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Expression-Based Functional Investigation of the Organ-Specific MicroRNAs in Arabidopsis

Overview of attention for article published in PLOS ONE, November 2012
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Title
Expression-Based Functional Investigation of the Organ-Specific MicroRNAs in Arabidopsis
Published in
PLOS ONE, November 2012
DOI 10.1371/journal.pone.0050870
Pubmed ID
Authors

Yijun Meng, Chaogang Shao, Xiaoxia Ma, Huizhong Wang, Ming Chen

Abstract

MicroRNAs (miRNAs) play a pivotal role in plant development. The expression patterns of the miRNA genes significantly influence their regulatory activities. By utilizing small RNA (sRNA) high-throughput sequencing (HTS) data, the miRNA expression patterns were investigated in four organs (flowers, leaves, roots and seedlings) of Arabidopsis. Based on a set of criteria, dozens of organ-specific miRNAs were discovered. A dominant portion of the organ-specific miRNAs identified from the ARGONAUTE 4-enriched sRNA HTS libraries were highly expressed in flowers. Additionally, the expression of the precursors of the organ-specific miRNAs was analyzed. Degradome sequencing data-based approach was employed to identify the targets of the organ-specific miRNAs. The miRNA-target interactions were used for network construction. Subnetwork analysis unraveled some novel regulatory cascades, such as the feedback regulation mediated by miR161, the potential self-regulation of the genes miR172, miR396, miR398 and miR860, and the miR863-guided cleavage of the SERRATE transcript. Our bioinformatics survey expanded the organ-specific miRNA-target list in Arabidopsis, and could deepen the biological view of the miRNA expression and their regulatory roles.

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The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 1%
Germany 1 1%
Argentina 1 1%
Unknown 65 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 32%
Researcher 14 21%
Student > Master 8 12%
Student > Bachelor 5 7%
Student > Postgraduate 4 6%
Other 10 15%
Unknown 5 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 47 69%
Biochemistry, Genetics and Molecular Biology 6 9%
Engineering 4 6%
Computer Science 2 3%
Psychology 2 3%
Other 1 1%
Unknown 6 9%