RNA-protein interactions: Central players in coordination of regulatory networks

RNA: The Unsuspected Conductor in the Orchestra of Macromolecular Crowding

This comprehensive Review delves into the chemical principles governing RNA-mediated crowding events, commonly referred to as granules or biological condensates. We explore the pivotal role played by RNA sequence, structure, and chemical modifications in these processes, uncovering their correlation with crowding phenomena under physiological conditions. Additionally, we investigate instances where crowding deviates from its intended function, leading to pathological consequences. By deepening our understanding of the delicate balance that governs molecular crowding driven by RNA and its implications for cellular homeostasis, we aim to shed light on this intriguing area of research. Our exploration extends to the methodologies employed to decipher the composition and structural intricacies of RNA granules, offering a comprehensive overview of the techniques used to characterize them, including relevant computational approaches. Through two detailed examples highlighting the significance of noncoding RNAs, NEAT1 and XIST, in the formation of phase-separated assemblies and their influence on the cellular landscape, we emphasize their crucial role in cellular organization and function. By elucidating the chemical underpinnings of RNA-mediated molecular crowding, investigating the role of modifications, structures, and composition of RNA granules, and exploring both physiological and aberrant phase separation phenomena, this Review provides a multifaceted understanding of the intriguing world of RNA-mediated biological condensates.

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.

RNA-dependent proteome solubility maintenance in Escherichia coli lysates analysed by quantitative mass spectrometry: Proteomic characterization in terms of isoelectric point, structural disorder, functional hub, and chaperone network

Protein aggregation, a consequence of misfolding and impaired proteostasis, can lead to cellular malfunctions such as various proteinopathies. The mechanisms protecting proteins from aggregation in complex cellular environments have long been investigated, often from a protein-centric viewpoint. However, our study provides insights into a crucial, yet overlooked actor: RNA. We found that depleting RNAs from Escherichia coli lysates induces global protein aggregation. Our quantitative mass spectrometry analysis identified over 900 statistically significant proteins from the Escherichia coli proteome whose solubility depends on RNAs. Proteome-wide characterization showed that the RNA dependency is particularly enriched among acidic proteins, intrinsically disordered proteins, and structural hub proteins. Moreover, we observed distinct differences in RNA-binding mode and Gene Ontology categories between RNA-dependent acidic and basic proteins. Notably, the solubility of key molecular chaperones [Trigger factor, DnaJ, and GroES] is largely dependent on RNAs, suggesting a yet-to-be-explored hierarchical relationship between RNA-based chaperone (termed as chaperna) and protein-based chaperones, both of which constitute the whole chaperone network. These findings provide new insights into the RNA-centric role in maintaining healthy proteome solubility in vivo, where proteins associate with a variety of RNAs, either stably or transiently.

Unveiling the role of PUS7-mediated pseudouridylation in host protein interactions specific for the SARS-CoV-2 RNA genome

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a positive single-stranded RNA virus, engages in complex interactions with host cell proteins throughout its life cycle. While these interactions enable the host to recognize and inhibit viral replication, they also facilitate essential viral processes such as transcription, translation, and replication. Many aspects of these virus-host interactions remain poorly understood. Here, we employed the catRAPID algorithm and utilized the RNA-protein interaction detection coupled with mass spectrometry technology to predict and validate the host proteins that specifically bind to the highly structured 5' and 3' terminal regions of the SARS-CoV-2 RNA. Among the interactions identified, we prioritized pseudouridine synthase PUS7, which binds to both ends of the viral RNA. Using nanopore direct RNA sequencing, we discovered that the viral RNA undergoes extensive post-transcriptional modifications. Modified consensus regions for PUS7 were identified at both terminal regions of the SARS-CoV-2 RNA, including one in the viral transcription regulatory sequence leader. Collectively, our findings offer insights into host protein interactions with the SARS-CoV-2 UTRs and highlight the likely significance of pseudouridine synthases and other post-transcriptional modifications in the viral life cycle. This new knowledge enhances our understanding of virus-host dynamics and could inform the development of targeted therapeutic strategies.

A Strategy Utilizing Protein-Protein Interaction Hubs for the Treatment of Cancer Diseases

We describe a strategy for the development of a rational approach of neoplastic disease therapy based on the demonstration that scale-free networks are susceptible to specific attacks directed against its connective hubs. This strategy involves the (i) selection of up-regulated hubs of connectivity in the tumors interactome, (ii) drug repurposing of these hubs, (iii) RNA silencing of non-druggable hubs, (iv) in vitro hub validation, (v) tumor-on-a-chip, (vi) in vivo validation, and (vii) clinical trial. Hubs are protein targets that are assessed as targets for rational therapy of cancer in the context of personalized oncology. We confirmed the existence of a negative correlation between malignant cell aggressivity and the target number needed for specific drugs or RNA interference (RNAi) to maximize the benefit to the patient's overall survival. Interestingly, we found that some additional proteins not generally targeted by drug treatments might justify the addition of inhibitors designed against them in order to improve therapeutic outcomes. However, many proteins are not druggable, or the available pharmacopeia for these targets is limited, which justifies a therapy based on encapsulated RNAi.

The PRALINE database: protein and Rna humAn singLe nucleotIde variaNts in condEnsates

SUMMARY

Biological condensates are membraneless organelles with different material properties. Proteins and RNAs are the main components, but most of their interactions are still unknown. Here, we introduce PRALINE, a database for the interrogation of proteins and RNAs contained in stress granules, processing bodies and other assemblies including droplets and amyloids. PRALINE provides information about the predicted and experimentally validated protein-protein, protein-RNA and RNA-RNA interactions. For proteins, it reports the liquid-liquid phase separation and liquid-solid phase separation propensities. For RNAs, it provides information on predicted secondary structure content. PRALINE shows detailed information on human single-nucleotide variants, their clinical significance and presence in protein and RNA binding sites, and how they can affect condensates' physical properties.

AVAILABILITY AND IMPLEMENTATION

PRALINE is freely accessible on the web at http://praline.tartaglialab.com.

Interactomics of CXXC proteins involved in epigenetic regulation of gene expression

Regulation of gene expression is an extremely complex and multicomponent biological phenomenon. Proteins containing the CXXC-domain “zinc fingers” (CXXC-proteins) are master regulators of expression of many genes and have conserved functions of methylation of DNA bases and histone proteins. CXXC proteins function as a part of multiprotein complexes, which indicates the fundamental importance of studying post-translational regulation through modulation of the protein-protein interaction spectrum (PPI) in both normal and pathological conditions. In this paper we discuss general aspects of the involvement of CXXC proteins and their protein partners in neoplastic processes, both from the literature data and our own studies. Special attention is paid to recent data on the particular interactomics of the CFP1 protein encoded by the CXXC1 gene located on the human chromosome 18. CFP1 is devoid of enzymatic activity and implements epigenetic regulation of expression through binding to chromatin and a certain spectrum of PPIs.

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.

Deep structural insights into RNA-binding disordered protein regions

Recent efforts to identify RNA binding proteins in various organisms and cellular contexts have yielded a large collection of proteins that are capable of RNA binding in the absence of conventional RNA recognition domains. Many of the recently identified RNA interaction motifs fall into intrinsically disordered protein regions (IDRs). While the recognition mode and specificity of globular RNA binding elements have been thoroughly investigated and described, much less is known about the way IDRs can recognize their RNA partners. Our aim was to summarize the current state of structural knowledge on the RNA binding modes of disordered protein regions and to propose a classification system based on their sequential and structural properties. Through a detailed structural analysis of the complexes that contain disordered protein regions binding to RNA, we found two major binding modes that represent different recognition strategies and, most likely, functions. We compared these examples with DNA binding disordered proteins and found key differences stemming from the nucleic acids as well as similar binding strategies, implying a broader substrate acceptance by these proteins. Due to the very limited number of known structures, we integrated molecular dynamics simulations in our study, whose results support the proposed structural preferences of specific RNA‐binding IDRs. To broaden the scope of our review, we included a brief analysis of RNA‐binding small molecules and compared their structural characteristics and RNA recognition strategies to the RNA‐binding IDRs.

This article is categorized under:

RNA Structure and Dynamics > RNA Structure, Dynamics, and Chemistry

RNA Interactions with Proteins and Other Molecules > Protein–RNA Recognition

RNA Interactions with Proteins and Other Molecules > Small Molecule–RNA Interactions

A comparative analysis of machine learning classifiers for predicting protein-binding nucleotides in RNA sequences

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RNA are master players in various cellular and biological processes and RNA-protein interactions are vital for proper functioning of cellular machineries.

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Knowledge of binding sites is crucial to decipher their functional implications.

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RNA NC-triplet and NC-quartet features could give reasonably high performance.

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RF model outperformed other machine learning classifiers with 85% accuracy and 0.93 AUC and performed better than few existing methods.

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An online webserver “Nucpred” is developed with trained model and freely accessible for scientific community.

RNA-protein interactions play vital roles in driving the cellular machineries. Despite significant involvement in several biological processes, the underlying molecular mechanism of RNA-protein interactions is still elusive. This may be due to the experimental difficulties in solving co-crystallized RNA-protein complexes. Inherent flexibility of RNA molecules to adopt different conformations makes them functionally diverse. Their interactions with protein have implications in RNA disease biology. Thus, study of binding interfaces can provide a mechanistic insight of the molecular functioning and aberrations caused due to altered interactions. Moreover, high-throughput sequencing technologies have generated huge sequence data compared to available structural data of RNA-protein complexes. In such a scenario, efficient computational algorithms are required for identification of protein-binding interfaces of RNA in the absence of known structures. We have investigated several machine learning classifiers and various features derived from nucleotide sequences to identify protein-binding nucleotides in RNA. We achieve best performance with nucleotide-triplet and nucleotide-quartet feature-based random forest models. An overall accuracy of 84.8%, sensitivity of 83.2%, specificity of 86.1%, MCC of 0.70 and AUC of 0.93 is achieved. We have further implemented the developed models in a user-friendly webserver “Nucpred”, which is freely accessible at “http://www.csb.iitkgp.ac.in/applications/Nucpred/index”.

Intrinsic disorder and phase transitions: Pieces in the puzzling role of the prion protein in health and disease

After four decades of prion protein research, the pressing questions in the literature remain similar to the common existential dilemmas. Who am I? Some structural characteristics of the cellular prion protein (PrP^C) and scrapie PrP (PrP^Sc) remain unknown: there are no high-resolution atomic structures for either full-length endogenous human PrP^C or isolated infectious PrP^Sc particles. Why am I here? It is not known why PrP^C and PrP^Sc are found in specific cellular compartments such as the nucleus; while the physiological functions of PrP^C are still being uncovered, the misfolding site remains obscure. Where am I going? The subcellular distribution of PrP^C and PrP^Sc is wide (reported in 10 different locations in the cell). This complexity is further exacerbated by the eight different PrP fragments yielded from conserved proteolytic cleavages and by reversible post-translational modifications, such as glycosylation, phosphorylation, and ubiquitination. Moreover, about 55 pathological mutations and 16 polymorphisms on the PrP gene (PRNP) have been described. Prion diseases also share unique, challenging features: strain phenomenon (associated with the heterogeneity of PrP^Sc conformations) and the possible transmissibility between species, factors which contribute to PrP undruggability. However, two recent concepts in biochemistry-intrinsically disordered proteins and phase transitions-may shed light on the molecular basis of PrP's role in physiology and disease.

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.