Large-scale analysis of small molecule-RNA interactions using multiplexed RNA structure libraries.

The large-scale analysis of small-molecule binding to diverse RNA structures is key to understanding the required interaction properties and selectivity for developing RNA-binding molecules toward RNA-targeted therapies. Here, we report a new system for performing the large-scale analysis of small molecule-RNA interactions using a multiplexed pull-down assay with RNA structure libraries. The system profiled the RNA-binding landscapes of G-clamp and thiazole orange derivatives, which recognizes an unpaired guanine base and are good probes for fluorescent indicator displacement (FID) assays, respectively. We discuss the binding preferences of these molecules based on their large-scale affinity profiles. In addition, we selected combinations of fluorescent indicators and different ranks of RNA based on the information and screened for RNA-binding molecules using FID. RNAs with high- and intermediate-rank RNA provided reliable results. Our system provides fundamental information about small molecule-RNA interactions and facilitates the discovery of novel RNA-binding molecules.

Large-Scale Analysis of RNA-Protein Interactions for Functional RNA Motif Discovery Using FOREST.

RNA transcripts can form a variety of higher-order structures. We developed a large-scale affinity analysis system, FOREST (Folded RNA Element Profiling with Structure Library), to investigate the function of these RNA structures on transcriptome-wide scale. Here we describe a protocol to analyze RNA-protein interactions using FOREST . Users of the protocol prepare an RNA structure library comprised of diverse species of transcripts and perform high-throughput characterization of the RNA-protein interactions to obtain quantitative and comprehensive information on the binding affinities and specificities. Moreover, we demonstrate how FOREST can be used to analyze a non-canonical structure, the RNA G-quadruplex, without sequencing bias, because the quantification is performed directly on a microarray without sequence amplification. FOREST will contribute to the discovery of RNA structure motifs that determine RNA-protein interactions.

Fluid flow-induced left-right asymmetric decay of Dand5 mRNA in the mouse embryo requires a Bicc1-Ccr4 RNA degradation complex.

Molecular left-right (L-R) asymmetry is established at the node of the mouse embryo as a result of the sensing of a leftward fluid flow by immotile cilia of perinodal crown cells and the consequent degradation of Dand5 mRNA on the left side. We here examined how the fluid flow induces Dand5 mRNA decay. We found that the first 200 nucleotides in the 3' untranslated region (3'-UTR) of Dand5 mRNA are necessary and sufficient for the left-sided decay and to mediate the response of a 3'-UTR reporter transgene to Ca2+, the cation channel Pkd2, the RNA-binding protein Bicc1 and their regulation by the flow direction. We show that Bicc1 preferentially recognizes GACR and YGAC sequences, which can explain the specific binding to a conserved GACGUGAC motif located in the proximal Dand5 3'-UTR. The Cnot3 component of the Ccr4-Not deadenylase complex interacts with Bicc1 and is also required for Dand5 mRNA decay at the node. These results suggest that Ca2+ currents induced by leftward fluid flow stimulate Bicc1 and Ccr4-Not to mediate Dand5 mRNA degradation specifically on the left side of the node.

RNA structure-wide discovery of functional interactions with multiplexed RNA motif library.

Biochemical assays and computational analyses have discovered RNA structures throughout various transcripts. However, the roles of these structures are mostly unknown. Here we develop folded RNA element profiling with structure library (FOREST), a multiplexed affinity assay system to identify functional interactions from transcriptome-wide RNA structure datasets. We generate an RNA structure library by extracting validated or predicted RNA motifs from gene-annotated RNA regions. The RNA structure library with an affinity enrichment assay allows for the comprehensive identification of target-binding RNA sequences and structures in a high-throughput manner. As a proof-of-concept, FOREST discovers multiple RNA-protein interaction networks with quantitative scores, including translational regulatory elements that function in living cells. Moreover, FOREST reveals different binding landscapes of RNA G-quadruplex (rG4) structures-binding proteins and discovers rG4 structures in the terminal loops of precursor microRNAs. Overall, FOREST serves as a versatile platform to investigate RNA structure-function relationships on a large scale.