RNA interactomics: recent advances and remaining challenges

ChimericFragments: computation, analysis and visualization of global RNA networks

RNA-RNA interactions are a key feature of post-transcriptional gene regulation in all domains of life. While ever more experimental protocols are being developed to study RNA duplex formation on a genome-wide scale, computational methods for the analysis and interpretation of the underlying data are lagging behind. Here, we present ChimericFragments, an analysis framework for RNA-seq experiments that produce chimeric RNA molecules. ChimericFragments implements a novel statistical method based on the complementarity of the base-pairing RNAs around their ligation site and provides an interactive graph-based visualization for data exploration and interpretation. ChimericFragments detects true RNA-RNA interactions with high precision and is compatible with several widely used experimental procedures such as RIL-seq, LIGR-seq or CLASH. We further demonstrate that ChimericFragments enables the systematic detection of novel RNA regulators and RNA-target pairs with crucial roles in microbial physiology and virulence. ChimericFragments is written in Julia and available at: https://github.com/maltesie/ChimericFragments.

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.

RNAcontacts: A Pipeline for Predicting Contacts from RNA Proximity Ligation Assays

High-throughput RNA proximity ligation assays are molecular methods that are used to simultaneously analyze the spatial proximity of many RNAs in living cells. Their principle is based on cross-linking, fragmentation, and subsequent religation of RNAs, followed by high-throughput sequencing. The generated fragments have two different types of splits, one resulting from pre-mRNA splicing and the other formed by the ligation of spatially close RNA strands. Here, we present RNAcontacts, a universal pipeline for detecting RNA-RNA contacts in high-throughput RNA proximity ligation assays. RNAcontacts circumvents the inherent problem of mapping sequences with two distinct types of splits using a two-pass alignment, in which splice junctions are inferred from a control RNA-seq experiment on the first pass and then provided to the aligner as bona fide introns on the second pass. Compared to previously developed methods, our approach allows for a more sensitive detection of RNA contacts and has a higher specificity with respect to splice junctions that are present in the biological sample. RNAcontacts automatically extracts contacts, clusters their ligation points, computes the read support, and generates tracks for visualizing through the UCSC Genome Browser. The pipeline is implemented in Snakemake, a reproducible and scalable workflow management system for rapid and uniform processing of multiple datasets. RNAcontacts is a generic pipeline for the detection of RNA contacts that can be used with any proximity ligation method as long as one of the interacting partners is RNA. RNAcontacts is available via the GitHub repository https://github.com/smargasyuk/ RNAcontacts/.

Bioinformatic and experimental methods to identify and validate bacterial RNA-human RNA interactions

Ample evidence supports the importance of the microbiota on human health and disease. Recent studies suggest that extracellular vesicles are an important means of bacterial-host communication, in part via the transport of small RNAs (sRNAs). Bacterial sRNAs have been shown to co-precipitate with human and mouse RNA-induced silencing complex, hinting that some may regulate gene expression as eukaryotic microRNAs do. Bioinformatic tools, including those that can incorporate an sRNA's secondary structure, can be used to predict interactions between bacterial sRNAs and human messenger RNAs (mRNAs). Validation of these potential interactions using reproducible experimental methods is essential to move the field forward. This review will cover the evidence of interspecies communication via sRNAs, bioinformatic tools currently available to identify potential bacterial sRNA-host (specifically, human) mRNA interactions, and experimental methods to identify and validate those interactions.

The snoGloBe interaction predictor reveals a broad spectrum of C/D snoRNA RNA targets

Box C/D small nucleolar RNAs (snoRNAs) are a conserved class of RNA known for their role in guiding ribosomal RNA 2'-O-ribose methylation. Recently, C/D snoRNAs were also implicated in regulating the expression of non-ribosomal genes through different modes of binding. Large scale RNA-RNA interaction datasets detect many snoRNAs binding messenger RNA, but are limited by specific experimental conditions. To enable a more comprehensive study of C/D snoRNA interactions, we created snoGloBe, a human C/D snoRNA interaction predictor based on a gradient boosting classifier. SnoGloBe considers the target type, position and sequence of the interactions, enabling it to outperform existing predictors. Interestingly, for specific snoRNAs, snoGloBe identifies strong enrichment of interactions near gene expression regulatory elements including splice sites. Abundance and splicing of predicted targets were altered upon the knockdown of their associated snoRNA. Strikingly, the predicted snoRNA interactions often overlap with the binding sites of functionally related RNA binding proteins, reinforcing their role in gene expression regulation. SnoGloBe is also an excellent tool for discovering viral RNA targets, as shown by its capacity to identify snoRNAs targeting the heavily methylated SARS-CoV-2 RNA. Overall, snoGloBe is capable of identifying experimentally validated binding sites and predicting novel sites with shared regulatory function.

The Emerging Role of Long Non-Coding RNAs in Development and Function of Gilthead Sea Bream (Sparus aurata) Fast Skeletal Muscle

Long non-coding RNAs (lncRNAs) are an emerging group of ncRNAs that can modulate gene expression at the transcriptional or translational levels. In the present work, previously published transcriptomic data were used to identify lncRNAs expressed in gilthead sea bream skeletal muscle, and their transcription levels were studied under different physiological conditions. Two hundred and ninety lncRNAs were identified and, based on transcriptomic differences between juveniles and adults, a total of seven lncRNAs showed potential to be important for muscle development. Our data suggest that the downregulation of most of the studied lncRNAs might be linked to increased myoblast proliferation, while their upregulation might be necessary for differentiation. However, with these data, as it is not possible to propose a formal mechanism to explain their effect, bioinformatic analysis suggests two possible mechanisms. First, the lncRNAs may act as sponges of myoblast proliferation inducers microRNAs (miRNAs) such as miR-206, miR-208, and miR-133 (binding energy MEF < -25.0 kcal). Secondly, lncRNA20194 had a strong predicted interaction towards the myod1 mRNA (ndG = -0.17) that, based on the positive correlation between the two genes, might promote its function. Our study represents the first characterization of lncRNAs in gilthead sea bream fast skeletal muscle and provides evidence regarding their involvement in muscle development.

Identifying proximal RNA interactions from cDNA-encoded crosslinks with ShapeJumper

SHAPE-JuMP is a concise strategy for identifying close-in-space interactions in RNA molecules. Nucleotides in close three-dimensional proximity are crosslinked with a bi-reactive reagent that covalently links the 2'-hydroxyl groups of the ribose moieties. The identities of crosslinked nucleotides are determined using an engineered reverse transcriptase that jumps across crosslinked sites, resulting in a deletion in the cDNA that is detected using massively parallel sequencing. Here we introduce ShapeJumper, a bioinformatics pipeline to process SHAPE-JuMP sequencing data and to accurately identify through-space interactions, as observed in complex JuMP datasets. ShapeJumper identifies proximal interactions with near-nucleotide resolution using an alignment strategy that is optimized to tolerate the unique non-templated reverse-transcription profile of the engineered crosslink-traversing reverse-transcriptase. JuMP-inspired strategies are now poised to replace adapter-ligation for detecting RNA-RNA interactions in most crosslinking experiments.

Bioinformatic Tools for the Analysis and Prediction of ncRNA Interactions

Noncoding RNAs (ncRNAs) play prominent roles in the regulation of gene expression via their interactions with other biological molecules such as proteins and nucleic acids. Although much of our knowledge about how these ncRNAs operate in different biological processes has been obtained from experimental findings, computational biology can also clearly substantially boost this knowledge by suggesting possible novel interactions of these ncRNAs with other molecules. Computational predictions are thus used as an alternative source of new insights through a process of mutual enrichment because the information obtained through experiments continuously feeds through into computational methods. The results of these predictions in turn shed light on possible interactions that are subsequently validated experimentally. This review describes the latest advances in databases, bioinformatic tools, and new in silico strategies that allow the establishment or prediction of biological interactions of ncRNAs, particularly miRNAs and lncRNAs. The ncRNA species described in this work have a special emphasis on those found in humans, but information on ncRNA of other species is also included.

Reprogramming Gene Expression by Targeting RNA-Based Interactions: A Novel Pipeline Utilizing RNA Array Technology

Regulatory RNA-based interactions are critical for coordinating gene expression and are increasingly being targeted in synthetic biology, antimicrobial, and therapeutic fields. Bacterial trans-encoded small RNAs (sRNAs) regulate the translation and/or stability of mRNA targets through base-pairing interactions. These interactions are often integral to complex gene circuits which coordinate critical bacterial processes. The ability to predictably modulate these gene circuits has potential for reprogramming gene expression for synthetic biology and antibacterial purposes. Here, we present a novel pipeline for targeting such RNA-based interactions with antisense oligonucleotides (ASOs) in order to reprogram gene expression. As proof-of-concept, we selected sRNA-mRNA interactions that are central to the Vibrio cholerae quorum sensing pathway, required for V. cholerae pathogenesis, as a regulatory RNA-based interaction input. We rationally designed anti-sRNA ASOs to target the sRNAs and synthesized them as peptide nucleic acids (PNAs). Next, we devised an RNA array-based interaction assay to allow screening of the anti-sRNA ASOs in vitro. Finally, an Escherichia coli-based gene expression reporter assay was developed and used to validate anti-sRNA ASO regulatory activity in a cellular environment. The output from the pipeline was an anti-sRNA ASO that targets sRNAs to inhibit sRNA-mRNA interactions and modulate gene expression. This anti-sRNA ASO has potential for reprogramming gene expression for synthetic biology and/or antibacterial purposes. We anticipate that this pipeline will find widespread use in fields targeting RNA-based interactions as modulators of gene expression.

RNAnue: efficient data analysis for RNA-RNA interactomics

RNA–RNA inter- and intramolecular interactions are fundamental for numerous biological processes. While there are reasonable approaches to map RNA secondary structures genome-wide, understanding how different RNAs interact to carry out their regulatory functions requires mapping of intermolecular base pairs. Recently, different strategies to detect RNA–RNA duplexes in living cells, so called direct duplex detection (DDD) methods, have been developed. Common to all is the Psoralen-mediated in vivo RNA crosslinking followed by RNA Proximity Ligation to join the two interacting RNA strands. Sequencing of the RNA via classical RNA-seq and subsequent specialised bioinformatic analyses the result in the prediction of inter- and intramolecular RNA–RNA interactions. Existing approaches adapt standard RNA-seq analysis pipelines, but often neglect inherent features of RNA–RNA interactions that are useful for filtering and statistical assessment. Here we present RNAnue, a general pipeline for the inference of RNA–RNA interactions from DDD experiments that takes into account hybridisation potential and statistical significance to improve prediction accuracy. We applied RNAnue to data from different DDD studies and compared our results to those of the original methods. This showed that RNAnue performs better in terms of quantity and quality of predictions.

Navigation through the twists and turns of RNA sequencing technologies: Application to bacterial regulatory RNAs

Discovered in the 1980s, small regulatory RNAs (sRNAs) are now considered key actors in virtually all aspects of bacterial physiology and virulence. Together with transcriptional and translational regulatory proteins, they integrate and often are hubs of complex regulatory networks, responsible for bacterial response/adaptation to various perceived stimuli. The recent development of powerful RNA sequencing technologies has facilitated the identification and characterization of sRNAs (length, structure and expression conditions) and their RNA targets in several bacteria. Nevertheless, it could be very difficult for non-experts to understand the advantages and drawbacks related to each offered option and, consequently, to make an informed choice. Therefore, the main goal of this review is to provide a guide to navigate through the twists and turns of high-throughput RNA sequencing technologies, with a specific focus on those applied to the study of sRNAs. This article is part of a Special Issue entitled: RNA and gene control in bacteria edited by Dr. M. Guillier and F. Repoila.

Bioinformatic Application of Fluorescence-Based In Vivo RNA Regional Accessibility Data to Identify Novel sRNA Targets

Data from fluorescence-based methods that measure in vivo hybridization efficacy of unique RNA regions can be used to infer regulatory activity and to identify novel RNA: RNA interactions. Here, we document the step-by-step analysis of fluorescence data collected using an in vivo regional RNA structural sensing system (iRS³) for the purpose of identifying potential functional sites that are likely to be involved in regulatory interactions. We also detail a step-by-step protocol that couples this in vivo accessibility data with computational mRNA target predictions to inform the selection of potentially true targets from long lists of thermodynamic predictions.