Advances in the characterization of RNA-binding proteins

High-throughput quantitation of protein-RNA UV-crosslinking efficiencies as a predictive tool for high-confidence identification of RNA-binding proteins

UV-crosslinking has proven to be an invaluable tool for the identification of RNA-protein interactomes. The paucity of methods for distinguishing background from bona fide RNA-protein interactions, however, makes attribution of RNA-binding function on UV-crosslinking alone challenging. To address this need, we previously reported an RNA-binding protein (RBP) confidence scoring metric (RCS), incorporating both signal-to-noise (S:N) and protein abundance determinations to distinguish high- and low-confidence candidate RBPs. Although RCS has utility, we sought a direct metric for quantification and comparative evaluation of protein-RNA interactions. Here we propose the use of protein-specific UV-crosslinking efficiency (%CL), representing the molar fraction of a protein that is crosslinked to RNA, for functional evaluation of candidate RBPs. Application to the HeLa RNA interactome yielded %CL values for 1097 proteins. Remarkably, %CL values span over five orders of magnitude. For the HeLa RNA interactome, %CL values comprise a range from high efficiency, high specificity interactions, e.g., the Elav protein HuR and the Pumilio homolog Pum2, with %CL values of 45.9 and 24.2, respectively, to very low efficiency and specificity interactions, for example, the metabolic enzymes glyceraldehyde-3-phosphate dehydrogenase, fructose-bisphosphate aldolase, and alpha-enolase, with %CL values of 0.0016, 0.006, and 0.008, respectively. We further extend the utility of %CL through prediction of protein domains and classes with known RNA-binding functions, thus establishing it as a useful metric for RNA interactome analysis. We anticipate that this approach will benefit efforts to establish functional RNA interactomes and support the development of more predictive computational approaches for RBP identification.

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.

Immunoprecipitation Methods to Isolate Messenger Ribonucleoprotein Complexes (mRNP)

Throughout their life cycle, messenger RNAs (mRNAs) associate with proteins to form ribonucleoproteins (mRNPs). Each mRNA is part of multiple successive mRNP complexes that participate in their biogenesis, cellular localization, translation and decay. The dynamic composition of mRNP complexes and their structural remodelling play crucial roles in the control of gene expression. Studying the endogenous composition of different mRNP complexes is a major challenge. In this chapter, we describe the variety of protein-centric immunoprecipitation methods available for the identification of mRNP complexes and the requirements for their experimental settings.

Utilizing Dichloromethane as an Extremely Proficient Substitute for Phenol/Chloroform in Extracting RNA with Exceptional Purity from Woody Tissues of Coconut

Procuring high-grade RNA from mature coconut tissues is a tricky and labor-intensive process due to the intricate scaffold of polysaccharides, polyphenols, lipids, and proteins that form firm complexes with nucleic acids. However, we have effectively developed a novel method for the first time, letting the retrieval of high-grade RNA from the roots, endosperm, and mesocarp of mature coconut trees take place. In this method, we exploited dichloromethane as a replacement to phenol/chloroform for RNA recovery from mature coconut tissues. The amount of high-grade RNA acquired from the roots of mature coconut trees was 120.7 µg/g, with an A260/280 ratio of 1.95. Similarly, the mature coconut mesocarp yielded 134.6 µg/g FW of quality RNA with A260/280 ratio of 1.98, whereas the mature coconut endosperm produced 120.4 µg/g FW of quality RNA with A260/280 ratio of 2.01. Furthermore, the RNA isolation using the dichloromethane method exhibited excellent performance in downstream experiments, particularly in RT-PCR for cDNA production and amplification. On the contrary, the RNA plant kit, TRIZOL, and Cetyl Trimethyl Ammonium Bromide (CTAB) methods were unsuccessful in isolating substantial quantities of RNA with exceptional purities from the mentioned coconut tissues. In view of these findings, we conclude that the newly developed method will be pivotal in effectively extracting RNA with high purity from mature coconut tissues.

Role of the RNA-binding protein family in gynecologic cancers

Gynecological cancers pose a threat to women's health. Although early-stage gynecological cancers show good outcomes after standardized treatment, the prognosis of patients with advanced, met-astatic, and recurrent cancers is poor. RNA-binding proteins (RBPs) are important cellular proteins that interact with RNA through RNA-binding domains and participate extensively in post-transcriptional regulatory processes, such as mRNA alternative splicing, polyadenylation, intracellular localization and stability, and translation. Abnormal RBP expression affects the normal function of oncogenes and tumor suppressor genes in many malignancies, thus leading to the occurrence or progression of cancers. Similarly, RBPs play crucial roles in gynecological carcinogenesis. We summarize the role of RBPs in gynecological malignancies and explore their potential in the diagnosis and treatment of cancers. The findings summarized in this review may provide a guide for future research on the functions of RBPs.

TFRC-RNA interactions show the regulation of gene expression and alternative splicing associated with IgAN in human renal tubule mesangial cells

Introduction: IgA nephropathy (IgAN) is the most common primary glomerular disease (PGD) which could progress to renal failure and is characterized by aberrant IgA immune complex deposition. Transferrin receptor1 (TFRC), an IgA receptor, is a potential RNA binding protein (RBP) which regulates expression of genes positively associated with the cell cycle and proliferation and is involved in IgAN. Molecular mechanisms by which TFRC affects IgAN development remain unclear. Methods: In this study, TFRC was overexpressed in human renal tubular mesangial cells (HRMCs) and RNA-sequencing (RNA-seq) and improved RNA immunoprecipitation sequencing (iRIP-seq) were performed. The aim was to identify potential RNA targets of TFRC at transcriptional and alternative splicing (AS) levels. Results: TFRC-regulated AS genes were enriched in mRNA splicing and DNA repair, consistent with global changes due to TFRC overexpression (TFRC-OE). Expression of TFRC-regulated genes potentially associated with IgAN, including CENPH, FOXM1, KIFC1, TOP2A, FABP4, ID1, KIF20A, ATF3, H19, IRF7, and H1-2, and with AS, CYGB, MCM7 and HNRNPH1, were investigated by RT-qPCR and iRIP-seq data analyzed to identify TFRC-bound RNA targets. RCC1 and RPPH1 were found to be TFRC-bound RNA targets involved in cell proliferation. Discussion: In conclusion, molecular TFRC targets were identified in HRMCs and TFRC found to regulate gene transcription and AS. TFRC is considered to have potential as a clinical therapeutic target.

Expression of targets of the RNA-binding protein AUF-1 in human airway epithelium indicates its role in cellular senescence and inflammation

Introduction

The RNA-binding protein AU-rich-element factor-1 (AUF-1) participates to posttranscriptional regulation of genes involved in inflammation and cellular senescence, two pathogenic mechanisms of chronic obstructive pulmonary disease (COPD). Decreased AUF-1 expression was described in bronchiolar epithelium of COPD patients versus controls and in vitro cytokine- and cigarette smoke-challenged human airway epithelial cells, prompting the identification of epithelial AUF-1-targeted transcripts and function, and investigation on the mechanism of its loss.

Results

RNA immunoprecipitation-sequencing (RIP-Seq) identified, in the human airway epithelial cell line BEAS-2B, 494 AUF-1-bound mRNAs enriched in their 3'-untranslated regions for a Guanine-Cytosine (GC)-rich binding motif. AUF-1 association with selected transcripts and with a synthetic GC-rich motif were validated by biotin pulldown. AUF-1-targets' steady-state levels were equally affected by partial or near-total AUF-1 loss induced by cytomix (TNFα/IL1β/IFNγ/10 nM each) and siRNA, respectively, with differential transcript decay rates. Cytomix-mediated decrease in AUF-1 levels in BEAS-2B and primary human small-airways epithelium (HSAEC) was replicated by treatment with the senescence- inducer compound etoposide and associated with readouts of cell-cycle arrest, increase in lysosomal damage and senescence-associated secretory phenotype (SASP) factors, and with AUF-1 transfer in extracellular vesicles, detected by transmission electron microscopy and immunoblotting. Extensive in-silico and genome ontology analysis found, consistent with AUF-1 functions, enriched RIP-Seq-derived AUF-1-targets in COPD-related pathways involved in inflammation, senescence, gene regulation and also in the public SASP proteome atlas; AUF-1 target signature was also significantly represented in multiple transcriptomic COPD databases generated from primary HSAEC, from lung tissue and from single-cell RNA-sequencing, displaying a predominant downregulation of expression.

Discussion

Loss of intracellular AUF-1 may alter posttranscriptional regulation of targets particularly relevant for protection of genomic integrity and gene regulation, thus concurring to airway epithelial inflammatory responses related to oxidative stress and accelerated aging. Exosomal-associated AUF-1 may in turn preserve bound RNA targets and sustain their function, participating to spreading of inflammation and senescence to neighbouring cells.

Arjunolic acid modulate pancreatic dysfunction by ameliorating pattern recognition receptor and canonical Wnt pathway activation in type 2 diabetic rats

BACKGROUND

Arjunolic acid (AA) is a potent phytochemical with multiple therapeutics effects. In this study, AA is evaluated on type 2 diabetic (T2DM) rats to understand the mechanism of β-cell linkage with Toll-like receptor 4 (TLR-4) and canonical Wnt signaling. However, its role in modulating TLR-4 and canonical Wnt/β-catenin crosstalk on insulin signaling remains unclear during T2DM. Aim The current study is aimed to examine the potential role of AA on insulin signaling and TLR-4-Wnt crosstalk in the pancreas of type 2 diabetic rats.

METHOD

Multiple methods were used to determine molecular cognizance of AA in T2DM rats, when treated with different dosage levels. Histopathological and histomorphometry analysis was conducted using masson trichrome and H&E stains. While, protein and mRNA expressions of TLR-4/Wnt and insulin signaling were assessed using automated Western blotting (jess), immunohistochemistry, and RT-PCR.

RESULTS

Histopathological findings revealed that AA had reversed back the T2DM-induced apoptosis and necrosis caused to rats pancreas. Molecular findings exhibited prominent effects of AA in downregulating the elevated level of TLR-4, MyD88, NF-κB, p-JNK, and Wnt/β-catenin by blocking TLR-4/MyD88 and canonical Wnt signaling in diabetic pancreas, while IRS-1, PI3K, and pAkt were all upregulated by altering the NF-κB and β-catenin crosstalk during T2DM.

CONCLUSION

Overall results, indicate that AA has potential to develop as an effective therapeutic in the treatment of T2DM associated meta-inflammation. However, future preclinical research at multiple dose level in a long-term chronic T2DM disease model is warranted to understand its clinical relevance in cardiometabolic disease.

Self-Attention Based Neural Network for Predicting RNA-Protein Binding Sites

Proteins binding to Ribonucleic Acid (RNA) inside cells are called RNA-binding proteins (RBP), which play a crucial role in gene regulation. The identification of RNA-protein binding sites helps to understand the function of RBP better. Although many computational methods have been developed to predict RNA-protein binding sites, their prediction accuracy on small sample datasets needs improvement. To overcome this limitation, we propose a novel model called SA-Net, which utilizes k-mer embedding to encode RNA sequences and a self-attention-based neural network to extract sequence features. K-mer embedding assists the model to discover significant subsequence fragments associated with binding sites. The self-attention mechanism captures contextual information from the entire input sequence globally, performing well in small sample sequence learning. Experimental results demonstrate that SA-Net attains state-of-the-art results on the RBP-24 dataset. We find that 4-mer embedding aids the model to achieve optimal performance. We also show that the self-attention network outperforms the commonly used CNN and CNN-BLSTM models in sequence feature extraction.

The Prognostic Role of HuR Varies Between Different Subtypes of Breast Cancer Patients: Data Mining and Retrospective Analysis

Objective

Human-antigen R (HuR) is an RNA-binding protein, which regulates the expression of several oncogenes and tumor suppressor genes through post-transcriptional mechanisms. But the role of HuR in breast cancer remains controversial. The aim of this study was to verify the association between cytoplasmic HuR level and prognosis of breast cancer patients.

Methods

Data mining from the Human Protein Atlas (HPA) and Kaplan-Meier Plotter (KMP) databases was performed. Then, 394 patients with stage I-III primary breast cancer were enrolled between January 2005 and December 2016. We investigated the association between cytoplasmic HuR level and clinicopathological characteristics or survival of these patients. Immunohistochemical analysis was performed to determine HuR expression level. SPSS 21.0 statistical software was used for analysis.

Results

In the HPA and KMP datasets, HuR protein and mRNA expression level were not significantly associated with overall survival of all breast cancer patients enrolled. Results from our 394 patients indicated that higher expression level of cytoplasmic HuR was associated with larger tumor size, lymph node positive, ER negative and triple-negative subtype. For all patients enrolled, the results indicated that compared with HuR negative patients, the DFS (disease-free survival) of HuR 1+ was longer (60.5% vs 78.8, P=0.053, HR=0.616, 95% CI: 0.378-1.005), the P value was borderline. In the triple-negative breast cancer (TNBC) subgroup, HuR positive patients had significantly longer DFS than HuR negative patients (65.5% vs 30.8%, P=0.001, HR=0.345, 95% CI: 0.180-0.658). In the HR+HER2- subgroup, HuR low (0~1+) patients had significantly longer OS than HuR high (2+~3+) patients (97.0% vs 89.5%, P=0.033, HR=2.482, 95% CI: 1.074-5.736).

Conclusion

In conclusion, our results revealed that higher expression level of HuR was related to aggressive biological characteristics which supported the findings from previous researches. In the HR+HER2- subgroup, lower HuR expression level patients had better survival time, while in the TNBC subgroup we got the opposite results. Our work indicated that HuR might play different roles in different breast cancer subtypes.

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.

A translational repression reporter assay for the analysis of RNA-binding protein consensus sites

RNA-binding proteins are essential regulators of RNA processing and function. Translational repression assays can be used to study how they interact with specific RNA sequences by insertion of such a consensus sequence into the 5' untranslated region of a reporter mRNA and measuring reporter protein translation. The straightforward set-up of these translational repression assays avoids the need for the isolation of the protein or the RNA providing speed, robustness and a low-cost method. Here, we report the optimization of the assay to function with linear RNA sequences instead of the previously reported hairpin type sequences to allow the study of a wider variety of RNA-binding proteins. Multiplication of a consensus sequence strongly improves the signal allowing analysis by both fluorescence intensity measurements and flow cytometry.

Revitalizing an important field in biophysics: The new frontiers of molecular crowding

Taking into account the presence of the crowded environment of a macromolecule has been an important goal of biology over the past 20 years. Molecular crowding affects the motions, stability and the kinetic behaviour of proteins. New powerful approaches have recently been developed to study molecular crowding, some of which make use of the synchrotron radiation light. The meeting "New Frontiers in Molecular Crowding" was organized in July 2022at the European Synchrotron Radiation facility of Grenoble to discuss the new frontiers of molecular crowding. The workshop brought together researchers from different disciplines to highlight the new developments of the field, including areas where new techniques allow the scientists to gain unprecedently expected information. A key conclusion of the meeting was the need to build an international and interdisciplinary research community through enhanced communication, resource-sharing, and educational initiatives that could let the molecular crowding field flourish further.

Establishment of a risk model correlated with metabolism based on RNA-binding proteins associated with cell pyroptosis in acute myeloid leukemia

Background

RNA-binding protein (RBP) regulates acute myeloid leukemia (AML) by participating in mRNA editing and modification. Pyroptosis also plays an immunomodulatory function in AML. Therefore, this study aimed to identify pyroptosis-related RBP genes that could predict the prognosis of AML patients.

Methods

AML related expression data were downloaded from the UCSC website and Gene Expression Omnibus (GEO) database. Pyroptosis-RPB-related differentially expressed genes (PRBP-DEGs) were conducted with a protein-protein interactions (PPI) network to screen out the key PRBP-DEGs, based on which a risk model was constructed by Cox analysis, and evaluated by plotting Receiver operating characteristic (ROC) curves and survival curves. Independent prognostic analysis was performed and a nomogram was constructed. Finally, enrichment analysis was performed for high and low risk groups.

Reuslts

A total of 71 PRBP-DEGs were obtained and a pyroptosis-RPB-related risk model was constructed based on IFIT5, MRPL14, MRPL21, MRPL39, MVP, and PUSL1 acquired from Cox analysis. RiskScore, age, and cytogenetics risk category were identified as independent prognostic factors, and the nomogram based on these independent prognostic factors could accurately predict 1-, 3- and 5-year survival of AML patients. Gene set enrichment analysis (GSEA) showed that the high-risk and low-risk groups were mainly enriched in metabolic- and immune-related processes and pathways.

Conclusion

In this study, a risk score model correlated with metabolism based on RNA-binding proteins associated with cell pyroptosis in acute myeloid leukemia was established, which provided a theoretical basis and reference value for therapeutic studies and prognosis of AML.

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.

LPI-CSFFR: Combining serial fusion with feature reuse for predicting LncRNA-protein interactions

Long non-coding RNAs (LncRNAs) play important roles in a series of life activities, and they function primarily with proteins. The wet experimental-based methods in lncRNA-protein interactions (lncRPIs) study are time-consuming and expensive. In this study, we propose for the first time a novel feature fusion method, the LPI-CSFFR, to train and predict LncRPIs based on a Convolutional Neural Network (CNN) with feature reuse and serial fusion in sequences, secondary structures, and physicochemical properties of proteins and lncRNAs. The experimental results indicate that LPI-CSFFR achieves excellent performance on the datasets RPI1460 and RPI1807 with an accuracy of 83.7 % and 98.1 %, respectively. We further compare LPI-CSFFR with the state-of-the-art existing methods on the same benchmark datasets to evaluate the performance. In addition, to test the generalization performance of the model, we independently test sample pairs of five model organisms, where Mus musculus are the highest prediction accuracy of 99.5 %, and we find multiple hotspot proteins after constructing an interaction network. Finally, we test the predictive power of the LPI-CSFFR for sample pairs with unknown interactions. The results indicate that LPI-CSFFR is promising for predicting potential LncRPIs. The relevant source code and the data used in this study are available at https://github.com/JianjunTan-Beijing/LPI-CSFFR.

Ribonomics Approaches to Identify RBPome in Plants and Other Eukaryotes: Current Progress and Future Prospects

RNA-binding proteins (RBPs) form complex interactions with RNA to regulate the cell’s activities including cell development and disease resistance. RNA-binding proteome (RBPome) aims to profile and characterize the RNAs and proteins that interact with each other to carry out biological functions. Generally, RNA-centric and protein-centric ribonomic approaches have been successfully developed to profile RBPome in different organisms including plants and animals. Further, more and more novel methods that were firstly devised and applied in mammalians have shown great potential to unravel RBPome in plants such as RNA-interactome capture (RIC) and orthogonal organic phase separation (OOPS). Despise the development of various robust and state-of-the-art ribonomics techniques, genome-wide RBP identifications and characterizations in plants are relatively fewer than those in other eukaryotes, indicating that ribonomics techniques have great opportunities in unraveling and characterizing the RNA–protein interactions in plant species. Here, we review all the available approaches for analyzing RBPs in living organisms. Additionally, we summarize the transcriptome-wide approaches to characterize both the coding and non-coding RBPs in plants and the promising use of RBPome for booming agriculture.

RBM24 in the Post-Transcriptional Regulation of Cancer Progression: Anti-Tumor or Pro-Tumor Activity?

Simple Summary

RBM24 is a highly conserved RNA-binding protein that plays critical roles in the post-transcriptional regulation of gene expression for initiating cell differentiation during embryonic development and for maintaining tissue homeostasis in adult life. Evidence is now accumulating that it is frequently dysregulated across human cancers. Importantly, RBM24 may act as a tumor suppressor or as an oncogene in a context- or background-dependent manner. Its activity can be regulated by protein–protein interactions and post-translational modifications, making it a potential therapeutic target for cancer treatment. However, molecular mechanisms underlying its function in tumor growth and metastasis remain elusive. Further investigation will be necessary to better understand how its post-transcriptional regulatory activity is controlled and how it is implicated in tumor progression. This review provides a comprehensive analysis of recent findings on the implication of RBM24 in cancer and proposes future research directions to delve more deeply into the mechanisms underlying its tumor-suppressive function or oncogenic activity.

Abstract

RNA-binding proteins are critical post-transcriptional regulators of gene expression. They are implicated in a wide range of physiological and pathological processes by modulating nearly every aspect of RNA metabolisms. Alterations in their expression and function disrupt tissue homeostasis and lead to the occurrence of various cancers. RBM24 is a highly conserved protein that binds to a large spectrum of target mRNAs and regulates many post-transcriptional events ranging from pre-mRNA splicing to mRNA stability, polyadenylation and translation. Studies using different animal models indicate that it plays an essential role in promoting cellular differentiation during organogenesis and tissue regeneration. Evidence is also accumulating that its dysregulation frequently occurs across human cancers. In several tissues, RBM24 clearly functions as a tumor suppressor, which is consistent with its inhibitory potential on cell proliferation. However, upregulation of RBM24 in other cancers appears to promote tumor growth. There is a possibility that RBM24 displays both anti-tumor and pro-tumor activities, which may be regulated in part through differential interactions with its protein partners and by its post-translational modifications. This makes it a potential biomarker for diagnosis and prognosis, as well as a therapeutic target for cancer treatment. The challenge remains to determine the post-transcriptional mechanisms by which RBM24 modulates gene expression and tumor progression in a context- or background-dependent manner. This review discusses recent findings on the potential function of RBM24 in tumorigenesis and provides future directions for better understanding its regulatory role in cancer cells.

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.

Identification of long non-coding RNAs and RNA binding proteins in breast cancer subtypes

Breast cancer is a heterogeneous disease classified into four main subtypes with different clinical outcomes, such as patient survival, prognosis, and relapse. Current genetic tests for the differential diagnosis of BC subtypes showed a poor reproducibility. Therefore, an early and correct diagnosis of molecular subtypes is one of the challenges in the clinic. In the present study, we identified differentially expressed genes, long non-coding RNAs and RNA binding proteins for each BC subtype from a public dataset applying bioinformatics algorithms. In addition, we investigated their interactions and we proposed interacting biomarkers as potential signature specific for each BC subtype. We found a network of only 2 RBPs (RBM20 and PCDH20) and 2 genes (HOXB3 and RASSF7) for luminal A, a network of 21 RBPs and 53 genes for luminal B, a HER2-specific network of 14 RBPs and 30 genes, and a network of 54 RBPs and 302 genes for basal BC. We validated the signature considering their expression levels on an independent dataset evaluating their ability to classify the different molecular subtypes with a machine learning approach. Overall, we achieved good performances of classification with an accuracy >0.80. In addition, we found some interesting novel prognostic biomarkers such as RASSF7 for luminal A, DCTPP1 for luminal B, DHRS11, KLC3, NAGS, and TMEM98 for HER2, and ABHD14A and ADSSL1 for basal. The findings could provide preliminary evidence to identify putative new prognostic biomarkers and therapeutic targets for individual breast cancer subtypes.

Correlation in Domain Fluctuations Navigates Target Search of a Viral Peptide along RNA

Biological macromolecules often exhibit correlations in fluctuations involving distinct domains. This study decodes their functional implications in RNA-protein recognition and target-specific binding. The target search of a peptide along RNA in a viral TAR-Tat complex is closely monitored using atomistic simulations, steered molecular dynamics simulations, free energy calculations, and a machine-learning-based clustering technique. An anticorrelated domain fluctuation is identified between the tetraloop and the bulge region in the apo form of TAR RNA that sets a hierarchy in the domain-specific fluctuations at each binding event and that directs the succeeding binding footsteps. Thus, at each binding footstep, the dynamic partner selects an RNA location for binding where it senses a higher fluctuation, which is conventionally reduced upon binding. This event stimulates an alternate domain fluctuation, which then dictates sequential binding footstep/s and thus the search progresses. Our cross-correlation maps show that the fluctuations relay from one domain to another specific domain until the anticorrelation between those interdomain fluctuations sustains. Artificial attenuation of that hierarchical domain fluctuation inhibits specific RNA binding. The binding is completed with the arrival of a few long-lived water molecules that mediate slightly distant RNA-protein sites and finally stabilize the overall complex. The study underscores the functional importance of naturally designed fluctuating RNA motifs (bulge, tetraloop) and their interplay in dictating the directionality of the search in a highly dynamic environment.

rBPDL:Predicting RNA-Binding Proteins Using Deep Learning

RNA-binding protein (RBP) is a powerful and wide-ranging regulator that plays an important role in cell development, differentiation, metabolism, health and disease. The prediction of RBPs provides valuable guidance for biologists. Although experimental methods have made great progress in predicting RBP, they are time-consuming and not flexible. Therefore, we developed a network model, rBPDL, by combining a convolutional neural network and long short-term memory for multilabel classification of RBPs. Moreover, to achieve better prediction results, we used a voting algorithm for ensemble learning of the model. We compared rBPDL with state-of-the-art methods and found that rBPDL significantly improved identification performance for the RBP68 dataset, with a macro-Area Under Curve (AUC), micro-AUC, and weighted AUC of 0.936, 0.962, and 0.946, respectively. Furthermore, through AUC statistical analysis of the RBP domain, we analyzed the performance of rBPDL and found that the RBP identification performance in the same domain was similar. In addition, we analyzed the performance preferences and physicochemical properties of the binding protein amino acids and explored the characteristics that affect the binding by using the RBP86 dataset.

Capturing Endogenous Long Noncoding RNAs and Their Binding Proteins Using Chromatin Isolation by RNA Purification

Long noncoding RNAs (lncRNAs) exert their functions through binding to other RNA, genomic DNA, or proteins. The identification of proteins that associate with the lncRNA of interest sheds light on the molecular basis of its biological functions. This can be achieved by tagging the lncRNA with chemically modified ribonucleotides, or by using in vitro transcribed lncRNA to retrieve proteins from cell lysates. Alternatively, endogenous lncRNAs can be pulled down from cells or tissues with biotinylated antisense DNA oligonucleotides, which may overcome the potential pitfalls of using tagged lncRNAs, such as artifacts caused by tagging or non-physiological interactions. Here we describe the detailed protocol for chromatin isolation by RNA purification (ChIRP) from mammalian cell lines and mouse tissues, which captures endogenous lncRNAs and enables subsequent identification of their physiologically relevant binding partners.

The Roles of CCCH Zinc-Finger Proteins in Plant Abiotic Stress Tolerance

Zinc-finger proteins, a superfamily of proteins with a typical structural domain that coordinates a zinc ion and binds nucleic acids, participate in the regulation of growth, development, and stress adaptation in plants. Most zinc fingers are C2H2-type or CCCC-type, named after the configuration of cysteine (C) and histidine (H); the less-common CCCH zinc-finger proteins are important in the regulation of plant stress responses. In this review, we introduce the domain structures, classification, and subcellular localization of CCCH zinc-finger proteins in plants and discuss their functions in transcriptional and post-transcriptional regulation via interactions with DNA, RNA, and other proteins. We describe the functions of CCCH zinc-finger proteins in plant development and tolerance to abiotic stresses such as salt, drought, flooding, cold temperatures and oxidative stress. Finally, we summarize the signal transduction pathways and regulatory networks of CCCH zinc-finger proteins in their responses to abiotic stress. CCCH zinc-finger proteins regulate the adaptation of plants to abiotic stress in various ways, but the specific molecular mechanisms need to be further explored, along with other mechanisms such as cytoplasm-to-nucleus shuttling and post-transcriptional regulation. Unraveling the molecular mechanisms by which CCCH zinc-finger proteins improve stress tolerance will facilitate the breeding and genetic engineering of crops with improved traits.

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

Interactions between proteins and RNA are at the base of numerous cellular regulatory and functional phenomena. The investigation of the biological relevance of non-coding RNAs has led to the identification of numerous novel RNA-binding proteins (RBPs). However, defining the RNA sequences and structures that are selectively recognised by an RBP remains challenging, since these interactions can be transient and highly dynamic, and may be mediated by unstructured regions in the protein, as in the case of many non-canonical RBPs. Numerous experimental and computational methodologies have been developed to predict, identify and verify the binding between a given RBP and potential RNA partners, but navigating across the vast ocean of data can be frustrating and misleading. In this mini-review, we propose a workflow for the identification of the RNA binding partners of putative, newly identified RBPs. The large pool of potential binders selected by in-cell experiments can be enriched by in silico tools such as catRAPID, which is able to predict the RNA sequences more likely to interact with specific RBP regions with high accuracy. The RNA candidates with the highest potential can then be analysed in vitro to determine the binding strength and to precisely identify the binding sites. The results thus obtained can furthermore validate the computational predictions, offering an all-round solution to the issue of finding the most likely RNA binding partners for a newly identified potential RBP.

Regulator of calcineurin 1 is a novel RNA-binding protein to regulate neuronal apoptosis

Posttranscriptional regulation of gene expression plays an important role in the maturation, transport, stability and translation of coding and noncoding RNAs. RNA-binding protein (RBP) is a key factor of the regulation. Regulator of calcineurin 1 (RCAN1) is a multifunctional protein involved in neurodegeneration, mitochondrial dysfunction, inflammation and protein glycosylation, and plays an important role in the pathogenesis of Down syndrome and Alzheimer's disease. In this report, we discovered that RCNA1 is a novel RNA-binding protein. A 23 nucleotide sequence of adenine nucleotide translocator (ANT1) mRNA was identified as the binding motif of RCAN1. Furthermore, we found that R1SR13, as the RNA aptamer of RCAN1 identified by SELEX, blocked RCAN1-induced inhibition of the nuclear factor of activated T cells (NFAT) and NF-κB signaling pathways, and reduced neuronal apoptosis. Taken together, our results demonstrate that RCAN1 is a novel RNA-binding protein and the RNA aptamer of RCAN1 plays a neuroprotective role.

Nuclear-Encoded lncRNA MALAT1 Epigenetically Controls Metabolic Reprogramming in HCC Cells through the Mitophagy Pathway

Mitochondrial dysfunction is a metabolic hallmark of cancer cells. In search of molecular factors involved in this dysregulation in hepatocellular carcinoma (HCC), we found that the nuclear-encoded long noncoding RNA (lncRNA) MALAT1 (metastasis-associated lung adenocarcinoma transcript 1) was aberrantly enriched in the mitochondria of hepatoma cells. Using RNA reverse transcription-associated trap sequencing (RAT-seq), we showed that MALAT1 interacted with multiple loci on mitochondrial DNA (mtDNA), including D-loop, COX2, ND3, and CYTB genes. MALAT1 knockdown induced alterations in the CpG methylation of mtDNA and in mitochondrial transcriptomes. This was associated with multiple abnormalities in mitochondrial function, including altered mitochondrial structure, low oxidative phosphorylation (OXPHOS), decreased ATP production, reduced mitophagy, decreased mtDNA copy number, and activation of mitochondrial apoptosis. These alterations in mitochondrial metabolism were associated with changes in tumor phenotype and in pathways involved in cell mitophagy, mitochondrial apoptosis, and epigenetic regulation. We further showed that the RNA-shuttling protein HuR and the mitochondria transmembrane protein MTCH2 mediated the transport of MALAT1 in this nuclear-mitochondrial crosstalk. This study provides the first evidence that the nuclear genome-encoded lncRNA MALAT1 functions as a critical epigenetic player in the regulation of mitochondrial metabolism of hepatoma cells, laying the foundation for further clarifying the roles of lncRNAs in tumor metabolic reprogramming.

A live-cell assay for the detection of pre-microRNA-protein interactions

Recent efforts in genome-wide sequencing and proteomics have revealed the fundamental roles that RNA-binding proteins (RBPs) play in the life cycle and function of coding and non-coding RNAs. While these methodologies provide a systems-level view of the networking of RNA and proteins, approaches to enable the cellular validation of discovered interactions are lacking. Leveraging the power of bioorthogonal chemistry- and split-luciferase-based assay technologies, we have devised a conceptually new assay for the live-cell detection of RNA-protein interactions (RPIs), RNA interaction with Protein-mediated Complementation Assay, or RiPCA. As proof-of-concept, we utilized the interaction of the pre-microRNA, pre-let-7, with its binding partner, Lin28. Using this system, we have demonstrated the selective detection of the pre-let-7-Lin28 RPI in both the cytoplasm and nucleus. Furthermore, we determined that this technology can be used to discern relative affinities for specific sequences as well as of individual RNA binding domains. Thus, RiPCA has the potential to serve as a useful tool in supporting the investigation of cellular RPIs.

Systematic Construction and Validation of an RNA-Binding Protein-Associated Model for Prognosis Prediction in Hepatocellular Carcinoma

BACKGROUND

Evidence from prevailing studies show that hepatocellular carcinoma (HCC) is among the top cancers with high mortality globally. Gene regulation at post-transcriptional level orchestrated by RNA-binding proteins (RBPs) is an important mechanism that modifies various biological behaviors of HCC. Currently, it is not fully understood how RBPs affects the prognosis of HCC. In this study, we aimed to construct and validate an RBP-related model to predict the prognosis of HCC patients.

METHODS

Differently expressed RBPs were identified in HCC patients based on the GSE54236 dataset from the Gene Expression Omnibus (GEO) database. Integrative bioinformatics analyses were performed to select hub genes. Gene expression patterns were validated in The Cancer Genome Atlas (TCGA) database, after which univariate and multivariate Cox regression analyses, as well as Kaplan-Meier analysis were performed to develop a prognostic model. Then, the performance of the prognostic model was assessed using receiver operating characteristic (ROC) curves and clinicopathological correlation analysis. Moreover, data from the International Cancer Genome Consortium (ICGC) database were used for external validation. Finally, a nomogram combining clinicopathological parameters and prognostic model was established for the individual prediction of survival probability.

RESULTS

The prognostic risk model was finally constructed based on two RBPs (BOP1 and EZH2), facilitating risk-stratification of HCC patients. Survival was markedly higher in the low-risk group relative to the high-risk group. Moreover, higher risk score was associated with advanced pathological grade and late clinical stage. Besides, the risk score was found to be an independent prognosis factor based on multivariate analysis. Nomogram including the risk score and clinical stage proved to perform better in predicting patient prognosis.

CONCLUSIONS

The RBP-related prognostic model established in this study may function as a prognostic indicator for HCC, which could provide evidence for clinical decision making.

The search for RNA-binding proteins: a technical and interdisciplinary challenge

RNA-binding proteins are customarily regarded as important facilitators of gene expression. In recent years, RNA–protein interactions have also emerged as a pervasive force in the regulation of homeostasis. The compendium of proteins with provable RNA-binding function has swelled from the hundreds to the thousands astride the partnership of mass spectrometry-based proteomics and RNA sequencing. At the foundation of these advances is the adaptation of RNA-centric capture methods that can extract bound protein that has been cross-linked in its native environment. These methods reveal snapshots in time displaying an extensive network of regulation and a wealth of data that can be used for both the discovery of RNA-binding function and the molecular interfaces at which these interactions occur. This review will focus on the impact of these developments on our broader perception of post-transcriptional regulation, and how the technical features of current capture methods, as applied in mammalian systems, create a challenging medium for interpretation by systems biologists and target validation by experimental researchers.

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

Changes in the abundance of protein and RNA molecules can impair the formation of complexes in the cell leading to toxicity and death. Here we exploit the information contained in protein, RNA and DNA interaction networks to provide a comprehensive view of the regulation layers controlling the concentration-dependent formation of assemblies in the cell. We present the emerging concept that RNAs can act as scaffolds to promote the formation ribonucleoprotein complexes and coordinate the post-transcriptional layer of gene regulation. We describe the structural and interaction network properties that characterize the ability of protein and RNA molecules to interact and phase separate in liquid-like compartments. Finally, we show that presence of structurally disordered regions in proteins correlate with the propensity to undergo liquid-to-solid phase transitions and cause human diseases. Also see the video abstract here https://youtu.be/kfpqibsNfS0.

econvRBP: Improved ensemble convolutional neural networks for RNA binding protein prediction directly from sequence

RNA binding proteins (RBPs) determine RNA process from synthesis to decay, which play a key role in RNA transport, translation and degradation. Therefore, exploring RBPs' function from the amino acid sequence using computational methods has become one of the momentous topics in genome annotation. However, there still have some challenges: (1) shallow feature: Although the sequence determines structure is self-evident, it is difficult to analyze the essential features from simple sequence. (2) Poorly understand: feature-based prediction methods mainly emphasize feature extraction, while in-depth understanding of protein mysteries limits the application of feature engineering. (3) Feature fusion: multi-feature fusion is often used, but the features are not well integrated. In view of these challenges, we propose a novel ensemble convolutional neural network (econvRBP) to predict RBPs. In order to capture the local and global features of RNA binding proteins simultaneously, first of all, One Hot and Conjoint Triad encoding methods are used to transform amino acid sequence into local and global features, respectively. After that the local and global features are combined for further high-level feature extraction using convolutional neural networks. Some experiments are constructed to evaluate our method with 10-fold cross validation and the results show that it has achieved the best performance among all the predictors so far. We correctly predicted 99% of 2875 RBPs and 99% of 6782 non-RBPs with accuracy of 0.99. In addition, the datasets provided by RBPPred are also used to validate our models with an accuracy of 0.87. These results indicate that the econvRBP is the most excellent method at present, and will provide reliable guidance for the detection of RBPs. econvRBP is available at http://47.100.203.218:3389/home.html/.

RNA-binding and prion domains: the Yin and Yang of phase separation

Proteins and RNAs assemble in membrane-less organelles that organize intracellular spaces and regulate biochemical reactions. The ability of proteins and RNAs to form condensates is encoded in their sequences, yet it is unknown which domains drive the phase separation (PS) process and what are their specific roles. Here, we systematically investigated the human and yeast proteomes to find regions promoting condensation. Using advanced computational methods to predict the PS propensity of proteins, we designed a set of experiments to investigate the contributions of Prion-Like Domains (PrLDs) and RNA-binding domains (RBDs). We found that one PrLD is sufficient to drive PS, whereas multiple RBDs are needed to modulate the dynamics of the assemblies. In the case of stress granule protein Pub1 we show that the PrLD promotes sequestration of protein partners and the RBD confers liquid-like behaviour to the condensate. Our work sheds light on the fine interplay between RBDs and PrLD to regulate formation of membrane-less organelles, opening up the avenue for their manipulation.

Comprehensive Survey and Comparative Assessment of RNA-Binding Residue Predictions with Analysis by RNA Type

With close to 30 sequence-based predictors of RNA-binding residues (RBRs), this comparative survey aims to help with understanding and selection of the appropriate tools. We discuss past reviews on this topic, survey a comprehensive collection of predictors, and comparatively assess six representative methods. We provide a novel and well-designed benchmark dataset and we are the first to report and compare protein-level and datasets-level results, and to contextualize performance to specific types of RNAs. The methods considered here are well-cited and rely on machine learning algorithms on occasion combined with homology-based prediction. Empirical tests reveal that they provide relatively accurate predictions. Virtually all methods perform well for the proteins that interact with rRNAs, some generate accurate predictions for mRNAs, snRNA, SRP and IRES, while proteins that bind tRNAs are predicted poorly. Moreover, except for DRNApred, they confuse DNA and RNA-binding residues. None of the six methods consistently outperforms the others when tested on individual proteins. This variable and complementary protein-level performance suggests that users should not rely on applying just the single best dataset-level predictor. We recommend that future work should focus on the development of approaches that facilitate protein-level selection of accurate predictors and the consensus-based prediction of RBRs.

Single and Combined Methods to Specifically or Bulk-Purify RNA-Protein Complexes

The ribonome interconnects the proteome and the transcriptome. Specific biology is situated at this interface, which can be studied in bulk using omics approaches or specifically by targeting an individual protein or RNA species. In this review, we focus on both RNA- and ribonucleoprotein-(RNP) centric methods. These methods can be used to study the dynamics of the ribonome in response to a stimulus or to identify the proteins that interact with a specific RNA species. The purpose of this review is to provide and discuss an overview of strategies to cross-link RNA to proteins and the currently available RNA- and RNP-centric approaches to study RNPs. We elaborate on some major challenges common to most methods, involving RNP yield, purity and experimental cost. We identify the origin of these difficulties and propose to combine existing approaches to overcome these challenges. The solutions provided build on the recently developed organic phase separation protocols, such as Cross-Linked RNA eXtraction (XRNAX), orthogonal organic phase separation (OOPS) and Phenol-Toluol extraction (PTex).

Identifying Regulatory Elements via Deep Learning

Deep neural networks have been revolutionizing the field of machine learning for the past several years. They have been applied with great success in many domains of the biomedical data sciences and are outperforming extant methods by a large margin. The ability of deep neural networks to pick up local image features and model the interactions between them makes them highly applicable to regulatory genomics. Instead of an image, the networks analyze DNA and RNA sequences and additional epigenomic data. In this review, we survey the successes of deep learning in the field of regulatory genomics. We first describe the fundamental building blocks of deep neural networks, popular architectures used in regulatory genomics, and their training process on molecular sequence data. We then review several key methods in different gene regulation domains. We start with the pioneering method DeepBind and its successors, which were developed to predict protein–DNA binding. We then review methods developed to predict and model epigenetic information, such as histone marks and nucleosome occupancy. Following epigenomics, we review methods to predict protein–RNA binding with its unique challenge of incorporating RNA structure information. Finally, we provide our overall view of the strengths and weaknesses of deep neural networks and prospects for future developments.

Classification and function of RNA-protein interactions

Almost all RNAs need to interact with proteins to fully exert their functions, and proteins also bind to RNAs to act as regulators. It has now become clear that RNA-protein interactions play important roles in many biological processes among organisms. Despite the great progress that has been made in the field, there is still no precise classification system for RNA-protein interactions, which makes it challenging to further decipher the functions and mechanisms of these interactions. In this review, we propose four different categories of RNA-protein interactions according to their basic characteristics: RNA motif-dependent RNA-protein interactions, RNA structure-dependent RNA-protein interactions, RNA modification-dependent RNA-protein interactions, and RNA guide-based RNA-protein interactions. Moreover, the integration of different types of RNA-protein interactions and the regulatory factors implicated in these interactions are discussed. Furthermore, we emphasize the functional diversity of these four types of interactions in biological processes and disease development and assess emerging trends in this exciting research field. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Processing > RNA Editing and Modification.

High throughput approaches to study RNA-protein interactions in vitro

To understand the regulation of gene expression it is critical to determine how proteins interact with and discriminate between different RNAs. In this review, we discuss experimental techniques that utilize high throughput approaches to characterize the interactions of proteins with large numbers of RNAs in vitro. We describe the underlying principles for the main methods, briefly discuss their scope and limitations, and outline how insight from the techniques contributes to our understanding of specificity for RNA-protein interactions.

RNAct: Protein-RNA interaction predictions for model organisms with supporting experimental data

Protein-RNA interactions are implicated in a number of physiological roles as well as diseases, with molecular mechanisms ranging from defects in RNA splicing, localization and translation to the formation of aggregates. Currently, ∼1400 human proteins have experimental evidence of RNA-binding activity. However, only ∼250 of these proteins currently have experimental data on their target RNAs from various sequencing-based methods such as eCLIP. To bridge this gap, we used an established, computationally expensive protein-RNA interaction prediction method, catRAPID, to populate a large database, RNAct. RNAct allows easy lookup of known and predicted interactions and enables global views of the human, mouse and yeast protein-RNA interactomes, expanding them in a genome-wide manner far beyond experimental data (http://rnact.crg.eu).

Upstream analysis of alternative splicing: a review of computational approaches to predict context-dependent splicing factors

Alternative splicing (AS) has shown to play a pivotal role in the development of diseases, including cancer. Specifically, all the hallmarks of cancer (angiogenesis, cell immortality, avoiding immune system response, etc.) are found to have a counterpart in aberrant splicing of key genes. Identifying the context-specific regulators of splicing provides valuable information to find new biomarkers, as well as to define alternative therapeutic strategies. The computational models to identify these regulators are not trivial and require three conceptual steps: the detection of AS events, the identification of splicing factors that potentially regulate these events and the contextualization of these pieces of information for a specific experiment. In this work, we review the different algorithmic methodologies developed for each of these tasks. Main weaknesses and strengths of the different steps of the pipeline are discussed. Finally, a case study is detailed to help the reader be aware of the potential and limitations of this computational approach.

Rbm24 controls poly(A) tail length and translation efficiency of crystallin mRNAs in the lens via cytoplasmic polyadenylation

Lens transparency critically requires the abundant accumulation of crystallin proteins, and deregulation of this process causes congenital cataracts in humans. Rbm24 is an RNA-binding protein with highly conserved expression in differentiating lens fiber cells among all vertebrates. We use a zebrafish model to demonstrate that loss of Rbm24 function specifically impedes lens fiber cell differentiation, resulting in cataract formation and blindness. Molecular analyses reveal that Rbm24 interacts with cytoplasmic polyadenylation complex and binds to a large number of lens-expressed messenger RNAs to maintain their stability and protect their poly(A) tail length, thereby crucially contributing to their efficient translation into functional proteins. This work identifies an important mechanism by which Rbm24 posttranscriptionally controls lens gene expression to establish transparency and refraction power.

Lens transparency is established by abundant accumulation of crystallin proteins and loss of organelles in the fiber cells. It requires an efficient translation of lens messenger RNAs (mRNAs) to overcome the progressively reduced transcriptional activity that results from denucleation. Inappropriate regulation of this process impairs lens differentiation and causes cataract formation. However, the regulatory mechanism promoting protein synthesis from lens-expressed mRNAs remains unclear. Here we show that in zebrafish, the RNA-binding protein Rbm24 is critically required for the accumulation of crystallin proteins and terminal differentiation of lens fiber cells. In the developing lens, Rbm24 binds to a wide spectrum of lens-specific mRNAs through the RNA recognition motif and interacts with cytoplasmic polyadenylation element-binding protein (Cpeb1b) and cytoplasmic poly(A)-binding protein (Pabpc1l) through the C-terminal region. Loss of Rbm24 reduces the stability of a subset of lens mRNAs encoding heat shock proteins and shortens the poly(A) tail length of crystallin mRNAs encoding lens structural components, thereby preventing their translation into functional proteins. This severely impairs lens transparency and results in blindness. Consistent with its highly conserved expression in differentiating lens fiber cells, the findings suggest that vertebrate Rbm24 represents a key regulator of cytoplasmic polyadenylation and plays an essential role in the posttranscriptional control of lens development.

A Genetically Encoded Diazirine Analogue for RNA-Protein Photo-crosslinking

Ultraviolent crosslinking is a key experimental step in the numerous protocols that have been developed for capturing and dissecting RNA-protein interactions in living cells. UV crosslinking covalently stalls dynamic interactions between RNAs and the directly contacting RNA-binding proteins and enables stringent denaturing downstream purification conditions needed for the enrichment and biochemical analysis of RNA-protein complexes. Despite its popularity, conventional 254 nm UV crosslinking possesses a set of intrinsic drawbacks, with the low photochemical efficiency being the central caveat. Here we show that genetically encoded photoreactive unnatural amino acids bearing a dialkyl diazirine photoreactive group can address this problem. Using the human iron regulatory protein 1 (IRP1) as a model RNA-binding protein, we show that the photoreactive amino acids can be introduced into the protein without diminishing its RNA-binding properties. A sevenfold increase in the crosslinking efficiency compared to conventional 254 nm UV crosslinking was achieved using the diazirine-based unnatural amino acid DiAzKs. This finding opens an avenue for new applications of the unnatural amino acids in studying RNA-protein interactions.

Approaches for measuring the dynamics of RNA-protein interactions

RNA-protein interactions are pivotal for the regulation of gene expression from bacteria to human. RNA-protein interactions are dynamic; they change over biologically relevant timescales. Understanding the regulation of gene expression at the RNA level therefore requires knowledge of the dynamics of RNA-protein interactions. Here, we discuss the main experimental approaches to measure dynamic aspects of RNA-protein interactions. We cover techniques that assess dynamics of cellular RNA-protein interactions that accompany biological processes over timescales of hours or longer and techniques measuring the kinetic dynamics of RNA-protein interactions in vitro. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes RNA Evolution and Genomics > Ribonomics.

An Integrative Study of Protein-RNA Condensates Identifies Scaffolding RNAs and Reveals Players in Fragile X-Associated Tremor/Ataxia Syndrome

Recent evidence indicates that specific RNAs promote the formation of ribonucleoprotein condensates by acting as scaffolds for RNA-binding proteins (RBPs). We systematically investigated RNA-RBP interaction networks to understand ribonucleoprotein assembly. We found that highly contacted RNAs are structured, have long UTRs, and contain nucleotide repeat expansions. Among the RNAs with such properties, we identified the FMR1 3' UTR that harbors CGG expansions implicated in fragile X-associated tremor/ataxia syndrome (FXTAS). We studied FMR1 binding partners in silico and in vitro and prioritized the splicing regulator TRA2A for further characterization. In a FXTAS cellular model, we validated the TRA2A-FMR1 interaction and investigated implications of its sequestration at both transcriptomic and post-transcriptomic levels. We found that TRA2A co-aggregates with FMR1 in a FXTAS mouse model and in post-mortem human samples. Our integrative study identifies key components of ribonucleoprotein aggregates, providing links to neurodegenerative disease and allowing the discovery of therapeutic targets.

Recent Advances in Machine Learning Based Prediction of RNA-protein Interactions

The interactions between RNAs and proteins play critical roles in many biological processes. Therefore, characterizing these interactions becomes critical for mechanistic, biomedical, and clinical studies. Many experimental methods can be used to determine RNA-protein interactions in multiple aspects. However, due to the facts that RNA-protein interactions are tissuespecific and condition-specific, as well as these interactions are weak and frequently compete with each other, those experimental techniques can not be made full use of to discover the complete spectrum of RNA-protein interactions. To moderate these issues, continuous efforts have been devoted to developing high quality computational techniques to study the interactions between RNAs and proteins. Many important progresses have been achieved with the application of novel techniques and strategies, such as machine learning techniques. Especially, with the development and application of CLIP techniques, more and more experimental data on RNA-protein interaction under specific biological conditions are available. These CLIP data altogether provide a rich source for developing advanced machine learning predictors. In this review, recent progresses on computational predictors for RNA-protein interaction were summarized in the following aspects: dataset, prediction strategies, and input features. Possible future developments were also discussed at the end of the review.

Comprehensive in vivo identification of the c-Myc mRNA protein interactome using HyPR-MS

Proteins bind mRNA through their entire life cycle from transcription to degradation. We analyzed c-Myc mRNA protein interactors in vivo using the HyPR-MS method to capture the crosslinked mRNA by hybridization and then analyzed the bound proteins using mass spectrometry proteomics. Using HyPR-MS, 229 c-Myc mRNA-binding proteins were identified, confirming previously proposed interactors, suggesting new interactors, and providing information related to the roles and pathways known to involve c-Myc. We performed structural and functional analysis of these proteins and validated our findings with a combination of RIP-qPCR experiments, in vitro results released in past studies, publicly available RIP- and eCLIP-seq data, and results from software tools for predicting RNA-protein interactions.

RNA structure drives interaction with proteins

The combination of high-throughput sequencing and in vivo crosslinking approaches leads to the progressive uncovering of the complex interdependence between cellular transcriptome and proteome. Yet, the molecular determinants governing interactions in protein-RNA networks are not well understood. Here we investigated the relationship between the structure of an RNA and its ability to interact with proteins. Analysing in silico, in vitro and in vivo experiments, we find that the amount of double-stranded regions in an RNA correlates with the number of protein contacts. This relationship -which we call structure-driven protein interactivity- allows classification of RNA types, plays a role in gene regulation and could have implications for the formation of phase-separated ribonucleoprotein assemblies. We validate our hypothesis by showing that a highly structured RNA can rearrange the composition of a protein aggregate. We report that the tendency of proteins to phase-separate is reduced by interactions with specific RNAs.

Analysis of NRAS RNA G-quadruplex binding proteins reveals DDX3X as a novel interactor of cellular G-quadruplex containing transcripts

RNA G-quadruplexes (rG4s) are secondary structures in mRNAs known to influence RNA post-transcriptional mechanisms thereby impacting neurodegenerative disease and cancer. A detailed knowledge of rG4–protein interactions is vital to understand rG4 function. Herein, we describe a systematic affinity proteomics approach that identified 80 high-confidence interactors that assemble on the rG4 located in the 5′-untranslated region (UTR) of the NRAS oncogene. Novel rG4 interactors included DDX3X, DDX5, DDX17, GRSF1 and NSUN5. The majority of identified proteins contained a glycine-arginine (GAR) domain and notably GAR-domain mutation in DDX3X and DDX17 abrogated rG4 binding. Identification of DDX3X targets by transcriptome-wide individual-nucleotide resolution UV-crosslinking and affinity enrichment (iCLAE) revealed a striking association with 5′-UTR rG4-containing transcripts which was reduced upon GAR-domain mutation. Our work highlights hitherto unrecognized features of rG4 structure–protein interactions that highlight new roles of rG4 structures in mRNA post-transcriptional control.