Workflow for a Computational Analysis of an sRNA Candidate in Bacteria

Differential expression of small RNAs in biofilm-producing clinical methicillin-susceptible Staphylococcus aureus recovered from human urine

Bacterial small RNAs (sRNAs) play crucial roles in coordinating gene regulatory networks in various physiological processes, including biofilm formation. In this study, RNA sequencing was performed on biofilm (n = 4) and planktonic (n = 4) cells harvested at 10 h (pre-stationary phase of biofilm development) to identify biofilm-associated sRNAs in human methicillin-susceptible Staphylococcus aureus (MSSA) recovered from urine isolate. A total of 56 highly expressed sRNAs were identified with 15 overlapping sRNA genes (srn_9348, sprD, sRNA205, sRNA288, srn_2467, Sau-25, srn_2468, sRNA260, sRNA200, RsaE, sRNA397, Teg55, Teg60, RsaX05 and Teg140). Further validation through RT-qPCR analysis of nine sRNAs revealed that srn_9348 and sRNA260 were significantly expressed in the biofilm cells of urine sample. Both sRNAs were predicted to interact with mRNA genes including intracellular adhesin A (icaA) and host factor protein (hfq) involved in biofilm formation via cis-acting and trans-acting using CopraRNA analysis. Therefore, both sRNAs merit further investigations via reverse genetic approaches to elucidate their mechanism of translational regulation. In summary, the transcriptomic analysis conducted in this study offers new insights into the potential regulatory roles of sRNAs in MSSA biofilm development within the urinary environment.

How to do RNA-RNA Interaction Prediction? A Use-Case Driven Handbook Using IntaRNA

Computational prediction of RNA-RNA interactions (RRI) is a central methodology for the specific investigation of inter-molecular RNA interactions and regulatory effects of non-coding RNAs like eukaryotic microRNAs or prokaryotic small RNAs. Available methods can be classified according to their underlying prediction strategies, each implicating specific capabilities and restrictions often not transparent to the non-expert user. Within this work, we review seven classes of RRI prediction strategies and discuss the advantages and limitations of respective tools, since such knowledge is essential for selecting the right tool in the first place.Among the RRI prediction strategies, accessibility-based approaches have been shown to provide the most reliable predictions. Here, we describe how IntaRNA, as one of the state-of-the-art accessibility-based tools, can be applied in various use cases for the task of computational RRI prediction. Detailed hands-on examples for individual RRI predictions as well as large-scale target prediction scenarios are provided. We illustrate the flexibility and capabilities of IntaRNA through the examples. Each example is designed using real-life data from the literature and is accompanied by instructions on interpreting the respective results from IntaRNA output. Our use-case driven instructions enable non-expert users to comprehensively understand and utilize IntaRNA's features for effective RRI predictions.

Thirty Years of sRNA-Mediated Regulation in Staphylococcus aureus: From Initial Discoveries to In Vivo Biological Implications

Staphylococcus aureus is a widespread livestock and human pathogen that colonizes diverse microenvironments within its host. Its adaptation to the environmental conditions encountered within humans relies on coordinated gene expression. This requires a sophisticated regulatory network, among which regulatory RNAs (usually called sRNAs) have emerged as key players over the last 30 years. In S. aureus, sRNAs regulate target genes at the post-transcriptional level through base–pair interactions. The functional characterization of a subset revealed that they participate in all biological processes, including virulence, metabolic adaptation, and antibiotic resistance. In this review, we report 30 years of S. aureus sRNA studies, from their discovery to the in-depth characterizations of some of them. We also discuss their actual in vivo contribution, which is still lagging behind, and their place within the complex regulatory network. These shall be key aspects to consider in order to clearly uncover their in vivo biological functions.

Riboregulation in bacteria: From general principles to novel mechanisms of the trp attenuator and its sRNA and peptide products

Gene expression strategies ensuring bacterial survival and competitiveness rely on cis- and trans-acting RNA-regulators (riboregulators). Among the cis-acting riboregulators are transcriptional and translational attenuators, and antisense RNAs (asRNAs). The trans-acting riboregulators are small RNAs (sRNAs) that bind proteins or base pairs with other RNAs. This classification is artificial since some regulatory RNAs act both in cis and in trans, or function in addition as small mRNAs. A prominent example is the archetypical, ribosome-dependent attenuator of tryptophan (Trp) biosynthesis genes. It responds by transcription attenuation to two signals, Trp availability and inhibition of translation, and gives rise to two trans-acting products, the attenuator sRNA rnTrpL and the leader peptide peTrpL. In Escherichia coli, rnTrpL links Trp availability to initiation of chromosome replication and in Sinorhizobium meliloti, it coordinates regulation of split tryptophan biosynthesis operons. Furthermore, in S. meliloti, peTrpL is involved in mRNA destabilization in response to antibiotic exposure. It forms two types of asRNA-containing, antibiotic-dependent ribonucleoprotein complexes (ARNPs), one of them changing the target specificity of rnTrpL. The posttranscriptional role of peTrpL indicates two emerging paradigms: (1) sRNA reprograming by small molecules and (2) direct involvement of antibiotics in regulatory RNPs. They broaden our view on RNA-based mechanisms and may inspire new approaches for studying, detecting, and using antibacterial compounds. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Small Molecule-RNA Interactions RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs.

An Integrated Database of Small RNAs and Their Interplay With Transcriptional Gene Regulatory Networks in Corynebacteria

Small RNAs (sRNAs) are one of the key players in the post-transcriptional regulation of bacterial gene expression. These molecules, together with transcription factors, form regulatory networks and greatly influence the bacterial regulatory landscape. Little is known concerning sRNAs and their influence on the regulatory machinery in the genus Corynebacterium, despite its medical, veterinary and biotechnological importance. Here, we expand corynebacterial regulatory knowledge by integrating sRNAs and their regulatory interactions into the transcriptional regulatory networks of six corynebacterial species, covering four human and animal pathogens, and integrate this data into the CoryneRegNet database. To this end, we predicted sRNAs to regulate 754 genes, including 206 transcription factors, in corynebacterial gene regulatory networks. Amongst them, the sRNA Cd-NCTC13129-sRNA-2 is predicted to directly regulate ydfH, which indirectly regulates 66 genes, including the global regulator glxR in C. diphtheriae. All of the sRNA-enriched regulatory networks of the genus Corynebacterium have been made publicly available in the newest release of CoryneRegNet(www.exbio.wzw.tum.de/coryneregnet/) to aid in providing valuable insights and to guide future experiments.

GLASSgo in Galaxy: high-throughput, reproducible and easy-to-integrate prediction of sRNA homologs

MOTIVATION

The correct prediction of bacterial sRNA homologs is a prerequisite for many downstream analyses based on comparative genomics, but it is frequently challenging due to the short length and distinct heterogeneity of such homologs. GLobal Automatic Small RNA Search go (GLASSgo) is an efficient tool for the prediction of sRNA homologs from a single input query. To make the algorithm available to a broader community, we offer a Docker container along with a free-access web service. For non-computer scientists, the web service provides a user-friendly interface. However, capabilities were lacking so far for batch processing, version control and direct interaction with compatible software applications as a workflow management system can provide.

RESULTS

Here, we present GLASSgo 1.5.2, an updated version that is fully incorporated into the workflow management system Galaxy. The improved version contains a new feature for extracting the upstream regions, allowing the search for conserved promoter elements. Additionally, it supports the use of accession numbers instead of the outdated GI numbers, which widens the applicability of the tool.

AVAILABILITY AND IMPLEMENTATION

GLASSgo is available at https://github.com/lotts/GLASSgo/ under the MIT license and is accompanied by instruction and application data. Furthermore, it can be installed into any Galaxy instance using the Galaxy ToolShed.

CopomuS-Ranking Compensatory Mutations to Guide RNA-RNA Interaction Verification Experiments

In silico RNA-RNA interaction prediction is widely applied to identify putative interaction partners and to assess interaction details in base pair resolution. To verify specific interactions, in vitro evidence can be obtained via compensatory mutation experiments. Unfortunately, the selection of compensatory mutations is non-trivial and typically based on subjective ad hoc decisions. To support the decision process, we introduce our COmPensatOry MUtation Selector CopomuS. CopomuS evaluates the effects of mutations on RNA-RNA interaction formation using a set of objective criteria, and outputs a reliable ranking of compensatory mutation candidates. For RNA-RNA interaction assessment, the state-of-the-art IntaRNA prediction tool is applied. We investigate characteristics of successfully verified RNA-RNA interactions from the literature, which guided the design of CopomuS. Finally, we evaluate its performance based on experimentally validated compensatory mutations of prokaryotic sRNAs and their target mRNAs. CopomuS predictions highly agree with known results, making it a valuable tool to support the design of verification experiments for RNA-RNA interactions. It is part of the IntaRNA package and available as stand-alone webserver for ad hoc application.

Riboregulation in Nitrogen-Fixing Endosymbiotic Bacteria

Small non-coding RNAs (sRNAs) are ubiquitous components of bacterial adaptive regulatory networks underlying stress responses and chronic intracellular infection of eukaryotic hosts. Thus, sRNA-mediated regulation of gene expression is expected to play a major role in the establishment of mutualistic root nodule endosymbiosis between nitrogen-fixing rhizobia and legume plants. However, knowledge about this level of genetic regulation in this group of plant-interacting bacteria is still rather scarce. Here, we review insights into the rhizobial non-coding transcriptome and sRNA-mediated post-transcriptional regulation of symbiotic relevant traits such as nutrient uptake, cell cycle, quorum sensing, or nodule development. We provide details about the transcriptional control and protein-assisted activity mechanisms of the functionally characterized sRNAs involved in these processes. Finally, we discuss the forthcoming research on riboregulation in legume symbionts.

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.

Freiburg RNA tools: a central online resource for RNA-focused research and teaching

The Freiburg RNA tools webserver is a well established online resource for RNA-focused research. It provides a unified user interface and comprehensive result visualization for efficient command line tools. The webserver includes RNA-RNA interaction prediction (IntaRNA, CopraRNA, metaMIR), sRNA homology search (GLASSgo), sequence-structure alignments (LocARNA, MARNA, CARNA, ExpaRNA), CRISPR repeat classification (CRISPRmap), sequence design (antaRNA, INFO-RNA, SECISDesign), structure aberration evaluation of point mutations (RaSE), and RNA/protein-family models visualization (CMV), and other methods. Open education resources offer interactive visualizations of RNA structure and RNA-RNA interaction prediction as well as basic and advanced sequence alignment algorithms. The services are freely available at http://rna.informatik.uni-freiburg.de.

Structure and Interaction Prediction in Prokaryotic RNA Biology

Many years of research in RNA biology have soundly established the importance of RNA-based regulation far beyond most early traditional presumptions. Importantly, the advances in "wet" laboratory techniques have produced unprecedented amounts of data that require efficient and precise computational analysis schemes and algorithms. Hence, many in silico methods that attempt topological and functional classification of novel putative RNA-based regulators are available. In this review, we technically outline thermodynamics-based standard RNA secondary structure and RNA-RNA interaction prediction approaches that have proven valuable to the RNA research community in the past and present. For these, we highlight their usability with a special focus on prokaryotic organisms and also briefly mention recent advances in whole-genome interactomics and how this may influence the field of predictive RNA research.