Reconstruction and topological analysis of metabolic network in the bovine rumen tissue

Document Type : Full Research Paper

Authors

1 1- Department of Animal Science, Faculty of Animal and Food Science, Khuzestan Agricultural sciences and Natural Resources University, Mollasani, Ahvaz, Iran

2 Department of Animal Sciences, Ramin Agricultural and Natural Resources University, Iran

3 2- School of Advanced Technologies in Medicine Shahid Beheshti University of Medical Sciences, Tehran, Iran

4 3- School of Advanced Technologies in Medicine Shahid Beheshti University of Medical Sciences, Tehran, Iran

Abstract

The rumen has a central role in production efficiency in ruminants. Understanding metabolism process of rumen tissue can improve production efficiency of ruminants. In the present work, the metabolic network in the rumen tissue of cattle was reconstructed using genome information and available knowledge of rumen tissue from BRENDA, KEGG, Uniprot and NCBI. The result of reconstruct network consisted of 410 enzymes, 1429 metabolites as nodes and 1771 reactions they take part as edges. The characteristics of the metabolite-centric network were analyzed using some plugins in Cytoscape software. The top 15 hub metabolites were determined. The result of search in KEGG pathway shown the most of hub metabolites include CoA, Acetyl-CoA, D-fructose-6-phosphate, ubiquinone, succinate, electron-transferring flavoprotein, pyruvate, tetrahydrofolate and 2-oxoglutarate, involved in reactions which participate in metabolic pathway. Also, the genes CPT2, CPT1A, CPT1B, CPT1C (EC 2.3.1.21), ETFDH (EC 2.3.1.41), GPAM, GPAT4, GPAT3, GPAT2 (EC 2.3.1.15) participate in fatty acid metabolism that genes associated with this pathway may be essential to improve meat production efficiency of cattle research. The degree distribution of network follow power-law distribution hence displays a scale-free property. The average path length was 13.142 and diameter was 38 that shows the network also has small world properties. The present work is the first study to reconstruct rumen tissue network that may provide information to greater understanding on metabolic potential of rumen. Hub metabolites in scale-free networks paly significant role in maintaining topological robustness, for this reason seem to be useful for bovine breeding researches.

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References

1. Artegoitia, V.M., A.P. Foote., R. M. Lewis and H. C. Freetly. 2017. Rumen Fluid Metabolomics Analysis Associated with Feed Efficiency on Crossbred Steers. Sci Rep 6;7(1):2864
2. Barabasi, A and R. Albert. 1999. Emergence of scaling in random networks. Science 286: 509-12.
3. Barabási, A.L and Z. N. Oltvai. 2004. Network biology: understanding the cell's functional organization. Nat Rev Genet 5(2):101-13.
4. Bornstein, B.J, S. M. Keating., A. Jouraku and M. Hucka. 2008. LibSBML: an API library for SBML. Bioinformatics 24: 880-1.
5. Bruggeman, F.J and H.V. Westerhoff. 2007. The nature of systems biology. Trends Microbiol 15(1):45-50.
6. Cline, M.S., M. Smoot., E. Cerami., A. Kuchinsky., N. Landys., C. Workman., R. Christmas., I. Avila-Campilo., M. Creech., B. Gross., K. Hanspers, R. Isserlin and R. Kelley. 2007. Integration of biological networks and gene expression data using Cytoscape. Nat Protoc 2(10):2366-82.
7. Cottle, D. and L. Kahn. 2014. Beef cattle production and trade. CSIRO Publishing p. 221.
8. Degtyarenko, K., P. de Matos., M. Ennis., J. Hastings., M. Zbinden., A. McNaught., R. Alcántara., Darsow M., M. Guedj., M. Ashburner. 2008. ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Res 36: D344-50.
9. Hadizadeh, M., Niazi, A.,Mohammad Abadi, M., Esmailizadeh, A., Mehdizadeh., Gazooei, Y. 2014. Bioinformatics analysis of the BMP15 exon 2 in Tali and Beetal goats. Modern Genetics 9 (1), 117-120.
10. Hadizadeh, M., Mohammadabadi, M.R., Niazi, A., Esmailizadeh Koshkoiyeh, A.K., Mehdizadeh Gazooei, Y., Molaei, S. 2013. Use of bioinformatics tools to study exon 2 of GDF9 gene in Tali and Beetal goats. Modern Genetics Journal (MGJ) 8 (334), 283-288.
11. Jeong, H., B. Tombor., R. Albert., Z.N. Oltvai and A.L. Barabási. 2000. The large-scale organization of metabolic networks. Nature 5;407(6804):651-4.
12. Kanehisa, M., M. Araki., S. Goto., M. Hattori and M. Hirakawa. 2008. KEGG for linking genomes to life and the environment. Nucleic Acids Res 36: D480-4.
13. Kharrati Koopaei, H., Mohammad Abadi, M.R., Ansari Mahyari, S., Tarang, A.R., Potki, P., Esmailizadeh, A.K. 2012. Effect of DGAT1 variants on milk composition traits in Iranian Holstein cattle population. Animal Science Papers & Reports 30 (3), 231-240
14. Kharrati Koopaei, H., Mohammadabadi, M.R., Ansari Mehyari, S., Esmailizadeh, A.K., Tarang, A., Nikbakhti, M. 2011. Genetic Variation of DGAT1 Gene and its Association with Milk Production in Iranian Holstein Cattle Breed Population. Iranian Journal of Animal Science Research 3 (2), 185-192 (In Farsi).
15. Ma, H and A. Zeng. 2003. Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms. Bioinformatics 19: 270-7.
16. Managbanag, JR, T.M. Witten., D. Bonchev., L.A. Fox., M. Tsuchiya., Kennedy BK and M. Kaeberlein. 2008. Shortest-path network analysis is a useful approach toward identifying genetic determinants of longevity. PLoS ONE 3: e3802
17. Mazurie, A., D. Bonchev., B. Schwikowski and G. A. Buck. 2010. Evolution of metabolic network organization. BMC Syst Biol 11; 4:59.
18. Nikolaev, E.V., A.P. Burgard and C. D. Maranas.2005. Elucidation and structural analysis of conserved pools for genome-scale metabolic reconstructions. Biophys J 88: 37-49.
19. Pasandideh, M., Mohammadabadi, M.R., Esmailizadeh, A.K., Tarang, A. 2015. Association of bovine PPARGC1A and OPN genes with milk production and composition in Holstein cattle. Czech J. Anim. Sci 60, 97-104.
20. Poolman, M.G., B.K. Bonde., A. Gevorgyan., H.H. Patel and D.A. Fell. 2006. Challenges to be faced in the reconstruction of metabolic networks from public databases. Syst Biol 153: 379-84.
21. Radrich, K., Y. Tsuruoka., P. Dobson., A. Gevorgyan., N. Swainston., G. Baart and J.M. Schwartz. 2010. Integration of metabolic databases for the reconstruction of genome-scale metabolic networks. BMC Syst Biol 16;4:114
22. Stein, S.E., S.R. Heller and D. Tchekhovskoi. 2003. An open standard for chemical structure representation: The IUPAC chemical identifier. International Chemical Information Conference, Infonortics 131-43.
23. Wang, C., J. Wang., Z. Ju., R. Zhai., L. Zhou., Q. Li., J. Li., R. Li., J. Huang and J. Zhong. 2012. Reconstruction of metabolic network in the bovine mammary gland tissue. Mol Biol Rep 39(7):7311-8.
24. Watts, D. J and S.H. Strogatz. 1998. Collective dynamics of 'small-world' networks. Nature 393: 440-2.
25. Zhang, P., H. Foerster., C.P. Tissier., L. Mueller., S. Paley., P.D. Karp and S.Y. Rhee. 2005. MetaCyc and AraCyc. Metabolic pathway databases for plant research. Plant Physiol 138: 27-37.
26. Xue, F., X. Pan., L. Jiang, Y. Guo and B. Xiong. 2018. GC-MS analysis of the ruminal metabolome response to thiamine supplementation during high grain feeding in dairy cows. Metabolomics 14(5):67.
Veterinary Research & Biological Products
Volume 32, Issue 4
December 2019
Pages 106-117

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History

  • Receive Date: 20 February 2019
  • Revise Date: 02 March 2019
  • Accept Date: 03 March 2019

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