A Newly Identified Peripheral Duplex Anchors and Stabilizes the MALAT1 Triplex.

The accumulation of the 8-kb oncogenic long noncoding MALAT1 RNA in cells is dependent on the presence of a protective triple helix structure at the 3' terminus. While recent studies have examined the functional importance of numerous base triples within the triplex and its immediately adjacent base pairs, the functional importance of peripheral duplex elements has not been thoroughly investigated. To investigate the functional importance of a peripheral linker region that was previously described as unstructured, we employed a variety of assays including thermal melting, protection from exonucleolytic degradation by RNase R, small-angle X-ray scattering, biochemical ligation and binding assays, and computational modeling. Our results demonstrate the presence of a duplex within this linker that enhances the functional stability of the triplex in vitro, despite its location more than 40 Å from the 3' terminus. We present a full-length model of the MALAT1 triple helix-containing RNA having an extended rod-like structure and comprising 33 layers of coaxial stacking interactions. Taken together with recent research on a homologous triplex, our results demonstrate that peripheral elements anchor and stabilize triplexes in vitro. Such peripheral elements may also contribute to the formation and stability of some triple helices in vivo.

G-quadruplexes in long non-coding RNAs and their interactions with proteins.

Long non-coding RNAs (lncRNAs) have emerged as crucial regulators of cellular processes, with their dysregulation linked to various disease states. Among the structural motifs in lncRNAs, RNA G-quadruplexes (rG4s) have gained increasing attention due to their diverse roles in cellular function and disease pathogenesis. This review provides an updated and comprehensive overview of rG4s in lncRNAs, elucidating their formation, interaction with proteins, and distinctive roles in cellular processes. We discuss current methodologies for experimentally probing RNA G4s, including the use of specific small molecules, biomolecular ligands and fluorescent probes. The commonly found RNA G4-interacting protein domains are summarised along with potential strategies for disrupting lncRNA G4-protein interactions from a therapeutic perspective.

Target-triggered assembly of plasmon resonance nanostructures for quantitative detection of lncRNA in liver cancer cells via surface enhanced Raman spectroscopy.

Long-stranded non-coding RNAs (lncRNA) have important roles in disease as transcriptional regulators, mRNA processing regulators and protein synthesis factors. However, traditional methods for detecting lncRNA are time-consuming and labor-intensive, and the functions of lncRNA are still being explored. Here, we present a surface enhanced Raman spectroscopy (SERS) based biosensor for the detection of lncRNA associated with liver cancer (LC) as well as in situ cellular imaging. Using the dual SERS probes, quantitative detection of lncRNA (DAPK1-215) can be achieved with an ultra-low detection limit of 952 aM by the target-triggered assembly of core-satellite nanostructures. And the reliability of this assay can be further improved with the R2 value of 0.9923 by an internal standard probe that enables the signal dynamic calibration. Meanwhile, the high expression of DAPK1-215 mainly distributed in the cytoplasm was observed in LC cells compared with the normal ones using the SERS imaging method. Moreover, results of cellular function assays showed that DAPK1-215 promoted the migration and invasion of LC by significantly reducing the expression of the structural domain of death associated protein kinase. The development of this biosensor based on SERS can provide a sensitive and specific method for exploring the expression of lncRNA that would be a potential biomarker for the screening of LC.

Massively parallel dissection of RNA in RNA-protein interactions in vivo.

Many of the biological functions performed by RNA are mediated by RNA-binding proteins (RBPs), and understanding the molecular basis of these interactions is fundamental to biology. Here, we present massively parallel RNA assay combined with immunoprecipitation (MPRNA-IP) for in vivo high-throughput dissection of RNA-protein interactions and describe statistical models for identifying RNA domains and parsing the structural contributions of RNA. By using custom pools of tens of thousands of RNA sequences containing systematically designed truncations and mutations, MPRNA-IP is able to identify RNA domains, sequences, and secondary structures necessary and sufficient for protein binding in a single experiment. We show that this approach is successful for multiple RNAs of interest, including the long noncoding RNA NORAD, bacteriophage MS2 RNA, and human telomerase RNA, and we use it to interrogate the hitherto unknown sequence or structural RNA-binding preferences of the DNA-looping factor CTCF. By integrating systematic mutation analysis with crosslinking immunoprecipitation, MPRNA-IP provides a novel high-throughput way to elucidate RNA-based mechanisms behind RNA-protein interactions in vivo.

The Multiscale Ernwin/SPQR RNA Structure Prediction Pipeline.

Aside from the well-known role in protein synthesis, RNA can perform catalytic, regulatory, and other essential biological functions which are determined by its three-dimensional structure. In this regard, a great effort has been made during the past decade to develop computational tools for the prediction of the structure of RNAs from the knowledge of their sequence, incorporating experimental data to refine or guide the modeling process. Nevertheless, this task can become exceptionally challenging when dealing with long noncoding RNAs, constituted by more than 200 nucleotides, due to their large size and the specific interactions involved. In this chapter, we describe a multiscale approach to predict such structures, incorporating SAXS experimental data into a hierarchical procedure which couples two coarse-grained representations: Ernwin, a helix-based approach, which deals with the global arrangement of secondary structure elements, and SPQR, a nucleotide-centered coarse-grained model, which corrects and refines the structures predicted at the coarser level.We describe the methodology through its application on the Braveheart long noncoding RNA, starting from the SAXS and secondary structure data to propose a refined, all-atom structure.

Biophysical Investigation of RNA ⋠DNA : DNA Triple Helix and RNA : DNA Heteroduplex Formation by the lncRNAs MEG3 and Fendrr.

Long non-coding RNAs (lncRNAs) are important regulators of gene expression and can associate with DNA as RNA : DNA heteroduplexes or RNA ⋅ DNA : DNA triple helix structures. Here, we review in vitro biochemical and biophysical experiments including electromobility shift assays (EMSA), circular dichroism (CD) spectroscopy, thermal melting analysis, microscale thermophoresis (MST), single-molecule Förster resonance energy transfer (smFRET) and nuclear magnetic resonance (NMR) spectroscopy to investigate RNA ⋅ DNA : DNA triple helix and RNA : DNA heteroduplex formation. We present the investigations of the antiparallel triplex-forming lncRNA MEG3 targeting the gene TGFB2 and the parallel triplex-forming lncRNA Fendrr with its target gene Emp2. The thermodynamic properties of these oligonucleotides lead to concentration-dependent heterogeneous mixtures, where a DNA duplex, an RNA : DNA heteroduplex and an RNA ⋅ DNA : DNA triplex coexist and their relative populations are modulated in a temperature-dependent manner. The in vitro data provide a reliable readout of triplex structures, as RNA ⋅ DNA : DNA triplexes show distinct features compared to DNA duplexes and RNA : DNA heteroduplexes. Our experimental results can be used to validate computationally predicted triple helix formation between novel disease-relevant lncRNAs and their DNA target genes.

Predicting lncRNA-protein interactions through deep learning framework employing multiple features and random forest algorithm.

RNA-protein interaction (RPI) is crucial to the life processes of diverse organisms. Various researchers have identified RPI through long-term and high-cost biological experiments. Although numerous machine learning and deep learning-based methods for predicting RPI currently exist, their robustness and generalizability have significant room for improvement. This study proposes LPI-MFF, an RPI prediction model based on multi-source information fusion, to address these issues. The LPI-MFF employed protein-protein interactions features, sequence features, secondary structure features, and physical and chemical properties as the information sources with the corresponding coding scheme, followed by the random forest algorithm for feature screening. Finally, all information was combined and a classification method based on convolutional neural networks is used. The experimental results of fivefold cross-validation demonstrated that the accuracy of LPI-MFF on RPI1807 and NPInter was 97.60% and 97.67%, respectively. In addition, the accuracy rate on the independent test set RPI1168 was 84.9%, and the accuracy rate on the Mus musculus dataset was 90.91%. Accordingly, LPI-MFF demonstrated greater robustness and generalization than other prevalent RPI prediction methods.

Deep Conservation and Unexpected Evolutionary History of Neighboring lncRNAs MALAT1 and NEAT1.

Long non-coding RNAs (lncRNAs) have begun to receive overdue attention for their regulatory roles in gene expression and other cellular processes. Although most lncRNAs are lowly expressed and tissue-specific, notable exceptions include MALAT1 and its genomic neighbor NEAT1, two highly and ubiquitously expressed oncogenes with roles in transcriptional regulation and RNA splicing. Previous studies have suggested that NEAT1 is found only in mammals, while MALAT1 is present in all gnathostomes (jawed vertebrates) except birds. Here we show that these assertions are incomplete, likely due to the challenges associated with properly identifying these two lncRNAs. Using phylogenetic analysis and structure-aware annotation of publicly available genomic and RNA-seq coverage data, we show that NEAT1 is a common feature of tetrapod genomes except birds and squamates. Conversely, we identify MALAT1 in representative species of all major gnathostome clades, including birds. Our in-depth examination of MALAT1, NEAT1, and their genomic context in a wide range of vertebrate species allows us to reconstruct the series of events that led to the formation of the locus containing these genes in taxa from cartilaginous fish to mammals. This evolutionary history includes the independent loss of NEAT1 in birds and squamates, since NEAT1 is found in the closest living relatives of both clades (crocodilians and tuataras, respectively). These data clarify the origins and relationships of MALAT1 and NEAT1 and highlight an opportunity to study the change and continuity in lncRNA structure and function over deep evolutionary time.

Structural basis for inactivation of PRC2 by G-quadruplex RNA.

Polycomb repressive complex 2 (PRC2) silences genes through trimethylation of histone H3K27. PRC2 associates with numerous precursor messenger RNAs (pre-mRNAs) and long noncoding RNAs (lncRNAs) with a binding preference for G-quadruplex RNA. In this work, we present a 3.3-Å-resolution cryo-electron microscopy structure of PRC2 bound to a G-quadruplex RNA. Notably, RNA mediates the dimerization of PRC2 by binding both protomers and inducing a protein interface composed of two copies of the catalytic subunit EZH2, thereby blocking nucleosome DNA interaction and histone H3 tail accessibility. Furthermore, an RNA-binding loop of EZH2 facilitates the handoff between RNA and DNA, another activity implicated in PRC2 regulation by RNA. We identified a gain-of-function mutation in this loop that activates PRC2 in zebrafish. Our results reveal mechanisms for RNA-mediated regulation of a chromatin-modifying enzyme.

Hit identification of novel small molecules interfering with MALAT1 triplex by a structure-based virtual screening.

Nowadays, RNA is an attractive target for the design of new small molecules with different pharmacological activities. Among several RNA molecules, long noncoding RNAs (lncRNAs) are extensively reported to be involved in cancer pathogenesis. In particular, the overexpression of lncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) plays an important role in the development of multiple myeloma (MM). Starting from the crystallographic structure of the triple-helical stability element at the 3'-end of MALAT1, we performed a structure-based virtual screening of a large commercial database, previously filtered according to the drug-like properties. After a thermodynamic analysis, we selected five compounds for the in vitro assays. Compound M5, characterized by a diazaindene scaffold, emerged as the most promising molecule enabling the destabilization of the MALAT1 triplex structure and antiproliferative activity on in vitro models of MM. M5 is proposed as a lead compound to be further optimized for improving its affinity toward MALAT1.

IRSOM2: a web server for predicting bifunctional RNAs.

Recent advances have shown that some biologically active non-coding RNAs (ncRNAs) are actually translated into polypeptides that have a physiological function as well. This paradigm shift requires adapted computational methods to predict this new class of 'bifunctional RNAs'. Previously, we developed IRSOM, an open-source algorithm to classify non-coding and coding RNAs. Here, we use the binary statistical model of IRSOM as a ternary classifier, called IRSOM2, to identify bifunctional RNAs as a rejection of the two other classes. We present its easy-to-use web interface, which allows users to perform predictions on large datasets of RNA sequences in a short time, to re-train the model with their own data, and to visualize and analyze the classification results thanks to the implementation of self-organizing maps (SOM). We also propose a new benchmark of experimentally validated RNAs that play both protein-coding and non-coding roles, in different organisms. Thus, IRSOM2 showed promising performance in detecting these bifunctional transcripts among ncRNAs of different types, such as circRNAs and lncRNAs (in particular those of shorter lengths). The web server is freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr.

Rolling circle extension-assisted loop-mediated isothermal amplification (Rol-LAMP) method for locus-specific and visible detection of RNA N6-methyladenosine.

N6-methyladenosine (m6A) is the most prevalent RNA modification in eukaryotic mRNAs. Currently available detection methods for locus-specific m6A marks rely on RT-qPCR, radioactive methods, or high-throughput sequencing. Here, we develop a non-qPCR, ultrasensitive, isothermal, and naked-eye visible method for m6A detection based on rolling circle amplification (RCA) and loop-mediated isothermal amplification (LAMP), named m6A-Rol-LAMP, to verify putative m6A sites in transcripts obtained from the high-throughput data. When padlock probes hybridize to the potential m6A sites on targets, they are converted to circular form by DNA ligase in the absence of m6A modification, while m6A modification hinders the sealing of padlock probes. Subsequently, Bst DNA polymerase-mediated RCA and LAMP allow the amplification of the circular padlock probe to achieve the locus-specific detection of m6A. Following optimization and validation, m6A-Rol-LAMP can ultra-sensitively and quantitatively determine the existence of m6A modification on a specific target site as low as 100 amol under isothermal conditions. Detections of m6A can be performed on rRNA, mRNA, lincRNA, lncRNA and pre-miRNA from biological samples with naked-eye observations after dye incubation. Together, we provide a powerful tool for locus-specific detection of m6A, which can simply, quickly, sensitively, specifically, and visually determine putative m6A modification on RNA.

Investigating RNA-RNA interactions through computational and biophysical analysis.

Numerous viruses utilize essential long-range RNA-RNA genome interactions, specifically flaviviruses. Using Japanese encephalitis virus (JEV) as a model system, we computationally predicted and then biophysically validated and characterized its long-range RNA-RNA genomic interaction. Using multiple RNA computation assessment programs, we determine the primary RNA-RNA interacting site among JEV isolates and numerous related viruses. Following in vitro transcription of RNA, we provide, for the first time, characterization of an RNA-RNA interaction using size-exclusion chromatography coupled with multi-angle light scattering and analytical ultracentrifugation. Next, we demonstrate that the 5' and 3' terminal regions of JEV interact with nM affinity using microscale thermophoresis, and this affinity is significantly reduced when the conserved cyclization sequence is not present. Furthermore, we perform computational kinetic analyses validating the cyclization sequence as the primary driver of this RNA-RNA interaction. Finally, we examined the 3D structure of the interaction using small-angle X-ray scattering, revealing a flexible yet stable interaction. This pathway can be adapted and utilized to study various viral and human long-non-coding RNA-RNA interactions and determine their binding affinities, a critical pharmacological property of designing potential therapeutics.

LncReader: identification of dual functional long noncoding RNAs using a multi-head self-attention mechanism.

Long noncoding ribonucleic acids (RNAs; LncRNAs) endowed with both protein-coding and noncoding functions are referred to as 'dual functional lncRNAs'. Recently, dual functional lncRNAs have been intensively studied and identified as involved in various fundamental cellular processes. However, apart from time-consuming and cell-type-specific experiments, there is virtually no in silico method for predicting the identity of dual functional lncRNAs. Here, we developed a deep-learning model with a multi-head self-attention mechanism, LncReader, to identify dual functional lncRNAs. Our data demonstrated that LncReader showed multiple advantages compared to various classical machine learning methods using benchmark datasets from our previously reported cncRNAdb project. Moreover, to obtain independent in-house datasets for robust testing, mass spectrometry proteomics combined with RNA-seq and Ribo-seq were applied in four leukaemia cell lines, which further confirmed that LncReader achieved the best performance compared to other tools. Therefore, LncReader provides an accurate and practical tool that enables fast dual functional lncRNA identification.

ortho2align: a sensitive approach for searching for orthologues of novel lncRNAs.

BACKGROUND: Many novel long noncoding RNAs have been discovered in recent years due to advances in high-throughput sequencing experiments. Finding orthologues of these novel lncRNAs might facilitate clarification of their functional role in living organisms. However, lncRNAs exhibit low sequence conservation, so specific methods for enhancing the signal-to-noise ratio were developed. Nevertheless, current methods such as transcriptomes comparison approaches or searches for conserved secondary structures are not applicable to novel, previously unannotated lncRNAs by design. RESULTS: We present ortho2align-a versatile sensitive synteny-based lncRNA orthologue search tool with statistical assessment of sequence conservation. This tool allows control of the specificity of the search process and optional annotation of found orthologues. ortho2align shows similar performance in terms of sensitivity and resource usage as the state-of-the-art method for aligning orthologous lncRNAs but also enables scientists to predict unannotated orthologous sequences for lncRNAs in question. Using ortho2align, we predicted orthologues of three distinct classes of novel human lncRNAs in six Vertebrata species to estimate their degree of conservation. CONCLUSIONS: Being designed for the discovery of unannotated orthologues of novel lncRNAs in distant species, ortho2align is a versatile tool applicable to any genomic regions, especially weakly conserved ones. A small amount of input files makes ortho2align easy to use in orthology studies as a single tool or in bundle with other steps that researchers will consider sensible. ortho2align is available as an Anaconda package with its source code hosted at https://github.com/dmitrymyl/ortho2align .

A toolkit for the identification of NEAT1_2/paraspeckle modulators.

Paraspeckles are ribonucleoprotein granules assembled by NEAT1_2 lncRNA, an isoform of Nuclear Paraspeckle Assembly Transcript 1 (NEAT1). Dysregulation of NEAT1_2/paraspeckles has been linked to multiple human diseases making them an attractive drug target. However currently NEAT1_2/paraspeckle-focused translational research and drug discovery are hindered by a limited toolkit. To fill this gap, we developed and validated a set of tools for the identification of NEAT1_2 binders and modulators comprised of biochemical and cell-based assays. The NEAT1_2 triple helix stability element was utilized as the target in the biochemical assays, and the cellular assay ('ParaQuant') was based on high-content imaging of NEAT1_2 in fixed cells. As a proof of principle, these assays were used to screen a 1,200-compound FDA-approved drug library and a 170-compound kinase inhibitor library and to confirm the screening hits. The assays are simple to establish, use only commercially-available reagents and are scalable for higher throughput. In particular, ParaQuant is a cost-efficient assay suitable for any cells growing in adherent culture and amenable to multiplexing. Using ParaQuant, we identified dual PI3K/mTOR inhibitors as potent negative modulators of paraspeckles. The tools we describe herein should boost paraspeckle studies and help guide the search, validation and optimization of NEAT1_2/paraspeckle-targeted small molecules.

Regulation and roles of RNA modifications in aging-related diseases.

With the aging of the global population, accumulating interest is focused on manipulating the fundamental aging-related signaling pathways to delay the physiological aging process and eventually slow or prevent the appearance or severity of multiple aging-related diseases. Recently, emerging evidence has shown that RNA modifications, which were historically considered infrastructural features of cellular RNAs, are dynamically regulated across most of the RNA species in cells and thereby critically involved in major biological processes, including cellular senescence and aging. In this review, we summarize the current knowledge about RNA modifications and provide a catalog of RNA modifications on different RNA species, including mRNAs, miRNAs, lncRNA, tRNAs, and rRNAs. Most importantly, we focus on the regulation and roles of these RNA modifications in aging-related diseases, including neurodegenerative diseases, cardiovascular diseases, cataracts, osteoporosis, and fertility decline. This would be an important step toward a better understanding of fundamental aging mechanisms and thereby facilitating the development of novel diagnostics and therapeutics for aging-related diseases.

Structural effects of m6A modification of the Xist A-repeat AUCG tetraloop and its recognition by YTHDC1.

The A-repeat region of the lncRNA Xist is critical for X inactivation and harbors several N6-methyladenosine (m6A) modifications. How the m6A modification affects the conformation of the conserved AUCG tetraloop hairpin of the A-repeats and how it can be recognized by the YTHDC1 reader protein is unknown. Here, we report the NMR solution structure of the (m6A)UCG hairpin, which reveals that the m6A base extends 5' stacking of the A-form helical stem, resembling the unmethylated AUCG tetraloop. A crystal structure of YTHDC1 bound to the (m6A)UCG tetraloop shows that the (m6A)UC nucleotides are recognized by the YTH domain of YTHDC1 in a single-stranded conformation. The m6A base inserts into the aromatic cage and the U and C bases interact with a flanking charged surface region, resembling the recognition of single-stranded m6A RNA ligands. Notably, NMR and fluorescence quenching experiments show that the binding requires local unfolding of the upper stem region of the (m6A)UCG hairpin. Our data show that m6A can be readily accommodated in hairpin loop regions, but recognition by YTH readers requires local unfolding of flanking stem regions. This suggests how m6A modifications may regulate lncRNA function by modulating RNA structure.

Distinct MUNC lncRNA structural domains regulate transcription of different promyogenic factors.

Many lncRNAs have been discovered using transcriptomic data; however, it is unclear what fraction of lncRNAs is functional and what structural properties affect their phenotype. MUNC lncRNA (also known as DRReRNA) acts as an enhancer RNA for the Myod1 gene in cis and stimulates the expression of other promyogenic genes in trans by recruiting the cohesin complex. Here, experimental probing of the RNA structure revealed that MUNC contains multiple structural domains not detected by prediction algorithms in the absence of experimental information. We show that these specific and structurally distinct domains are required for induction of promyogenic genes, for binding genomic sites and gene expression regulation, and for binding the cohesin complex. Myod1 induction and cohesin interaction comprise only a subset of MUNC phenotype. Our study reveals unexpectedly complex, structure-driven functions for the MUNC lncRNA and emphasizes the importance of experimentally determined structures for understanding structure-function relationships in lncRNAs.