Dataset Information

Improvement of the design and generation of highly specific plant knockdown lines using primary synthetic microRNAs (pri-smiRNAs).

ABSTRACT:

Background

microRNAs (miRNAs) are endogenous small non-coding RNAs that post-transcriptionally regulate gene expression. In plants, they typically show high complementarity to a single sequence motif within their target mRNAs and act by catalyzing specific mRNA cleavage and degradation. miRNAs are processed from much longer primary transcripts via precursor miRNAs containing fold-back structures. Leaving these secondary structures intact, miRNAs can be re-designed experimentally to target mRNAs of choice.

Results

We designed primary synthetic miRNAs (pri-smiRNAs) on the basis of the primary transcript of the Arabidopsis MIR159A gene by replacing the original miR159a and the corresponding miR159a* with novel sequences, keeping the overall secondary structure as predicted by the program RNAfold. We used the program RNAhybrid to optimize smiRNA design and to screen the complete Arabidopsis transcriptome for potential off-targets. To improve the molecular cloning of the pri-smiRNA we inserted restriction sites in the original MIR159A primary transcript to easily accommodate the smiRNA/smiRNA* DNA fragment. As a proof-of-concept, we targeted the single gene encoding chalcone synthase (CHS) in Arabidopsis. We demonstrate smiRNA(CHS) expression and CHS mRNA cleavage in different transgenic lines. Phenotypic changes in these lines were observed for seed color and flavonol derivatives, and quantified with respect to anthocyanin content. We also tested the effect of mismatches and excess G:U base pairs on knockdown efficiency.

Conclusions

RNAhybrid-assisted design of smiRNAs and generation of pri-smiRNAs using a novel vector containing restriction sites greatly improves specificity and speed of the generation of stable knockdown lines for functional analyses in plants.

SUBMITTER: Niemeier S

PROVIDER: S-EPMC2845148 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Improvement of the design and generation of highly specific plant knockdown lines using primary synthetic microRNAs (pri-smiRNAs).

Niemeier Sandra S Alves Junior Leonardo L Merkle Thomas T

BMC research notes 20100304

<h4>Background</h4>microRNAs (miRNAs) are endogenous small non-coding RNAs that post-transcriptionally regulate gene expression. In plants, they typically show high complementarity to a single sequence motif within their target mRNAs and act by catalyzing specific mRNA cleavage and degradation. miRNAs are processed from much longer primary transcripts via precursor miRNAs containing fold-back structures. Leaving these secondary structures intact, miRNAs can be re-designed experimentally to targe ...[more]

PMID: 20202197

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Project description:BACKGROUND: MicroRNAs (miRNAs) regulate gene expression via mRNA cleavage or translation inhibition. In spite of barley being a cereal of great economic importance, very little data is available concerning its miRNA biogenesis. There are 69 barley miRNA and 67 pre-miRNA sequences available in the miRBase (release 19). However, no barley pri-miRNA and MIR gene structures have been shown experimentally. In the present paper, we examine the biogenesis of selected barley miRNAs and the developmental regulation of their pri-miRNA processing to learn more about miRNA maturation in barely. RESULTS: To investigate the organization of barley microRNA genes, nine microRNAs - 156g, 159b, 166n, 168a-5p/168a-3p, 171e, 397b-3p, 1120, and 1126 - were selected. Two of the studied miRNAs originate from one MIR168a-5p/168a-3p gene. The presence of all miRNAs was confirmed using a Northern blot approach. The miRNAs are encoded by genes with diverse organizations, representing mostly independent transcription units with or without introns. The intron-containing miRNA transcripts undergo complex splicing events to generate various spliced isoforms. We identified miRNAs that were encoded within introns of the noncoding genes MIR156g and MIR1126. Interestingly, the intron that encodes miR156g is spliced less efficiently than the intron encoding miR1126 from their specific precursors. miR397b-3p was detected in barley as a most probable functional miRNA, in contrast to rice where it has been identified as a complementary partner miRNA*. In the case of miR168a-5p/168a-3p, we found the generation of stable, mature molecules from both pre-miRNA arms, confirming evolutionary conservation of the stability of both species, as shown in rice and maize. We suggest that miR1120, located within the 3' UTR of a protein-coding gene and described as a functional miRNA in wheat, may represent a siRNA generated from a mariner-like transposable element. CONCLUSIONS: Seven of the eight barley miRNA genes characterized in this study contain introns with their respective transcripts undergoing developmentally specific processing events prior to the dicing out of pre-miRNA species from their pri-miRNA precursors. The observed tendency to maintain the intron encoding miR156g within the transcript, and preferences in splicing the miR1126-harboring intron, may suggest the existence of specific regulation of the levels of intron-derived miRNAs in barley.

Project description:Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes. A single enhancer, of a few hundred base pairs in length, can autonomously and independently of its location and orientation drive cell-type specific expression of a gene or transgene. It has been a long-standing goal in the field to decode the regulatory logic of an enhancer and to understand the details of how spatiotemporal gene expression is encoded in an enhancer sequence. Recently, deep learning models have yielded unprecedented insight into the enhancer code, and well-trained models are reaching a level of understanding that may be close to complete. As a consequence, we hypothesized that deep learning models can be used to guide the directed design of synthetic, cell type specific enhancers, and that this process would allow for a detailed tracing of all enhancer features at nucleotide-level resolution. Here we implemented and compared three different design strategies, each built on a deep learning model: (1) directed sequence evolution; (2) directed iterative motif implanting; and (3) generative design. We evaluated the function of fully synthetic enhancers to specifically target Kenyon cells or glial cells in the fruit fly brain using transgenic animals. We then exploited this concept further by creating “dual-code” enhancers that target two cell types, and minimal enhancers smaller than 50 base pairs that are fully functional. By examining the trajectories followed during state space searches towards functional enhancers, we could accurately define the enhancer code as the optimal strength, combination, and relative distance of TF activator motifs, and the absence of TF repressor motifs. Finally, we applied the same three strategies to successfully design human enhancers, finding highly similar design principles as in Drosophila. In conclusion, enhancer design guided by deep learning leads to better understanding of how enhancers work and shows that their code can be exploited to manipulate cell states.

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Dataset Information

Improvement of the design and generation of highly specific plant knockdown lines using primary synthetic microRNAs (pri-smiRNAs).

Background

Results

Conclusions

Publications

Improvement of the design and generation of highly specific plant knockdown lines using primary synthetic microRNAs (pri-smiRNAs).

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