عنوان مقاله [English]
RNA-Sequencing technique is a powerful tool for analysis of cellular transcriptome in many research areas. In recent years, network constructing on data resulted from this technique and gene relationships have been investigated for better understanding the complex physiological mechanisms. In this study, construction of gene regulatory network and surveying gene relationships which involve in skeletal muscle synthesis were discussed by growing muscle tissue’s RNA-Seq data. This research introduced six transcription factors which were involved in formation and growth of skeletal muscle tissue by fitting an integrated network. These transcription factors included KLF4, FOXM1, FOS, MYBL2, KLF10 and FOSB. Their biological roles have been identified in proliferation and differentiation of stem cells, elongation and fusion of muscle cells, myofibril generation and self-renewal of satellite cells. Ontology of these transcription factors and their regulated genes showed that theyare involved in hexose pathway and nucleotides production. Theyare also involved in regulation of basic cell activities such as gene expression and transcription. These genes may be used as molecular markersfor improvement of meat production in breeding programs.
1. Anders, S., P.T. Pyl and W. Huber. 2014. HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics btu638.
2. Bolger, A.M., M. Lohse and B. Usadel. 2014. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformaticsbtu 170.
3. Chaplais, E., H.J. Garchon, M.E. Chaplais and G. AnnotationDbi. 2015. Package ‘stringgaussnet. http://www.et.bs.ehu.es/cran/web/packages/stringgaussnet/stringgaussnet.pdf. Accessed 25 March 2017
4.Deato, M.D.E., M.T. Marr, T. Sottero, C. Inouye, P. Hu and R. Tjian. 2008. MyoD targets TAF3/TRF3 to activate myogenin transcription. Molecular cell 32(1): 96-105.
5. Dehmer, M., L.A. Mueller and F. Emmert-Streib. 2013. Quantitative network measures as biomarkers for classifying prostate cancer disease states: a systems approach to diagnostic biomarkers.PloS One 8(11): e77602.
6. Du, P., J. Gong, E.S. Wurteleand J.A. Dickerson. 2005. Modeling gene expression networks using fuzzy logic.IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 35(6): 1351-1359.
7. Dunner, S., N. Sevane, D. García, O. Cortés, A. Valentini, J. Williams, B. Mangin, J. Cañón, H. Levéziel and G. Consortium. 2013. Association of genes involved in carcass and meat quality traits in 15 European bovine breeds. Livestock Science 154(1): 34-44.
8. Friedman, N., M. Linial, I. Nachman and D. Pe'er. 2000. Using Bayesian networks to analyze expression data. Journal of computational biology 7(4): 601-620.
9. Glass, L. and S.A. Kauffman. 1973. The logical analysis of continuous, non-linear biochemical control networks. Journal of theoretical Biology 39(1): 103-129.
10. Hedden, M.P. and M.G. Buse. 1982. Effects of glucose, pyruvate, lactate, and amino acids on muscle protein synthesis. American Journal of Physiology-Endocrinology And Metabolism 242(3): E184-E192.
11. Imoto, S., T. Goto and S. Miyano. 2001. Estimation of genetic networks and functional structures between genes by using Bayesian networks and nonparametric regression. In:Pacific symposium on Biocomputing. Kauai, Hawaii.Volume. 7, pp. 175-186.
12. Jiang, J., Y.-S. Chan, Y.-H. Loh, J. Cai, G.-Q. Tong, C.-A. Lim, P. Robson, S. Zhong and H.-H. Ng. 2008. A core Klf circuitry regulates self-renewal of embryonic stem cells. Nature cell biology 10(3): 353-360.
13. Johansen, K.A. and K. Overturf. 2006. Alterations in expression of genes associated with muscle metabolism and growth during nutritional restriction and refeeding in rainbow trout.Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology 144(1): 119-127.
14. Keogh, K., D.A. Kenny, P. Cormican, M.S. McCabe, A.K. Kelly and S.M. Waters. 2016. Effect of dietary restriction and subsequent re-alimentation on the transcriptional profile of bovine skeletal muscle. PloSOne 11(2): e0149373.
15. Li, Z., J.A. Gilbert, Y. Zhang, M. Zhang, Q. Qiu, K. Ramanujan, T. Shavlakadze, J.K. Eash, A. Scaramozza and M.M. Goddeeris. 2012. An HMGA2-IGF2BP2 axis regulates myoblast proliferation and myogenesis. Developmental cell 23(6): 1176-1188.
16. Mangadzuwa, D.A., J. Thiengtham and S. Prasanpanich. 2016. A case study on compensatory growth of emaciated cattle fed on total mixed ration. African Journal of Agricultural Research 11(27): 2397-2402.
17. Martinez, R., J.F. Rocha, D. Bejarano, Y. Gomez, Y. Abuabara and J. Gallego. 2016. Identification of SNPs in growth-related genes in Colombian creole cattle. Genetics and molecular research 15(3).
18. Mauro, A. 1961. Satellite cell of skeletal muscle fibers.The Journal of biophysical and biochemical cytology 9(2): 493-495.
19. Molinelli, E.J., A. Korkut, W. Wang, M.L. Miller, N.P. Gauthier, X. Jing, P. Kaushik, Q. He, G. Millsand D.B. Solit. 2013. Perturbation biology: inferring signaling networks in cellular systems.PLoS Computational Biology 9(12): e1003290.
20. Na, H.H., H.M. Cheong and K.C. Kim. 2016. BMB Reports: SETDB1 mediated FosB expression increases the cell proliferation rate during anticancer drug therapy. BMB reports 49(4): 238-243.
21. Parakati, R. and J.X. DiMario. 2013. Repression of myoblast proliferation and fibroblast growth factor receptor 1 promoter activity by KLF10 protein. Journal of Biological Chemistry 288(19): 13876-13884.
22. Plank, J.L., M.T. Suflita, C.L. Galindo and P.A. Labosky. 2014. Transcriptional targets of Foxd3 in murine ES cells. Stem cell research 12(1): 233-240.
23. Robinson, M.D., D.J. McCarthy and G.K. Smyth. 2010. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1): 139-140.
24. Trapnell, C., L. Pachter and S.L. Salzberg. 2009. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25(9):1105-1111.
25. Woo, D.-H., S.-J. Yun, E.-K. Kim, J.-M. Ha, H.-K. Shin and S.-S. Bae. 2012. Regulation of Skeletal Muscle Differentiation by Akt. Journal of Life Science 22(4): 447-455.
26. Yoshida, T., K.H. Kaestner and G.K. Owens. 2008. Conditional deletion of Krüppel-like factor 4 delays downregulation of smooth muscle cell differentiation markers but accelerates neointimal formation following vascular injury. Circulation research 102(12): 1548-1557.
27. Yoshida, Y., I.C. Wang, H.M. Yoder, N.O. Davidson and R.H. Costa. 2007. The forkhead box M1 transcription factor contributes to the development and growth of mouse colorectal cancer. Gastroenterology 132(4): 1420-1431.
28. Yu, Y., L. Qi, J. Wu, Y. Wang, W. Fang and H. Zhang. 2013. Kindlin 2 regulates myogenic related factor myogenin via a canonical Wnt signaling in myogenic differentiation. PloS One 8(5): e63490.
29. Zhan, M., D.R. Riordon, B. Yan, Y.S. Tarasova, S. Bruweleit, K.V. Tarasov, R.A. Li, R.P. Wersto and K.R. Boheler. 2012. The B-MYB transcriptional network guides cell cycle progression and fate decisions to sustain self-renewal and the identity of pluripotent stem cells. PloS One 7(8): e42350.