Abstract and References
Transactions on Science and Technology Vol. 4, No. 3-3, 391 - 395, 2017

Profiling of MicroRNA Expression in Obese and Diabetic-Induced Mice for Biomarker Discovery

Janan N. Hadi, Mohammad Iqbal, Vijay Kumar

ABSTRACT
MicroRNAs (miRNAs) are short (-22 nucleotides) regulatory RNAs involved in many fundamental biological processes. They are involved in post-transcriptional regulation of gene expression. Dysregulated expression of microRNAs has been associated with a variety of diseases, including obesity and diabetes. Obesity is a potential risk factor contributing to the development of type 2 diabetes. Meanwhile, diabetes is one of the most prevalent chronic diseases, affecting 6.4% of the world’s adult population. The aim of this study is to identify microRNAs that are differentially expressed in obese, diabetic and control C57BL/6 mice by using small RNA sequencing. Total RNAs were extracted from the serum of the target groups of animals. Next, the small RNAs were sequenced using the TruSeq small RNA Library Prep Kit in a MiSeq Illumina sequencer. A total of 52 up-regulated and 54 down-regulated miRNAs were identified based on the comparison of the log2 fold change of obese and diabetic (with normal mice as control; FC ≥ 2). The obese groups showed 22 up-regulated and 25 down-regulated microRNAs. Meanwhile, in the diabetic group, 32 microRNAs were up-regulated and 29 were down-regulated. This finding will help better understand the mechanism of metabolic disorders and may influence future approaches for the diagnosis and treatment of obesity and diabetes.

KEYWORDS: MicroRNA, Gene expression, Biomarker Obesity, Diabetes, C57BL/6N

Download Full Text PDF

REFERENCES

Bolstad, B. M., Irizarry, R. A., Astrand, M. & Speed, T. P. (2003). A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics, 19(2), 185 - 193.

Chen, X., Ba Y., Ma, L., Xing, C., Yuan, Y., Kehui, W., Jigang G., Yujing, Z., Jiangning C., Xing G., Qibin, L., Xiaoying L., Wenjing, W., Yan, Z, Jin, W., Xueyuan J., Yang, X., Chen, X.,  Pingping, Z., Juanbin, Z.,  Ruiqiang, L., Hongjie Z., Xiaobin, S., Ting, G., Guang, N., Jun, W., Ke, Z., Junfeng, Z. & Chen,Y. Z. (2008). Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases. Cell Research, 18, 997-1006.

Chu, Y. & Corey, D. R. (2012). RNA sequencing: platform selection, experimental design, and data interpretation . Nucleic Acid Ther, 22(4), 271–4. 

Deiuliis, J. A. (2016).  MicroRNAs as regulators of metabolic disease: pathophysiologic significance and emerging role as biomarkers and therapeutics. Int J Obes,  40(1),88-101.

Friedlander, M. R., Chen, W., Adamidi, C., Maaskola, J., Einspanier, R., Knespel, S. & Rajewsky, N. (2008). Discovering microRNAs from deep sequencing data using miRDeep. Nature Biotechnology, 26(4), 407-415.

Gallo, A., Tandon, M., Alevizos, I. & Illei, G. G. (2012). The Majority of MicroRNAs Detectable in Serum and Saliva Is Concentrated in Exosomes. PLoS ONE 7(3): e30679.

Griffiths-Jones, S. (2006) miRBase:The MicroRNA Sequence Database. In: Ying, S. Y. (eds). MicroRNA Protocols. Methods in Molecular Biology™, vol 342. Humana Press.

Hirst, M. & Marra, M. A. (2010).  Next generation sequencing based approaches to epigenomics. Briefings in Functional Genomics, 9(5-6), 455–465.

Kopelman, P. G. (2000). Obesity as a medical problem. Nature, 404(3), 635-643.

Krützfeldt, J. & Stoffel, M. (2006). MicroRNAs: a new class of regulatory genes affecting metabolism. Cell Metabolism; 4(1), 9 - 12.

Lopez, J. P., Cruceanu, A. D., Cristiana, Fiori, L M., Laboissiere, S., Guillet I., Fontaine, I., Ragoussis, J., Benes, V., Turecki, G. & Ernst, C. (2015). Biomarker discovery: quantification of microRNAs and other small non-coding RNAs using next generation sequencing. BMC Med Genomics, 8: 35. http://doi.org/10.1186/s12920-015-0109-x.

Moret, I., Sánchez-Izquierdo, D., Marisa, I.,Luis, T., Ana, N. P., Pilar, N., José, C. & Belén, B. (2013). Assessing an Improved Protocol for Plasma MicroRNA Extraction. PLoS ONE, 8(12), Article e82753.

Xie, X., Li, W., Lan, T., Liu, W., Peng, J., Huang, K., Huang, J., Shen, X., Liu, P. & Huang, H. (2011). Berberine ameliorates hyperglycemia in alloxan-induced diabetic C57BL/6 mice through activation of akt signalling pathway. Journal of Endocr, 58(9), 761-8