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A Combined Approach of High-Throughput Sequencing and Degradome Analysis Reveals Tissue Specific Expression of MicroRNAs and Their Targets in Cucumber

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Title
A Combined Approach of High-Throughput Sequencing and Degradome Analysis Reveals Tissue Specific Expression of MicroRNAs and Their Targets in Cucumber
Published in
PLOS ONE, March 2012
DOI 10.1371/journal.pone.0033040
Pubmed ID
Authors

Weihua Mao, Zeyun Li, Xiaojian Xia, Yadan Li, Jingquan Yu

Abstract

MicroRNAs (miRNAs) are endogenous small RNAs playing an important regulatory function in plant development and stress responses. Among them, some are evolutionally conserved in plant and others are only expressed in certain species, tissue or developmental stages. Cucumber is among the most important greenhouse species in the world, but only a limited number of miRNAs from cucumber have been identified and the experimental validation of the related miRNA targets is still lacking. In this study, two independent small RNA libraries from cucumber leaves and roots were constructed, respectively, and sequenced with the high-throughput Illumina Solexa system. Based on sequence similarity and hairpin structure prediction, a total of 29 known miRNA families and 2 novel miRNA families containing a total of 64 miRNA were identified. QRT-PCR analysis revealed that some of the cucumber miRNAs were preferentially expressed in certain tissues. With the recently developed 'high throughput degradome sequencing' approach, 21 target mRNAs of known miRNAs were identified for the first time in cucumber. These targets were associated with development, reactive oxygen species scavenging, signaling transduction and transcriptional regulation. Our study provides an overview of miRNA expression profile and interaction between miRNA and target, which will help further understanding of the important roles of miRNAs in cucumber plants.

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Geographical breakdown

Country Count As %
Malaysia 1 1%
Norway 1 1%
Slovenia 1 1%
United States 1 1%
Poland 1 1%
Unknown 88 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 24 26%
Student > Ph. D. Student 21 23%
Student > Master 14 15%
Student > Postgraduate 7 8%
Student > Bachelor 5 5%
Other 13 14%
Unknown 9 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 61 66%
Biochemistry, Genetics and Molecular Biology 11 12%
Computer Science 3 3%
Business, Management and Accounting 1 1%
Mathematics 1 1%
Other 5 5%
Unknown 11 12%