gut and catabolised in liver. Since boar taint affects consumer’s preference, the aim of this study was to perform transcriptome profiling in liver of boars with divergent skatole levels in backfat by using RNA-Seq. The total number of reads produced for each liver sample ranged from 11.8 to 39.0 million. Approximately 448 genes were differentially regulated (p-adjusted <0.05). Among them, 383 genes were up-regulated in higher skatole group and 65 were down-regulated (p<0.01, FC>1.5). Differentially regulated genes in the high skatole liver samples were enriched in metabolic processes such as small molecule biochemistry, protein synthesis, lipid and amino acid metabolism. Pathway analysis identified the remodeling of epithelial adherens junction and TCA cycle as the most dominant pathways which may play important roles in skatole metabolism. Differential gene expression analysis identified candidate genes in ATP synthesis, cytochrome P450, keratin, phosphoglucomutase, isocitrate dehydrogenase and solute carrier family. Additionally, polymorphism and association analysis revealed that mutations in ATP5B, KRT8, PGM1, SLC22A7 and IDH1 genes could be potential markers for skatole levels in boars. Furthermore, expression analysis of exon usage of three genes ( ATP5B, KRT8 and PGM1) revealed significant differential expression of exons of these genes in different skatole levels. These polymorphisms and exon expression differences may have impacts on the gene activity ultimately leading to skatole variation and could be used as genetic marker for boar taint related traits. However, further validation is required to confirm the effect of these genetic markers in other pig populations in order to be used in genomic selection against boar taint in pig breeding programs.

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Most cited references 43

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Cell adhesion: the molecular basis of tissue architecture and morphogenesis.

B. M. Gumbiner (1996)

A variety of cell adhesion mechanisms underlie the way that cells are organized in tissues. Stable cell interactions are needed to maintain the structural integrity of tissues, and dynamic changes in cell adhesion participate in the morphogenesis of developing tissues. Stable interactions actually require active adhesion mechanisms that are very similar to those involved in tissue dynamics. Adhesion mechanisms are highly regulated during tissue morphogenesis and are intimately related to the processes of cell motility and cell migration. In particular, the cadherins and the integrins have been implicated in the control of cell movement. Cadherin mediated cell compaction and cellular rearrangements may be analogous to integrin-mediated cell spreading and motility on the ECM. Regulation of cell adhesion can occur at several levels, including affinity modulation, clustering, and coordinated interactions with the actin cytoskeleton. Structural studies have begun to provide a picture of how the binding properties of adhesion receptors themselves might be regulated. However, regulation of tissue morphogenesis requires complex interactions between the adhesion receptors, the cytoskeleton, and networks of signaling pathways. Signals generated locally by the adhesion receptors themselves are involved in the regulation of cell adhesion. These regulatory pathways are also influenced by extrinsic signals arising from the classic growth factor receptors. Furthermore, signals generated locally be adhesion junctions can interact with classic signal transduction pathways to help control cell growth and differentiation. This coupling between physical adhesion and developmental signaling provides a mechanism to tightly integrate physical aspects of tissue morphogenesis with cell growth and differentiation, a coordination that is essential to achieve the intricate patterns of cells in tissues.

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RNA-seq: from technology to biology

Samuel Marguerat, Jurg Bahler (2009)

Next-generation sequencing technologies are now being exploited not only to analyse static genomes, but also dynamic transcriptomes in an approach termed RNA-seq. Although these powerful and rapidly evolving technologies have only been available for a couple of years, they are already making substantial contributions to our understanding of genome expression and regulation. Here, we briefly describe technical issues accompanying RNA-seq data generation and analysis, highlighting differences to array-based approaches. We then review recent biological insight gained from applying RNA-seq and related approaches to deeply sample transcriptomes in different cell types or physiological conditions. These approaches are providing fascinating information about transcriptional and post-transcriptional gene regulation, and they are also giving unique insight into the richness of transcript structures and processing on a global scale and at unprecedented resolution.

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RNA-seq: technical variability and sampling

Lauren McIntyre, Kenneth Lopiano, Alison Morse … (2011)

Background RNA-seq is revolutionizing the way we study transcriptomes. mRNA can be surveyed without prior knowledge of gene transcripts. Alternative splicing of transcript isoforms and the identification of previously unknown exons are being reported. Initial reports of differences in exon usage, and splicing between samples as well as quantitative differences among samples are beginning to surface. Biological variation has been reported to be larger than technical variation. In addition, technical variation has been reported to be in line with expectations due to random sampling. However, strategies for dealing with technical variation will differ depending on the magnitude. The size of technical variance, and the role of sampling are examined in this manuscript. Results In this study three independent Solexa/Illumina experiments containing technical replicates are analyzed. When coverage is low, large disagreements between technical replicates are apparent. Exon detection between technical replicates is highly variable when the coverage is less than 5 reads per nucleotide and estimates of gene expression are more likely to disagree when coverage is low. Although large disagreements in the estimates of expression are observed at all levels of coverage. Conclusions Technical variability is too high to ignore. Technical variability results in inconsistent detection of exons at low levels of coverage. Further, the estimate of the relative abundance of a transcript can substantially disagree, even when coverage levels are high. This may be due to the low sampling fraction and if so, it will persist as an issue needing to be addressed in experimental design even as the next wave of technology produces larger numbers of reads. We provide practical recommendations for dealing with the technical variability, without dramatic cost increases.

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Author and article information

Contributors

Raffaele A. Calogero: Role: Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS ONE

Journal ID (publisher-id): plos

Journal ID (pmc): plosone

Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, USA )

ISSN (Electronic): 1932-6203

Publication date Collection: 2013

Publication date (Electronic): 26 August 2013

Volume: 8

Issue: 8

Electronic Location Identifier: e72298

Affiliations

[1 ]Institute of Animal Science, Faculty of Agriculture, University of Bonn, Bonn, Germany

[2 ]Department of Animal Production and Technology, Faculty of Animal Science, Bogor Agricultural University, Bogor, Indonesia

[3 ]Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, Sankt Augustin, Germany

[4 ]Department of Animal Science, Faculty of Agriculture, Erciyes University, Kayseri, Turkey

University of Torino, Italy

Author notes

* E-mail: judd@ 123456itw.uni-bonn.de

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

Conceived and designed the experiments: AG ET CL KS MUC MJU. Performed the experiments: AG SS CN LF. Analyzed the data: SS ET CG-B. Contributed reagents/materials/analysis tools: MUC DT DSW MH. Wrote the paper: AG SS MJU KS.

Article

Publisher ID: PONE-D-13-19771

DOI: 10.1371/journal.pone.0072298

PMC ID: 3753299

PubMed ID: 23991084

SO-VID: 7924d16b-1baa-4de9-8074-9c57fbf3d515

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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

Date received : 14 May 2013

Date accepted : 9 July 2013

Page count

Pages: 19

Funding

This work was financially supported by the Federal Ministry of Food, Agriculture and Consumer Protection, Germany (BMELV) and State of North Rhine Westphalia with the programme USL “Umweltverträgliche und Standortgerechte Landwirtschaft”. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Identification of the Novel Candidate Genes and Variants in Boar Liver Tissues with Divergent Skatole Levels Using RNA Deep Sequencing

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Most cited references 43

Cell adhesion: the molecular basis of tissue architecture and morphogenesis.

RNA-seq: from technology to biology

RNA-seq: technical variability and sampling

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