8954856055505db

شناسایی miRNAها و ایزومیرهای جدید در بافت کبد گاوهای شیری

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، گروه علوم دام و طیور، پردیس ابوریحان دانشگاه تهران

2 استادیار، گروه علوم دام و طیور، پردیس ابوریحان دانشگاه تهران

3 دانشیار، گروه علوم دام و طیور، پردیس ابوریحان دانشگاه تهران

چکیده

تعادل منفی انرژی (NEB) در گاوهای شیری پرتولید در چند هفتة نخست پس از زایمان رخ می­دهد و به دلیل اثرگذاری منفی بر باروری و سلامتی اهمیت اقتصادی زیادی در گله­های گاو شیری دارد. بنابراین، شناسایی هر چه بهتر سازوکارهای تنظیمی مؤثر در این اختلال سوخت‌وسازی (متابولیکی) اهمیت دارد. یکی از عامل‌های تنظیمی مؤثر در NEB، miRNAها هستند. به‌رغم اهمیت NEB، سازوکارهای تنظیمی مربوط به miRNAها در این دوره به‌خوبی شناخته نشده­اند. در این بررسی داده­های miRNA-seq مربوط به بافت کبد هشت گاو شیری هلشتاین موجود در بخش GEO بانک اطلاعاتی NCBI برای شناسایی miRNAها و ایزومیرهای جدید تجزیه‌وتحلیل شدند. در مجموع، 291 miRNA جدید که ژن همتا (همولوگ) در دیگر گونه­ها داشتند، و 164 miRNA جدید بدون همتا شناسایی شد. بررسی عملکرد ژن­های هدف miRNAهای شناسایی‌شده نشان داد، این ژن­ها در مسیرهای زیستی (بیولوژیکی) مرتبط با NEB نقش دارند. افزون بر این 446 ایزومیر و 95 miRNA* جدید برای نخستین بار در ژنگان (ژنوم) گاو گزارش شد. یافته‌های به‌دست‌آمده از این بررسی اطلاعات جدیدی برای درک بهتر نقش تنظیمی miRNAها در NEB فراهم می­کند.

کلیدواژه‌ها


عنوان مقاله [English]

Identification of new miRNAs and isomirs in liver tissue of dairy cows

نویسندگان [English]

  • Zohre Mozduri 1
  • Mohammad Reza Bakhtiarizadeh 2
  • Abdolreza Salehi 3
1 M. Sc. Student, Department of Animal and Poultry Sciences, Aburaihan Campus, University of Tehran, Iran
2 Assistant Professor, Department of Animal and Poultry Sciences, Aburaihan Campus, University of Tehran, Iran
3 Associate Professor, Department of Animal and Poultry Sciences, Aburaihan Campus, University of Tehran, Iran
چکیده [English]

Negative energy balance (NEB) occurs inhigh-producing dairy cows in first few weeks after parturition, that energy demand for maintenance and milk production exceeds the dietary energy intake. NEB has a considerable economic importance due to negative effect on health and fertility in dairy herds, therefore, the identification of its effective regulatory mechanism is important. miRNAs are one of these effective regulatory factors in NEB. Despite of the importance of NEB, the regulatory mechanisms related to miRNAs has not been well documented. In this study miRNA-seq data from liver tissue of eight Holstein dairy cows were analyzed to identify new miRNAs and isomirs. All data have been achieved from GEO in NCBI database. A total of 291 new miRNAs with homologous gene in other species were identified. Moreover, 164 new miRNAs without homologous were identified. Investigation of target genes of these miRNAs lead to identify biological paths related to NEB. Also 466 new isomiR and 95 new miRNA* were detected for the first time in cow genome. The results of the current study provide new information for better understanding of the regulatory roles of miRNAs in NEB.

کلیدواژه‌ها [English]

  • IsomiR
  • negative energy balance
  • liver tissue
  1. Bartel, D. P. (2004). MicroRNAs: genomics, biogenesis, mechanism, and function. Cell, 116, 281-297.
  2. Barturen, G., Rueda, A., Hamberg, M., Alganza, A., Lebron, R., Kotsyfakis, M., Shi, B. J,. Koppers-Lalic, D. & Hackenberg, M. (2014).  sRNAbench: profiling of small RNAs and its sequence variants in single or multi-species high-throughput experiments. Methods in Next Generation Sequencing, 1, 21-311.
  3. Bolger, A. M., Lohse, M. & Usadel, B. (2014). Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, btu170, 1-77.
  4. Bortoluzzi, S., Bisognin, A., Biasiolo, M., Guglielmelli, P.,  Biamonte, F., Norfo, R., Manfredini, R. & Vannucchi, A. M. (2012). Characterization and discovery of novel miRNAs and moRNAs in JAK2V617F-mutated SET2 cells. Blood, 119, e120-e130.
  5. Bu, P., Wang, L., Chen, K. Y., Rakhilin, N., Sun, J., Closa, A., Tung, K. L., King, S., Varanko, A. K. &  Xu, Y. (2015). miR-1269 promotes metastasis and forms a positive feedback loop with TGF-[beta]. Naturecommunications, 6, 1-122.
  6. Clemens, J. C., Worby, C. A., Simonson-Leff, N., Muda, M., Maehama, R., Hemmings, B. A. & Dixon, J. E. (2000). Use of double-stranded RNA interference in Drosophila cell lines to dissect signal transduction pathways. Proceedings of the National Academy of Sciences,97, 6499-6503.
  7. Denman, R. (1993). Using RNAFOLD to predict the activity of small catalytic RNAs.Biotechniques, 15, 1090-1095.
  8. Diskin, M. & Morris, D. (2008). Embryonic and early foetal losses in cattle and other ruminants. Reproduction in Domestic Animals, 43, 260-2677.
  9. Fatima, A., Analysis of hepatic microRNA expression in postpartum dairy cows in negative energy balance. (2014). National University of Ireland, Galway.
  10. Fatima, A. & Morris, D. G. (2013). MicroRNAs in domestic livestock. Physiological Genomics, 45, 685-696.
  11. Fatima, A., Waters,  S., O’Boyle, P., Seoighe, C. & Morris, D. G. (2014a). Alterations in hepatic miRNA expression during negative energy balance in postpartum dairy cattle. BMC Genomics, 15, 1-100.
  12. Fatima, A., Lynn, D.  J., O’Boyle, P., Seoighe, C. & Morris, D. (2014b). The miRNAome of the postpartum dairy cow liver in negative energy balance. BMC genomics,15, 1-8.
  13. Fenwick, M. A., Fitzpatrick, R., Kenny, D. A., Diskin, M. G., Patton, J., Murphy, J. J. & Wathes, D. C. (2008). Interrelationships between negative energy balance (NEB) and IGF regulation in liver of lactating dairy cows. Domestic Animal Endocrinology, 34, 31-444.
  14. Ferland-McCollough, D., Ozanne, S., Siddle, K., Willis, A. & Bushell, M. (2010). The involvement of microRNAs in Type2 diabetes. Biochemical Society Transactions, 38, 1565-1570.
  15. Fernandez-Valverde, S. L., Taft, R. J. & Mattick, J. S. (2010). Dynamic isomiR regulation in Drosophila development. Rna16, 1881-1888.
  16. Friedländer, M. R., Mackowiak, S. D., Li, N., Chen, W. & Rajewsky, N. (2012). miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Research, 40, 37-52.
  17. Friedman, R. C., Farh, K. K. H., Burge, C. B. & Bartel, D. P. (2009). Most mammalian mRNAs are conserved targets of microRNAs. Genome Research, 19, 92-105.
  18. Go, G. W. & Mani, A. (2012). Low-density lipoprotein receptor (LDLR) family orchestrates cholesterol homeostasis. The Yale Journal of Biology and Medicine, 85, 19-288.
  19. IUM, T. (2012). ENCODE project writes eulogy for junk DNA. SCIENCE, 337, 1159-1161.
  20. Jin, W., Grant, J. R., Stothard, P., Moore, S. S. & Guan, L. L. (2009). Characterization of bovine miRNAs by sequencing and bioinformatics analysis. BMC Molecular Biology, 10, 1-111.
  21. John, B., Enright, A. J., Aravin, A., Tuschl, T., Sander, C. & Marks, D. S. (2004). Human microRNA targets. PLoS Biol,2,1-11.
  22. Jopling, C. (2012). Liver-specific microRNA-122: Biogenesis and function. RNA biology, 9, 137-142.
  23. Katoh, T., Sakaguchi, Y., Miyauchi, K., Suzuki, T., Kashiwabara, S.i., Baba, T. & Suzuki, T. (2009). Selective stabilization of mammalian microRNAs by 3′ adenylation mediated by the cytoplasmic poly (A) polymerase GLD-2. Genes & Development, 23, 433-438.
  24. Kaur, K., Pandey, A. K,. Srivastava, S., Srivastava, A. K. & Datta, M. (2011). Comprehensive miRNome and in silico analyses identify the Wnt signaling pathway to be altered in the diabetic liver. Molecular BioSystems, 7, 3234-3244.
  25. Kertesz, M., Iovino, N., Unnerstall,  U., Gaul, U. &  Segal, E. (2007).  The role of site accessibility in microRNA target recognition. Nature Genetics, 39, 1278-12844.
  26. Langmead, B. (2010).  Aligning short sequencing reads with Bowtie. Current Protocols in Bioinformatics, 11.17. 11-11.17. 14.
  27. Lawless, N., Foroushani, A. B., McCabe, M. S., O’Farrelly, C. & Lynn, D. J. (2013).  Next generation sequencing reveals the expression of a unique miRNA profile in response to a Gram-positive bacterial infection. PLOS ONE, 8, 1-13.
  28. Li, J. Y., Yong, T. Y., Michael, M. Z. &  Gleadle, J. M. (2010).  Review: The role of microRNAs in kidney disease. Nephrology, 15, 599-6088.
  29. Li, M., Yang, Y., He, Z. X.,  Zhou, Z. W., Yang, T., Guo, P., Zhang, X. &. Zhou, S. F. (2014). Microrna-561 promotes acetaminophen-induced hepatotoxicity in hepg2 cells and primary human hepatocytes through downregulation of the nuclear receptor corepressor dosage-sensitive sex-reversal adrenal hypoplasia congenital critical region on the x chromosome, gene 1 (dax-1). Drug Metabolism and Disposition , 42, 44-611.
  30. Li, N., You, X., Chen, T., Mackowiak, S. D., Friedländer, M. R., Weigt, M., Du, H., Gogol-Döring, A., Chang, Z. & Dieterich, C. (2013).  Global profiling of miRNAs and the hairpin precursors: insights into miRNA processing and novel miRNA discovery. Nucleic Acids Research, gkt072, 1-166.
  31. Li, R., Zhang, C. L., Liao, X. X., Chen, D., Wang, W.Q., Zhu, Y. H., Geng, X. H., Ji, D. J., Mao, Y. J. & Gong Y. C. (2015). Transcriptome microRNA profiling of bovine mammary glands infected with Staphylococcus aureus. International Journal of Molecular Sciences, 16, 4997-50133.
  32. Loor, J. J., Everts, R. E., Bionaz, M., Dann, H. M., Morin, D. E., Oliveira, R., Rodriguez-Zas, S.L., Drackley, J. K. & Lewin, H. A. (2007).  Nutrition-induced ketosis alters metabolic and signaling gene networks in liver of periparturient dairy cows. Physiological Genomics, 32, 105-1166.
  33. Lucy, M. (2006). Fertility in high-producing dairy cows: reasons for decline and corrective strategies for sustainable improvement. Society of Reproduction and Fertility Supplement, 64,237-2544.
  34. McCabe, M., Waters, S., Morris, D., Kenny, D., Lynn, D. & Creevey, C. (2012). RNA-seq analysis of differential gene expression in liver from lactating dairy cows divergent in negative energy balance.  BMC Genomics, 13, 1-11.
  35. McCarthy, S. D., Waters, S. M., Kenny, D. A., Diskin, M. G., Fitzpatrick, R., Patton, J., Wathes, D. C. &  Morris, D. G. (2010). Negative energy balance and hepatic gene expression patterns in high-yielding dairy cows during the early postpartum period: a global approach. Physiological Genomics, 42, 188-199.
  36. Mehta, J. P. (2014). Sequencing Small RNA: Introduction and Data Analysis Fundamentals. RNA MappingSpringer, 1182, 93-103.
  37. Nielsen, M., Hansen, J., Hedegaard, J., Nielsen, R., Panitz, F., Bendixen, C. & Thomsen, B. (2010). MicroRNA identity and abundance in porcine skeletal muscles determined by deep sequencing. Animal Genetics, 41, 159-168.
  38. Papa, S., Zazzeroni, F., Fu, Y. X., Bubici, C., Alvarez, K., Dean, K., Christiansen, P. A., Anders, R. A. & Franzoso, G. (2008). Gadd45β promotes hepatocyte survival during liver regeneration in mice by modulating JNK signaling. The Journal of Clinical Investigation, 118, 1911-19233.
  39. Peng, Z., Cheng, Y., Tan, B.C.M., Kang, L., Tian, Z., Zhu, Y., Zhang, W., Liang, Y., Hu, X. & Tan, X. (2012). Comprehensive analysis of RNA-Seq data reveals extensive RNA editing in a human transcriptome. Nature Biotechnology, 30, 253-260.
  40. Phillips, T. (2008). Regulation of transcription and gene expression in eukaryotes. Nature Education, 6, 775-781.
  41. Pogribny, I. P., Starlard-Davenport,  A., Tryndyak, V. P., Han, T., Ross, S. A., Rusyn, I. & Beland, F. A. (2010). Difference in expression of hepatic microRNAs miR-29c, miR-34a, miR-155, and miR-200b is associated with strain-specific susceptibility to dietary nonalcoholic steatohepatitis in mice. Laboratory Investigation, 90, 1437-1446.
  42. Schmieder, R. & Edwards, R. (2011). Quality control and preprocessing of metagenomic datasets. Bioinformatics, 27, 863-864.
  43. Sheldon, I. M. (2004). The postpartum uterus. Veterinary Clinics of North America: Food Animal Practice, 20, 569-5911.
  44. Sturm, M., Hackenberg, M., Langenberger, D. & Frishman, D. (2010).  TargetSpy: a supervised machine learning approach for microRNA target prediction. BMC bioinformatics, 11, 1-177.
  45. Tabas-Madrid, D., Muniategui, A., Sánchez-Caballero, I., Martínez-Herrera, D. J., Sorzano, C. O. S., Rubio, A. & Pascual-Montano, A. (2014). Improving miRNA-mRNA interaction predictions. BMC Genomics, 15, 1-12.
  46. Timoneda, O., Balcells, I., Núñez, J. I., Egea, R., Vera, G., Castelló, A., Tomàs, A. & Sánchez, A. (2013). miRNA expression profile analysis in kidney of different porcine breeds. PLoS One, 8, 1-13.
  47. Wang, K., Zhang, S., Marzolf, B., Troisch, P., Brightman, A., Hu, Z., Hood, L. E. & Galas, D. J. (2009). Circulating microRNAs, potential biomarkers for drug-induced liver injury. In: Proceedings of the national academy of sciences, 106, 4402-4407.
  48. Wang, L., Jia, X. J., Jiang, H.J,. Du, Y., Yang, F., Si, S. Y. & Hong, B. (2013). MicroRNAs 185, 96, and 223 repress selective high-density lipoprotein cholesterol uptake through posttranscriptional inhibition. Molecular and Cellular Biology, 33, 1956-1964.
  49. Wathes, D. C., Cheng, Z., Chowdhury, W., Fenwick, M. A., Fitzpatrick, R., Morris, D. G., Patton, J. & Murphy, J. J. (2009). Negative energy balance alters global gene expression and immune responses in the uterus of postpartum dairy cows. Physiological Genomics, 39, 1-13.
  50. Xia, K., Zhang, Y., Cao, S., Wu, Y., Guo, W., Yuan, W. & Zhang, S. (2015). miR-411 regulated ITCH expression and promoted cell proliferation in human hepatocellular carcinoma cells. Biomedicine &Pharmacotherapy,70, 158-163.
  51. Xu, G., Gao, Z., He, W., Ma, Y., Feng, X., Cai, T., Lu, F., Liu, L. & Li, W. (2014). microRNA expression in hepatitis B virus infected primary treeshrew hepatocytes and the independence of intracellular miR-122 level for de novo HBV infection in culture. Virology, 448, 247-2544.