Identification of bacterial sRNA regulatory targets using ribosome profiling

Bacteria express large numbers of non-coding, regulatory RNAs known as ‘small RNAs’ (sRNAs). sRNAs typically regulate expression of multiple target messenger RNAs (mRNAs) through base-pairing interactions. sRNA:mRNA base-pairing often results in altered mRNA stability and/or altered translation initiation. Computational identification of sRNA targets is challenging due to the requirement for only short regions of base-pairing that can accommodate mismatches. Experimental approaches have been applied to identify sRNA targets on a genomic scale, but these focus only on those targets regulated at the level of mRNA stability. Here, we utilize ribosome profiling (Ribo-seq) to experimentally identify regulatory targets of the Escherichia coli sRNA RyhB. We not only validate a majority of known RyhB targets using the Ribo-seq approach, but also discover many novel ones. We further confirm regulation of a selection of known and novel targets using targeted reporter assays. By mutating nucleotides in the mRNA of a newly discovered target, we demonstrate direct regulation of this target by RyhB. Moreover, we show that Ribo-seq distinguishes between mRNAs regulated at the level of RNA stability and those regulated at the level of translation. Thus, Ribo-seq represents a powerful approach for genome-scale identification of sRNA targets.     

Small RNA interactome of pathogenic E. coli revealed through crosslinking of RNase E

RNA sequencing studies have identified hundreds of non‐coding RNAs in bacteria, including regulatory small RNA (sRNA). However, our understanding of sRNA function has lagged behind their identification due to a lack of tools for the high‐throughput analysis of RNA–RNA interactions in bacteria. Here we demonstrate that in vivo sRNA–mRNA duplexes can be recovered using UV‐crosslinking, ligation and sequencing of hybrids (CLASH). Many sRNAs recruit the endoribonuclease, RNase E, to facilitate processing of mRNAs. We were able to recover base‐paired sRNA–mRNA duplexes in association with RNase E, allowing proximity‐dependent ligation and sequencing of cognate sRNA–mRNA pairs as chimeric reads. We verified that this approach captures bona fide sRNA–mRNA interactions. Clustering analyses identified novel sRNA seed regions and sets of potentially co‐regulated target mRNAs. We identified multiple mRNA targets for the pathotype‐specific sRNA Esr41, which was shown to regulate colicin sensitivity and iron transport in E. coli. Numerous sRNA interactions were also identified with non‐coding RNAs, including sRNAs and tRNAs, demonstrating the high complexity of the sRNA interactome.     

Proteins that interact with bacterial small RNA regulators

Small regulatory RNAs have been identified in a wide range of organisms, where they modify mRNA stability, translation or protein function. Small RNA regulators (sRNAs) either pair with mRNA targets or modify protein activities. Here we discuss current knowledge of the various proteins that interact with RNA regulators and review the physiologic implications of sRNA-protein complexes in DNA, RNA and protein metabolism, as well as in RNA and protein quality control in prokaryotes. Proteins that interact with the sRNAs can possess catalytic activity, induce conformational changes of the sRNA, or be sequestered by the sRNA to prevent the action of the protein.     

Quantitative effect of target translation on small RNA efficacy reveals a novel mode of interaction

Small regulatory RNAs (sRNAs) in bacteria regulate many important cellular activities under normal conditions and in response to stress. Many sRNAs bind to the mRNA targets at or near the 5′ untranslated region (UTR) resulting in translation inhibition and accelerated degradation. Often the sRNA-binding site is adjacent to or overlapping with the ribosomal binding site (RBS), suggesting a possible interplay between sRNA and ribosome binding. Here we combine quantitative experiments with mathematical modeling to reveal novel features of the interaction between small RNAs and the translation machinery at the 5′UTR of a target mRNA. By measuring the response of a library of reporter targets with varied RBSs, we find that increasing translation rate can lead to increased repression. Quantitative analysis of these data suggests a recruitment model, where bound ribosomes facilitate binding of the sRNA. We experimentally verified predictions of this model for the cell-to-cell variability of target expression. Our findings offer a framework for understanding sRNA silencing in the context of bacterial physiology.     

SPORTS1.0: A Tool for Annotating and Profiling Non-coding RNAs Optimized for rRNA- and tRNA-derived Small RNAs

High-throughput RNA-seq has revolutionized the process of small RNA (sRNA) discovery, leading to a rapid expansion of sRNA categories. In addition to the previously well-characterized sRNAs such as microRNAs (miRNAs), piwi-interacting RNAs (piRNAs), and small nucleolar RNA (snoRNAs), recent emerging studies have spotlighted on tRNA-derived sRNAs (tsRNAs) and rRNA-derived sRNAs (rsRNAs) as new categories of sRNAs that bear versatile functions. Since existing software and pipelines for sRNA annotation are mostly focused on analyzing miRNAs or piRNAs, here we developed the sRNA annotation pipelineoptimized for rRNA- and tRNA-derived sRNAs (SPORTS1.0). SPORTS1.0 is optimized for analyzing tsRNAs and rsRNAs from sRNA-seq data, in addition to its capacity to annotate canonical sRNAs such as miRNAs and piRNAs. Moreover, SPORTS1.0 can predict potential RNA modification sites based on nucleotide mismatches within sRNAs. SPORTS1.0 is precompiled to annotate sRNAs for a wide range of 68 species across bacteria, yeast, plant, and animal kingdoms, while additional species for analyses could be readily expanded upon end users’ input. For demonstration, by analyzing sRNA datasets using SPORTS1.0, we reveal that distinct signatures are present in tsRNAs and rsRNAs from different mouse cell types. We also find that compared to other sRNA species, tsRNAs bear the highest mismatch rate, which is consistent with their highly modified nature. SPORTS1.0 is an open-source software and can be publically accessed at https://github.com/junchaoshi/sports1.0.     

Identification of Novel Oryza sativa miRNAs in Deep Sequencing-Based Small RNA Libraries of Rice Infected with Rice Stripe Virus

MicroRNAs (miRNAs) play essential regulatory roles in the development of eukaryotes. Methods based on deep-sequencing have provided a powerful high-throughput strategy for identifying novel miRNAs and have previously been used to identify over 100 novel miRNAs from rice. Most of these reports are related to studies of rice development, tissue differentiation, or abiotic stress, but novel rice miRNAs related to viral infection have rarely been identified. In previous work, we constructed and pyrosequenced the small RNA (sRNA) libraries of rice infected with Rice stripe virus and described the character of the small interfering RNAs (siRNA) derived from the RSV RNA genome. We now report the identification of novel miRNAs from the abundant sRNAs (with a minimum of 100 sequencing reads) in the sRNA library of RSV-infected rice. 7 putative novel miRNAs (pn-miRNAs) whose precursor sequences have not previously been described were identified and could be detected by Northern blot or RT-PCR, and were recognized as novel miRNAs (n-miRNAs). Further analysis showed that 5 of the 7 n-miRNAs were up-expressed while the other 2 n-miRNAs were down-expressed in RSV-infected rice. In addition, 23 pn-miRNAs that were newly produced from 19 known miRNA precursors were also identified. This is first report of novel rice miRNAs produced from new precursors related to RSV infection.     

Plant Mobile Small RNAs

In plants, RNA silencing is a fundamental regulator of gene expression, heterochromatin formation, suppression of transposable elements, and defense against viruses. The sequence specificity of these processes relies on small noncoding RNA (sRNA) molecules. Although the spreading of RNA silencing across the plant has been recognized for nearly two decades, only recently have sRNAs been formally demonstrated as the mobile silencing signals. Here, we discuss the various types of mobile sRNA molecules, their short- and long-range movement, and their function in recipient cells.RNA silencing is a regulatory mechanism that controls the expression of endogenous genes and exogenous molecular parasites such as viruses, transgenes, and transposable elements. One of the most fascinating aspects of RNA silencing found in plants and invertebrates is its mobile nature—in other words, its ability to spread from the cell where it has been initiated to neighboring cells. This phenomenon relies on the movement of small noncoding RNA molecules (sRNA, 21–24 nucleotides [nt] in length) that provide the sequence specificity of the silencing effects. In plants, there are two major classes of sRNAs: short interfering RNAs (siRNAs) and micro RNAs (miRNAs). These sRNAs are generated by diverse and sometimes interacting biochemical pathways, which may influence their mobility. Movement of plant sRNAs falls into two main categories: cell-to-cell (short-range) and systemic (long-range) movement (Melnyk et al. 2011).     

Analysis of plant-derived miRNAs in animal small RNA datasets

ABSTRACT: BACKGROUND: Plants contain significant quantities of small RNAs (sRNAs) derived from various sRNA biogenesis pathways. Many of these sRNAs play regulatory roles in plants. Previous analysis revealed that numerous sRNAs in corn, rice and soybean seeds have high sequence similarity to animal genes. However, exogenous RNA is considered to be unstable within the gastrointestinal tract of many animals, thus limiting potential for any adverse effects from consumption of dietary RNA. A recent paper reported that putative plant miRNAs were detected in animal plasma and serum, presumably acquired through ingestion, and may have a functional impact in the consuming organisms. RESULTS: To address the question of how common this phenomenon could be, we searched for plant miRNAs sequences in public sRNA datasets from various tissues of mammals, chicken and insects. Our analyses revealed that plant miRNAs were present in the animal sRNA datasets, and significantly miR168 was extremely over-represented. Furthermore, all or nearly all (>96%) miR168 sequences were monocot derived for most datasets, including datasets for two insects reared on dicot plants in their respective experiments. To investigate if plant-derived miRNAs, including miR168, could accumulate and move systemically in insects, we conducted insect feeding studies for three insects including corn rootworm, which has been shown to be responsive to plant-produced long double-stranded RNAs. CONCLUSIONS: Our analyses suggest that the observed plant miRNAs in animal sRNA datasets can originate in the process of sequencing, and that accumulation of plant miRNAs via dietary exposure is not universal in animals.     

Hfq proximity and orientation controls RNA annealing

Regulation of bacterial gene networks by small non-coding RNAs (sRNAs) requires base pairing with messenger RNA (mRNA) targets, which is facilitated by Hfq protein. Hfq is recruited to sRNAs and mRNAs through U-rich- and A-rich-binding sites, respectively, but their distance from the sRNA–mRNA complementary region varies widely among different genes. To determine whether distance and binding orientation affect Hfq’s chaperone function, we engineered ‘toy’ RNAs containing strong Hfq-binding sites at defined distances from the complementary target site. We show that RNA annealing is fastest when the distal face of Hfq binds an A-rich sequence immediately 3′ of the target. This recruitment advantage is lost when Hfq binds >20 nt away from the target, but is partially restored by secondary structure that shortens this distance. Although recruitment through Hfq’s distal face accelerates RNA annealing, tight binding of six Us to Hfq’s proximal face inhibits annealing. Finally, we show that ectopic A-rich motifs dramatically accelerate base pairing between DsrA sRNA and a minimal rpoS mRNA in the presence of Hfq, demonstrating that proximity and orientation predict the activity of Hfq on long RNAs.