| تعداد نشریات | 284 |
| تعداد نشریات سازمان | 82 |
| تعداد شماره‌ها | 3,044 |
| تعداد مقالات | 34,047 |
| تعداد مشاهده مقاله | 46,439,387 |
| تعداد دریافت فایل اصل مقاله | 77,507,070 |
استخراج رابطه علّی- معلولی ترانسکریپتومی در بافت غدد پستانی گاو شیری با استفاده از شبکه بیزی | ||
| علوم دامی | ||
| مقاله 15، دوره 32، شماره 123، شهریور 1398، صفحه 197-216 اصل مقاله (2.86 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/asj.2018.121024.1656 | ||
| نویسندگان | ||
| امین مرتضوی1؛ امیر رشیدی* 2؛ مصطفی قادری زفره‌ای3؛ پرهام مرادی4؛ محمد رزم کبیر5 | ||
| 1دانشجوی دکتری، ژنتیک و اصلاح دام، گروه علوم دامی، دانشگاه کردستان، سنندج، ایران | ||
| 2استاد ژنتیک و اصلاح دام، گروه علوم دامی، دانشگاه کردستان، سنندج، ایران. | ||
| 3استادیار زیست سامانه‌های محاسباتی، گروه علوم دامی دانشگاه یاسوج، یاسوج، ایران | ||
| 4دانشیار رایانه، گروه مهندسی کامپیوتر، دانشگاه کردستان، سنندج، کردستان، ایران | ||
| 5استادیار ژنتیک و اصلاح دام، گروه علوم دامی، دانشگاه کردستان، سنندج، ایران | ||
| چکیده | ||
| هدف از این مطالعه، شناسایی ژن‌های تنظیمی مؤثر در بروز بیماری ورم پستان در گاو شیری با استفاده از داده-های ریزآرایهDNA بود. بدین منظور فایل بیان ژنی با بیشترین تعداد آرایه و متعلق به پلت‌فرم افی‌متریکس GPL1221 با شماره دسترسی GSE24560از پایگاه GEO استخراج شد. برای آزمون کنترل کیفیت داده از بسته‌یArrayQualityMetrics و برای پیش‌پردازش داده‌ها از تابع Threestep بسته‌ی AffyPLM در محیط R استفاده شد. پس از شناسایی ژن‌های متفاوت بیان‌شده، الگوریتم جستجوی تابو در بسته bnlearn برای شناسایی ژن‌های تنظیمی مورداستفاده قرار گرفت. نتایج حاصل از این پژوهش بیانگر نقش علّی و تنظیمی ژن‌های BCL2A، CCL2، S100A12، AOX1 و MGP بر بیان سایر ژن‌ها در بافت پستانی گاو شیری بود. بررسی هستی‌شناسی ژن‌ها، تفاوت معنی‌داری را در 7 گروه عملکردی مولکولی، 48 گروه فرآیندهای زیستی و 11 گروه اجزای سلولی در شرایط بروز بیماری ورم پستان در بافت غدد پستانی نشان داد. همچنین نتایج غنی-سازی مجموعه دادههای بیان ژنی نشان داد بیشتر ژن‌های متفاوت بیان‌شده در این پژوهش به طور معنی داری(p < 0.05) در مسیرهای متابولیکی (GO:0009605، GO:0002376 و GO:0006954) فعال بوده و در فرآیندهای پاسخ به بروز عوامل بیماری‌زا، پاسخ ایمنی و پاسخ به التهاب در بافت پستانی گاو شیری نقش دارند. | ||
| کلیدواژه‌ها | ||
| ریزآرایهDNA؛ ورم پستان؛ ژن‌های تنظیمی؛ هستی‌شناسی | ||
| عنوان مقاله [English] | ||
| Extraction of cause-and-effect transcriptomic relationship in mammary gland tissue of dairy cattle using Bayesian network | ||
| نویسندگان [English] | ||
| Amin Mortazavi1؛ Amir Rashidi2؛ Mostafa Ghaderi-Zefrehei Ghaderi-Zefrehei3؛ Parham Moradi4؛ Mohammad Razm Razm Kabir5 | ||
| 1Department of Animal Science, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran | ||
| 2Department of Animal Science, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran | ||
| 3Department of Animal Science, ّFaculty of Agriculture and Natural Resources, Yasouj university, Yasouj, Iran | ||
| 4Department of Computer Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran | ||
| 5Department of Animal Science, ّFaculty of Agriculture, University of Kurdistan, Sanandaj, Iran | ||
| چکیده [English] | ||
| 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. | ||
| کلیدواژه‌ها [English] | ||
| DNA Microarray, Mastitis, Regulatory Genes, Anthology | ||
| مراجع | ||
|
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 research, 45(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. | ||
|
آمار تعداد مشاهده مقاله: 529 تعداد دریافت فایل اصل مقاله: 399 |
||