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http://dx.doi.org/10.5713/ajas.2012.12375

Gene Co-expression Analysis to Characterize Genes Related to Marbling Trait in Hanwoo (Korean) Cattle  

Lim, Dajeong (National Institute of Animal Science, RDA)
Lee, Seung-Hwan (National Institute of Animal Science, RDA)
Kim, Nam-Kuk (National Agricultural products Quality management Service(NAQS))
Cho, Yong-Min (National Institute of Animal Science, RDA)
Chai, Han-Ha (National Institute of Animal Science, RDA)
Seong, Hwan-Hoo (National Institute of Animal Science, RDA)
Kim, Heebal (Department of Food and Animal Biotechnology, Seoul National University)
Publication Information
Asian-Australasian Journal of Animal Sciences / v.26, no.1, 2013 , pp. 19-29 More about this Journal
Abstract
Marbling (intramuscular fat) is an important trait that affects meat quality and is a casual factor determining the price of beef in the Korean beef market. It is a complex trait and has many biological pathways related to muscle and fat. There is a need to identify functional modules or genes related to marbling traits and investigate their relationships through a weighted gene co-expression network analysis based on the system level. Therefore, we investigated the co-expression relationships of genes related to the 'marbling score' trait and systemically analyzed the network topology in Hanwoo (Korean cattle). As a result, we determined 3 modules (gene groups) that showed statistically significant results for marbling score. In particular, one module (denoted as red) has a statistically significant result for marbling score (p = 0.008) and intramuscular fat (p = 0.02) and water capacity (p = 0.006). From functional enrichment and relationship analysis of the red module, the pathway hub genes (IL6, CHRNE, RB1, INHBA and NPPA) have a direct interaction relationship and share the biological functions related to fat or muscle, such as adipogenesis or muscle growth. This is the first gene network study with m.logissimus in Hanwoo to observe co-expression patterns in divergent marbling phenotypes. It may provide insights into the functional mechanisms of the marbling trait.
Keywords
Gene Co-expression Network; Marbling; Hanwoo;
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1 McIntyre, B. A. S., P. Brouillard, V. Aerts, I. Gutierrez-Roelens and M. Vikkula. 2004. Glomulin is predominantly expressed in vascular smooth muscle cells in the embryonic and adult mouse. Gene Expr. Patterns 4:351-358.   DOI   ScienceOn
2 Miller, J. A., M. C. Oldham and D. H. Geschwind. 2008. A systems level analysis of transcriptional changes in Alzheimer's disease and normal aging. J. Neurosci. 28:1410-1420.   DOI   ScienceOn
3 Moore, S. S. and E. F. Marques. 2008. Associations of polymorphisms in the fibroblast growth factor 8 (FGF8) and its haplotypes with carcass quality, growth and feed efficiency in beef cattle, Google Patents.
4 Nikitin, A., S. Egorov, N. Daraselia and I. Mazo. 2003. Pathway studio-the analysis and navigation of molecular networks. Bioinformatics 19:2155-2157.   DOI   ScienceOn
5 Nobis, W., X. Ren, S. P. Suchyta, T. R. Suchyta, A. J. Zanella and P. M. Coussens. 2003. Development of a porcine brain cDNA library, EST database, and microarray resource. Physiol. Genomics 16:153-159.   DOI   ScienceOn
6 Oldham, M. C., S. Horvath and D. H. Geschwind. 2006. Conservation and evolution of gene coexpression networks in human and chimpanzee brains. Proc. Natl. Acad. Sci. 103: 17973-17978.   DOI   ScienceOn
7 Park, B., A. D. Whittaker, R. K. Miller and D. S. Hale. 1994. Predicting intramuscular fat in beef longissimus muscle from speed of sound. J. Anim. Sci. 72:109-116.
8 Peter, L. and H. Steve. 2008. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9:599
9 Ravasz, E., A. Somera, D. Mongru, Z. Oltvai and A. Barabasi. 2002. Hierarchical organization of modularity in metabolic networks. Science 297:1551-1555.   DOI   ScienceOn
10 Reverter, A., N. Hudson, Y. Wang, S. Tan, W. Barris, K. Byrne, S. McWilliam, C. Bottema, A. Kister and P. Greenwood. 2006. A gene coexpression network for bovine skeletal muscle inferred from microarray data. Physiol. Genomics 28:76-83.   DOI   ScienceOn
11 Rhoads, R. P., M. E. Fernyhough, X. Liu, D. C. McFarland, S. G. Velleman, G. J. Hausman and M. V. Dodson. 2009. Extrinsic regulation of domestic animal-derived myogenic satellite cells II. Domest. Anim. Endocrinol. 36:111-126.   DOI   ScienceOn
12 Rosenthal, S. M. and Z. Q. Cheng. 1995. Opposing early and late effects of insulin-like growth factor I on differentiation and the cell cycle regulatory retinoblastoma protein in skeletal myoblasts. Proc. Natl. Acad. Sci. 92:10307-10311.   DOI   ScienceOn
13 Saris, C. G., S. Horvath, P. W. van Vught, M. A. van Es, H. M. Blauw, T. F. Fuller, P. Langfelder, J. DeYoung, J. H. Wokke, J. H. Veldink. 2009. Weighted gene co-expression network analysis of the peripheral blood from Amyotrophic Lateral Sclerosis patients. BMC Genomics 10:405.   DOI   ScienceOn
14 Schadt, E. E., S. A. Monks, T. A. Drake, A. J. Lusis, N. Che, V. Colinayo, T. G. Ruff, S. B. Milligan, J. R. Lamb and G. Cavet. 2003. Genetics of gene expression surveyed in maize, mouse and man. Nature 422:297-302.   DOI   ScienceOn
15 Sevane, N., I. Crespo, J. Canon and S. Dunner. 2011. A Primer-Extension Assay for simultaneous use in cattle Genotype Assisted Selection, parentage and traceability analysis. Livest. Sci. 137:141-150.   DOI   ScienceOn
16 Shin, J., B. Li, M. E. Davis, Y. Suh and K. Lee. 2009. Comparative analysis of fatty acid-binding protein 4 promoters: conservation of peroxisome proliferator-activated receptor binding sites. J. Anim. Sci. 87:3923-3934.   DOI   ScienceOn
17 Sibut, V., C. Hennequet-Antier, E. Le Bihan-Duval, S. Marthey, M. J. Duclos and C. Berri. 2011. Identification of differentially expressed genes in chickens differing in muscle glycogen content and meat quality. BMC Genomics 12:112.   DOI   ScienceOn
18 Suarez, E., D. Bach, J. Cadefau, M. Palacin, A. Zorzano and A. Guma. 2001. A novel role of neuregulin in skeletal muscle. Neuregulin stimulates glucose uptake, glucose transporter translocation, and transporter expression in muscle cells. J. Biol. Chem. 276:18257-18264.   DOI   ScienceOn
19 Smith, G. W. and G. J. Rosa. 2007. Interpretation of microarray data: trudging out of the abyss towards elucidation of biological significance. J. Anim. Sci. 85(13 Suppl):E20-E23.   DOI
20 Su, H. Y., T. J. Bos, F. S. Monteclaro and P. K. Vogt. 1991. Jun inhibits myogenic differentiation. Oncogene 6:1759-1766.
21 Subramanian, A., P. Tamayo, V. K. Mootha, S. Mukherjee, B. L. Ebert, M. A. Gillette, A. Paulovich, S. L. Pomeroy, T. R. Golub and E. S. Lander. 2005. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. 102:15545-15550.   DOI   ScienceOn
22 Tashiro, E., Y. Minato, H. Maruki, M. Asagiri and M. Imoto. 2003. Regulation of FGF receptor-2 expression by transcription factor E2F-1. Oncogene 22:5630-5635.   DOI   ScienceOn
23 Wayne, M. L. and L. M. McIntyre. 2002. Combining mapping and arraying: An approach to candidate gene identification. Proc. Natl. Acad. Sci. USA. 99:14903-14906.   DOI   ScienceOn
24 Wibowo, T. A., J. J. Michal and Z. Jiang. 2007. Corticotropin releasing hormone is a promising candidate gene for marbling and subcutaneous fat depth in beef cattle. Genome 50:939-945.   DOI
25 Ye, Y. and A. Godzik. 2004. Comparative analysis of protein domain organization. Genome Res. 14:343-353.   DOI   ScienceOn
26 Crews Jr, D., E. Pollak, R. Weaber, R. Quaas and R. Lipsey. 2003. Genetic parameters for carcass traits and their live animal indicators in Simmental cattle. J. Anim. Sci. 81:1427-1433.
27 Al-Khalili, L., K. Bouzakri, S. Glund, F. Lonnqvist, H. A. Koistinen and A. Krook. 2006. Signaling specificity of interleukin-6 action on glucose and lipid metabolism in skeletal muscle. Mol. Endocrinol. 20:3364-3375.   DOI   ScienceOn
28 Brem, R. B., G. Yvert, R. Clinton and L. Kruglyak. 2002. Genetic dissection of transcriptional regulation in budding yeast. Science 296:752-755.   DOI   ScienceOn
29 Chen, P. L., D. J. Riley, Y. Chen and W. H. Lee. 1996. Retinoblastoma protein positively regulates terminal adipocyte differentiation through direct interaction with C/EBPs. Genes Dev. 10:2794-2804.   DOI   ScienceOn
30 D'Andrea, M., S. Dal Monego, A. Pallavicini, M. Modonut, R. Dreos, B. Stefanon and F. Pilla. 2011. Muscle transcriptome profiling in divergent phenotype swine breeds during growth using microarray and RT PCR tools. Anim. Genet. 42:501-509   DOI   ScienceOn
31 Davies, J. D., K. L. Carpenter, I. R. Challis, N. L. Figg, R. McNair, D. Proudfoot, P. L. Weissberg and C. M. Shanahan. 2005. Adipocytic differentiation and liver x receptor pathways regulate the accumulation of triacylglycerols in human vascular smooth muscle cells. J. Biol. Chem. 280:3911-3919.   DOI   ScienceOn
32 Dewey, F. E., M. V. Perez, M. T. Wheeler, C. Watt, J. Spin, P. Langfelder, S. Horvath, S. Hannenhalli, T. P. Cappola and E. A. Ashley. 2011. Gene coexpression network topology of cardiac development, hypertrophy, and failure clinical perspective. Circ. Cardiovasc. Genet. 4:26-35.   DOI
33 Donaldson, L., T. Vuocolo, C. Gray, Y. Strandberg, A. Reverter, S. McWilliam, Y. Wang, K. Byrne and R. Tellam. 2005. Construction and validation of a bovine innate immune microarray. BMC Genomics 6:135.   DOI   ScienceOn
34 Fan, B., S. Onteru, M. Nikkila, K. Stalder and M. Rothschild. 2009. The COL9A1 gene is associated with longissimus dorsi muscle area in the pig. Anim. Genet. 40:788.   DOI   ScienceOn
35 Florini, J. R., D. S. Samuel, D. Z. Ewton, C. Kirk and R. M. Sklar. 1996. Stimulation of myogenic differentiation by a neuregulin, glial growth factor 2. Are neuregulins the long-sought muscle trophic factors secreted by nerves? J. Biol. Chem. 271:12699-12702.   DOI   ScienceOn
36 Fuller, T. F., A. Ghazalpour, J. E. Aten, T. A. Drake, A. J. Lusis and S. Horvath. 2007. Weighted gene coexpression network analysis strategies applied to mouse weight. Mamm. Genome 18:463-472.   DOI
37 Gautier, L., L. Cope, B. Bolstad and R. Irizarry. 2004. Affy-analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20:307-315.   DOI   ScienceOn
38 Grefte, S., A. M. Kuijpers-Jagtman, R. Torensma and J. W. Von den Hoff. 2007. Skeletal muscle development and regeneration. Stem Cells Dev. 16:857-868.   DOI   ScienceOn
39 Ghazalpour, A., S. Doss, B. Zhang, S. Wang, C. Plaisier, R. Castellanos, A. Brozell, E. E. Schadt, T. A. Drake and A. J. Lusis. 2006. Integrating genetic and network analysis to characterize genes related to mouse weight. PLoS Genet. 2: e130.   DOI   ScienceOn
40 Gibson, G. and B. Weir. 2005. The quantitative genetics of transcription. Trends Genet. 21:616-623.   DOI   ScienceOn
41 Haley, C. and D. J. de Koning. 2006. Genetical genomics in livestock: potentials and pitfalls. Anim. Genet. 37(Suppl 1):10-12.
42 Harper, G., D. Pethick, V. Oddy, R. Tume, W. Barendse and L. Hygate. 2001. Biological determinants of intramuscular fat deposition in beef cattle: current mechanistic knowledge and sources of variation. Meat Livest. Australia, Sydney.
43 Hocquette, J., F. Gondret, E. Baeza, F. Medale, C. Jurie and D. Pethick. 2010. Intramuscular fat content in meat-producing animals: development, genetic and nutritional control, and identification of putative markers. Animal 4:303-319.   DOI   ScienceOn
44 Horvath, S. and J. Dong. 2008. Geometric interpretation of gene coexpression network analysis. PLoS Comput. Biol. 4: e1000117.   DOI   ScienceOn
45 Irizarry, R. A., B. M. Bolstad, F. Collin, L. M. Cope, B. Hobbs and T. P. Speed. 2003. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31:e15.   DOI   ScienceOn
46 Jiang, Z., J. J. Michal, J. Chen, T. F. Daniels, T. Kunej, M. D. Garcia, C. T. Gaskins, J. R. Busboom, L. J. Alexander and R. W. Wright. 2009. Discovery of novel genetic networks associated with 19 economically important traits in beef cattle. Int. J. Biol. Sci. 5:528-542.
47 Lee, S. H., C. Gondro, J. van der Werf, N. K. Kim, D. Lim, E. W. Park, S. J. Oh, J. Gibson and J. Thompson. 2010. Use of a bovine genome array to identify new biological pathways for beef marbling in Hanwoo (Korean Cattle). BMC Genomics 11: 623.   DOI   ScienceOn
48 Kim, N. K., D. Lim, S. H. Lee, Y. M. Cho, E. W. Park, C. S. Lee, B. S. Shin, T. H. Kim and D. Yoon. 2011. Heat shock protein B1 and its regulator genes are negatively correlated with intramuscular fat content in the Longissimus thoracis muscle of Hanwoo (Korean Cattle) steers. J. Agric. Food Chem. 25:5657-5664.
49 Kokta, T., M. Dodson, A. Gertler and R. Hill. 2004. Intercellular signaling between adipose tissue and muscle tissue. Domest. Anim. Endocrinol. 27:303-331.   DOI   ScienceOn
50 Lebrasseur, N. K., G. M. Cote, T. A. Miller, R. A. Fielding and D. B. Sawyer. 2003. Regulation of neuregulin/ErbB signaling by contractile activity in skeletal muscle. Am. J. Physiol. Cell Physiol. 284:C1149-1155.   DOI   ScienceOn
51 Lee, S. H., E. W. Park, Y. M. Cho, S. K. Kim, J. H. Lee, J. T. Jeon, C. S. Lee, S. K. Im, S. J. Oh and J. M. Thompson. 2007. Identification of differentially expressed genes related to intramuscular fat development in the early and late fattening stages of hanwoo steers. J. Biochem. Mol. Biol. 40:757-764.   과학기술학회마을   DOI   ScienceOn
52 Li, L., J. C. Chambard, M. Karin and E. N. Olson. 1992. Fos and Jun repress transcriptional activation by myogenin and MyoD: the amino terminus of Jun can mediate repression. Genes Dev. 6:676-689.   DOI   ScienceOn
53 Li, Y., Z. Xu, H. Li, Y. Xiong and B. Zuo. 2010. Differential transcriptional analysis between red and white skeletal muscle of Chinese Meishan pigs. Int. J. Biol. Sci. 6:350-360.
54 Lim, D., N. K. Kim, H. S. Park, S. H. Lee, Y. M. Cho, S. J. Oh, T. H. Kim and H. Kim. 2011. Identification of candidate genes related to bovine marbling using protein-protein interaction networks. Int. J. Biol. Sci. 7:992-1002.