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Identification of Conserved and Novel microRNAs in Cerebral Ischemia-Reperfusion Injury of Rat Using Deep Sequencing

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Abstract

MicroRNAs are a class of noncoding small RNAs that regulate gene expression by inhibiting target genes at post-transcriptional levels. MicroRNAs have been highlighted in many organs and tissues, including the brain. To identify special microRNAs involved in ischemia-reperfusion injury, we performed a comprehensive small RNA profiling in rat model and the control using Illumina high-throughput sequencing. A total of 9,444,562 and 10,290,391 clean reads were sequenced from two small RNA libraries constructed, respectively. Three hundred fifty-eight known microRNAs were identified, in which 78 microRNAs exhibited significantly differential expression between model and control. In addition, 62 and 68 novel miRNAs were found in model and control, respectively. Comparative analysis showed that 24 novel microRNAs were differentially expressed with greater than six-fold change. The GO annotation suggested that predicted targets of microRNAs were enriched into the category of metabolic process, cell part, cell-extracellular communications, and so on. KEGG pathway analysis suggested that these genes were involved in many important pathways, mainly including signaling transduction, MAPK signaling pathway, NF-κB signaling pathway, and neurotrophin signaling pathway. Our findings provided a deeper understanding to the regulatory mechanism of microRNAs underlying cerebral ischemia, therefore benefitting the improvement of the protection and treatment strategies of this disease.

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Acknowledgments

The present work was supported by grants from Shandong Province Natural Science Foundation (Nos. ZR2013CQ031), National Nature Science Foundation of China (Nos. 30971081, 31271243, 81070961 and 81241052), and Taishan Scholar Construction Special Fund.

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Correspondence to Jing Chen or Bo Bai.

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Wang, C., Pan, Y., Cheng, B. et al. Identification of Conserved and Novel microRNAs in Cerebral Ischemia-Reperfusion Injury of Rat Using Deep Sequencing. J Mol Neurosci 54, 671–683 (2014). https://doi.org/10.1007/s12031-014-0383-7

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  • DOI: https://doi.org/10.1007/s12031-014-0383-7

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