NDLI: Identification of microRNA-like RNAs from Curvularia lunata associated with maize leaf spot by bioinformation analysis and deep sequencing

Content Provider	Springer Nature Link
Author	Liu, Tong Hu, John Zuo, Yuhu Jin, Yazhong Hou, Jumei
Copyright Year	2015
Abstract	Deep sequencing of small RNAs is a useful tool to identify novel small RNAs that may be involved in fungal growth and pathogenesis. In this study, we used HiSeq deep sequencing to identify 747,487 unique small RNAs from Curvularia lunata. Among these small RNAs were 1012 microRNA-like RNAs (milRNAs), which are similar to other known microRNAs, and 48 potential novel milRNAs without homologs in other organisms have been identified using the miRBase© database. We used quantitative PCR to analyze the expression of four of these milRNAs from C. lunata at different developmental stages. The analysis revealed several changes associated with germinating conidia and mycelial growth, suggesting that these milRNAs may play a role in pathogen infection and mycelial growth. A total of 8334 target mRNAs for the 1012 milRNAs that were identified, and 256 target mRNAs for the 48 novel milRNAs were predicted by computational analysis. These target mRNAs of milRNAs were also performed by gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis. To our knowledge, this study is the first report of C. lunata’s milRNA profiles. This information will provide a better understanding of pathogen development and infection mechanism.
Starting Page	587
Ending Page	596
Page Count	10
File Format	PDF
ISSN	16174615
Journal	Molecular and General Genetics MGG
Volume Number	291
Issue Number	2
e-ISSN	16174623
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2015-10-19
Publisher Place	Berlin/Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Curvularia lunata HiSeq deep sequencing Small RNA milRNA Cell Biology Biochemistry Microbial Genetics and Genomics Plant Genetics & Genomics Animal Genetics and Genomics
Content Type	Text
Resource Type	Article
Subject	Genetics Medicine Molecular Biology

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