RNAi: double-stranded RNA directs the ATP-dependent cleavage of mRNA at 21 to 23 nucleotide intervals

Double-stranded RNA (dsRNA) directs the sequence-specific degradation of mRNA through a process known as RNA interference (RNAi). Using a recently developed Drosophila in vitro system, we examined the molecular mechanism underlying RNAi. We find that RNAi is ATP dependent yet uncoupled from mRNA translation. During the RNAi reaction, both strands of the dsRNA are processed to RNA segments 21-23 nucleotides in length. Processing of the dsRNA to the small RNA fragments does not require the targeted mRNA. The mRNA is cleaved only within the region of identity with the dsRNA. Cleavage occurs at sites 21-23 nucleotides apart, the same interval observed for the dsRNA itself, suggesting that the 21-23 nucleotide fragments from the dsRNA are guiding mRNA cleavage

Normal microRNA Maturation and Germ-Line Stem Cell Maintenance Requires Loquacious, a Double-Stranded RNA-Binding Domain Protein

microRNAs (miRNAs) are single-stranded, 21- to 23-nucleotide cellular RNAs that control the expression of cognate target genes. Primary miRNA (pri-miRNA) transcripts are transformed to mature miRNA by the successive actions of two RNase III endonucleases. Drosha converts pri-miRNA transcripts to precursor miRNA (pre-miRNA); Dicer, in turn, converts pre-miRNA to mature miRNA. Here, we show that normal processing of Drosophila pre-miRNAs by Dicer-1 requires the double-stranded RNA-binding domain (dsRBD) protein Loquacious (Loqs), a homolog of human TRBP, a protein first identified as binding the HIV trans-activator RNA (TAR). Efficient miRNA-directed silencing of a reporter transgene, complete repression of white by a dsRNA trigger, and silencing of the endogenous Stellate locus by Suppressor of Stellate, all require Loqs. In loqs (f00791) mutant ovaries, germ-line stem cells are not appropriately maintained. Loqs associates with Dcr-1, the Drosophila RNase III enzyme that processes pre-miRNA into mature miRNA. Thus, every known Drosophila RNase-III endonuclease is paired with a dsRBD protein that facilitates its function in small RNA biogenesis

AAV-mediated in vivo knockdown of luciferase using combinatorial RNAi and U1i

RNA interference (RNAi) has been successfully employed for specific inhibition of gene expression; however, safety and delivery of RNAi remain critical issues. We investigated the combinatorial use of RNAi and U1 interference (U1i). U1i is a gene-silencing technique that acts on the pre-mRNA by preventing polyadenylation. RNAi and U1i have distinct mechanisms of action in different cellular compartments and their combined effect allows usage of minimal doses, thereby avoiding toxicity while retaining high target inhibition. As a proof of concept, we investigated knockdown of the firefly luciferase reporter gene by combinatorial use of RNAi and U1i, and evaluated their inhibitory potential both in vitro and in vivo. Co-transfection of RNAi and U1i constructs showed additive reduction of luciferase expression up to 95% in vitro. We attained similar knockdown when RNAi and U1i constructs were hydrodynamically transfected into murine liver, demonstrating for the first time successful in vivo application of U1i. Moreover, we demonstrated long-term gene silencing by AAV-mediated transduction of murine muscle with RNAi/U1i constructs targeting firefly luciferase. In conclusion, these results provide a proof of principle for the combinatorial use of RNAi and U1i to enhance target gene knockdown in vivo

Regulatory feedback response mechanisms to phosphate starvation in rice

Phosphorus is a growth-limiting nutrient for plants. The growing scarcity of phosphate stocks threatens global food security. Phosphate-uptake regulation is so complex and incompletely known that attempts to improve phosphorus use efficiency have had extremely limited success. This study improves our understanding of the molecular mechanisms underlying phosphate uptake by investigating the transcriptional dynamics of two regulators: the Ubiquitin ligase PHO2 and the long non-coding RNA IPS1. Temporal measurements of RNA levels have been integrated into mechanistic mathematical models using advanced statistical techniques. Models based solely on current knowledge could not adequately explain the temporal expression profiles. Further modeling and bioinformatics analysis have led to the prediction of three regulatory features: the PHO2 protein mediates the degradation of its own transcriptional activator to maintain constant PHO2 mRNA levels; the binding affinity of the transcriptional activator of PHO2 is impaired by a phosphate-sensitive transcriptional repressor/inhibitor; and the extremely high levels of IPS1 and its rapid disappearance upon Pi re-supply are best explained by Pi-sensitive RNA protection. This work offers both new opportunities for plant phosphate research that will be essential for informing the development of phosphate efficient crop varieties, and a foundation for the development of models integrating phosphate with other stress responses

RNAi technology and its use in studying the function of nuclear receptors and coregulators

Until just a few years ago, RNA interference (RNAi) technology was restricted to the research fields of plants, C. elegans or Drosophila. The discovery of gene silencing by in vitro synthesized double-stranded RNA (dsRNA) in mammalian cells has made the use of RNAi possible in nearly the entire life science kingdom. DNA vectors delivering small interfering RNA (siRNA) directed by polymerase III or polymerase II promoters to persistently inhibit target genes expression have extended this technology to study in vivo function of these genes. Recently, RNAi has been used as a powerful tool in the functional analysis of nuclear receptors and their coregulators. This short review will cover studies in this area

An accurate and interpretable model for siRNA efficacy prediction

BACKGROUND: The use of exogenous small interfering RNAs (siRNAs) for gene silencing has quickly become a widespread molecular tool providing a powerful means for gene functional study and new drug target identification. Although considerable progress has been made recently in understanding how the RNAi pathway mediates gene silencing, the design of potent siRNAs remains challenging. RESULTS: We propose a simple linear model combining basic features of siRNA sequences for siRNA efficacy prediction. Trained and tested on a large dataset of siRNA sequences made recently available, it performs as well as more complex state-of-the-art models in terms of potency prediction accuracy, with the advantage of being directly interpretable. The analysis of this linear model allows us to detect and quantify the effect of nucleotide preferences at particular positions, including previously known and new observations. We also detect and quantify a strong propensity of potent siRNAs to contain short asymmetric motifs in their sequence, and show that, surprisingly, these motifs alone contain at least as much relevant information for potency prediction as the nucleotide preferences for particular positions. CONCLUSION: The model proposed for prediction of siRNA potency is as accurate as a state-of-the-art nonlinear model and is easily interpretable in terms of biological features. It is freely available on the web a

Polymerase II Promoter Strength Determines Efficacy of microRNA Adapted shRNAs

Since the discovery of RNAi and microRNAs more than 10 years ago, much research has focused on the development of systems that usurp microRNA pathways to downregulate gene expression in mammalian cells. One of these systems makes use of endogenous microRNA pri-cursors that are expressed from polymerase II promoters where the mature microRNA sequence is replaced by gene specific duplexes that guide RNAi (shRNA-miRs). Although shRNA-miRs are effective in directing target mRNA knockdown and hence reducing protein expression in many cell types, variability of RNAi efficacy in cell lines has been an issue. Here we show that the choice of the polymerase II promoter used to drive shRNA expression is of critical importance to allow effective mRNA target knockdown. We tested the abundance of shRNA-miRs expressed from five different polymerase II promoters in 6 human cell lines and measured their ability to drive target knockdown. We observed a clear positive correlation between promoter strength, siRNA expression levels, and protein target knockdown. Differences in RNAi from the shRNA-miRs expressed from the various promoters were particularly pronounced in immune cells. Our findings have direct implications for the design of shRNA-directed RNAi experiments and the preferred RNAi system to use for each cell type

Reconsideration of In-Silico siRNA Design Based on Feature Selection: A Cross-Platform Data Integration Perspective

RNA interference via exogenous short interference RNAs (siRNA) is increasingly more widely employed as a tool in gene function studies, drug target discovery and disease treatment. Currently there is a strong need for rational siRNA design to achieve more reliable and specific gene silencing; and to keep up with the increasing needs for a wider range of applications. While progress has been made in the ability to design siRNAs with specific targets, we are clearly at an infancy stage towards achieving rational design of siRNAs with high efficacy. Among the many obstacles to overcome, lack of general understanding of what sequence features of siRNAs may affect their silencing efficacy and of large-scale homogeneous data needed to carry out such association analyses represents two challenges. To address these issues, we investigated a feature-selection based in-silico siRNA design from a novel cross-platform data integration perspective. An integration analysis of 4,482 siRNAs from ten meta-datasets was conducted for ranking siRNA features, according to their possible importance to the silencing efficacy of siRNAs across heterogeneous data sources. Our ranking analysis revealed for the first time the most relevant features based on cross-platform experiments, which compares favorably with the traditional in-silico siRNA feature screening based on the small samples of individual platform data. We believe that our feature ranking analysis can offer more creditable suggestions to help improving the design of siRNA with specific silencing targets. Data and scripts are available at http://csbl.bmb.uga.edu/publications/materials/qiliu/siRNA.html