RNase P: at last, the key finds its lock

A screening platform to monitor RNA processing and protein-RNA interactions in ribonuclease P uncovers a small molecule inhibitor

Ribonucleoprotein (RNP) complexes and RNA-processing enzymes are attractive targets for antibiotic development owing to their central roles in microbial physiology. For many of these complexes, comprehensive strategies to identify inhibitors are either lacking or suffer from substantial technical limitations. Here, we describe an activity-binding-structure platform for bacterial ribonuclease P (RNase P), an essential RNP ribozyme involved in 5' tRNA processing. A novel, real-time fluorescence-based assay was used to monitor RNase P activity and rapidly identify inhibitors using a mini-helix and a pre-tRNA-like bipartite substrate. Using the mini-helix substrate, we screened a library comprising 2560 compounds. Initial hits were then validated using pre-tRNA and the pre-tRNA-like substrate, which ultimately verified four compounds as inhibitors. Biolayer interferometry-based binding assays and molecular dynamics simulations were then used to characterize the interactions between each validated inhibitor and the P protein, P RNA and pre-tRNA. X-ray crystallographic studies subsequently elucidated the structure of the P protein bound to the most promising hit, purpurin, and revealed how this inhibitor adversely affects tRNA 5' leader binding. This integrated platform affords improved structure-function studies of RNA processing enzymes and facilitates the discovery of novel regulators or inhibitors.

Geneticin Stabilizes the Open Conformation of the 5' Region of Hepatitis C Virus RNA and Inhibits Viral Replication

The aminoglycoside Geneticin (G418) is known to inhibit cell culture proliferation, via virus-specific mechanisms, of two different virus genera from the family Flaviviridae. Here, we tried to determine whether Geneticin can selectively alter the switching of the nucleotide 1 to 570 RNA region of hepatitis C virus (HCV) and, if so, whether this inhibits viral growth. Two structure-dependent RNases known to specifically cleave HCV RNA were tested in the presence or absence of the drug. One was the Synechocystis sp. RNase P ribozyme, which cleaves the tRNA-like domain around the AUG start codon under high-salt buffer conditions; the second was Escherichia coli RNase III, which recognizes a double-helical RNA switch element that changes the internal ribosome entry site (IRES) from a closed (C) conformation to an open (O) one. While the drug did not affect RNase P activity, it did inhibit RNase III in the micromolar range. Kinetic studies indicated that the drug favors the switch from the C to the O conformation of the IRES by stabilizing the distal double-stranded element and inhibiting further processing of the O form. We demonstrate that, because the RNA in this region is highly conserved and essential for virus survival, Geneticin inhibits HCV Jc1 NS3 expression, the release of the viral genomic RNA, and the propagation of HCV in Huh 7.5 cells. Our study highlights the crucial role of riboswitches in HCV replication and suggests the therapeutic potential of viral-RNA-targeted antivirals.

Messenger RNAs bearing tRNA-like features exemplified by interferon alfa 5 mRNA

The purpose of this work was to ascertain whether liver mRNA species share common structural features with hepatitis C virus (HCV) mRNA that allow them to support the RNase-P (pre-tRNA/processing enzyme) cleavage reaction in vitro. The presence of RNase-P competitive elements in the liver mRNA population was determined by means of biochemical techniques, and a set of sensitive mRNA species were identified through microarray screening. Cleavage specificity and substrate length requirement of around 200 nts, were determined for three mRNA species. One of these cleavage sites was found in interferon-alpha 5 (IFNA5) mRNA between specific base positions and with the characteristic RNase-P chemistry of cleavage. It was mapped within a cloverleaf-like structure revealed by a comparative structural analysis based on several direct enzymes and chemical probing methods of three RNA fragments of increasing size, and subsequently contrasted against site-directed mutants. The core region was coincident with the reported signal for the cytoplasmic accumulation region (CAR) in IFNAs. Striking similarities with the tRNA-like element of the antagonist HCV mRNA were found. In general, this study provides a new way of looking at a variety of viral tRNA-like motifs as this type of structural mimicry might be related to specific host mRNA species rather than, or in addition to, tRNA itself.

Structure-based whole-genome realignment reveals many novel noncoding RNAs

Recent genome-wide computational screens that search for conservation of RNA secondary structure in whole-genome alignments (WGAs) have predicted thousands of structural noncoding RNAs (ncRNAs). The sensitivity of such approaches, however, is limited, due to their reliance on sequence-based whole-genome aligners, which regularly misalign structural ncRNAs. This suggests that many more structural ncRNAs may remain undetected. Structure-based alignment, which could increase the sensitivity, has been prohibitive for genome-wide screens due to its extreme computational costs. Breaking this barrier, we present the pipeline REAPR (RE-Alignment for Prediction of structural ncRNA), which efficiently realigns whole genomes based on RNA sequence and structure, thus allowing us to boost the performance of de novo ncRNA predictors, such as RNAz. Key to the pipeline's efficiency is the development of a novel banding technique for multiple RNA alignment. REAPR significantly outperforms the widely used predictors RNAz and EvoFold in genome-wide screens; in direct comparison to the most recent RNAz screen on D. melanogaster, REAPR predicts twice as many high-confidence ncRNA candidates. Moreover, modENCODE RNA-seq experiments confirm a substantial number of its predictions as transcripts. REAPR's advancement of de novo structural characterization of ncRNAs complements the identification of transcripts from rapidly accumulating RNA-seq data.