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

CheRRI-Accurate classification of the biological relevance of putative RNA-RNA interaction sites

BACKGROUND

RNA-RNA interactions are key to a wide range of cellular functions. The detection of potential interactions helps to understand the underlying processes. However, potential interactions identified via in silico or experimental high-throughput methods can lack precision because of a high false-positive rate.

RESULTS

We present CheRRI, the first tool to evaluate the biological relevance of putative RNA-RNA interaction sites. CheRRI filters candidates via a machine learning-based model trained on experimental RNA-RNA interactome data. Its unique setup combines interactome data and an established thermodynamic prediction tool to integrate experimental data with state-of-the-art computational models. Applying these data to an automated machine learning approach provides the opportunity to not only filter data for potential false positives but also tailor the underlying interaction site model to specific needs.

CONCLUSIONS

CheRRI is a stand-alone postprocessing tool to filter either predicted or experimentally identified potential RNA-RNA interactions on a genomic level to enhance the quality of interaction candidates. It is easy to install (via conda, pip packages), use (via Galaxy), and integrate into existing RNA-RNA interaction pipelines.

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.

A hybrid approach to assess the structural impact of long noncoding RNA mutations uncovers key NEAT1 interactions in colorectal cancer

Long noncoding RNAs (lncRNAs) are emerging players in cancer and they entail potential as prognostic biomarkers or therapeutic targets. Earlier studies have identified somatic mutations in lncRNAs that are associated with tumor relapse after therapy, but the underlying mechanisms behind these associations remain unknown. Given the relevance of secondary structure for the function of some lncRNAs, some of these mutations may have a functional impact through structural disturbance. Here, we examined the potential structural and functional impact of a novel A > G point mutation in NEAT1 that has been recurrently observed in tumors of colorectal cancer patients experiencing relapse after treatment. Here, we used the nextPARS structural probing approach to provide first empirical evidence that this mutation alters NEAT1 structure. We further evaluated the potential effects of this structural alteration using computational tools and found that this mutation likely alters the binding propensities of several NEAT1-interacting miRNAs. Differential expression analysis on these miRNA networks shows upregulation of Vimentin, consistent with previous findings. We propose a hybrid pipeline that can be used to explore the potential functional effects of lncRNA somatic mutations.

Posttranscriptional Regulation in Response to Different Environmental Stresses in Campylobacter jejuni

The survival strategies that Campylobacter jejuni (C. jejuni) employ throughout its transmission and infection life cycles remain largely elusive. Specifically, there is a lack of understanding about the posttranscriptional regulation of stress adaptations resulting from small noncoding RNAs (sRNAs). Published C. jejuni sRNAs have been discovered in specific conditions but with limited insights into their biological activities. Many more sRNAs are yet to be discovered as they may be condition-dependent. Here, we have generated transcriptomic data from 21 host- and transmission-relevant conditions. The data uncovered transcription start sites, expression patterns and posttranscriptional regulation during various stress conditions. This data set helped predict a list of putative sRNAs. We further explored the sRNAs' biological functions by integrating differential gene expression analysis, coexpression analysis, and genome-wide sRNA target prediction. The results showed that the C. jejuni gene expression was influenced primarily by nutrient deprivation and food storage conditions. Further exploration revealed a putative sRNA (CjSA21) that targeted tlp1 to 4 under food processing conditions. tlp1 to 4 are transcripts that encode methyl-accepting chemotaxis proteins (MCPs), which are responsible for chemosensing. These results suggested CjSA21 inhibits chemotaxis and promotes survival under food processing conditions. This study presents the broader research community with a comprehensive data set and highlights a novel sRNA as a potential chemotaxis inhibitor. IMPORTANCE The foodborne pathogen C. jejuni is a significant challenge for the global health care system. It is crucial to investigate C. jejuni posttranscriptional regulation by small RNAs (sRNAs) in order to understand how it adapts to different stress conditions. However, limited data are available for investigating sRNA activity under stress. In this study, we generate gene expression data of C. jejuni under 21 stress conditions. Our data analysis indicates that one of the novel sRNAs mediates the adaptation to food processing conditions. Results from our work shed light on the posttranscriptional regulation of C. jejuni and identify an sRNA associated with food safety.

sRNARFTarget: a fast machine-learning-based approach for transcriptome-wide sRNA target prediction

Bacterial small regulatory RNAs (sRNAs) are key regulators of gene expression in many processes related to adaptive responses. A multitude of sRNAs have been identified in many bacterial species; however, their function has yet to be elucidated. A key step to understand sRNAs function is to identify the mRNAs these sRNAs bind to. There are several computational methods for sRNA target prediction, and the most accurate one is CopraRNA which is based on comparative-genomics. However, species-specific sRNAs are quite common and CopraRNA cannot be used for these sRNAs. The most commonly used transcriptome-wide sRNA target prediction method and second-most-accurate method is IntaRNA. However, IntaRNA can take hours to run on a bacterial transcriptome. Here we present sRNARFTarget, a machine-learning-based method for transcriptome-wide sRNA target prediction applicable to any sRNA. We comparatively assessed the performance of sRNARFTarget, CopraRNA and IntaRNA in three bacterial species. Our results show that sRNARFTarget outperforms IntaRNA in terms of accuracy, ranking of true interacting pairs, and running time. However, CopraRNA substantially outperforms the other two programsin terms of accuracy. Thus, we suggest using CopraRNA when homolog sequences of the sRNA are available, and sRNARFTarget for transcriptome-wide prediction or for species-specific sRNAs. sRNARFTarget is available at https://github.com/BioinformaticsLabAtMUN/sRNARFTarget.

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.