期刊
PLANT CELL
卷 25, 期 5, 页码 1507-1522出版社
AMER SOC PLANT BIOLOGISTS
DOI: 10.1105/tpc.113.112235
关键词
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资金
- National Science Foundation [IOS-0843244]
- National Institutes of Health [R01 GM60493, R01 GM70567]
- Massachusetts General Hospital Executive Committee on Research Postdoctoral Fellowship for Medical Discovery
- Direct For Biological Sciences [0843244] Funding Source: National Science Foundation
- Division Of Integrative Organismal Systems [0843244] Funding Source: National Science Foundation
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 similar to 100% gene silencing in plant cells and null phenotypes in transgenic plants. Optimal amiRNAs predominantly mediated highly specific translational repression at 59 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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