Zooming in on protein-RNA interactions: a multi-level workflow to identify interaction partners

Prediction of protein-RNA interactions from single-cell transcriptomic data

Proteins are crucial in regulating every aspect of RNA life, yet understanding their interactions with coding and noncoding RNAs remains limited. Experimental studies are typically restricted to a small number of cell lines and a limited set of RNA-binding proteins (RBPs). Although computational methods based on physico-chemical principles can predict protein-RNA interactions accurately, they often lack the ability to consider cell-type-specific gene expression and the broader context of gene regulatory networks (GRNs). Here, we assess the performance of several GRN inference algorithms in predicting protein-RNA interactions from single-cell transcriptomic data, and propose a pipeline, called scRAPID (single-cell transcriptomic-based RnA Protein Interaction Detection), that integrates these methods with the catRAPID algorithm, which can identify direct physical interactions between RBPs and RNA molecules. Our approach demonstrates that RBP-RNA interactions can be predicted from single-cell transcriptomic data, with performances comparable or superior to those achieved for the well-established task of inferring transcription factor-target interactions. The incorporation of catRAPID significantly enhances the accuracy of identifying interactions, particularly with long noncoding RNAs, and enables the identification of hub RBPs and RNAs. Additionally, we show that interactions between RBPs can be detected based on their inferred RNA targets. The software is freely available at https://github.com/tartaglialabIIT/scRAPID.

Preparation of Cardiac Extracts from Embryonal Hearts to Capture RNA-protein Interactions by CLIP

The interaction of RNA with specific RNA-binding proteins (RBP) leads to the establishment of complex regulatory networks through which gene expression is controlled. Careful consideration should be given to the exact environment where a given RNA/RBP interplay occurs, as the functional responses might depend on the type of organism as well as the specific cellular or subcellular contexts. This requisite becomes particularly crucial for the study of long non-coding RNAs (lncRNA), as a consequence of their peculiar tissue-specificity and timely regulated expression. The functional characterization of lncRNAs has traditionally relied on the use of established cell lines that, although useful, are unable to fully recapitulate the complexity of a tissue or organ. Here, we detail an optimized protocol, with comments and tips, to identify the RNA interactome of given RBPs by performing cross-linking immunoprecipitation (CLIP) from mouse embryonal hearts. We tested the efficiency of this protocol on the murine pCharme, a muscle-specific lncRNA interacting with Matrin3 (MATR3) and forming RNA-enriched condensates of biological significance in the nucleus. Key features • The protocol refines previous methods of cardiac extracts preparation to use for CLIP assays. • The protocol allows the quantitative RNA-seq analysis of transcripts interacting with selected proteins. • Depending on the embryonal stage, a high number of hearts can be required as starting material. • The steps are adaptable to other tissues and biochemical assays.

Exploring the landscape of tools and resources for the analysis of long non-coding RNAs

In recent years, research on long non-coding RNAs (lncRNAs) has gained considerable attention due to the increasing number of newly identified transcripts. Several characteristics make their functional evaluation challenging, which called for the urgent need to combine molecular biology with other disciplines, including bioinformatics. Indeed, the recent development of computational pipelines and resources has greatly facilitated both the discovery and the mechanisms of action of lncRNAs. In this review, we present a curated collection of the most recent computational resources, which have been categorized into distinct groups: databases and annotation, identification and classification, interaction prediction, and structure prediction. As the repertoire of lncRNAs and their analysis tools continues to expand over the years, standardizing the computational pipelines and improving the existing annotation of lncRNAs will be crucial to facilitate functional genomics studies.

A high-throughput approach to predict A-to-I effects on RNA structure indicates a change of double-stranded content in non-coding RNAs

RNA molecules undergo a number of chemical modifications whose effects can alter their structure and molecular interactions. Previous studies have shown that RNA editing can impact the formation of ribonucleoprotein complexes and influence the assembly of membrane-less organelles such as stress-granules. For instance, N6-methyladenosine (m6A) enhances SG formation and N1-methyladenosine (m1A) prevents their transition to solid-like aggregates. Yet, very little is known about adenosine to inosine (A-to-I) modification that is very abundant in human cells and not only impacts mRNAs but also non-coding RNAs. Here, we built the CROSSalive predictor of A-to-I effects on RNA structure based on high-throughput in-cell experiments. Our method shows an accuracy of 90% in predicting the single and double-stranded content of transcripts and identifies a general enrichment of double-stranded regions caused by A-to-I in long intergenic non-coding RNAs (lincRNAs). For the individual cases of NEAT1, NORAD and XIST, we investigated the relationship between A-to-I editing and interactions with RNA-binding proteins using available CLIP data and catRAPID predictions. We found that A-to-I editing is linked to alteration of interaction sites with proteins involved in phase-separation, which suggests that RNP assembly can be influenced by A-to-I. CROSSalive is available at http://service.tartaglialab.com/new_submission/crossalive. This article is protected by copyright. All rights reserved.

Environmentally sensitive fluorescent nucleoside analogues as probes for nucleic acid - protein interactions: molecular design and biosensing applications

Fluorescent nucleoside analogues (FNAs) are indispensable in studying the interactions of nucleic acids with nucleic acid-binding proteins. By replacing one of the poorly emissive natural nucleosides, FNAs enable real-time optical monitoring of the binding interactions in solutions, under physiologically relevant conditions, with high sensitivity. Besides that, FNAs are widely used to probe conformational dynamics of biomolecular complexes using time-resolved fluorescence methods. Because of that, FNAs are tools of high utility for fundamental biological research, with potential applications in molecular diagnostics and drug discovery. Here I review the structural and physical factors that can be used for the conversion of the molecular binding events into a detectable fluorescence output. Typical environmentally sensitive FNAs, their properties and applications, and future challenges in the field are discussed.

Towards an Ideal In Cell Hybridization-Based Strategy to Discover Protein Interactomes of Selected RNA Molecules

RNA-binding proteins are crucial to the function of coding and non-coding RNAs. The disruption of RNA–protein interactions is involved in many different pathological states. Several computational and experimental strategies have been developed to identify protein binders of selected RNA molecules. Amongst these, ‘in cell’ hybridization methods represent the gold standard in the field because they are designed to reveal the proteins bound to specific RNAs in a cellular context. Here, we compare the technical features of different ‘in cell’ hybridization approaches with a focus on their advantages, limitations, and current and potential future applications.

Roles of Emerging RNA-Binding Activity of cGAS in Innate Antiviral Response

cGAS, a DNA sensor in mammalian cells, catalyzes the generation of 2'-3'-cyclic AMP-GMP (cGAMP) once activated by the binding of free DNA. cGAMP can bind to STING, activating downstream TBK1-IRF-3 signaling to initiate the expression of type I interferons. Although cGAS has been considered a traditional DNA-binding protein, several lines of evidence suggest that cGAS is a potential RNA-binding protein (RBP), which is mainly supported by its interactions with RNAs, RBP partners, RNA/cGAS-phase-separations as well as its structural similarity with the dsRNA recognition receptor 2'-5' oligoadenylate synthase. Moreover, two influential studies reported that the cGAS-like receptors (cGLRs) of fly Drosophila melanogaster sense RNA and control 3'-2'-cGAMP signaling. In this review, we summarize and discuss in depth recent studies that identified or implied cGAS as an RBP. We also comprehensively summarized current experimental methods and computational tools that can identify or predict RNAs that bind to cGAS. Based on these discussions, we appeal that the RNA-binding activity of cGAS cannot be ignored in the cGAS-mediated innate antiviral response. It will be important to identify RNAs that can bind and regulate the activity of cGAS in cells with or without virus infection. Our review provides novel insight into the regulation of cGAS by its RNA-binding activity and extends beyond its DNA-binding activity. Our review would be significant for understanding the precise modulation of cGAS activity, providing the foundation for the future development of drugs against cGAS-triggering autoimmune diseases such as Aicardi-Gourtières syndrome.

The Interplay Between Disordered Regions in RNAs and Proteins Modulates Interactions Within Stress Granules and Processing Bodies

Condensation, or liquid-like phase separation, is a phenomenon indispensable for the spatiotemporal regulation of molecules within the cell. Recent studies indicate that the composition and molecular organization of phase-separated organelles such as Stress Granules (SGs) and Processing Bodies (PBs) are highly variable and dynamic. A dense contact network involving both RNAs and proteins controls the formation of SGs and PBs and an intricate molecular architecture, at present poorly understood, guarantees that these assemblies sense and adapt to different stresses and environmental changes. Here, we investigated the physico-chemical properties of SGs and PBs components and studied the architecture of their interaction networks. We found that proteins and RNAs establishing the largest amount of contacts in SGs and PBs have distinct properties and intrinsic disorder is enriched in all protein-RNA, protein-protein and RNA-RNA interaction networks. The increase of disorder in proteins is accompanied by an enrichment in single-stranded regions of RNA binding partners. Our results suggest that SGs and PBs quickly assemble and disassemble through dynamic contacts modulated by unfolded domains of their components.