Extraction of cause-and-effect transcriptomic relationship in mammary gland tissue of dairy cattle using Bayesian network

Document Type : Research Paper

Authors

1 Department of Animal Science, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran

2 Department of Animal Science, ّFaculty of Agriculture and Natural Resources, Yasouj university, Yasouj, Iran

3 Department of Computer Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran

4 Department of Animal Science, ّFaculty of Agriculture, University of Kurdistan, Sanandaj, Iran

Abstract

The aim of this study was to identify regulatory genes affecting mastitis in dairy cattle using DNA microarray data. To reach this goal, the gene expression data with the largest number of arrays pertained to GPL1221 Platform with accession number GSE24560 was extracted from the GEO database. For quality control of data, ArrayQualityMetrics package and for preprocessing of data, three step function in AffyPLM, an add-in package in R environment were used. After identifying differentially expressed genes, a Tabu search algorithm was used to determine regulatory genes using bnlearn package in R environment. The results of this study revealed the causative and regulatory role for BCL2A, CCL2, S100A12, AOX1 and MGP genes on expression of other genes in mastitis of dairy cattle. Gene anthology analysis, revealed significant differences in 7 groups of molecular function, 48 groups of biological process and 11 groups of cellular components. Also, the results of the enrichment of the gene expression data set showed that most of the differentially genes expressed in this study that were significantly (p < 0.05) active in metabolic pathways (GO: 0009605, GO: 0002376 and GO: 0006954) involved in response to pathogens, immune response and response to inflammation in the mammary tissue of dairy cattle.

Keywords


شریفی، س.، پاکدل، ع. و ابراهیمی، ا. (1396). فراتحلیل (متا-آنالیز) داده­های بیان ژن بافت پستان آلوده شده با باکتری اشریشیا­کلی در گاوهای شیری. مجله علوم دامی ایران، دوره 48، شماره 3، ص ص. 352-343.
Abad, S. V., Alijani, S., Kia, H. D., Zali, H., Karim, S. K., and Pashaie, M. B. (2015). Bioinformatics analysis of E. coli causing mastitis in Holstein dairy cattle by using microarray data. Koomesh, 17(1).‏
Adomas, A., Heller G., Olson, A., Osborne, J., Karlsson, M., Nahalkova, J. et al. (2008). Comparative analysis of transcript abundance in pinus sylvestris after challenge with a saprotrophic, pathogenic or mutualistic fungus. Physiology. 28: 885-897.
Alexandre, P.A., Kogelman, L.J., Santana, M.H., Passarelli, D., Pulz L.H., Fantinato-Neto, P. et al. (2015). Liver transcriptomic networks reveal main biological processes associated with feed efficiency in beef cattle. BMC genomics. 16: 1.
Bar, D., Grohn, Y.T., Bennt, G., Gonzalez, R.N., Hertl, J.A., Schulte, H.F. et al. (2008). The cost of generic clinical mastitis in dairy cows as estimated by using dynamic programming. Journal of Dairy Science, 91: 2205- 2214.
Bartel, D.P. (2009). MicroRNAs: target recognition and regulatory functions. Cell, 136: 215-33.
Bolstad, B.M., Collin, F., Brettschneider, J., Simpson, K., Cope, L., Irizarry, R.A. et al. (2005). Quality Assessment of Affymetrix GeneChip Data.” In Gentleman R, Carey V, Huber W, Irizarry R and Dudoit S (eds.), Bioinformatics and Computational Biology Solutions using R and Bioconductor, chapter 3, pp. 33–47. Springer, New York.
Cánovas, A., Reverter, A., DeAtley, K.L., Ashley, R.L., Colgrave, M.L., Fortes, M.R., et al. (2014). Multi-tissue omics analyses reveal molecular regulatory networks for puberty in composite beef cattle. PloS one. 9: e102551.
Davis, S. and Meltzer, P. (2007). GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics. 14: 1846–1847.
Fortes, M.R., Nguyen, L.T., Neto, L.R.P., Reverter, A., Moore, S.S., Lehnert, S.A. et al. (2016). Polymorphisms and genes associated with puberty in heifers. Theriogenology. 86(1): 333-339.
Gámez, J.A., Mateo, J.L. and Puerta, J.M. (2011). Learning Bayesian networks by hill climbing: efficient methods based on progressive restriction of the neighborhood. Data Mining and Knowledge Discovery. 22:106-148.
Gene expression omnibus (GEO). (2017) Available at https://www.ncbi.nlm.nih.gov/geo/. NCBI, USA.
Gholami, A. and Siahkoohi, H.R. (2009). A two-step wavelet-based regularization for linear inversion of geophysical data. Geophysical Prospecting, 57: 847–862.
 Grisart, B., Coppieters, W., Farnir, F., Karim, L., Ford, C., Berzi, P. et al. (2002). Positional candidate cloning of a QTL in dairy cattle: identification of a missense mutation in the bovine DGAT1 gene with major effect on milk yield and composition. Genome research, 12(2): 222-231.
Gronlund, U., Hulten, C., Eckersall, P.D., Hogarth, C., Waller, K.P., (2003). Haptoglobin and serum amyloid A in milk and serum during acute and chronic experimentally induced Staphylococcus aureus mastitis. Journal of Dairy Research, 70: 379–386.
Hajibemani, A., Sharifiyazdi, H., Mirzaei, A., and Ghasrodashti, A. R. (2012). Characterization of single nucleotide polymorphism in the 5'-untranslated region (5'-UTR) of Lactoferrin gene and its association with reproductive parameters and uterine infection in dairy cattle. In Veterinary Research Forum (Vol. 3, No. 1, p. 37). Faculty of Veterinary Medicine, Urmia University, Urmia, Iran.‏ ‏
Heckerman, D. (1997). Bayesian networks for data mining. Data Mining and Knowledge Discovery. 1: 79–119.
Horvath, S. (2011). Weighted network analysis: applications in genomics and systems biology. Springer Science & Business Media, Berlin, Germany. pp: 90-149.
Huang, D.W., Sherman, B.T, Lempicki, R.A. (2009) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res, 37(1):1-13.
Jorgensen, H. B., Buitenhuis, B., Rontved, C. M., Jiang, L., Ingvartsen, K. L. and Sorensen, P. (2012). Transcriptional profiling of the bovine hepatic response to experimentally induced E. coli mastitis. Physiol Genomics, 44: 595-606.
Kauffmann, A., Gentleman, R. and Huber, W. (2009). arrayQualityMetrics–a bioconductor package for quality assessment of microarray data. Bioinformatics, 25(3): 415–6.
Kent, W.J., Sugnet, C.W., Furey, T.S., Roskin, K.M., Pringle, T.H., Zahler, A.M. and Haussler, D. (2002). The human genome browser at UCSC. Genome research, 12(6): 996-1006.
Kgwatalala, P. M., Ibeagha‐Awemu, E. M., Hayes, J. F., and Zhao, X. (2009). Stearoyl‐CoA desaturase 1 3′ UTR SNPs and their influence on milk fatty acid composition of Canadian Holstein cows. Journal of Animal Breeding and Genetics, 126(5): 394-403.‏
Langfelder, P. and Horvath, S. (2008). WGCNA: an R package for weighted correlation network analysis. BMC bioinformatics. 9: 559.
Li, K., Chen, Y., Li, W., He, J. and Xue, Y. (2018). Improved gene expression programming to solve the inverse problem for ordinary differential equations. Swarm and Evolutionary Computation, 38: 231-239.‏
Li, Q. L., Zhang, Z. F., Xia, P., Wang, Y. J., Wu, Z. Y., Jia, Y. H. et al. (2015). A SNP in the 3′-UTR of HSF1 in dairy cattle affects binding of target bta-miR-484. Genet Mol Res, 14(4): 12746-55.‏
Moran, B., Butler, S.T., Moore, S.G., MacHugh, D. and Creevey, C.J. (2015). Differential gene expression in the endometrium reveals cytoskeletal and immunological genes in lactating dairy cows genetically divergent for fertility traits. Reproduction, Fertility and Development. 29(2): 274-82.
Mungloo-Dilmohamud, Z., Jaufeerally-Fakim, Y. and Peña-Reyes, C. (2017). A Meta-Review of Feature Selection Techniques in the Context of Microarray Data. In: Proceedings of International Conference on Bioinformatics and Biomedical Engineering. Switzerland, Springer, Cham, 33-49.
R Core Team (2017). R:A language and environment for statistical computing. R Foundation for Statistical Computing. URL https://www.R-project.org/. Vienna, Austria.
Ramayo-Caldas, Y., Fortes, M.R.S., Hudson, N.J., Porto-Neto, L.R., Bolormaa, S., Barendse, W., et al. (2014). A marker-derived gene network reveals the regulatory role of, and in intramuscular fat deposition of beef cattle. Journal of Animal Science. 92: 2832-2845.
Rebhan, M., Chalifa-Caspi, V., Prilusky, J. and Lancet, D. (1997). GeneCards: integrating information about genes, proteins and diseases. Trends in Genetics, 163(4): 13.
Regennhard, P., Petzl, W., Zerbe, H., and Sauerwin, H. (2010). The antibacterial psoriasin is induced by E. coli infection in the bovine udder. Veterinary microbiology, 143(2): 293-298.
Schwerin, M. (2003). Applicattion of disease-associated differentially expressed genes-mining for fuctional candidate genes for mastitis resitance in cattle. Genetics selection Evolution, 35: 19-34.
Scutari, M. (2014). Bayesian network constraint-based structure learning algorithms: Parallel and optimised implementations in the bnlearn r package. arXiv preprint arXiv:1406.7648.
Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D. et al. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome research. 13: 2498-2504.
Strunz, S. (2014) Inferring a Core Transcriptional Regulatory Network in Cows. In: Proceedings of 10th World Congress on Genetics Applied to Livestock Production. Vancouver, BC, Canada.
Tian, T., Liu, Y., Yan, H., You, Q., Yi, X., Du, Z. et al. (2017). agriGO v2. 0: a GO analysis toolkit for the agricultural community, 2017 update. Nucleic acids research45(W1): W122-W129.‏
Weber, K. (2015). Regulatory Networks for Feedlot Feed Efficiency in Angus Cattle Using Multiple Tissue Transcriptomics. In: Proceedings of Plant and Animal Genome XXIII Conference. Plant and Animal Genome. San Diego, California, USA.
Yang, J., Sang, Y., Meade, K,G., and Ross, C. (2011). The role of oct-1 in the regulation of tracheal antimicrobial peptid (ATP) and lingual antimicrobial peptid (LAP) exertion in bovine mammary epithelial cells. Immunogenetics, 63(11): 715-725.