Patrick R. Wright

CopraRNA and IntaRNA: predicting small RNA targets, networks and interaction domains

CopraRNA (Comparative prediction algorithm for small RNA targets) is the most recent asset to the Freiburg RNA Tools webserver. It incorporates and extends the functionality of the existing tool IntaRNA (Interacting RNAs) in order to predict targets, interaction domains and consequently the regulatory networks of bacterial small RNA molecules. The CopraRNA prediction results are accompanied by extensive postprocessing methods such as functional enrichment analysis and visualization of interacting regions. Here, we introduce the functionality of the CopraRNA and IntaRNA webservers and give detailed explanations on their postprocessing functionalities. Both tools are freely accessible at http://rna.informatik.uni-freiburg.de.

Comparative genomics boosts target prediction for bacterial small RNAs

Significance This study presents a unique approach (CopraRNA, for Comparative Prediction Algorithm for sRNA Targets) towards reliably predicting the targets of bacterial small regulatory RNAs (sRNAs). These molecules are important regulators of gene expression. Their detailed analysis thus far has been hampered by the lack of reliable algorithms to predict their mRNA targets. CopraRNA integrates phylogenetic information to predict sRNA targets at the genomic scale, reconstructs regulatory networks upon functional enrichment and network analysis, and predicts the sRNA domains for target recognition and interaction. Our results demonstrate that CopraRNA substantially improves the bioinformatic prediction of target genes and opens the field for the application to nonmodel bacteria. Small RNAs (sRNAs) constitute a large and heterogeneous class of bacterial gene expression regulators. Much like eukaryotic microRNAs, these sRNAs typically target multiple mRNAs through short seed pairing, thereby acting as global posttranscriptional regulators. In some bacteria, evidence for hundreds to possibly more than 1,000 different sRNAs has been obtained by transcriptome sequencing. However, the experimental identification of possible targets and, therefore, their confirmation as functional regulators of gene expression has remained laborious. Here, we present a strategy that integrates phylogenetic information to predict sRNA targets at the genomic scale and reconstructs regulatory networks upon functional enrichment and network analysis (CopraRNA, for Comparative Prediction Algorithm for sRNA Targets). Furthermore, CopraRNA precisely predicts the sRNA domains for target recognition and interaction. When applied to several model sRNAs, CopraRNA revealed additional targets and functions for the sRNAs CyaR, FnrS, RybB, RyhB, SgrS, and Spot42. Moreover, the mRNAs gdhA, lrp, marA, nagZ, ptsI, sdhA, and yobF-cspC were suggested as regulatory hubs targeted by up to seven different sRNAs. The verification of many previously undetected targets by CopraRNA, even for extensively investigated sRNAs, demonstrates its advantages and shows that CopraRNA-based analyses can compete with experimental target prediction approaches. A Web interface allows high-confidence target prediction and efficient classification of bacterial sRNAs.

Global RNA recognition patterns of post-transcriptional regulators Hfq and CsrA revealed by UV crosslinking in vivo

The molecular roles of many RNA‐binding proteins in bacterial post‐transcriptional gene regulation are not well understood. Approaches combining in vivo UV crosslinking with RNA deep sequencing (CLIP‐seq) have begun to revolutionize the transcriptome‐wide mapping of eukaryotic RNA‐binding protein target sites. We have applied CLIP‐seq to chart the target landscape of two major bacterial post‐transcriptional regulators, Hfq and CsrA, in the model pathogen Salmonella Typhimurium. By detecting binding sites at single‐nucleotide resolution, we identify RNA preferences and structural constraints of Hfq and CsrA during their interactions with hundreds of cellular transcripts. This reveals 3′‐located Rho‐independent terminators as a universal motif involved in Hfq–RNA interactions. Additionally, Hfq preferentially binds 5′ to sRNA‐target sites in mRNAs, and 3′ to seed sequences in sRNAs, reflecting a simple logic in how Hfq facilitates sRNA–mRNA interactions. Importantly, global knowledge of Hfq sites significantly improves sRNA‐target predictions. CsrA binds AUGGA sequences in apical loops and targets many Salmonella virulence mRNAs. Overall, our generic CLIP‐seq approach will bring new insights into post‐transcriptional gene regulation by RNA‐binding proteins in diverse bacterial species.

MOF-associated complexes ensure stem cell identity and Xist repression

Histone acetyl transferases (HATs) play distinct roles in many cellular processes and are frequently misregulated in cancers. Here, we study the regulatory potential of MYST1-(MOF)-containing MSL and NSL complexes in mouse embryonic stem cells (ESCs) and neuronal progenitors. We find that both complexes influence transcription by targeting promoters and TSS-distal enhancers. In contrast to flies, the MSL complex is not exclusively enriched on the X chromosome, yet it is crucial for mammalian X chromosome regulation as it specifically regulates Tsix, the major repressor of Xist lncRNA. MSL depletion leads to decreased Tsix expression, reduced REX1 recruitment, and consequently, enhanced accumulation of Xist and variable numbers of inactivated X chromosomes during early differentiation. The NSL complex provides additional, Tsix-independent repression of Xist by maintaining pluripotency. MSL and NSL complexes therefore act synergistically by using distinct pathways to ensure a fail-safe mechanism for the repression of X inactivation in ESCs. DOI: http://dx.doi.org/10.7554/eLife.02024.001

IntaRNA 2.0: enhanced and customizable prediction of RNA–RNA interactions

Abstract The IntaRNA algorithm enables fast and accurate prediction of RNA–RNA hybrids by incorporating seed constraints and interaction site accessibility. Here, we introduce IntaRNAv2, which enables enhanced parameterization as well as fully customizable control over the prediction modes and output formats. Based on up to date benchmark data, the enhanced predictive quality is shown and further improvements due to more restrictive seed constraints are highlighted. The extended web interface provides visualizations of the new minimal energy profiles for RNA–RNA interactions. These allow a detailed investigation of interaction alternatives and can reveal potential interaction site multiplicity. IntaRNAv2 is freely available (source and binary), and distributed via the conda package manager. Furthermore, it has been included into the Galaxy workflow framework and its already established web interface enables ad hoc usage.

A Stress-Induced Small RNA Modulates Alpha-Rhizobial Cell Cycle Progression

Mechanisms adjusting replication initiation and cell cycle progression in response to environmental conditions are crucial for microbial survival. Functional characterization of the trans-encoded small non-coding RNA (trans-sRNA) EcpR1 in the plant-symbiotic alpha-proteobacterium Sinorhizobium meliloti revealed a role of this class of riboregulators in modulation of cell cycle regulation. EcpR1 is broadly conserved in at least five families of the Rhizobiales and is predicted to form a stable structure with two defined stem-loop domains. In S. meliloti, this trans-sRNA is encoded downstream of the divK-pleD operon. ecpR1 belongs to the stringent response regulon, and its expression was induced by various stress factors and in stationary phase. Induced EcpR1 overproduction led to cell elongation and increased DNA content, while deletion of ecpR1 resulted in reduced competitiveness. Computationally predicted EcpR1 targets were enriched with cell cycle-related mRNAs. Post-transcriptional repression of the cell cycle key regulatory genes gcrA and dnaA mediated by mRNA base-pairing with the strongly conserved loop 1 of EcpR1 was experimentally confirmed by two-plasmid differential gene expression assays and compensatory changes in sRNA and mRNA. Evidence is presented for EcpR1 promoting RNase E-dependent degradation of the dnaA mRNA. We propose that EcpR1 contributes to modulation of cell cycle regulation under detrimental conditions.

Two separate modules of the conserved regulatory RNA AbcR1 address multiple target mRNAs in and outside of the translation initiation region

The small RNA AbcR1 regulates the expression of ABC transporters in the plant pathogen Agrobacterium tumefaciens, the plant symbiont Sinorhizobium meliloti, and the human pathogen Brucella abortus. A combination of proteomic and bioinformatic approaches suggested dozens of AbcR1 targets in A. tumefaciens. Several of these newly discovered targets are involved in the uptake of amino acids, their derivatives, and sugars. Among the latter is the periplasmic sugar-binding protein ChvE, a component of the virulence signal transduction system. We examined 16 targets and their interaction with AbcR1 in close detail. In addition to the previously described mRNA interaction site of AbcR1 (M1), the CopraRNA program predicted a second functional module (M2) as target-binding site. Both M1 and M2 contain single-stranded anti-SD motifs. Using mutated AbcR1 variants, we systematically tested by band shift experiments, which sRNA region is responsible for mRNA binding and gene regulation. On the target site, we find that AbcR1 interacts with some mRNAs in the translation initiation region and with others far into their coding sequence. Our data show that AbcR1 is a versatile master regulator of nutrient uptake systems in A. tumefaciens and related bacteria.

Photorhabdus-nematode symbiosis is dependent on hfq-mediated regulation of secondary metabolites.

Photorhabdus luminescens maintains a symbiotic relationship with the nematodes Heterorhabditis bacteriophora and together they infect and kill insect larvae. To maintain this symbiotic relationship, the bacteria must produce an array of secondary metabolites to assist in the development and replication of nematodes. The regulatory mechanisms surrounding production of these compounds are mostly unknown. The global post-transcriptional regulator, Hfq, is widespread in bacteria and performs many functions, one of which is the facilitation of sRNA binding to target mRNAs, with recent research thoroughly exploring its various pleiotropic effects. Here we generate and characterize an hfq deletion mutant and show that in the absence of hfq, the bacteria are no longer able to maintain a healthy symbiosis with nematodes due to the abolishment of the production of all known secondary metabolites. RNAseq led us to produce a second deletion of a known repressor, HexA, in the same strain, which restored both metabolite production and symbiosis.

Differentiation of ncRNAs from small mRNAs in Escherichia coli O157:H7 EDL933 (EHEC) by combined RNAseq and RIBOseq – ryhB encodes the regulatory RNA RyhB and a peptide, RyhP

BackgroundWhile NGS allows rapid global detection of transcripts, it remains difficult to distinguish ncRNAs from short mRNAs. To detect potentially translated RNAs, we developed an improved protocol for bacterial ribosomal footprinting (RIBOseq). This allowed distinguishing ncRNA from mRNA in EHEC. A high ratio of ribosomal footprints per transcript (ribosomal coverage value, RCV) is expected to indicate a translated RNA, while a low RCV should point to a non-translated RNA.ResultsBased on their low RCV, 150 novel non-translated EHEC transcripts were identified as putative ncRNAs, representing both antisense and intergenic transcripts, 74 of which had expressed homologs in E. coli MG1655. Bioinformatics analysis predicted statistically significant target regulons for 15 of the intergenic transcripts; experimental analysis revealed 4-fold or higher differential expression of 46 novel ncRNA in different growth media. Out of 329 annotated EHEC ncRNAs, 52 showed an RCV similar to protein-coding genes, of those, 16 had RIBOseq patterns matching annotated genes in other enterobacteriaceae, and 11 seem to possess a Shine-Dalgarno sequence, suggesting that such ncRNAs may encode small proteins instead of being solely non-coding. To support that the RIBOseq signals are reflecting translation, we tested the ribosomal-footprint covered ORF of ryhB and found a phenotype for the encoded peptide in iron-limiting condition.ConclusionDetermination of the RCV is a useful approach for a rapid first-step differentiation between bacterial ncRNAs and small mRNAs. Further, many known ncRNAs may encode proteins as well.

Computational analysis of CLIP-seq data

CLIP-seq experiments are currently the most important means for determining the binding sites of RNA binding proteins on a genome-wide level. The computational analysis can be divided into three steps. In the first pre-processing stage, raw reads have to be trimmed and mapped to the genome. This step has to be specifically adapted for each CLIP-seq protocol. The next step is peak calling, which is required to remove unspecific signals and to determine bona fide protein binding sites on target RNAs. Here, both protocol-specific approaches as well as generic peak callers are available. Despite some peak callers being more widely used, each peak caller has its specific assets and drawbacks, and it might be advantageous to compare the results of several methods. Although peak calling is often the final step in many CLIP-seq publications, an important follow-up task is the determination of binding models from CLIP-seq data. This is central because CLIP-seq experiments are highly dependent on the transcriptional state of the cell in which the experiment was performed. Thus, relying solely on binding sites determined by CLIP-seq from different cells or conditions can lead to a high false negative rate. This shortcoming can, however, be circumvented by applying models that predict additional putative binding sites.