Visualizing the structure and motion of the long noncoding RNA HOTAIR

RNA sample optimization for cryo-EM analysis

RNAs play critical roles in most biological processes. Although the three-dimensional (3D) structures of RNAs primarily determine their functions, it remains challenging to experimentally determine these 3D structures due to their conformational heterogeneity and intrinsic dynamics. Cryogenic electron microscopy (cryo-EM) has recently played an emerging role in resolving dynamic conformational changes and understanding structure-function relationships of RNAs including ribozymes, riboswitches and bacterial and viral noncoding RNAs. A variety of methods and pipelines have been developed to facilitate cryo-EM structure determination of challenging RNA targets with small molecular weights at subnanometer to near-atomic resolutions. While a wide range of conditions have been used to prepare RNAs for cryo-EM analysis, correlations between the variables in these conditions and cryo-EM visualizations and reconstructions remain underexplored, which continue to hinder optimizations of RNA samples for high-resolution cryo-EM structure determination. Here we present a protocol that describes rigorous screenings and iterative optimizations of RNA preparation conditions that facilitate cryo-EM structure determination, supplemented by cryo-EM data processing pipelines that resolve RNA dynamics and conformational changes and RNA modeling algorithms that generate atomic coordinates based on moderate- to high-resolution cryo-EM density maps. The current protocol is designed for users with basic skills and experience in RNA biochemistry, cryo-EM and RNA modeling. The expected time to carry out this protocol may range from 3 days to more than 3 weeks, depending on the many variables described in the protocol. For particularly challenging RNA targets, this protocol could also serve as a starting point for further optimizations.

The conformational space of RNase P RNA in solution

RNA conformational diversity has fundamental biological roles1-5, but direct visualization of its full conformational space in solution has not been possible using traditional biophysical techniques. Using solution atomic force microscopy, a deep neural network and statistical analyses, we show that the ribonuclease P (RNase P) RNA adopts heterogeneous conformations consisting of a conformationally invariant core and highly flexible peripheral structural elements that sample a broad conformational space, with amplitudes as large as 20-60 Å in a multitude of directions, with very low net energy cost. Increasing Mg2+ drives compaction and enhances enzymatic activity, probably by narrowing the conformational space. Moreover, analyses of the correlations and anticorrelations between spatial flexibility and sequence conservation suggest that the functional roles of both the structure and dynamics of key regions are embedded in the primary sequence. These findings reveal the structure-dynamics basis for the embodiment of both enzymatic precision and substrate promiscuity in the RNA component of the RNase P. Mapping the conformational space of the RNase P RNA demonstrates a new general approach to studying RNA structure and dynamics.

Advances in the field of RNA 3D structure prediction and modeling, with purely theoretical approaches, and with the use of experimental data

Recent advancements in RNA three-dimensional (3D) structure prediction have provided significant insights into RNA biology, highlighting the essential role of RNA in cellular functions and its therapeutic potential. This review summarizes the latest developments in computational methods, particularly the incorporation of artificial intelligence and machine learning, which have improved the efficiency and accuracy of RNA structure predictions. We also discuss the integration of new experimental data types, including cryoelectron microscopy (cryo-EM) techniques and high-throughput sequencing, which have transformed RNA structure modeling. The combination of experimental advances with computational methods represents a significant leap in RNA structure determination. We review the outcomes of RNA-Puzzles and critical assessment of structure prediction (CASP) challenges, which assess the state of the field and limitations of existing methods. Future perspectives are discussed, focusing on the impact of RNA 3D structure prediction on understanding RNA mechanisms and its implications for drug discovery and RNA-targeted therapies, opening new avenues in molecular biology.

Advances in understanding the role of lncRNA in ferroptosis

LncRNA is a type of transcript with a length exceeding 200 nucleotides, which was once considered junk transcript with no biological function during the transcription process. In recent years, lncRNA has been shown to act as an important regulatory factor at multiple levels of gene expression, affecting various programmed cell death modes including ferroptosis. Ferroptosis, as a new form of programmed cell death, is characterized by a deficiency of cysteine or inactivation of glutathione peroxidase, leading to depletion of glutathione, aggregation of iron ions, and lipid peroxidation. These processes are influenced by many physiological processes, such as the Nrf2 pathway, autophagy, p53 pathway and so on. An increasing number of studies have shown that lncRNA can block the expression of specific molecules through decoy effect, guide specific proteins to function, or promote interactions between molecules as scaffolds. These modes of action regulate the expression of key factors in iron metabolism, lipid metabolism, and antioxidant metabolism through epigenetic or genetic regulation, thereby regulating the process of ferroptosis. In this review, we snapshotted the regulatory mechanism of ferroptosis as an example, emphasizing the regulation of lncRNA on these pathways, thereby helping to fully understand the evolution of ferroptosis in cell fate.

sincFold: end-to-end learning of short- and long-range interactions in RNA secondary structure

MOTIVATION

Coding and noncoding RNA molecules participate in many important biological processes. Noncoding RNAs fold into well-defined secondary structures to exert their functions. However, the computational prediction of the secondary structure from a raw RNA sequence is a long-standing unsolved problem, which after decades of almost unchanged performance has now re-emerged due to deep learning. Traditional RNA secondary structure prediction algorithms have been mostly based on thermodynamic models and dynamic programming for free energy minimization. More recently deep learning methods have shown competitive performance compared with the classical ones, but there is still a wide margin for improvement.

RESULTS

In this work we present sincFold, an end-to-end deep learning approach, that predicts the nucleotides contact matrix using only the RNA sequence as input. The model is based on 1D and 2D residual neural networks that can learn short- and long-range interaction patterns. We show that structures can be accurately predicted with minimal physical assumptions. Extensive experiments were conducted on several benchmark datasets, considering sequence homology and cross-family validation. sincFold was compared with classical methods and recent deep learning models, showing that it can outperform the state-of-the-art methods.

Conserved structures and dynamics in 5'-proximal regions of Betacoronavirus RNA genomes

Betacoronaviruses are a genus within the Coronaviridae family of RNA viruses. They are capable of infecting vertebrates and causing epidemics as well as global pandemics in humans. Mitigating the threat posed by Betacoronaviruses requires an understanding of their molecular diversity. The development of novel antivirals hinges on understanding the key regulatory elements within the viral RNA genomes, in particular the 5'-proximal region, which is pivotal for viral protein synthesis. Using a combination of cryo-electron microscopy, atomic force microscopy, chemical probing, and computational modeling, we determined the structures of 5'-proximal regions in RNA genomes of Betacoronaviruses from four subgenera: OC43-CoV, SARS-CoV-2, MERS-CoV, and Rousettus bat-CoV. We obtained cryo-electron microscopy maps and determined atomic-resolution models for the stem-loop-5 (SL5) region at the translation start site and found that despite low sequence similarity and variable length of the helical elements it exhibits a remarkable structural conservation. Atomic force microscopy imaging revealed a common domain organization and a dynamic arrangement of structural elements connected with flexible linkers across all four Betacoronavirus subgenera. Together, these results reveal common features of a critical regulatory region shared between different Betacoronavirus RNA genomes, which may allow targeting of these RNAs by broad-spectrum antiviral therapeutics.

miR-221 and Parkinson's disease: A biomarker with therapeutic potential

Parkinson's disease (PD) is a neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra, leading to various motor and non-motor symptoms. Several cellular and molecular mechanisms such as alpha-synuclein (α-syn) accumulation, mitochondrial dysfunction, oxidative stress and neuroinflammation are involved in the pathogenesis of this disease. MicroRNAs (miRNAs) play important roles in post-transcriptional gene regulation. They are typically about 21-25 nucleotides in length and are involved in the regulation of gene expression by binding to the messenger RNA (mRNA) molecules. miRNAs like miR-221 play important roles in various biological processes, including development, cell proliferation, differentiation and apoptosis. miR-221 promotes neuronal survival against oxidative stress and neurite outgrowth and neuronal differentiation. Additionally, the role of miR-221 in PD has been investigated in several studies. According to the results of these studies, (1) miR-221 protects PC12 cells against oxidative stress induced by 6-hydroxydopamine; (2) miR-221 prevents Bax/caspase-3 signalling activation by stopping Bim; (3) miR-221 has moderate predictive power for PD; (4) miR-221 directly targets PTEN, and PTEN over-expression eliminates the protective action of miR-221 on p-AKT expression in PC12 cells; and (5) miRNA-221 controls cell viability and apoptosis by manipulating the Akt signalling pathway in PD. This review study suggested that miR-221 has the potential to be used as a clinical biomarker for PD diagnosis and stage assignment.

Systematic detection of tertiary structural modules in large RNAs and RNP interfaces by Tb-seq

Compact RNA structural motifs control many aspects of gene expression, but we lack methods for finding these structures in the vast expanse of multi-kilobase RNAs. To adopt specific 3-D shapes, many RNA modules must compress their RNA backbones together, bringing negatively charged phosphates into close proximity. This is often accomplished by recruiting multivalent cations (usually Mg2+), which stabilize these sites and neutralize regions of local negative charge. Coordinated lanthanide ions, such as terbium (III) (Tb3+), can also be recruited to these sites, where they induce efficient RNA cleavage, thereby revealing compact RNA 3-D modules. Until now, Tb3+ cleavage sites were monitored via low-throughput biochemical methods only applicable to small RNAs. Here we present Tb-seq, a high-throughput sequencing method for detecting compact tertiary structures in large RNAs. Tb-seq detects sharp backbone turns found in RNA tertiary structures and RNP interfaces, providing a way to scan transcriptomes for stable structural modules and potential riboregulatory motifs.

Determining structures of individual RNA conformers using atomic force microscopy images and deep neural networks

The vast percentage of the human genome is transcribed into RNA, many of which contain various structural elements and are important for functions. RNA molecules are conformationally heterogeneous and functionally dyanmics1, even when they are structured and well-folded2, which limit the applicability of methods such as NMR, crystallography, or cryo-EM. Moreover, because of the lack of a large structure RNA database, and no clear correlation between sequence and structure, approaches like AlphaFold3 for protein structure prediction, do not apply to RNA. Therefore determining the structures of heterogeneous RNA is an unmet challenge. Here we report a novel method of determining RNA three-dimensional topological structures using deep neural networks and atomic force microscopy (AFM) images of individual RNA molecules in solution. Owing to the high signal-to-noise ratio of AFM, our method is ideal for capturing structures of individual conformationally heterogeneous RNA. We show that our method can determine 3D topological structures of any large folded RNA conformers, from ~ 200 to ~ 420 residues, the size range that most functional RNA structures or structural elements fall into. Thus our method addresses one of the major challenges in frontier RNA structural biology and may impact our fundamental understanding of RNA structure.

Review: Advanced Atomic Force Microscopy Modes for Biomedical Research

Visualization of biomedical samples in their native environments at the microscopic scale is crucial for studying fundamental principles and discovering biomedical systems with complex interaction. The study of dynamic biological processes requires a microscope system with multiple modalities, high spatial/temporal resolution, large imaging ranges, versatile imaging environments and ideally in-situ manipulation capabilities. Recent development of new Atomic Force Microscopy (AFM) capabilities has made it such a powerful tool for biological and biomedical research. This review introduces novel AFM functionalities including high-speed imaging for dynamic process visualization, mechanobiology with force spectroscopy, molecular species characterization, and AFM nano-manipulation. These capabilities enable many new possibilities for novel scientific research and allow scientists to observe and explore processes at the nanoscale like never before. Selected application examples from recent studies are provided to demonstrate the effectiveness of these AFM techniques.

Long non-coding RNA and RNA-binding protein interactions in cancer: Experimental and machine learning approaches

Understanding the complex and specific roles played by non-coding RNAs (ncRNAs), which comprise the bulk of the genome, is important for understanding virtually every hallmark of cancer. This large group of molecules plays pivotal roles in key regulatory mechanisms in various cellular processes. Regulatory mechanisms, mediated by long non-coding RNA (lncRNA) and RNA-binding protein (RBP) interactions, are well documented in several types of cancer. Their effects are enabled through networks affecting lncRNA and RBP stability, RNA metabolism including N⁶-methyladenosine (m⁶A) and alternative splicing, subcellular localization, and numerous other mechanisms involved in cancer. In this review, we discuss the reciprocal interplay between lncRNAs and RBPs and their involvement in epigenetic regulation via histone modifications, as well as their key role in resistance to cancer therapy. Other aspects of RBPs including their structural domains, provide a deeper knowledge on how lncRNAs and RBPs interact and exert their biological functions. In addition, current state-of-the-art knowledge, facilitated by machine and deep learning approaches, unravels such interactions in better details to further enhance our understanding of the field, and the potential to harness RNA-based therapeutics as an alternative treatment modality for cancer are discussed.

De novo annotation of lncRNA HOTAIR transcripts by long-read RNA capture-seq reveals a differentiation-driven isoform switch

Background

LncRNAs are tissue-specific and emerge as important regulators of various biological processes and as disease biomarkers. HOTAIR is a well-established pro-oncogenic lncRNA which has been attributed a variety of functions in cancer and native contexts. However, a lack of an exhaustive, cell type-specific annotation questions whether HOTAIR functions are supported by the expression of multiple isoforms.

Results

Using a capture long-read sequencing approach, we characterize HOTAIR isoforms expressed in human primary adipose stem cells. We find HOTAIR isoforms population displays varied splicing patterns, frequently leading to the exclusion or truncation of canonical LSD1 and PRC2 binding domains. We identify a highly cell type-specific HOTAIR isoform pool regulated by distinct promoter usage, and uncover a shift in the HOTAIR TSS usage that modulates the balance of HOTAIR isoforms at differentiation onset.

Conclusion

Our results highlight the complexity and cell type-specificity of HOTAIR isoforms and open perspectives on functional implications of these variants and their balance to key cellular processes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08887-w.

Recent advances in RNA structurome

RNA structures are essential to support RNA functions and regulation in various biological processes. Recently, a range of novel technologies have been developed to decode genome-wide RNA structures and novel modes of functionality across a wide range of species. In this review, we summarize key strategies for probing the RNA structurome and discuss the pros and cons of representative technologies. In particular, these new technologies have been applied to dissect the structural landscape of the SARS-CoV-2 RNA genome. We also summarize the functionalities of RNA structures discovered in different regulatory layers-including RNA processing, transport, localization, and mRNA translation-across viruses, bacteria, animals, and plants. We review many versatile RNA structural elements in the context of different physiological and pathological processes (e.g., cell differentiation, stress response, and viral replication). Finally, we discuss future prospects for RNA structural studies to map the RNA structurome at higher resolution and at the single-molecule and single-cell level, and to decipher novel modes of RNA structures and functions for innovative applications.

Secondary structure prediction of long noncoding RNA: review and experimental comparison of existing approaches

MOTIVATION

In contrast to messenger RNAs, the function of the wide range of existing long noncoding RNAs (lncRNAs) largely depends on their structure, which determines interactions with partner molecules. Thus, the determination or prediction of the secondary structure of lncRNAs is critical to uncover their function. Classical approaches for predicting RNA secondary structure have been based on dynamic programming and thermodynamic calculations. In the last 4 years, a growing number of machine learning (ML)-based models, including deep learning (DL), have achieved breakthrough performance in structure prediction of biomolecules such as proteins and have outperformed classical methods in short transcripts folding. Nevertheless, the accurate prediction for lncRNA still remains far from being effectively solved. Notably, the myriad of new proposals has not been systematically and experimentally evaluated.

RESULTS

In this work, we compare the performance of the classical methods as well as the most recently proposed approaches for secondary structure prediction of RNA sequences using a unified and consistent experimental setup. We use the publicly available structural profiles for 3023 yeast RNA sequences, and a novel benchmark of well-characterized lncRNA structures from different species. Moreover, we propose a novel metric to assess the predictive performance of methods, exclusively based on the chemical probing data commonly used for profiling RNA structures, avoiding any potential bias incorporated by computational predictions when using dot-bracket references. Our results provide a comprehensive comparative assessment of existing methodologies, and a novel and public benchmark resource to aid in the development and comparison of future approaches.

AVAILABILITY

Full source code and benchmark datasets are available at: https://github.com/sinc-lab/lncRNA-folding.

CONTACT

lbugnon@sinc.unl.edu.ar.

Getting to the bottom of lncRNA mechanism: structure-function relationships

While long non-coding RNAs are known to play key roles in disease and development, relatively few structural studies have been performed for this important class of RNAs. Here, we review functional studies of long non-coding RNAs and expose the need for high-resolution 3-D structural studies, discussing the roles of long non-coding RNAs in the cell and how structure-function relationships might be used to elucidate further understanding. We then describe structural studies of other classes of RNAs using chemical probing, nuclear magnetic resonance, small-angle X-ray scattering, X-ray crystallography, and cryogenic electron microscopy (cryo-EM). Next, we review early structural studies of long non-coding RNAs to date and describe the way forward for the structural biology of long non-coding RNAs in terms of cryo-EM.

The multiple molecular dimensions of long noncoding RNAs that regulate gene expression and tumorigenesis

PURPOSE OF REVIEW

LncRNAs are emerging as key regulators of gene expression and they ensure homeostasis during cell differentiation and development, replication, and adaptation to the environment. Because of their key central role in regulating the biology of living cells, it is crucial to characterize how lncRNAs function at the genetic, transcriptomic, and mechanistic level.

RECENT FINDINGS

The low endogenous abundance and high molecular complexity of lncRNAs pose unique challenges for their characterization but new methodological advances in biochemistry, biophysics and cell biology have recently made it possible to characterize an increasing number of these transcripts, including oncogenic and tumor suppressor lncRNAs. These recent studies specifically address important issues that had remained controversial, such as the selectivity of lncRNA mechanisms of action, the functional importance of lncRNA sequences, secondary and tertiary structures, and the specificity of lncRNA interactions with proteins.

SUMMARY

These recent achievements, coupled to population-wide medical and genomic approaches that connect lncRNAs with human diseases and to recent advances in RNA-targeted drug development, open unprecedented new perspectives for exploiting lncRNAs as pharmacological targets or biomarkers to monitor and cure cancer, in addition to metabolic, developmental and cardiovascular diseases.

The Roles of Non-coding RNA in the Development and Regeneration of Hair Follicles: Current Status and Further Perspectives

Alopecia is a common problem that affects almost every age group and is considered to be an issue for cosmetic or psychiatric reasons. The loss of hair follicles (HFs) and hair caused by alopecia impairs self-esteem, thermoregulation, tactile sensation and protection from ultraviolet light. One strategy to solve this problem is HF regeneration. Many signalling pathways and molecules participate in the morphology and regeneration of HF, such as Wnt/β-catenin, Sonic hedgehog, bone morphogenetic protein and Notch. Non-coding RNAs (ncRNAs), especially microRNAs and long ncRNAs, have significant modulatory roles in HF development and regeneration via regulation of these signalling pathways. This review provides a comprehensive overview of the status and future prospects of ncRNAs in HF regeneration and could prompt novel ncRNA-based therapeutic strategies.

Current Knowledge of Long Non-Coding RNA HOTAIR in Breast Cancer Progression and Its Application

Breast cancer is one of the most devastating cancers with high morbidity and mortality in females worldwide. Breast tumorigenesis and further development present great uncertainty and complexity, and efficient therapeutic approaches still lack. Accumulating evidence indicates HOX transcript antisense intergenic RNA (HOTAIR) is dysregulated in cancers and has emerged as a novel hotspot in the field. In breast cancer, aberrant HOTAIR expression is responsible for advanced tumor progression by regulating multifarious signaling pathways. Besides, HOTAIR may act as competitive endogenous RNA to bind to several microRNAs and suppress their expressions, which can subsequently upregulate the levels of targeted downstream messenger RNAs, thereby leading to further cancer progression. In addition, HOTAIR works as a promising biomarker and predictor for breast cancer patients’ diagnosis or outcome prediction. Recently, HOTAIR is potentially considered to be a drug target. Here, we have summarized the induction of HOTAIR in breast cancer and its impacts on cell proliferation, migration, apoptosis, and therapeutic resistance, as well as elucidating the underlying mechanisms. This review aims to provide new insights into investigations between HOTAIR and breast cancer development and inspire new methods for studying the association in depth.

An exon-biased biophysical approach and NMR spectroscopy define the secondary structure of a conserved helical element within the HOTAIR long non-coding RNA

HOTAIR is a large, multi-exon spliced non-coding RNA proposed to function as a molecular scaffold and competes with chromatin to bind to histone modification enzymes. Previous sequence analysis and biochemical experiments identified potential conserved regions and characterized the full length HOTAIR secondary structure. Here, we examine the thermodynamic folding properties and structural propensity of the individual exonic regions of HOTAIR using an array of biophysical methods and NMR spectroscopy. We demonstrate that different exons of HOTAIR contain variable degrees of heterogeneity, and identify one exonic region, exon 4, that adopts a stable and compact fold under low magnesium concentrations. Close agreement of NMR spectroscopy and chemical probing unambiguously confirm conserved base pair interactions within the structural element, termed helix 10 of exon 4, located within domain I of human HOTAIR. This combined exon-biased and integrated biophysical approach introduces a new strategy to examine conformational heterogeneity in lncRNAs and emphasizes NMR as a key method to validate base pair interactions and corroborate large RNA secondary structures.

The lncRNA Toolkit: Databases and In Silico Tools for lncRNA Analysis

Long non-coding RNAs (lncRNAs) are a rapidly expanding field of research, with many new transcripts identified each year. However, only a small subset of lncRNAs has been characterized functionally thus far. To aid investigating the mechanisms of action by which new lncRNAs act, bioinformatic tools and databases are invaluable. Here, we review a selection of computational tools and databases for the in silico analysis of lncRNAs, including tissue-specific expression, protein coding potential, subcellular localization, structural conformation, and interaction partners. The assembled lncRNA toolkit is aimed primarily at experimental researchers as a useful starting point to guide wet-lab experiments, mainly containing multi-functional, user-friendly interfaces. With more and more new lncRNA analysis tools available, it will be essential to provide continuous updates and maintain the availability of key software in the future.

Deciphering LncRNA-protein interactions using docking complexes

Deciphering RNA-protein interactions are important to study principal biological mechanisms including transcription and translation regulation, gene silencing, among others. Predicting RNA molecule interaction with the target protein could allow us to understand important cellular processes and design novel treatment therapies for various diseases. As non-coding RNAs do not have coding potential our knowledge about their functions is still limited. Therefore, RNA-binding proteins of non-coding RNAs regulating functions, viz. including cellular maturation, nuclear export and stability may play a very important role. Keeping in view of the need for refined methods to understand protein-RNA interactions, we have attempted a docking model to infer binding sites between lncRNA NONHSAT02007 and protein KIF13A for a rare disease phenotype that we are studying in our lab.Communicated by Ramaswamy H. Sarma.

From structure to function: Route to understanding lncRNA mechanism

RNAs have emerged as a major target for diagnostics and therapeutics approaches. Regulatory nonprotein-coding RNAs (ncRNAs) in particular display remarkable versatility. They can fold into complex structures and interact with proteins, DNA, and other RNAs, thus modulating activity, localization, or interactome of multi-protein complexes. Thus, ncRNAs confer regulatory plasticity and represent a new layer of regulatory control. Interestingly, long noncoding RNAs (lncRNAs) tend to acquire complex secondary and tertiary structures and their function-in many cases-is dependent on structural conservation rather than primary sequence conservation. Whereas for many proteins, structure and its associated function are closely connected, for lncRNAs, the structural domains that determine functionality and its interactome are still not well understood. Numerous approaches for analyzing the structural configuration of lncRNAs have been developed recently. Here, will provide an overview of major experimental approaches used in the field, and discuss the potential benefit of using combinatorial strategies to analyze lncRNA modes of action based on structural information.

The molecular structure of long non-coding RNAs: emerging patterns and functional implications

Long non-coding RNAs (lncRNAs) are recently-discovered transcripts that regulate vital cellular processes and are crucially connected to diseases. Despite their unprecedented molecular complexity, it is emerging that lncRNAs possess distinct structural motifs. Remarkably, the 3D shape and topology of full-length, native lncRNAs have been visualized for the first time in the last year. These studies reveal that lncRNA structures dictate lncRNA functions. Here, we review experimentally determined lncRNA structures and emphasize that lncRNA structural characterization requires synergistic integration of computational, biochemical and biophysical approaches. Based on these emerging paradigms, we discuss how to overcome the challenges posed by the complex molecular architecture of lncRNAs, with the goal of obtaining a detailed understanding of lncRNA functions and molecular mechanisms in the future.

Site-specific covalent labeling of large RNAs with nanoparticles empowered by expanded genetic alphabet transcription

Conjugation of RNAs with nanoparticles (NPs) is of significant importance because of numerous applications in biology and medicine, which, however, remains challenging especially for large ones. So far, the majority of RNA labeling relies on solid-phase chemical synthesis, which is generally limited to RNAs smaller than 100 nucleotides (nts). We, here, present an efficient and generally applicable labeling strategy for site-specific covalent conjugation of large RNAs with a gold nanoparticle (Nanogold) empowered by transcription of an expanded genetic alphabet containing the A-T/U and G-C natural base pairs (bps) and the TPT3-NaM unnatural base pair (UBP). We synthesize an amine-derivatized TPT3 (TPT3^A), which is site specifically incorporated into a 97-nt 3'SL RNA and a 719-nt minigenomic RNA (DENV-mini) from Dengue virus serotype 2 (DENV2) by in vitro T7 transcription. The TPT3^A-modified RNAs are covalently conjugated with mono-Sulfo-N-hydroxysuccinimidyl (NHS)-Nanogold NPs via an amine and NHS ester reaction and further purified under nondenaturing conditions. TPT3 modification and Nanogold labeling cause minimal structural perturbations to the RNAs by circular dichroism, small angle X-ray scattering (SAXS), and binding activity assay. We demonstrate the application of the Nanogold-RNA conjugates in large RNA structural biology by an emerging molecular ruler, X-ray scattering interferometry (XSI). The internanoparticle distance distributions in the 3'SL and DENV-mini RNAs derived from XSI measurements support the hypothetical model of flavivirus genome circularization, thus, validate the applicability of this labeling strategy. The presented strategy overcomes the size constraints in conventional RNA labeling strategies and is expected to have wide applications in large RNA structural biology and RNA nanotechnology.

Yeast Telomerase RNA Flexibly Scaffolds Protein Subunits: Results and Repercussions

It is said that "hindsight is 20-20", so, given the current year, it is an opportune time to review and learn from experiences studying long noncoding RNAs. Investigation of the Saccharomyces cerevisiae telomerase RNA, TLC1, has unveiled striking flexibility in terms of both structural and functional features. Results support the "flexible scaffold" hypothesis for this 1157-nt telomerase RNA. This model describes TLC1 acting as a tether for holoenzyme protein subunits, and it also may apply to a plethora of RNAs beyond telomerase, such as types of lncRNAs. In this short perspective review, I summarize findings from studying the large yeast telomerase ribonucleoprotein (RNP) complex in the hope that this hindsight will sharpen foresight as so many of us seek to mechanistically understand noncoding RNA molecules from vast transcriptomes.