Identification of candidate genes related to bovine marbling using pro-tein-protein interaction networks

International Journal of Biological Sciences

Volumn 7, Issue 7, 2011, Pages 992-1002

  Lim, Dajeong ^a,b   Kim, Nam Kuk ^a,c   Park, Hye Sun ^a   Lee, Seung Hwan ^a   Cho, Yong Min ^d   Oh, Sung Jong ^a   Kim, Tae Hun ^a   Kim, Heebal ^b  

^a National Institute of Animal Science   (South Korea)

^b Seoul National University   (South Korea)

^c Ministry of Agriculture and Forestry Korea   (South Korea)

^d Functional Food and Nutrition Division   (South Korea)

Author keywords

Bovine; Hanwoo; Marbling; Network; Protein protein interaction (PPI)

Indexed keywords

BOVINAE;

EID: 80052738377     PISSN: 14492288     EISSN: None     Source Type: Journal    
DOI: 10.7150/ijbs.7.992     Document Type: Article

References (60)

1. Hocquette JF, Lehnert S, Barendse W, Cassar-Malek I, Picard B. Recent advances in cattle functional genomics and their application to beef quality. animal. 2007; 1:159-173.
2. Taylor JF, Coutinho LL, Herring KL, Gallagher DSJr., Brenneman RA, Burney N, Sanders JO, Turner JW, Smith SB, Miller RK et al. Candidate gene analysis of GH1 for effects on growth and carcass composition of cattle. Anim Genet. 1998; 29:194-201.
3. Michal J, Zhang Z, Gaskins C, Jiang Z. The bovine fatty acid binding protein 4 gene is significantly associated with marbling and subcutaneous fat depth in Wagyu x Limousin F2 crosses. Animal Genet. 2006; 37:400-402.
4. Wood IA, Moser G, Burrell DL, Mengersen KL, Hetzel DJ. A meta-analytic assessment of a thyroglobulin marker for marbling in beef cattle. Genet Sel Evol. 2006; 38:479-494.
5. Zhu M, Zhao S. Candidate gene identification approach: progress and challenges. Int J Biol Sci. 2007; 3:420-427.
6. Thaller G, Kuhn C, Winter A, Ewald G, Bellmann O, Wegner J, Zuhlke H, Fries R. DGAT1, a new positional and functional candidate gene for intramuscular fat deposition in cattle. Anim Genet. 2003; 34:354-357.
7. Lee SH, Cho YM, Kim B, Kim N, Choy Y, Kim K, Yoon D, Im S, Oh S, Park E. Identification of marbling-related candidate genes in M. longissimus dorsi of high-and low marbled Hanwoo (Korean Native Cattle) steers. BMB rep. 2008; 41:846.
8. Lee SH, Gondro C, van der Werf J, Kim NK, Lim D, Park EW, Oh SJ, Gibson J, Thompson J. Use of a bovine genome array to identify new biological pathways for beef marbling in Hanwoo (Korean Cattle). BMC Genomics. 2010; 11:623.
9. Kim NK, Lim D, Lee SH, Cho YM, Park EW, Lee CS, Shin BS, Kim TH, Yoon D. 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. 2011; 59:5657-5664.
10. Giot L, Bader JS, Brouwer C, Chaudhuri A, Kuang B, Li Y, Hao YL, Ooi CE, Godwin B, Vitols E et al. A protein interaction map of Drosophila melanogaster. Science. 2003; 302:1727-1736.
11. Graugnard DE, Piantoni P, Bionaz M, Berger LL, Faulkner DB, Loor JJ. Adipogenic and energy metabolism gene networks in longissimus lumborum during rapid post-weaning growth in Angus and Angus x Simmental cattle fed high-starch or low-starch diets. BMC Genomics. 2009; 10:142.
12. Wang X, Xue C, Liu H, Xu Y, Zhao R, Jiang Z, Dodson MV, Chen J. Differential display of expressed genes reveals a novel function of SFRS18 in regulation of intramuscular fat deposition. Int J Biol Sci. 2009; 5:28-33.
13. Reverter A, Hudson NJ, Wang Y, Tan SH, Barris W, Byrne KA, McWilliam SM, Bottema CD, Kister A, Greenwood PL et al. A gene coexpression network for bovine skeletal muscle inferred from microarray data. Physiol Genomics. 2006; 28:76-83.
14. Jiang Z, Michal JJ, Chen J, Daniels TF, Kunej T, Garcia MD, Gaskins CT, Busboom JR, Alexander LJ, Wright RWJr, et al. Discovery of novel genetic networks associated with 19 economically important traits in beef cattle. Int J Biol Sci. 2009; 5:528-542.
15. Giot L, Bader J, Brouwer C, Chaudhuri A, Kuang B, Li Y, Hao Y, Ooi C, Godwin B, Vitols E. A protein interaction map of Drosophila melanogaster. Science. 2003; 302:1727.
16. Gandhi T, Zhong J, Mathivanan S, Karthick L, Chandrika K, Mohan S, Sharma S, Pinkert S, Nagaraju S, Periaswamy B. Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets. Nat Genet. 2006; 38:285-293.
17. Chuang HY, Hofree M, Ideker T. A decade of systems biology. Annu Rev Cell Dev Biol. 2010; 26:721-744.
18. Bandyopadhyay S, Kelley R, Krogan NJ, Ideker T. Functional maps of protein complexes from quantitative genetic interaction data. PLoS Comput Biol. 2008; 4:e1000065.
19. Chua HN, Sung WK, Wong L. Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions. Bioinformatics. 2006; 22:1623-1630.
20. Nabieva E, Jim K, Agarwal A, Chazelle B, Singh M. Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics. 2005; 21 (Suppl 1):i302-310.
21. Hu L, Huang T, Shi X, Lu WC, Cai YD, Chou KC. Predicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid properties. PLoS One. 2011; 6:e14556.
22. Liu Y, Patel S, Nibbe R, Maxwell S, Chowdhury SA, Koyuturk M, Zhu X, Larkin EK, Buxbaum SG, Punjabi NM et al. Systems biology analyses of gene expression and genome wide association study data in obstructive sleep apnea. Pac Symp Biocomput. 2011;:14-25.
23. Novichkova S, Egorov S, Daraselia N. MedScan, a natural language processing engine for MEDLINE abstracts. Bioinformatics. 2003; 19:1699.
24. Peri S, Navarro JD, Kristiansen TZ, Amanchy R, Surendranath V, Muthusamy B, Gandhi TK, Chandrika KN, Deshpande N, Suresh S et al. Human protein reference database as a discovery resource for proteomics. Nucleic Acids Res. 2004; 32:D497-501.
25. Radde N, Gebert J, Forst C. Systematic component selection for gene-network refinement. Bioinformatics. 2006; 22:2674.
26. Hwang S, Son SW, Kim SC, Kim YJ, Jeong H, Lee D. A protein interaction network associated with asthma. J Theor Biol. 2008; 252:722-731.
27. Barabasi AL, Albert R. Emergence of scaling in random networks. Science. 1999; 286:509-512.
28. Barabasi AL, Oltvai ZN. Network biology: understanding the cell's functional organization. Nat Rev Genet. 2004; 5:101-113.
29. Brandes U. A faster algorithm for betweenness centrality. J Math Soci. 2001; 25:163-177.
30. Nikitin A, Egorov S, Daraselia N, Mazo I. Pathway studio--the analysis and navigation of molecular networks. Bioinformatics. 2003; 19:2155-2157.
31. Hocquette JF, Brandstetter AM. Common practice in molecular biology may introduce statistical bias and misleading biological interpretation. J Nutr Biochem. 2002; 13:370-377.
32. Albert R, Barabasi A. Statistical mechanics of complex networks. Rev Mod Phys. 2002; 74:47-97.
33. Albert R. Scale-free networks in cell biology. J Cell Sci. 2005; 118:4947-4957.
34. Watts DJ, Strogatz SH. Collective dynamics of 'small-world' networks. Nature. 1998; 393:440-442.
35. Albert R, Jeong H, Barabasi A. Error and attack tolerance of complex networks. Nature 2000; 406:378-382.
36. Li HX, Luo X, Liu RX, Yang YJ, Yang GS. Roles of Wnt/[beta]-catenin signaling in adipogenic differentiation potential of adipose-derived mesenchymal stem cells. Mol Cell Endocrinol. 2008; 291:116-124.
37. Lee S, Park E, Cho Y, Kim S, Lee J, Jeon J, Lee C, Im S, Oh S, Thompson J. Identification of differentially expressed genes related to intramuscular fat development in the early and late fattening stages of hanwoo steers. J Biochem Mol. 2007; 40:757-764.
38. Son S, Kim D, Ahn Y, Jeong H. Response network emerging from simple perturbation. J Korean Phys Soc. 2004; 44:628-632.
39. Kubi ski K, Doma ska K, Sajnaga E, Mazur E, Zieli ski R, Szyszka R. Yeast holoenzyme of protein kinase CK2 requires beta and beta' regulatory subunits for its activity. Mol Cell Biochem. 2007; 295:229-236.
40. Bennett CN, Ross SE, Longo KA, Bajnok L, Hemati N, Johnson KW, Harrison SD, MacDougald OA. Regulation of Wnt signaling during adipogenesis. J Biol Chem. 2002; 277:30998-31004.
41. Hausman GJ, Dodson MV, Ajuwon K, Azain M, Barnes KM, Guan LL, Jiang Z, Poulos SP, Sainz RD, Smith S et al. Board-invited review: the biology and regulation of preadipocytes and adipocytes in meat animals. J Anim Sci. 2009; 87:1218-1246.
42. Martelli F, Cenciarelli C, Santarelli G, Polikar B, Felsani A, Caruso M. MyoD induces retinoblastoma gene expression during myogenic differentiation. Oncogene. 1994; 9:3579-3590.
43. Puri PL, Iezzi S, Stiegler P, Chen TT, Schiltz RL, Muscat GE, Giordano A, Kedes L, Wang JY, Sartorelli V. Class I histone deacetylases sequentially interact with MyoD and pRb during skeletal myogenesis. Mol Cell. 2001; 8:885-897.
44. Rosen ED, Spiegelman BM. Molecular regulation of adipogenesis. Annu Rev Cell Dev Biol. 2000; 16:145-171.
45. Gallagher CJ, Langefeld CD, Gordon CJ, Campbell JK, Mychaleckyj JC, Bryer-Ash M, Rich SS, Bowden DW, Sale MM. Association of the estrogen receptor-alpha gene with the metabolic syndrome and its component traits in African-American families: the Insulin Resistance Atherosclerosis Family Study. Diabetes. 2007; 56:2135-2141.
46. Huang Q, Wang TH, Lu WS, Mu PW, Yang YF, Liang WW, Li CX, Lin GP. Estrogen receptor alpha gene polymorphism associated with type 2 diabetes mellitus and the serum lipid concentration in Chinese women in Guangzhou. Chin Med J (Engl). 2006; 119:1794-1801.
47. Lkhagvadorj S, Qu L, Cai W, Couture OP, Barb CR, Hausman GJ, Nettleton D, Anderson LL, Dekkers JC, Tuggle CK. Microarray gene expression profiles of fasting induced changes in liver and adipose tissues of pigs expressing the melanocortin-4 receptor D298N variant. Physiol Genomics. 2009; 38:98-111.
48. Taniguchi M, Guan LL, Zhang B, Dodson MV, Okine E, Moore SS. Gene expression patterns of bovine perimuscular preadipocytes during adipogenesis. Biochem Biophys Res Commun. 2008; 366:346-351.
49. Barendse W, Bunch R, Harrison B. The effect of variation at the retinoic acid receptor-related orphan receptor C gene on intramuscular fat percent and marbling score in Australian cattle. J Anim Sci. 2010; 88:47.
50. Barendse W, Bunch R, Kijas J, Thomas M. The effect of genetic variation of the retinoic acid receptor-related orphan receptor C gene on fatness in cattle. Genetics. 2007; 175:843.
51. Miller M, Cross H, Lunt D, Smith S. Lipogenesis in acute and 48-hour cultures of bovine intramuscular and subcutaneous adipose tissue explants. J Anim Sci. 1991; 69:162.
52. Tourniaire F, Gouranton E, Von Lintig J, Keijer J, Luisa Bonet M, Amengual J, Lietz G, Landrier J. B-Carotene conversion products and their effects on adipose tissue. Gene Nutr. 2009; 4:179-187.
53. Solomon C, White JH, Kremer R. Mitogen-activated protein kinase inhibits 1,25-dihydroxyvitamin D3-dependent signal transduction by phosphorylating human retinoid X receptor alpha. J Clin Invest. 1999; 103:1729-1735.
54. Wolford JK, Bogardus C, Prochazka M. Polymorphism in the 3' Untranslated Region ofMTG8Is Associated with Obesity in Pima Indian Males. Biochem Biophys Res Commun. 1998; 246:624-626.
55. Marques E, Nkrumah JD, Sherman EL, Moore SS. Polymorphisms in positional candidate genes on BTA14 and BTA26 affect carcass quality in beef cattle. J Anim Sci. 2009; 87:2475-2484.
56. Kong I, Koh S, Bayguinov O, Sanders K. Small conductance Ca2+-activated K+ channels are regulated by Ca2+-calmodulin-dependent protein kinase II in murine colonic myocytes. J Physiol. 2000; 524:331.
57. Chin E. The role of calcium and calcium/calmodulin-dependent kinases in skeletal muscle plasticity and mitochondrial biogenesis. Proc Nutr Soc. 2007; 63:279-286.
58. Zhao YM, Basu U, Dodson MV, Basarb JA, Guan le L. Proteome differences associated with fat accumulation in bovine subcutaneous adipose tissues. Proteome Sci. 2010; 8:14.
59. SAKOYAMA R, TANIGUCHI Y, KITAGAWA M, YAMADA T, SASAKI Y. Comparison of Age-dependent Expression Patterns of C/EBP family and PPAR. GAMMA. in Musculus longissimus between Japanese Black and Holstein Breeds. Nihon Chikusan Gakkaiho. 2004; 75:527-533.
60. Vitevitch MS. What can graph theory tell us about word learning and lexical retrieval? J Speech Lang Hear Res. 2008; 51:408-422.