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. 2011;6(6):e21330.
doi: 10.1371/journal.pone.0021330. Epub 2011 Jun 23.

Regulation of gene expression in plants through miRNA inactivation

Sergey Ivashuta  1 , Isaac R Banks, B Elizabeth Wiggins, Yuanji Zhang, Todd E Ziegler, James K Roberts, Gregory R Heck
Affiliations

Regulation of gene expression in plants through miRNA inactivation

Sergey Ivashuta et al. PLoS One. 2011.

Abstract

Eukaryotic organisms possess a complex RNA-directed gene expression regulatory network allowing the production of unique gene expression patterns. A recent addition to the repertoire of RNA-based gene regulation is miRNA target decoys, endogenous RNA that can negatively regulate miRNA activity. miRNA decoys have been shown to be a valuable tool for understanding the function of several miRNA families in plants and invertebrates. Engineering and precise manipulation of an endogenous RNA regulatory network through modification of miRNA activity also affords a significant opportunity to achieve a desired outcome of enhanced plant development or response to environmental stresses. Here we report that expression of miRNA decoys as single or heteromeric non-cleavable microRNA (miRNA) sites embedded in either non-protein-coding or within the 3' untranslated region of protein-coding transcripts can regulate the expression of one or more miRNA targets. By altering the sequence of the miRNA decoy sites, we were able to attenuate miRNA inactivation, which allowed for fine regulation of native miRNA targets and the production of a desirable range of plant phenotypes. Thus, our results demonstrate miRNA decoys are a flexible and robust tool, not only for studying miRNA function, but also for targeted engineering of gene expression in plants. Computational analysis of the Arabidopsis transcriptome revealed a number of potential miRNA decoys, suggesting that endogenous decoys may have an important role in natural modulation of expression in plants.

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Conflict of interest statement

Competing Interests: The authors have the following competing interests. All authors are employed by Monsanto Company. The authors have made a patent application for decoy work described in this submission (US patent application publication 2009/070898 published 12 March 2009). There are no products in development or marketed products to declare. This does not alter the authors' adherence to all the PLoS ONE policies on sharing data and materials, as detailed online in the guide for authors.

Figures

Figure 1
Figure 1. Effect of different RNA scaffolds on miRNA decoy efficacy.
(A) miRMON1 bulge decoy sequence and diagrams of miRMON1 decoy embedded in various backbone configurations: miRMON1 decoys in maize non-coding RNA and soybean miRMON1 precursor. (B) Expression of miRMON1-targeted GFP reporter co-expressed with miRNA decoys in different scaffolds in N. benthamiana leaves. (C) Diagram of miRMON1 decoys in the 3′ UTR of the GUS coding transcript, with and without a 75 nt spacer after the open reading frame. (D) Expression of miRMON1-targeted GFP reporter co-expressed with decoys in a GUS protein-coding cassette in N. benthamiana leaves. GFP expression was measured by fluorescence microscopy and Western blot. The Rubisco band visualized by Ponceau staining shows the loading control. Relative GFP protein expression normalized to Rubisco is indicated. GUS expression was measured by the 4-methylumbelliferone assay. Leaves were co-transformed with one part GFP reporter, 5 parts miRMON1, and 10 parts miRNA decoy. Agrobacterium transformed with an empty vector was added to achieve a total OD600 = 1 of Agrobacterium for each transformation.
Figure 2
Figure 2. Inactivation of multiple miRNAs using a heteromeric miRNA decoy cassette.
(A) Diagram of heterodimeric miRNA decoy transcript designed to bind both miRMON1 and a-miRGL1. (B) Quantitative analysis of micrographs of miRMON1-targeted GFP reporter intensity and of miRGL1-targeted GUS reporter in N. benthamiana leaves. Leaves were co-transformed with one part GFP and/or one part GUS reporter, 5 parts miRMON1 and/or 5 parts miRGL1, and ten parts miRNA decoy. Agrobacterium transformed with a control vector was added to achieve a total OD600 = 1 of Agrobacterium for each transformation. GFP intensity values are averages of six individual transformations. Tissue from the seven individual transformations per inoculum mix was used to measure GUS activity.
Figure 3
Figure 3. Effect of stoichiometry on activity of two miRMON1 decoys (bulged, miRMON1_3 B decoy, and mismatched, miRMON1_2 M decoy).
Diagram of miRMON1_3B and 2M decoys embedded in the 3′ UTR of a GUS coding transcript and expression of miRMON1-targeted GFP reporter co-expressed with various concentrations of miRNA decoys in N. benthamiana leaves. Leaves were co-transformed with one part GFP reporter, 5 parts miRMON1, and variable parts miRNA decoy. Agrobacterium transformed with an empty vector was added to achieve a total OD600 = 1 of Agrobacterium for each transformation. GFP expression was measured by fluorescence microscopy and protein blot. The Rubisco band visualized by Ponceau staining shows the loading control. Relative GFP expression normalized to Rubisco and relative GUS expression as measured by qRT-PCR are indicated.
Figure 4
Figure 4. Effect of miRNA decoy structure on efficacy.
(A) Sequence of miRMON1 decoys with bulged (4B, 3B, 2B, 1B) or mismatched (1M, 2M, 3M) structures at various positions between 10 and 12 of the miRNA (position of mutation in parentheses). (B) Expression of miRMON1-targeted GFP reporter co-expressed with various miRNA decoys in N. benthamiana leaves measured by fluorescence microscopy and Western blot. Leaves were co-transformed with one part GFP reporter, 5 parts miRMON1, and 10 parts miRNA decoy. Agrobacterium transformed with an empty vector was added to achieve a total OD600 = 1 of Agrobacterium for each transformation. The Rubisco band visualized by Ponceau staining shows the loading control. Relative GFP expression normalized to Rubisco is indicated. Relative GUS expression as measured by qRT-PCR indicates decoy expression, and miRMON1 expression was determined by semi-quantitative RT-PCR.
Figure 5
Figure 5. Phenotypic and expression analysis of transgenic Arabidopsis expressing miR171_2M and miR171_3B decoys.
(A) Leaf and flower phenotypes of plants expressing miR171 decoys as compared to wild-type (WT). Expression of SCL6-III in miR171_2M and miR171_3B decoy plants verified by (B) northern blot analysis in leaves from 4 week old plants, with two independent events per group shown and (C) qRT-PCR. Log2-transformed expression values are plotted; error bar is plotted with +/− one standard error estimated from a two-way ANOVA. An “A” indicates significantly higher expression compared with control. miR171_3B events with significantly higher expression compared with miR171_2M events are indicated with a “B”. All statistical analysis can be found in Tables S2 and S3, online. (D) The corresponding expression of the decoy transgene for each event, using qRT-PCR. (E) Detection of mature miR171 via northern blot. miR159 is used as the control.
Figure 6
Figure 6. Characteristics of transgenic Arabidopsis over-expressing miR171_2M and miR171_3B decoys.
(A) Wild type and transgenic plants (T3 generation) expressing miR171_2M and miR171_3B decoys grown on vertical agar plates with M&S media at 25°C and 16/8 hours day/night. (B) Rosette leaf area of wild type and transgenic plants expressing miR171_2M and miR171_3B decoys. Leaves were detached from plants and leaf area measured. Six three week old plants were used per measurement. (C) Chlorophyll content of miR171 decoy plants. Chlorophyll content of 3 rosette leaves of 12 plants per event was measured using Minolta SPAD 502 meter.
Figure 7
Figure 7. Computational prediction of endogenous miRNA decoys in Arabidopsis.
(A) Distribution of predicted decoy sites in 5′ UTR, CDS and 3′ UTR of 286 protein-coding transcripts. If a decoy site spans two regions, i.e., 5′ UTR – CDS or CDS – 3′ UTR, the decoy site is assigned to the region in which the majority of the site is contained. The length of the 5′ UTR, CDS, and 3′ UTR of 286 transcripts is tallied respectively, and then the number of decoy sites for each feature is normalized to 1 kb sequence length. (B) Predicted decoy sites are classified into the ‘bulge’ type if there are bulges corresponding to miRNA bases 10–11, otherwise, they are classified into the ‘mismatch’ type, decoys of which have at least one mismatch to miRNA base 10 or 11.
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