Identification of biological pathways involved in body growth of cattle using gene expression profiles | ||
| علوم دامی | ||
| Article 5, Volume 31, Issue 119, September 2018, Pages 59-70 PDF (1.16 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.22092/asj.2017.110826.1460 | ||
| Authors | ||
| M. Alipanah* ; Zahra Roudbari1; ali javadmanesh2; Morteza Setaei mokhtari3; hamid reza seyedabadi4; Faezeh Gharari5 | ||
| 1Department of animal science, faculaty of Agriculture, Jiroft university, Jiroft,Iran | ||
| 2University of Mashhad | ||
| 3Jiroft University | ||
| 4Animal Science Research Institute of Iran | ||
| 5University of Torbay Heydarieh | ||
| Abstract | ||
| Today, exiting of high relationship and overlap between bioinformatics science and molecular biotechnology, can be play important role in genetic improvement of production traits. Growth trait is one of important production traits in animal husbandry, it affected via several biological pathways. Identification of the biological pathways is available using expression gene profiles and genomic information, and can cause of expand systemic idea about of the complicated procedure and better understand. In the study, collected data information about of gene expression in muscle tissue in two group cow 1 month and 24 month, from data base of Array Express. In first step investigated difference between expression genes using ArrayAnalysis, candidate 2732 genes for identifying biological pathway. Analysis of biological pathway conducted by PathVision software. Results of study gene expression in two group show that in two treatments, 503 gene had increasing in expression and 411 gene had decrease in expression. Study morphophonemic of the genes show that the genes play role in 23 biological pathways, most of the pathways play role in cellular process as growth, proliferation, differentiation, homeostasis and death was scheduled. The results can be lead to identification biological markers candidate for growth trait, that is a positive step forward to improve of procedure of cattle genomic evaluation and selection. | ||
| Keywords | ||
| Bioinformatics; growth of muscle; biological pathways and cattle breeding | ||
| References | ||
|
Bachelot, A. and Binart, N. (2007). Reproductive role of prolactin. Reproduction Review. 133: 361-369. Cole, J.B., Lewis, R.M., Maltecca, C., Newman, S., Olson, K.M. and Tait, R.G. (2013). Breeding and genetics symphosium: Systems biology in animal breeding: Identifying relationships among markers, genes, and phenotypes. Journal of Animal Science. 91(2): 521–522. Fortes, M.R.S., Reverter, A., Nagaraj, S.H., Zhang, Y., Jonsson, N.N., Barris, W. et al. (2011). A single nucleotide polymorphism-derived regulatory gene network underlying puberty in 2 tropical breeds of beef cattle. Journal of Animal Science. 89:1669–1683. Fortes, M.R., Reverter, A., Zhang, Y., Collis, E., Nagaraj, S.H., Jonsson, N., et al. (2010). Association weight matrix for the genetic dissection of puberty in beef cattle. In: Proceeding of National Acadic Science. USA 107:13642–13647. Froman, D.P. and D.D. Rhoads. (2013). A systems biology definition for chicken semen quality. Jornal of Animal Science. 91:523–529. Guifen, L., Xiaomu, L., Fachun, W., Xiuwen, T., Haijian, C. and Enliang, S. (2012). Use of a bovine genome chip to identify new biological pathways for beef quality in cattle. Molecular Biology Reproduction. 39(12):10979–86. Hobert, O. (2008). Gene regulation by transcription Factors and microRNAs. Science. 277:1630-1635. Horbelt, D., Denkis, A. and Knaus, P. (2012). A portrait of Transforming Growth Factor beta superfamily signalling: Background matters. The international journal of biochemistry & cell biology. 44(3): 469–474. Jiang, H. and Ge, X. (2013). Mechanism of growth hormone stimulation of skeletal muscle growth in cattle. Journal of animal sience. 92(10): 21-29. Kitano, H. (2000). Perspectives on systems biology. New Generation Computing. 18:199– 216. Koltes, J.E., Tait, R. G, Fritz, E.R., Mishra, B.P., Van Eenennaam, A.L., Mateescu, R.G., et al. (2012). A systems-genetics analysis of bovine skeletal muscle iron content. Journal of Animal Science. 90(Suppl. 3):163 (Abstr.). Kutmon, M., Van Iersel, M.P., Bohler, A., Kelder, T., Nunes, N., Pico, A.R. and Evelo, C.T. (2015). PathVisio 3: An Extendable Pathway Analysis Toolbox. PLoS Comput Biology. 11(2): e1004085. Lee, S.H, Gondro, C., Van der Werf. J., Kim, N.K., Lim, D., Park, E.W., et al. (2010). Use of a bovine genome array to identify new biological pathways for beef marbling in Hanwoo (Korean Cattle). BMC Genomics. 11(1):1–11. Nguyen, H.T., Ramstein, G., Leray, P. and Jacques,Y. (2009). Reconstruction of gene regulation networks from microarray data by Bayesian networks. Journées Ouvertes en Biologie, Informatique et Mathématiques (JOBIM. 1: 42-46. Reverter, A., and M. R. S. Fortes. 2013. Building single nucleotide polymorphism-derived gene regulatory networks: Towards functional genome wide association studies. Journal of Animal Science. 91:530–536. Rosa, G. J. M., and B. D. Valente. 2013. Inferring causal effects from observational data in livestock. Journal of Animal Science. 91:553–564. Roudbari, Z., Coort, S.L., Kutmon, M., Eijssen, L., Melius, J., Nassiri, M.M. et al. (2015). Pathway analysis of transcriptomics profiling as a key tool to improve beef cattle breeding in the meat-producing industry. In: Proceeding of Biosb confrance. Lunteren, Netherlands. Roudbari, Z., Coort, SL., Kutmon, M., Eijssen, L., Nassiri, M.M., Evelo, C. (2015). Cellular signaling pathway analysis of transcriptomics profiling as a key tool to improve marbling in beef cattle. A possible approach for artificial meat quality improvement. In: 1st International Symposium on Cultured Meat. Mastrich, Netherlands. Sadkowski, T., Ciecierska, A., Majewska, A., Oprzadek, J., Dasiewicz, K., Ollik, M., Wicik, Z. and Motyl, T. (2014). Transcriptional background of beef marbling - Novel genes implicated in intramuscular fat deposition, Meat Science. 97(1): 32–41. Shlomi, T., Cabili, MN., Herrgard, M.J., Palsson, B., Ruppin, E. (2008). Network-based prediction of human tissue-specific metabolism. Nature Biotechnology. 26:1003–1010. Snelling, W.M., Cushman, R.A., Keele, J.W., Maltecca, C., Thomas, M.G., Fortes, M.R.S. and Reverter. A. (2013). Networks and pathways to guide genomic selection. Journal of Animal Science. 91:537–552. Van Iersel, M.P., Kelder, T., Pico, A.R., Hanspers, K., Coort, S., Conklin, B.R., and Evelo, C. (2008). Presenting and exploring biological pathways with PathVisio. BMC Bioinformatics. 9(1): 1–9. | ||
|
Statistics Article View: 857 PDF Download: 631 |
||
Journal management system. designed by sinaweb