The effect of loop size in antisense and target RNAs on the efficiency of antisense RNA control.

Most natural antisense RNAs display a high degree of secondary structure with stem-loops as their most prominent feature. Mutations affecting the inhibitory activity of these RNAs most often map in or close to loop regions in both the antisense and target RNAs. The primary recognition loops often contain 5-7 unpaired nucleotides. Nucleotide changes in the loops affect the binding rate and, hence, the inhibitory effect on the activity of the target RNA. Here we address the question whether loop sizes affect binding rates between antisense and target RNAs, using the replication control system of plasmid R1 as a model system. By creating a series of loop size mutants we show that loop size alterations have strong effects on the binding rates between the two reactant RNAs in vitro, and that most of the mutations analyzed display corresponding effects on antisense RNA control in vivo. Our data suggest that the three-dimensional structures of antisense and target RNA stem-loops are crucial for determining binding rates. The implications of these results for the design of efficient artificial antisense RNA control systems are discussed.     

The origin of RNA interference: Adaptive or neutral evolution?

The origin of RNA interference (RNAi) is usually explained by a defense-based hypothesis, in which RNAi evolved as a defense against transposable elements (TEs) and RNA viruses and was already present in the last eukaryotic common ancestor (LECA). However, since RNA antisense regulation and double-stranded RNAs (dsRNAs) are ancient and widespread phenomena, the origin of defensive RNAi should have occurred in parallel with its regulative functions to avoid imbalances in gene regulation. Thus, we propose a neutral evolutionary hypothesis for the origin of RNAi in which qualitative system drift from a prokaryotic antisense RNA gene regulation mechanism leads to the formation of RNAi through constructive neutral evolution (CNE). We argue that RNAi was already present in the ancestor of LECA before the need for a new defense system arose and that its presence helped to shape eukaryotic genomic architecture and stability.

Where does RNA interference come from? This Essay describes a new step-by-step evolutionary model of how RNA interference might have originated in early eukaryotes through neutral events from the molecular machinery present in prokaryotes.     

Generalized structures of the 5S ribosomal RNAs.

The sequences of 5S ribosomal RNAs from a wide-range of organisms have been compared. All sequences fit a generalized 5S RNA secondary structural model. Twenty-three nucleotide positions are found universally, i.e., in 5S RNAs of eukaryotes, prokaryotes, archaebacteria, chloroplasts and mitochondria. One major distinguishing feature between the prokaryotic and eukaryotic 5S RNAs is the number of nucleotide positions between certain universal positions, e.g., prokaryotic 5S RNAs have three positions between the universal positions PuU40 and G44 (using the E. coli numbering system) and eukaryotic 5S RNAs have two. The archaebacterial 5S RNAs appear to resemble the eukaryotic 5S RNAs to varying degrees depending on the species of archaebacteria although all the RNAs conform with the prokaryotic "rule" of chain length between PuU40 and G44. The green plant chloroplast and wheat mitochondrial 5S RNAs appear prokaryotic-like when comparing the number of positions between universal nucleotides. Nucleotide positions common to eukaryotic 5S RNAs have been mapped; in addition, nucleotide sequences, helix lengths and looped-out residues specific to phyla are proposed. Several of the common nucleotides found in the 5S RNAs of metazoan somatic tissue differ in the 5S RNAs of oocytes. These changes may indicate an important functional role of the 5S RNA during oocyte maturation.     

HIV-1 TAR as anchoring site for optimized catalytic RNAs

Ribozymes have a great potential for developing specific gene silencing molecules. One of the main limitations to ensure the efficient application of ribozymes is to achieve effective binding to the target. Stem-loop domains support efficient formation of the kissing complex between natural antisense molecules and their target sequence. We have characterized catalytic antisense RNA hybrid molecules composed of a hammerhead ribozyme and a stem-loop antisense domain. A series of artificial RNA substrates containing the TAR-RNA stem-loop and a target for the hammerhead ribozyme were constructed and challenged with a catalytic antisense RNA carrying the TAR complementary stem-loop. The catalytic antisense RNA cleaves each of these substrates significantly more efficiently than the parental hammerhead ribozyme. Deletion of the TAR domain in the substrate abolishes the positive effect. These results suggest that the enhancement is due to the interaction of both complementary stem-loop motifs. A similar improvement was corroborated when targeting the LTR region of HIV-1 with either hammerhead- and hairpin-based catalytic antisense RNAs. Our results indicate that the TAR domain can be used as an anchoring site to facilitate the access of ribozymes to their specific target sequences within TAR-containing RNAs. Finally, we propose the addition of stable stem-loop motifs to the ribozyme domain as a rational way for constructing catalytic antisense RNAs.     

天然反义转录物及其调控基因的表达机制

天然反义转录(NATs)是一组编码蛋白质或非编码蛋白质的RNAs, 与其他(有义)转录物具有互补序列, 可以调节有义链的表达。这种调节可以发生在转录水平或转录后水平, 调节方式有转录干扰、RNA封闭、双链依赖机制或染色质重建(修饰)等。正义链和反义链分别加工成小RNAs调节基因表达, 也是NATs调节基因表达的重要方式, 如piRNAs的“乒乓机制”。实验或计算机研究已经证明了NATs在生物中广泛存在, 是一种重要的基因表达调节方式。文章论述了NATs的重要作用和机理, 重点论述了NATs的调节机制和相关的小RNAs。     

Conditional gene silencing of multiple genes with antisense RNAs and generation of a mutator strain of Escherichia coli

In this study, we describe a method of simultaneous conditional gene silencing of up to four genes in Escherichia coli by using antisense RNAs. We used antisense RNAs with paired termini, which carried flanking inverted repeats to create paired double-stranded RNA termini; these RNAs have been proven to have high silencing efficacy. To express antisense RNAs, we constructed four IPTG-inducible vectors carrying different but compatible replication origins. When the lacZ antisense RNA was expressed using these vectors, lacZ expression was successfully silenced by all the vectors, but the expression level of the antisense RNA and silencing efficacy differed depending on the used vectors. All the vectors were co-transformable; the antisense RNAs against lacZ, ackA, pta and pepN were co-expressed, and silencing of all the target genes was confirmed. Furthermore, when antisense RNAs were targeted to the mutator genes mutS, mutD (dnaQ) and ndk, which are involved in DNA replication or DNA mismatch repair, spontaneous mutation frequencies increased over 2000-fold. The resulting mutator strain is useful for random mutagenesis of plasmids. The method provides a robust tool for investigating functional relationships between multiple genes or altering cell phenotypes for biotechnological and industrial applications.     

Identification of novel non-coding RNAs as potential antisense regulators in the archaeon Sulfolobus solfataricus

By generating a specialized cDNA library from the archaeon Sulfolobus solfataricus, we have identified 57 novel small non-coding RNA (ncRNA) candidates and confirmed their expression by Northern blot analysis. The majority was found to belong to one of two classes, either antisense or antisense-box RNAs, where the latter only exhibit partial complementarity to RNA targets. The most prominent group of antisense RNAs is transcribed in the opposite orientation to the transposase genes, encoded by insertion elements (transposons). Thus, these antisense RNAs may regulate transposition of insertion elements by inhibiting expression of the transposase mRNA. Surprisingly, the class of antisense RNAs also contained RNAs complementary to tRNAs or sRNAs (small-nucleolar-like RNAs). For the antisense-box ncRNAs, the majority could be assigned to the class of C/D sRNAs, which specify 2'-O-methylation sites on rRNAs or tRNAs. Five C/D sRNAs of this group are predicted to target methylation at six sites in 13 different tRNAs, thus pointing to the widespread role of these sRNA species in tRNA modification in Archaea. Another group of antisense-box RNAs, lacking typical C/D sRNA motifs, was predicted to target the 3'-untranslated regions of certain mRNAs. Furthermore, one of the ncRNAs that does not show antisense elements is transcribed from a repeat unit of a cluster of small regularly spaced repeats in S. solfataricus which is potentially involved in replicon partitioning. In conclusion, this is the first report of stably expressed antisense RNAs in an archaeal species and it raises the prospect that antisense-based mechanisms are also used widely in Archaea to regulate gene expression.     

Functional roles of non-coding Y RNAs

Non-coding RNAs are involved in a multitude of cellular processes but the biochemical function of many small non-coding RNAs remains unclear. The family of small non-coding Y RNAs is conserved in vertebrates and related RNAs are present in some prokaryotic species. Y RNAs are also homologous to the newly identified family of non-coding stem-bulge RNAs (sbRNAs) in nematodes, for which potential physiological functions are only now emerging. Y RNAs are essential for the initiation of chromosomal DNA replication in vertebrates and, when bound to the Ro60 protein, they are involved in RNA stability and cellular responses to stress in several eukaryotic and prokaryotic species. Additionally, short fragments of Y RNAs have recently been identified as abundant components in the blood and tissues of humans and other mammals, with potential diagnostic value. While the number of functional roles of Y RNAs is growing, it is becoming increasingly clear that the conserved structural domains of Y RNAs are essential for distinct cellular functions. Here, we review the biochemical functions associated with these structural RNA domains, as well as the functional conservation of Y RNAs in different species. The existing biochemical and structural evidence supports a domain model for these small non-coding RNAs that has direct implications for the modular evolution of functional non-coding RNAs.     

Using binding profiles to predict binding sites of target RNAs

Prediction of RNA-RNA interaction is a key to elucidating possible functions of small non-coding RNAs, and a number of computational methods have been proposed to analyze interacting RNA secondary structures. In this article, we focus on predicting binding sites of target RNAs that are expected to interact with regulatory antisense RNAs in a general form of interaction. For this purpose, we propose bistaRNA, a novel method for predicting multiple binding sites of target RNAs. bistaRNA employs binding profiles that represent scores for hybridized structures, leading to reducing the computational cost for interaction prediction. bistaRNA considers an ensemble of equilibrium interacting structures and seeks to maximize expected accuracy using dynamic programming. Experimental results on real interaction data validate good accuracy and fast computation time of bistaRNA as compared with several competitive methods. Moreover, we aim to find new targets given specific antisense RNAs, which provides interesting insights into antisense RNA regulation. bistaRNA is implemented in C++. The program and Supplementary Material are available at http://rna.naist.jp/program/bistarna/.     

U-turns and regulatory RNAs

Conventional antisense RNAs, such as those controlling plasmid replication and maintenance, inhibit the function of their target RNAs rapidly and efficiently. Novel findings show that a common U-turn loop structure mediates fast RNA pairing in the majority of these RNA controlled systems. Usually, an antisense RNA regulates a single, cognate target RNA only. Recent reports, however, show that antisense RNAs can act as promiscuous regulators that control multiple genes in concert to integrate complex physiological responses in Escherichia coli.