Journal of Biomedical and Translational Research

Research Institute of Veterinary Medicine Chungbuk National University (충북대학교 동물의학연구소)
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Stage specific transcriptome analysis of liver tissue from a crossbred Korean Native Pig (KNP × Yorkshire)

  • Kumar, Himansu (Division of Animal Genomics and Bioinformatics, National Institute of Animal Science, RDA) ;
  • Srikanth, Krishnamoorthy (Division of Animal Genomics and Bioinformatics, National Institute of Animal Science, RDA) ;
  • Park, Woncheol (Division of Animal Genomics and Bioinformatics, National Institute of Animal Science, RDA) ;
  • Lee, Kyung-Tai (Division of Animal Genetics and Breeding, National Institute of Animal Science, RDA) ;
  • Choi, Bong-Hwan (Division of Animal Genomics and Bioinformatics, National Institute of Animal Science, RDA) ;
  • Kim, Jun-Mo (Department of Animal Science and Technology, Chung-Ang University) ;
  • Lim, Dajeong (Division of Animal Genomics and Bioinformatics, National Institute of Animal Science, RDA) ;
  • Park, Jong-Eun (Division of Animal Genomics and Bioinformatics, National Institute of Animal Science, RDA)
  • Received : 2018.11.15
  • Accepted : 2018.12.28
  • Published : 2018.12.31
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Abstract

Korean Native Pig (KNP) has a uniform black coat color, excellent meat quality, white colored fat, solid fat structure and good marbling. However, its growth performance is low, while the western origin Yorkshire pig has high growth performance. To take advantage of the unique performance of the two pig breeds, we raised crossbreeds (KNP ${\times}$ Yorkshire to make use of the heterotic effect. We then analyzed the liver transcriptome as it plays an important role in fat metabolism. We sampled at two stages: 10 weeks and at 26 weeks. The stages were chosen to correspond to the change in feeding system. A total of 16 pigs (8 from each stage) were sampled and RNA sequencing was performed. The reads were mapped to the reference genome and differential expression analysis was performed with edgeR package. A total of 324 genes were found to be significantly differentially expressed (${\left|log2FC\right|}$ > 1 & q < 0.01), out of which 180 genes were up-regulated and 144 genes were down-regulated. Principal Component Analysis (PCA) showed that the samples clustered according to stages. Functional annotation of significant DEGs (differentially expressed genes) showed that GO terms such as DNA replication, cell division, protein phosphorylation, regulation of signal transduction by p53 class mediator, ribosome, focal adhesion, DNA helicase activity, protein kinase activity etc. were enriched. KEGG pathway analysis showed that the DEGs functioned in cell cycle, Ras signaling pathway, p53 signaling pathway, MAPK signaling pathway etc. Twenty-nine transcripts were also part of the DEGs, these were predominantly Cys2His2-like fold group (C2H2) family of zinc fingers. A protein-protein interaction (PPI) network analysis showed that there were three highly interconnected clusters, suggesting an enrichment of genes with similar biological function. This study presents the first report of liver tissue specific gene regulation in a cross-bred Korean pig.

Keywords

Acknowledgement

Grant : Porcine epigenomic map construction and investigation of the imprinted genes

Supported by : RDA

References

  1. Cho SH, Seong PN, Kim JH, Park BY, Kwon OS, Hah KH, Kim DH, Ahn CN. Comparison of meat quality, nutritional, and sensory properties of Korean native pigs by gender. Korean J Food Sci Anim Resour 2007;27:475-481. https://doi.org/10.5851/kosfa.2007.27.4.475
  2. Oh HS, Kim HY, Yang HS, Lee JI, Joo YK, Kim CU. Comparison of meat quality characteristics between crossbreeds. Korean J Food Sci Anim Resour 2008;28:171-180. https://doi.org/10.5851/kosfa.2008.28.2.171
  3. Kim GW, Kim HY. Physicochemical properties of M. longissimus dorsi of Korean native pigs. J Anim Sci Technol 2018;60:6. https://doi.org/10.1186/s40781-018-0163-y
  4. Lundstrom K, Enfalt AC, Tornberg E, Agerhem H. Sensory and technological meat quality in carriers and non-carriers of the RN- allele in Hampshire crosses and in purebred Yorkshire pigs. Meat Sci 1998;48:115-124. https://doi.org/10.1016/S0309-1740(97)00082-X
  5. Warriss PD, Brown SN, Edwards JE, Knowles TG. Effect of lairage time on levels of stress and meat quality in pigs. Anim Sci 1998;66:255-261. https://doi.org/10.1017/S1357729800009036
  6. O'Hea EK, Leveille GA. Significance of adipose tissue and liver as sites of fatty acid synthesis in the pig and the efficiency of utilization of various substrates for lipogenesis. J Nutr 1969;99:338-344. https://doi.org/10.1093/jn/99.3.338
  7. Enser M, Richardson R, Wood JD, Gill BP, Sheard PR. Feeding linseed to increase the n-3 PUFA of pork: fatty acid composition of muscle, adipose tissue, liver and sausages. Meat Sci 2000;55:201-212. https://doi.org/10.1016/S0309-1740(99)00144-8
  8. Warr A. High quality re-assembly of the pig genome using PacBio sequencing. In: The 3rd Livestock Genomics Meeting; 2016 Sep 14-16. vol. 15.
  9. Robert C, Kapetanovic R, Beraldi D, Watson M, Archibald AL, Hume DA. Identification and annotation of conserved promoters and macrophage-expressed genes in the pig genome. BMC Genomics 2015;16:970. https://doi.org/10.1186/s12864-015-2111-2
  10. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, Owens SM, Betley J, Fraser L, Bauer M, Gormley N, Gilbert JA, Smith G, Knight R. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J 2012;6:1621-1624. https://doi.org/10.1038/ismej.2012.8
  11. Andrews S. FastQC: a quality control tool for high throughput sequence data [Internet]. 2010 [cited 2018 Dec 24]. Available from: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/.
  12. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014;30:2114-2210. https://doi.org/10.1093/bioinformatics/btu170
  13. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods 2012;9:357. https://doi.org/10.1038/nmeth.1923
  14. Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nature Methods 2015;12:357-360. https://doi.org/10.1038/nmeth.3317
  15. Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter L. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc 2012;7:562-578. https://doi.org/10.1038/nprot.2012.016
  16. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010;26:139-140. https://doi.org/10.1093/bioinformatics/btp616
  17. Liao Y, Smyth GK, Shi W. FeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 2013;30:923-930.
  18. Pan G, Ameur A, Enroth S, Bysani M, Nord H, Cavalli M, Essand M, Gyllensten U, Wadelius C. PATZ1 downregulates FADS1 by binding to rs174557 and is opposed by SP1/SREBP1c. Nucleic Acids Res 2017;45:2408-2422. https://doi.org/10.1093/nar/gkw1186
  19. Keskin N, Deniz E, Eryilmaz J, Un M, Batur T, Ersahin T, Cetin Atalay R, Sakaguchi S, Ellmeier W, Erman B. PATZ1 is a DNA damage responsive transcription factor that inhibits p53 function. Mol Cell Bio 2015;MCB:01475-01414.
  20. Reynolds LM, Howard TD, Ruczinski I, Kanchan K, Seeds MC, Mathias RA, Chilton FH. Tissue-specific impact of FADS cluster variants on FADS1 and FADS2 gene expression. PLOS ONE 2018;13:e0194610. https://doi.org/10.1371/journal.pone.0194610
  21. Wagner S, Hess MA, Ormonde-Hanson P, Malandro J, Hu H, Chen M, Kehrer R, Frodsham M, Schumacher C, Beluch M , Honer C, Skolnick M, Ballinger D, Bowen BR. A broad role for the zinc finger protein ZNF202 in human lipid metabolism. J Biol Chem 2000;275:15685-15690. https://doi.org/10.1074/jbc.M910152199
  22. Keller M, Hopp L, Liu X, Wohland T, Rohde K, Cancello R, Klos M, Bacos K, Kern M, Eichelmann F, Dietrich A, Schon MR, Gartner D, Lohmann T, Dressler M, Stumvoll M, Kovacs P, DiBlasio AM, Ling C, Binder H, Bluher M, Bottcher Y. Genome-wide DNA promoter methylation and transcriptome analysis in human adipose tissue unravels novel candidate genes for obesity. Mol Metab 2017;6:86-100. https://doi.org/10.1016/j.molmet.2016.11.003
  23. Tai CC, Ding ST. N-3 polyunsaturated fatty acids regulate lipid metabolism through several inflammation mediators: mechanisms and implications for obesity prevention. J Nutr Biochem 2010;21:357-363. https://doi.org/10.1016/j.jnutbio.2009.09.010
  24. Mullick A, Groulx N, Trasler D, Gros P. Nhlh1, a basic helix-loop-helix transcription factor, is very tightly linked to the mouse looptail (Lp) mutation. Mamm Genome 1995;6:700-704. https://doi.org/10.1007/BF00354291
  25. Weber JR, Sokol SY. Identification of a phylogenetically conserved activin-responsive enhancer in the Zic3 gene. Mech Dev 2003;120:955-964. https://doi.org/10.1016/S0925-4773(03)00082-0
  26. Utsunomiya YT, Carmo AS, Neves HH, Carvalheiro R, Matos MC, Zavarez LB, Ito PK, O'Brien AMP, Solkner J, Porto-Neto LR, Schenkel FS, McEwan J, Cole JB, da Silva MV, Van Tassell CP, Sonstegard TS, Garcia JF. Genome-wide mapping of loci explaining variance in scrotal circumference in Nellore cattle. PLOS ONE 2014;9:e88561. https://doi.org/10.1371/journal.pone.0088561
  27. Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, Snyder M. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 2008;320:1344-1349. https://doi.org/10.1126/science.1158441
  28. Core LJ, Waterfall JJ, Lis JT. Nascent RNA sequencing reveals widespread pausing and divergent initiation at human promoters. Science 2008;322:1845-1848. https://doi.org/10.1126/science.1162228
  29. Camarena L, Bruno V, Euskirchen G, Poggio S, Snyder M. Molecular mechanisms of ethanol-induced pathogenesis revealed by RNA-sequencing. PLOS Pathog 2010;6:e1000834. https://doi.org/10.1371/journal.ppat.1000834
  30. Sun L, Lamont SJ, Cooksey AM, McCarthy F, Tudor CO, Vijay-Shanker K, DeRita RM, Rothschild M, Ashwell C, Persia ME. Transcriptome response to heat stress in a chicken hepatocellular carcinoma cell line. Cell Stress Chaperones 2015;20:939-950. https://doi.org/10.1007/s12192-015-0621-0