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Genome‐wide identification of small RNA targets based on target enrichment and microarray hybridizations
Author(s) -
FrancoZorrilla José M.,
Del Toro Francisco J.,
Godoy Marta,
PérezPérez Julián,
LópezVidriero Irene,
Oliveros Juan C.,
GarcíaCasado Gloria,
Llave César,
Solano Roberto
Publication year - 2009
Publication title -
the plant journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.058
H-Index - 269
eISSN - 1365-313X
pISSN - 0960-7412
DOI - 10.1111/j.1365-313x.2009.03904.x
Subject(s) - biology , computational biology , rna , microrna , small interfering rna , dna microarray , gene silencing , genetics , small rna , gene expression , gene
Summary MicroRNAs (miRNAs) and small interfering RNAs (siRNAs) are two classes of abundant 21–24 nucleotide small RNAs (smRNAs) that control gene expression in plants, mainly by guiding cleavage and degradation of target transcripts. Target identification based on predictive algorithms for base‐paired complementarity requires further experimental validation and often fails to recognize miRNA::target pairs that escape from stringent complementarity rules. Here, we report on a microarray‐based methodology to identify target mRNAs of miRNAs and siRNAs at a genomic scale. This strategy takes advantage of the RNA ligase‐mediated amplification of 5′ cDNA ends (RLM‐RACE) to isolate miRNA or siRNA cleavage products from biological samples. Cleaved transcripts are then subjected to T7 RNA polymerase‐mediated amplification and microarray hybridizations. The use of suitable hybridization controls is what makes our strategy outperform previous analyses. We applied this method and identified more than 100 putative novel miRNA or siRNA target mRNAs that had not been previously predicted by computational or microarray‐based methods. Our data expand the regulatory role of endogenous smRNAs to a wide range of cellular processes, with prevalence in the regulation of cellular solute homeostasis. The methodology described here is straightforward, avoids extensive computational analysis and allows simultaneous analyses of several biological replicates, thus reducing the biological variability inherent in genomic analysis. The application of this simple methodology offers a framework for systematic analysis of smRNA‐guided cleaved transcriptomes in different plant tissues, genotypes or stress conditions, and should contribute to understanding of the physiological role of smRNAs in plants.