Identification of bacterial small non-coding RNAs: experimental approaches

Almost 140 bacterial small RNAs (sRNAs; sometimes referred to as non-coding RNAs) have been discovered in the past six years. The majority of these sRNAs were discovered in Escherichia coli, and a smaller subset was characterized in other bacteria, many of which were pathogenic. Many of these genes were identified as a result of systematic screens using computational prediction of sRNAs and experimental-based approaches, including microarray and shotgun cloning. A smaller number of sRNAs were discovered by direct labeling or by functional genetic screens. Many of the discovered genes, ranging in size from 50 to 500 nucleotides, are conserved and located in intergenic regions, in-between open reading frames. The expression of many of these genes is growth phase dependent or stress related. As each search employed specific parameters, this led to the identification of genes with distinct characteristics. Consequently, unique sRNAs such as those that are species-specific, sRNA genes that are transcribed under unique conditions or genes located on the antisense strand of protein-encoding genes, were probably missed.     

Predicting sRNAs and Their Targets in Bacteria

Bacterial small RNAs (sRNAs) are an emerging class of regulatory RNAs of about 40-500 nucleotides in length and, by binding to their target mRNAs or proteins, get involved in many biological processes such as sensing environmental changes and regulating gene expression. Thus, identification of bacterial sRNAs and their targets has become an important part of sRNA biology. Current strategies for discovery of sRNAs and their targets usually involve bioinformatics prediction followed by experimental validation, emphasizing a key role for bioinformatics prediction. Here, therefore, we provided an overview on prediction methods, focusing on the merits and limitations of each class of models. Finally, we will present our thinking on developing related bioinformatics models in future.     

细菌sRNA数据库

细菌sRNA是一类长度在50～500 nt的调控小RNA（small regulatory RNA）,主要通过与靶标mRNA或靶标蛋白质结合发挥多种生物学功能。目前,随着生物信息学与高通量测序的应用,发现了越来越多的细菌sRNA,开发了多个相关数据库。为了sRNA工作者系统了解与应用这些数据,本文拟对包含细菌sRNA的综合数据库和细菌sRNA专业数据库作一概述,并对sRNA数据库的未来发展进行展望。     

Human microRNA target identification by RRSM

MicroRNAs (miRNAs) are small endogenously expressed non-coding RNAs that regulate target messenger RNAs in various biological processes. In recent years, there have been many studies concentrated on the discovery of new miRNAs and identification of their mRNA targets. Although researchers have identified many miRNAs, few miRNA targets have been identified by actual experimental methods. To expedite the identification of miRNA targets for experimental verification, in the literature approaches based on the sequence or microarray expression analysis have been established to discover the potential miRNA targets. In this study, we focus on the human miRNA target prediction and propose a generalized relative R2 method (RRSM) to find many high-confidence targets. Many targets have been confirmed from previous studies. The targets for several miRNAs discovered by the HITS-CLIP method in a recent study have also been selected by our study.     

Non-coding RNAs in marine Synechococcus and their regulation under environmentally relevant stress conditions

Regulatory small RNAs (sRNAs) have crucial roles in the adaptive responses of bacteria to changes in the environment. Thus far, potential regulatory RNAs have been studied mainly in marine picocyanobacteria in genetically intractable Prochlorococcus, rendering their molecular analysis difficult. Synechococcus sp. WH7803 is a model cyanobacterium, representative of the picocyanobacteria from the mesotrophic areas of the ocean. Similar to the closely related Prochlorococcus it possesses a relatively streamlined genome and a small number of genes, but is genetically tractable. Here, a comparative genome analysis was performed for this and four additional marine Synechococcus to identify the suite of possible sRNAs and other RNA elements. Based on the prediction and on complementary microarray profiling, we have identified several known as well as 32 novel sRNAs. Some sRNAs overlap adjacent coding regions, for instance for the central photosynthetic gene psbA. Several of these novel sRNAs responded specifically to environmentally relevant stress conditions. Among them are six sRNAs changing their accumulation level under cold stress, six responding to high light and two to iron limitation. Target predictions suggested genes encoding components of the light-harvesting apparatus as targets of sRNAs originating from genomic islands and that one of the iron-regulated sRNAs might be a functional homolog of RyhB. These data suggest that marine Synechococcus mount adaptive responses to these different stresses involving regulatory sRNAs.     

Identification of small RNAs in diverse bacterial species

Small, non-coding bacterial RNAs (sRNAs) have been shown to regulate a plethora of biological processes. Up until recently, most sRNAs had been identified and characterized in E. coli. However, in the past few years, dozens of sRNAs have been discovered in a wide variety of bacterial species. Whereas numerous sRNAs have been isolated or detected through experimental approaches, most have been identified in predictive bioinformatic searches. Recently developed computational tools have greatly facilitated the efficient prediction of sRNAs in diverse species. Although the number of known sRNAs has dramatically increased in recent years, many challenges in the identification and characterization of sRNAs lie ahead.     

Small RNAs: Efficient and miraculous effectors that play key roles in plant–microbe interactions

Plants' response to pathogens is highly complex and involves changes at different levels, such as activation or repression of a vast array of genes. Recently, many studies have demonstrated that many RNAs, especially small RNAs (sRNAs), are involved in genetic expression and reprogramming affecting plant–pathogen interactions. The sRNAs, including short interfering RNAs and microRNAs, are noncoding RNA with 18–30 nucleotides, and are recognized as key genetic and epigenetic regulators. In this review, we summarize the new findings about defence-related sRNAs in the response to pathogens and our current understanding of their effects on plant–pathogen interactions. The main content of this review article includes the roles of sRNAs in plant–pathogen interactions, cross-kingdom sRNA trafficking between host and pathogen, and the application of RNA-based fungicides for plant disease control.     

细菌sRNA基因及其靶标预测研究进展

摘要：细菌sRNA是一类长度在40～500 nt之间的非编码RNA,主要以不完全碱基配对方式与靶标mRNA5′端相互作用进而发挥其生物学功能。鉴于预测方法可以为细菌sRNA及其靶标的实验发现提供指导,因此,细菌sRNA与靶标预测研究受到了广泛重视。文章首先将sRNA预测方法分为3类,分别是基于比较基因组学的预测方法、基于转录单元的预测方法和基于机器学习的预测方法;其次,将sRNA靶标预测方法分为2类,分别是序列比较方法与基于RNA二级结构的预测方法;最后对各类方法的原理、核心思想、优点和局限性进行了分析,并探讨了进一步的发展方向。     

Multifaceted mammalian transcriptome

Despite surprisingly a small number of protein-coding gene in mammalian genomes, a large variety of different RNAs is being produced. These RNAs are amazingly different in their number, size, cell localization, and mechanism of actions. Although new classes of short RNAs (sRNAs) are being continuously discovered, it is not yet obvious how many of the sRNAs are originated. Altogether, the research in the recent few years has identified an unexpectedly rich variety of mechanisms by which noncoding RNAs act, suggesting that we have identified probably only few of the many potential functional mechanism and more investigation will be needed to comprehensively understand the complex nature and biology of mammalian RNAome. Here, we focus on various aspects of the diversity of the biological role of these nonprotein-coding RNAs (ncRNAs), with emphasis on functional mechanisms recently elucidated.