Quantitative and Qualitative Assessment of Small RNA Preparations.

Recent advances in high-throughput sequencing have shed new light on the diversity of small noncoding RNA (sncRNA) classes and their crucial roles in gene regulation and disease. One key step in sncRNA profiling consists in their quantification and assessment of their degradation extent. In this chapter, we will describe different gold standard methods used to achieve both purposes before using the sncRNAs in downstream applications.

The impact of various seed, accessibility and interaction constraints on sRNA target prediction- a systematic assessment.

BACKGROUND: Seed and accessibility constraints are core features to enable highly accurate sRNA target screens based on RNA-RNA interaction prediction. Currently, available tools provide different (sets of) constraints and default parameter sets. Thus, it is hard to impossible for users to estimate the influence of individual restrictions on the prediction results. RESULTS: Here, we present a systematic assessment of the impact of established and new constraints on sRNA target prediction both on a qualitative as well as computational level. This is done exemplarily based on the performance of IntaRNA, one of the most exact sRNA target prediction tools. IntaRNA provides various ways to constrain considered seed interactions, e.g. based on seed length, its accessibility, minimal unpaired probabilities, or energy thresholds, beside analogous constraints for the overall interaction. Thus, our results reveal the impact of individual constraints and their combinations. CONCLUSIONS: This provides both a guide for users what is important and recommendations for existing and upcoming sRNA target prediction approaches.We show on a large sRNA target screen benchmark data set that only by altering the parameter set, IntaRNA recovers 30% more verified interactions while becoming 5-times faster. This exemplifies the potential of seed, accessibility and interaction constraints for sRNA target prediction.

Global assessment of small RNAs reveals a non-coding transcript involved in biofilm formation and attachment in Acinetobacter baumannii ATCC 17978.

Many strains of Acinetobacter baumannii have been described as being able to form biofilm. Small non-coding RNAs (sRNAs) control gene expression in many regulatory circuits in bacteria. The aim of the present work was to provide a global description of the sRNAs produced both by planktonic and biofilm-associated (sessile) cells of A. baumannii ATCC 17978, and to compare the corresponding gene expression profiles to identify sRNAs molecules associated to biofilm formation and virulence. sRNA was extracted from both planktonic and sessile cells and reverse transcribed. cDNA was subjected to 454-pyrosequencing using the GS-FLX Titanium chemistry. The global analysis of the small RNA transcriptome revealed different sRNA expression patterns in planktonic and biofilm associated cells, with some of the transcripts only expressed or repressed in sessile bacteria. A total of 255 sRNAs were detected, with 185 of them differentially expressed in the different types of cells. A total of 9 sRNAs were expressed only in biofilm cells, while the expression of other 21 coding regions were repressed only in biofilm cells. Strikingly, the expression level of the sRNA 13573 was 120 times higher in biofilms than in planktonic cells, an observation that prompted us to further investigate the biological role of this non-coding transcript. Analyses of an isogenic mutant and over-expressing strains revealed that the sRNA 13573 gene is involved in biofilm formation and attachment to A549 human alveolar epithelial cells. The present work serves as a basis for future studies examining the complex regulatory network that regulate biofilm biogenesis and attachment to eukaryotic cells in A. baumannii ATCC 17978.

DETECTION OF BACTERIAL SMALL TRANSCRIPTS FROM RNA-SEQ DATA: A COMPARATIVE ASSESSMENT.

Small non-coding RNAs (sRNAs) are regulatory RNA molecules that have been identified in a multitude of bacterial species and shown to control numerous cellular processes through various regulatory mechanisms. In the last decade, next generation RNA sequencing (RNA-seq) has been used for the genome-wide detection of bacterial sRNAs. Here we describe sRNA-Detect, a novel approach to identify expressed small transcripts from prokaryotic RNA-seq data. Using RNA-seq data from three bacterial species and two sequencing platforms, we performed a comparative assessment of five computational approaches for the detection of small transcripts. We demonstrate that sRNA-Detect improves upon current standalone computational approaches for identifying novel small transcripts in bacteria.

Structural model of an mRNA in complex with the bacterial chaperone Hfq.

The Sm-like protein Hfq (host factor Q-beta phage) facilitates regulation by bacterial small noncoding RNAs (sRNAs) in response to stress and other environmental signals. Here, we present a low-resolution model of Escherichia coli Hfq bound to the rpoS mRNA, a bacterial stress response gene that is targeted by three different sRNAs. Selective 2'-hydroxyl acylation and primer extension, small-angle X-ray scattering, and Monte Carlo molecular dynamics simulations show that the distal face and lateral rim of Hfq interact with three sites in the rpoS leader, folding the RNA into a compact tertiary structure. These interactions are needed for sRNA regulation of rpoS translation and position the sRNA target adjacent to an sRNA binding region on the proximal face of Hfq. Our results show how Hfq specifically distorts the structure of the rpoS mRNA to enable sRNA base pairing and translational control.

A universal TaqMan-based RT-PCR protocol for cost-efficient detection of small noncoding RNA.

Several methods for the detection of RNA have been developed over time. For small RNA detection, a stem-loop reverse primer-based protocol relying on TaqMan RT-PCR has been described. This protocol requires an individual specific TaqMan probe for each target RNA and, hence, is highly cost-intensive for experiments with small sample sizes or large numbers of different samples. We describe a universal TaqMan-based probe protocol which can be used to detect any target sequence and demonstrate its applicability for the detection of endogenous as well as artificial eukaryotic and bacterial small RNAs. While the specific and the universal probe-based protocol showed the same sensitivity, the absolute sensitivity of detection was found to be more than 100-fold lower for both than previously reported. In subsequent experiments, we found previously unknown limitations intrinsic to the method affecting its feasibility in determination of mature template RISC incorporation as well as in multiplexing. Both protocols were equally specific in discriminating between correct and incorrect small RNA targets or between mature miRNA and its unprocessed RNA precursor, indicating the stem-loop RT-primer, but not the TaqMan probe, triggers target specificity. The presented universal TaqMan-based RT-PCR protocol represents a cost-efficient method for the detection of small RNAs.

Extracting intracellular diffusive states and transition rates from single-molecule tracking data.

We provide an analytical tool based on a variational Bayesian treatment of hidden Markov models to combine the information from thousands of short single-molecule trajectories of intracellularly diffusing proteins. The method identifies the number of diffusive states and the state transition rates. Using this method we have created an objective interaction map for Hfq, a protein that mediates interactions between small regulatory RNAs and their mRNA targets.