DOI QR코드

DOI QR Code

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)
  • 투고 : 2012.07.06
  • 심사 : 2012.08.07
  • 발행 : 2013.01.01

초록

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.

키워드

참고문헌

  1. 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. https://doi.org/10.1210/me.2005-0490
  2. Brem, R. B., G. Yvert, R. Clinton and L. Kruglyak. 2002. Genetic dissection of transcriptional regulation in budding yeast. Science 296:752-755. https://doi.org/10.1126/science.1069516
  3. 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. https://doi.org/10.1101/gad.10.21.2794
  4. 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.
  5. 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 https://doi.org/10.1111/j.1365-2052.2010.02164.x
  6. 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. https://doi.org/10.1074/jbc.M410075200
  7. 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. https://doi.org/10.1161/CIRCGENETICS.110.941757
  8. 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. https://doi.org/10.1186/1471-2164-6-135
  9. 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. https://doi.org/10.1111/j.1365-2052.2009.01885.x
  10. 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. https://doi.org/10.1074/jbc.271.22.12699
  11. 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. https://doi.org/10.1007/s00335-007-9043-3
  12. Gautier, L., L. Cope, B. Bolstad and R. Irizarry. 2004. Affy-analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20:307-315. https://doi.org/10.1093/bioinformatics/btg405
  13. 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. https://doi.org/10.1371/journal.pgen.0020130
  14. Gibson, G. and B. Weir. 2005. The quantitative genetics of transcription. Trends Genet. 21:616-623. https://doi.org/10.1016/j.tig.2005.08.010
  15. 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. https://doi.org/10.1089/scd.2007.0058
  16. Haley, C. and D. J. de Koning. 2006. Genetical genomics in livestock: potentials and pitfalls. Anim. Genet. 37(Suppl 1):10-12.
  17. 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.
  18. 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. https://doi.org/10.1017/S1751731109991091
  19. Horvath, S. and J. Dong. 2008. Geometric interpretation of gene coexpression network analysis. PLoS Comput. Biol. 4: e1000117. https://doi.org/10.1371/journal.pcbi.1000117
  20. 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. https://doi.org/10.1093/nar/gng015
  21. 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.
  22. 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.
  23. Kokta, T., M. Dodson, A. Gertler and R. Hill. 2004. Intercellular signaling between adipose tissue and muscle tissue. Domest. Anim. Endocrinol. 27:303-331. https://doi.org/10.1016/j.domaniend.2004.05.004
  24. 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. https://doi.org/10.1152/ajpcell.00487.2002
  25. 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. https://doi.org/10.1186/1471-2164-11-623
  26. 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. https://doi.org/10.5483/BMBRep.2007.40.5.757
  27. 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. https://doi.org/10.1101/gad.6.4.676
  28. 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.
  29. 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.
  30. 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. https://doi.org/10.1016/j.modgep.2003.09.007
  31. 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. https://doi.org/10.1523/JNEUROSCI.4098-07.2008
  32. 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.
  33. Nikitin, A., S. Egorov, N. Daraselia and I. Mazo. 2003. Pathway studio-the analysis and navigation of molecular networks. Bioinformatics 19:2155-2157. https://doi.org/10.1093/bioinformatics/btg290
  34. 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. https://doi.org/10.1152/physiolgenomics.00099.2003
  35. 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. https://doi.org/10.1073/pnas.0605938103
  36. 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.
  37. Peter, L. and H. Steve. 2008. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9:599
  38. Ravasz, E., A. Somera, D. Mongru, Z. Oltvai and A. Barabasi. 2002. Hierarchical organization of modularity in metabolic networks. Science 297:1551-1555. https://doi.org/10.1126/science.1073374
  39. 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. https://doi.org/10.1152/physiolgenomics.00105.2006
  40. 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. https://doi.org/10.1016/j.domaniend.2008.12.005
  41. 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. https://doi.org/10.1073/pnas.92.22.10307
  42. 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. https://doi.org/10.1186/1471-2164-10-405
  43. 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. https://doi.org/10.1038/nature01434
  44. 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. https://doi.org/10.1016/j.livsci.2010.10.011
  45. 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. https://doi.org/10.2527/jas.2009-2124
  46. 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. https://doi.org/10.1186/1471-2164-12-112
  47. 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. https://doi.org/10.2527/jas.2006-479
  48. Su, H. Y., T. J. Bos, F. S. Monteclaro and P. K. Vogt. 1991. Jun inhibits myogenic differentiation. Oncogene 6:1759-1766.
  49. 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. https://doi.org/10.1074/jbc.M008100200
  50. 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. https://doi.org/10.1073/pnas.0506580102
  51. 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. https://doi.org/10.1038/sj.onc.1206636
  52. 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. https://doi.org/10.1073/pnas.222549199
  53. 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. https://doi.org/10.1139/G07-075
  54. Ye, Y. and A. Godzik. 2004. Comparative analysis of protein domain organization. Genome Res. 14:343-353. https://doi.org/10.1101/gr.1610504

피인용 문헌

  1. MicroRNA-mRNA regulatory networking fine-tunes the porcine muscle fiber type, muscular mitochondrial respiratory and metabolic enzyme activities vol.17, pp.1, 2016, https://doi.org/10.1186/s12864-016-2850-8
  2. Construction of Gene Network System Associated with Economic Traits in Cattle vol.26, pp.8, 2016, https://doi.org/10.5352/JLS.2016.26.8.904
  3. Multi-omic data integration and analysis using systems genomics approaches: methods and applications in animal production, health and welfare vol.48, pp.1, 2016, https://doi.org/10.1186/s12711-016-0217-x
  4. Whole-genome association analysis of pork meat pH revealed three significant regions and several potential genes in Finnish Yorkshire pigs vol.18, pp.1, 2017, https://doi.org/10.1186/s12863-017-0482-x
  5. TRIENNIAL GROWTH AND DEVELOPMENT SYMPOSIUM: Intramuscular fat deposition in ruminants and pigs: A transcriptomics perspective1 vol.95, pp.5, 2017, https://doi.org/10.2527/jas.2016.1112
  6. Integrative analysis of microRNAs and mRNAs revealed regulation of composition and metabolism in Nelore cattle vol.19, pp.1, 2018, https://doi.org/10.1186/s12864-018-4514-3
  7. RNA-seq of muscle from pigs divergent in feed efficiency and product quality identifies differences in immune response, growth, and macronutrient and connective tissue metabolism vol.19, pp.1, 2018, https://doi.org/10.1186/s12864-018-5175-y
  8. Global transcriptional differences in myokine and inflammatory genes in muscle of mature steer progeny are related to maternal lactation diet and muscle composition vol.50, pp.10, 2018, https://doi.org/10.1152/physiolgenomics.00060.2018
  9. Genomic regions and enrichment analyses associated with carcass composition indicator traits in Nellore cattle vol.136, pp.2, 2018, https://doi.org/10.1111/jbg.12373
  10. 일루미나에서 제작된 TSLRH (Truseq Synthetic Long-Read Haplotyping)와 10X Genomics에서 제작된 The Chromium Genome 시퀀싱 플랫폼을 이용하여 생산된 한우(한국 재래 소)의 반수체형 페이징 및 단일염기서열변 vol.29, pp.1, 2013, https://doi.org/10.5352/jls.2019.29.1.1
  11. The Construction and Analysis of lncRNA–miRNA–mRNA Competing Endogenous RNA Network of Schwann Cells in Diabetic Peripheral Neuropathy vol.8, pp.None, 2013, https://doi.org/10.3389/fbioe.2020.00490
  12. Genome Wide Assessment of Genetic Variation and Population Distinctiveness of the Pig Family in South Africa vol.11, pp.None, 2013, https://doi.org/10.3389/fgene.2020.00344
  13. Identification of genetic loci associated with growth traits at weaning in yak through a genome‐wide association study vol.51, pp.2, 2020, https://doi.org/10.1111/age.12897
  14. Genome-wide association study for residual concentrate intake using different approaches in Italian Brown Swiss vol.20, pp.1, 2013, https://doi.org/10.1080/1828051x.2021.1963864
  15. Unraveling Admixture, Inbreeding, and Recent Selection Signatures in West African Indigenous Cattle Populations in Benin vol.12, pp.None, 2021, https://doi.org/10.3389/fgene.2021.657282