Comprehensive Protein-Based Artificial MicroRNA Screens for Effective Gene Silencing in Plants
Author(s) -
Jianfeng Li,
Hoo Sun Chung,
Yajie Niu,
Jenifer Bush,
Matthew McCormack,
Jen Sheen
Publication year - 2013
Publication title -
the plant cell
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.324
H-Index - 341
eISSN - 1532-298X
pISSN - 1040-4651
DOI - 10.1105/tpc.113.112235
Subject(s) - biology , nicotiana benthamiana , gene silencing , gene , computational biology , rna interference , arabidopsis , oryza sativa , transgene , microrna , genetics , microbiology and biotechnology , rna , mutant
Artificial microRNA (amiRNA) approaches offer a powerful strategy for targeted gene manipulation in any plant species. However, the current unpredictability of amiRNA efficacy has limited broad application of this promising technology. To address this, we developed epitope-tagged protein-based amiRNA (ETPamir) screens, in which target mRNAs encoding epitope-tagged proteins were constitutively or inducibly coexpressed in protoplasts with amiRNA candidates targeting single or multiple genes. This design allowed parallel quantification of target proteins and mRNAs to define amiRNA efficacy and mechanism of action, circumventing unpredictable amiRNA expression/processing and antibody unavailability. Systematic evaluation of 63 amiRNAs in 79 ETPamir screens for 16 target genes revealed a simple, effective solution for selecting optimal amiRNAs from hundreds of computational predictions, reaching ∼100% gene silencing in plant cells and null phenotypes in transgenic plants. Optimal amiRNAs predominantly mediated highly specific translational repression at 5' coding regions with limited mRNA decay or cleavage. Our screens were easily applied to diverse plant species, including Arabidopsis thaliana, tobacco (Nicotiana benthamiana), tomato (Solanum lycopersicum), sunflower (Helianthus annuus), Catharanthus roseus, maize (Zea mays) and rice (Oryza sativa), and effectively validated predicted natural miRNA targets. These screens could improve plant research and crop engineering by making amiRNA a more predictable and manageable genetic and functional genomic technology.
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