RNAstructure: software for RNA secondary structure prediction and analysis

Fingerprinting Tertiary Structure in Complex RNAs Using Single-Molecule Correlated Chemical Probing

Single-molecule correlated chemical probing (smCCP) is an experimentally concise strategy for characterizing higher-order structural interactions in RNA. smCCP data yield rich, but complex, information about base pairing, conformational ensembles, and tertiary interactions. To date, through-space communication specifically measuring RNA tertiary structure has been difficult to isolate from structural communication reflective of other interactions. Here, we introduce mutual information as a filtering metric to isolate tertiary structure communication contained within smCCP data and use this strategy to characterize the structural ensemble of the SAM-III riboswitch. We identified an smCCP fingerprint that is selective for states containing a tertiary structure that forms concurrently with cognate ligand binding. We then successfully applied mutual information filters to independent RNAs and isolated through-space tertiary interactions in riboswitches and large RNAs with complex structures. smCCP, coupled with mutual information criteria, can now be used as a tertiary structure discovery tool, including to identify specific states in an ensemble that have a higher-order structure. These studies pave the way for the use of the straightforward smCCP experiment for discovery and characterization of tertiary structure motifs in complex RNAs.

A specific domain within the 3' untranslated region of Usutu virus confers resistance to the exonuclease ISG20

Usutu virus (USUV) and West Nile virus (WNV) are two closely related emerging mosquito-borne flaviviruses. Their natural hosts are wild birds, but they can also cause severe neurological disorders in humans. Both viruses are efficiently suppressed by type I interferon (IFN), which interferes with viral replication, dissemination, pathogenesis and transmission. Here, we show that the replication of USUV and WNV are inhibited through a common set of IFN-induced genes (ISGs), with the notable exception of ISG20, which USUV is resistant to. Strikingly, USUV was the only virus among all the other tested mosquito-borne flaviviruses that demonstrated resistance to the 3'-5' exonuclease activity of ISG20. Our findings highlight that the intrinsic resistance of the USUV genome, irrespective of the presence of cellular or viral proteins or protective post-transcriptional modifications, relies on a unique sequence present in its 3' untranslated region. Importantly, this genomic region alone can confer ISG20 resistance to a susceptible flavivirus, without compromising its infectivity, suggesting that it could be acquired by other flaviviruses. This study provides new insights into the strategy employed by emerging flaviviruses to overcome host defense mechanisms.

Identification of RNA structures and their roles in RNA functions

The development of high-throughput RNA structure profiling methods in the past decade has greatly facilitated our ability to map and characterize different aspects of RNA structures transcriptome-wide in cell populations, single cells and single molecules. The resulting high-resolution data have provided insights into the static and dynamic nature of RNA structures, revealing their complexity as they perform their respective functions in the cell. In this Review, we discuss recent technical advances in the determination of RNA structures, and the roles of RNA structures in RNA biogenesis and functions, including in transcription, processing, translation, degradation, localization and RNA structure-dependent condensates. We also discuss the current understanding of how RNA structures could guide drug design for treating genetic diseases and battling pathogenic viruses, and highlight existing challenges and future directions in RNA structure research.

Predicting RNA sequence-structure likelihood via structure-aware deep learning

BACKGROUND

The active functionalities of RNA are recognized to be heavily dependent on the structure and sequence. Therefore, a model that can accurately evaluate a design by giving RNA sequence-structure pairs would be a valuable tool for many researchers. Machine learning methods have been explored to develop such tools, showing promising results. However, two key issues remain. Firstly, the performance of machine learning models is affected by the features used to characterize RNA. Currently, there is no consensus on which features are the most effective for characterizing RNA sequence-structure pairs. Secondly, most existing machine learning methods extract features describing entire RNA molecule. We argue that it is essential to define additional features that characterize nucleotides and specific sections of RNA structure to enhance the overall efficacy of the RNA design process.

RESULTS

We develop two deep learning models for evaluating RNA sequence-secondary structure pairs. The first model, NU-ResNet, uses a convolutional neural network architecture that solves the aforementioned problems by explicitly encoding RNA sequence-structure information into a 3D matrix. Building upon NU-ResNet, our second model, NUMO-ResNet, incorporates additional information derived from the characterizations of RNA, specifically the 2D folding motifs. In this work, we introduce an automated method to extract these motifs based on fundamental secondary structure descriptions. We evaluate the performance of both models on an independent testing dataset. Our proposed models outperform the models from literatures in this independent testing dataset. To assess the robustness of our models, we conduct 10-fold cross validation. To evaluate the generalization ability of NU-ResNet and NUMO-ResNet across different RNA families, we train and test our proposed models in different RNA families. Our proposed models show superior performance compared to the models from literatures when being tested across different independent RNA families.

CONCLUSIONS

In this study, we propose two deep learning models, NU-ResNet and NUMO-ResNet, to evaluate RNA sequence-secondary structure pairs. These two models expand the field of data-driven approaches for learning RNA. Furthermore, these two models provide the new method to encode RNA sequence-secondary structure pairs.

Mimicking a Cellular Crowding Environment for Enzyme-Free Paper-Based Nucleic Acid Tests at the Point of Care

Point of care (PoC) nucleic acid amplification tests (NAATs) are a cornerstone of public health, providing the earliest and most accurate diagnostic method for many communicable diseases in the same location where the patient receives treatment. Communicable diseases, such as human immunodeficiency virus (HIV), disproportionately impact low-resource communities where NAATs are often unobtainable due to the resource-intensive enzymes that drive the tests. Enzyme-free nucleic acid detection methods, such as hybridization chain reaction (HCR), use DNA secondary structures for self-driven amplification schemes, producing large DNA nanostructures, capable of single-molecule detection in cellulo. These thermodynamically driven DNA-based tests have struggled to penetrate the PoC diagnostic field due to their inadequate limits of detection or complex workflows. Here, we present a proof-of-concept NAAT that combines HCR-based amplification of a target nucleic acid sequence with paper-based nucleic acid filtration and enrichment capable of detecting sub-pM levels of synthetic DNA. We reconstruct the favorable hybridization conditions of an in cellulo reaction in vitro by incubating HCR in an evaporating, microvolume environment containing poly(ethylene glycol) as a crowding agent. We demonstrate that the kinetics and thermodynamics of DNA-DNA and DNA-RNA hybridization is enhanced by the dynamic evaporating environment and inclusion of crowding agents, bringing HCR closer to meeting PoC NAAT needs.

Functional Validation of SAM Riboswitch Element A from Listeria monocytogenes

SreA is one of seven candidate S-adenosyl methionine (SAM) class I riboswitches identified in Listeria monocytogenes, a saprophyte and opportunistic foodborne pathogen. SreA precedes genes encoding a methionine ATP-binding cassette (ABC) transporter, which imports methionine and is presumed to regulate transcription of its downstream genes in a SAM-dependent manner. The proposed role of SreA in controlling the transcription of genes encoding an ABC transporter complex may have important implications for how the bacteria senses and responds to the availability of the metabolite SAM in the diverse environments in which L. monocytogenes persists. Here we validate SreA as a functional SAM-I riboswitch through ligand binding studies, structure characterization, and transcription termination assays. We determined that SreA has both a structure and SAM binding properties similar to those of other well-characterized SAM-I riboswitches. Despite the apparent structural similarities to previously described SAM-I riboswitches, SreA induces transcription termination in response to comparatively lower (nanomolar) ligand concentrations. Furthermore, SreA is a leaky riboswitch that permits some transcription of the downstream gene even in the presence of millimolar SAM, suggesting that L. monocytogenes may "dampen" the expression of genes for methionine import but likely does not turn them "OFF".

Protocol for profiling virus-to-host RNA-RNA interactions in infected cells by RIC-seq

Virus-to-host RNA-RNA interactions directly regulate host mRNA stability and viral replication. However, globally profiling virus-to-host in situ RNA-RNA interactions remains challenging. Here, we present an RNA in situ conformation sequencing (RIC-seq)-based protocol for mapping high-confidence virus-to-host in situ RNA-RNA interactions in infected cells. We detail steps for formaldehyde crosslinking, pCp-biotin labeling, in situ proximity ligation, chimeric RNA enrichment, strand-specific library construction, and data analysis. This protocol allows unbiased identification of virus-to-host RNA-RNA interactions for various RNA viruses and is potentially applicable to DNA virus-derived transcripts. For complete details on the use and execution of this protocol, please refer to Zhao et al.1.

Disulfide Tethering to Map Small Molecule Binding Sites Transcriptome-wide

We report the development of Tether-seq, a transcriptome-wide screen to probe RNA-small molecule interactions using disulfide tethering. This technique uses s4U metabolic labeling to provide sites for reversible and covalent attachment of small molecule disulfides to the transcriptome. By screening under reducing conditions, we identify interactions that are stabilized by binding over those driven by the reactivity of the RNA sites. When applied to cellular RNA, Tether-seq with a disulfide analogue of risdiplam, an FDA-approved drug that targets RNA to treat spinal muscular atrophy (SMA), revealed a number of potential binding sites, most prominently at a site within the cytochrome C oxidase 1 (COX1) transcript. Structure probing by SHAPE-MaP revealed a structured motif and confirmed binding to the lead molecule. This work demonstrates that these screens have the power to identify binding sites throughout the transcriptome and provide invaluable insight into the thermodynamic properties that define small molecule binding.

Prediction of aptamer affinity using an artificial intelligence approach

Aptamers are oligonucleotide sequences that can connect to particular target molecules, similar to monoclonal antibodies. They can be chosen by systematic evolution of ligands by exponential enrichment (SELEX), and are modifiable and can be synthesized. Even if the SELEX approach has been improved a lot, it is frequently challenging and time-consuming to identify aptamers experimentally. In particular, structure-based methods are the most used in computer-aided design and development of aptamers. For this purpose, numerous web-based platforms have been suggested for the purpose of forecasting the secondary structure and 3D configurations of RNAs and DNAs. Also, molecular docking and molecular dynamics (MD), which are commonly utilized in protein compound selection by structural information, are suitable for aptamer selection. On the other hand, from a large number of sequences, artificial intelligence (AI) may be able to quickly discover the possible aptamer candidates. Conversely, sophisticated machine and deep-learning (DL) models have demonstrated efficacy in forecasting the binding properties between ligands and targets during drug discovery; as such, they may provide a reliable and precise method for forecasting the binding of aptamers to targets. This research looks at advancements in AI pipelines and strategies for aptamer binding ability prediction, such as machine and deep learning, as well as structure-based approaches, molecular dynamics and molecular docking simulation methods.

Mapping the structural landscape of the yeast Ty3 retrotransposon RNA genome

Long terminal repeat (LTR)-retrotransposons are significant contributors to the evolution and diversity of eukaryotic genomes. Their RNA genomes (gRNA) serve as a template for protein synthesis and reverse transcription to a DNA copy, which can integrate into the host genome. Here, we used the SHAPE-MaP strategy to explore Ty3 retrotransposon gRNA structure in yeast and under cell-free conditions. Our study reveals the structural dynamics of Ty3 gRNA and the well-folded core, formed independently of the cellular environment. Based on the detailed map of Ty3 gRNA structure, we characterized the structural context of cis-acting sequences involved in reverse transcription and frameshifting. We also identified a novel functional sequence as a potential initiator for Ty3 gRNA dimerization. Our data indicate that the dimer is maintained by direct interaction between short palindromic sequences at the 5' ends of the two Ty3 gRNAs, resembling the model characteristic for other retroelements like HIV-1 and Ty1. This work points out a range of cell-dependent and -independent Ty3 gRNA structural changes that provide a solid background for studies on RNA structure-function relationships important for retroelement biology.

The separation between mRNA-ends is more variable than expected

Effective circularization of mRNA molecules is a key step for the efficient initiation of translation. Research has shown that the intrinsic separation of the ends of mRNA molecules is rather small, suggesting that intramolecular arrangements could provide this effective circularization. Considering that the innate proximity of RNA ends might have important unknown biological implications, we aimed to determine whether the close proximity of the ends of mRNA molecules is a conserved feature across organisms and gain further insights into the functional effects of the proximity of RNA ends. To do so, we studied the secondary structure of 274 full native mRNA molecules from 17 different organisms to calculate the contour length (CL) of the external loop as an index of their end-to-end separation. Our computational predictions show bigger variations (from 0.59 to 31.8 nm) than previously reported and also than those observed in random sequences. Our results suggest that separations larger than 18.5 nm are not favored, whereas short separations could be related to phenotypical stability. Overall, our work implies the existence of a biological mechanism responsible for the increase in the observed variability, suggesting that the CL features of the exterior loop could be relevant for the initiation of translation and that a short CL could contribute to the stability of phenotypes.

Machine learning in RNA structure prediction: Advances and challenges

RNA molecules play a crucial role in various biological processes, with their functionality closely tied to their structures. The remarkable advancements in machine learning techniques for protein structure prediction have shown promise in the field of RNA structure prediction. In this perspective, we discuss the advances and challenges encountered in constructing machine learning-based models for RNA structure prediction. We explore topics including model building strategies, specific challenges involved in predicting RNA secondary (2D) and tertiary (3D) structures, and approaches to these challenges. In addition, we highlight the advantages and challenges of constructing RNA language models. Given the rapid advances of machine learning techniques, we anticipate that machine learning-based models will serve as important tools for predicting RNA structures, thereby enriching our understanding of RNA structures and their corresponding functions.

Aptamer-associated colorimetric reverse transcription loop-mediated isothermal amplification assay for detection of dengue virus

Current diagnostic methods for dengue, such as serological tests, have limitations in terms of cross-reactivity with other viruses. To address this issue, we explored the potential of combining the loop-mediated isothermal amplification (LAMP) technique with the affinity of aptamers to develop point-of-care testing. In this study, we utilized 60 serum samples. An aptamer capable of binding to the dengue virus was employed as a platform for capturing genetic material, and its performance was compared to a commercial kit. Dengue virus was detected through RT-PCR and colorimetric reverse transcription loop-mediated isothermal amplification (RT-LAMP), allowing visual observation of the results without the need for equipment. In the context of the aptamer LAMP assay, our analysis revealed the detection of the dengue virus in 38 out of 60 samples, with 95% sensitivity and 100% specificity compared to RT-PCR and/or APTA-RT-PCR. Importantly, we observed no cross-reaction when assessing samples positive for the zika virus, underscoring the assay's selectivity. This innovative aptameric capture of the viral RNA in combination with the RT-LAMP (APTA-RT-LAMP) method has the potential to offer valuable molecular insights into neglected infectious diseases in a simpler and faster manner.

IMPORTANCE

Dengue is a neglected tropical disease of significant epidemiological importance in tropical and subtropical countries. Current diagnostics for this infection present challenges, such as cross-reactivity in serological tests. Finding ways to enhance the diagnosis of this disease is crucial, given the absence of specific treatments. An accurate, simple, and effective diagnosis contributes to the improved management of infected individuals. In this context, our work combines molecular biology techniques, such as isothermal loop amplification, with aptamers to detect the dengue virus in biological samples. Our method produces colorimetric results based on a color change, with outcomes available in less than 2 hours. Moreover, it requires simpler equipment compared to molecular PCR tests.

Characterization of the complete mitochondrial genome of a newly discovered torrent catfish, Liobagrus geumgangensis, and their phylogenetic relationships

BACKGROUND

A new Liobagrus fish was reported from the Korean Peninsula, but research on this taxon is lacking. Moreover, existing research on the mitogenome of the genus Liobagrus in Korea is very limited, and no studies have been conducted on structural characteristics of transfer RNA (tRNA) or gene order comparisons between taxa; instead, research has been restricted to basic phylogeny.

OBJECTIVE

The complete mitochondrial genome of Liobagrus geumgangensis was analyzed for the first time. We then aimed to reconstruct the phylogenetic relationships of the genus Liobagrus and estimate the divergence time of speciation events.

METHODS

We used a dissected fin clip from an adult of Liobagrus geumgangensis. Genomic DNA was extracted and analyzed with whole genome sequencing (WGS) and assembled by the NOVOPlasty method. The mitogenome sequence was annotated, and a genome map, tRNA structure, and phylogenetic tree were constructed using maximum likelihood analysis. In addition, divergence time was estimated.

RESULTS

The mitochondrial genome was 16,522 bp in length and comprised 37 genes. The overall base composition was 30.5% A, 25.5% T, 28.4% C, and 15.7% G. Most tRNAs exhibited the typical clover leaf shape, except trnS1. Phylogenetic analysis revealed that Liobagrus geumgangensis clustered within a clade with four other Liobagrus species exclusive to the southern region of the Korean Peninsula. Its divergence was estimated to have occurred during the late Miocene.

CONCLUSION

Characteristics of Liobagrus geumgangensis mitogenome were consistent with those of other torrent catfish species. Time scale estimation revealed distinct groupings, with some distributed across mainland Asia and others in the southern region of the Korean Peninsula. Notably, the Korean Peninsula group was identified as its own lineage, comprising entirely endemic species.

NNDB: An Expanded Database of Nearest Neighbor Parameters for Predicting Stability of Nucleic Acid Secondary Structures

Nearest neighbor thermodynamic parameters are widely used for RNA and DNA secondary structure prediction and to model thermodynamic ensembles of secondary structures. The Nearest Neighbor Database (NNDB) is a freely available web resource (https://rna.urmc.rochester.edu/NNDB) that provides the functional forms, parameter values, and example calculations. The NNDB provides the 1999 and 2004 set of RNA folding nearest neighbor parameters. We expanded the database to include a set of DNA parameters and a set of RNA parameters that includes m6A in addition to the canonical RNA nucleobases. The site was redesigned using the Quarto open-source publishing system. A downloadable PDF version of the complete resource and downloadable sets of nearest neighbor parameters are available.

A systematic search for RNA structural switches across the human transcriptome

RNA structural switches are key regulators of gene expression in bacteria, but their characterization in Metazoa remains limited. Here, we present SwitchSeeker, a comprehensive computational and experimental approach for systematic identification of functional RNA structural switches. We applied SwitchSeeker to the human transcriptome and identified 245 putative RNA switches. To validate our approach, we characterized a previously unknown RNA switch in the 3' untranslated region of the RORC (RAR-related orphan receptor C) transcript. In vivo dimethyl sulfate (DMS) mutational profiling with sequencing (DMS-MaPseq), coupled with cryogenic electron microscopy, confirmed its existence as two alternative structural conformations. Furthermore, we used genome-scale CRISPR screens to identify trans factors that regulate gene expression through this RNA structural switch. We found that nonsense-mediated messenger RNA decay acts on this element in a conformation-specific manner. SwitchSeeker provides an unbiased, experimentally driven method for discovering RNA structural switches that shape the eukaryotic gene expression landscape.

Unveiling the role of PA0730.1 sRNA in Pseudomonas aeruginosa virulence and biofilm formation: Exploring rpoS and mucA regulation

Small RNA (sRNA) in bacteria serve as the key messengers in regulating genes associated with quorum sensing controlled bacterial virulence. This study was aimed to unveil the regulatory role of sRNA PA0730.1 on the expression of various traits of Pseudomonas aeruginosa linked to pathogenicity, with special emphasis on the growth, colony morphology, cell motility, biofilm formation, and the expression of diverse virulence factors. PA0730.1 sRNA was found to be upregulated both during planktonic stationary growth phase and at biofilm state of P. aeruginosa PAO1. PA0730.1 deleted strain showed significant growth retardation with increased doubling time. Overexpression of PA0730.1 led to enhanced motility and biofilm formation, while the ∆PA0730.1 strain displayed significant inhibition in motility and biofilm formation. Furthermore, PA0730.1 was found to regulate the synthesis of selected virulence factors of P. aeruginosa. These observations in PA0730.1+ and ∆PA0730.1 were found to be correlated with the PA0730.1-mediated repression of transcription regulators, mucA and rpoS, both at transcriptional and translational levels. The results suggest that PA0730.1 sRNA might be a promising target for developing new drug to counter P. aeruginosa pathogenesis, and could also help in RNA oligonucleotide based therapeutic research for formulating a novel therapeutant.

Characterization of the SARS-CoV-2 Genome 3'-Untranslated Region Interactions with Host MicroRNAs

The 2019 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has marked the spread of a novel human coronavirus. SARS-CoV-2 has exhibited increased disease severity and immune evasion across its variants, and the molecular mechanisms behind these phenomena remain largely unknown. Conserved elements of the viral genome, such as secondary structures within the 3'-untranslated region (UTR), could prove crucial in furthering our understanding of the host-virus interface. Analysis of the SARS-CoV-2 viral genome 3'-UTR revealed the potential for host microRNA (miR) binding sites, allowing for sequence-specific interactions. In this study, we demonstrate that the SARS-CoV-2 genome 3'-UTR binds the host cellular miRs miR-34a-5p, miR-34b-5p, and miR-760-3p in vitro. Native gel electrophoresis and steady-state fluorescence spectroscopy were utilized to biophysically characterize the binding of these miRs to their predicted sites within the SARS-CoV-2 genome 3'-UTR. Additionally, we investigated 2'-fluoro-d-arabinonucleic acid (FANA) analogs as competitive binding inhibitors for these interactions. These miRs modulate the translation of granulin (GRN), interleukin-6 (IL-6), and the IL-6 receptor (IL-6R), all of which are key modulators and activators of JAK/STAT3 signaling and are implicated in regulation of the immune response. Thus, we propose that hijacking of these miRs by SARS-CoV-2 could identify a mechanism of host immune modulation by the virus. The mechanisms detailed in this study have the potential to drive the development of antiviral treatments for SARS-CoV-2, through direct targeting of the virus-host interface.

Sub-genomic flaviviral RNA elements increase the stability and abundance of recombinant AAV vector transcripts

Many viruses have evolved structured RNA elements that can influence transcript abundance and translational efficiency, and help evade host immune factors by hijacking cellular machinery during replication. Here, we evaluated the functional impact of sub-genomic flaviviral RNAs (sfRNAs) known to stall exoribonuclease activity, by incorporating these elements into recombinant adeno-associated viral (AAV) genome cassettes. Specifically, sfRNAs from Dengue, Zika, Japanese Encephalitis, Yellow Fever, Murray Valley Encephalitis, and West Nile viruses increased transcript stability and transgene expression compared to a conventional woodchuck hepatitis virus element (WPRE). Further dissection of engineered transcripts revealed that sfRNA elements (i) require incorporation in cis within the 3' untranslated region (UTR) of AAV genomes, (ii) require minimal dumbbell structures to exert the observed effects, and (iii) can stabilize AAV transcripts independent of 5'-3' exoribonuclease 1 (XRN1)-mediated decay. Additionally, preliminary in vivo assessment of AAV vectors bearing sfRNA elements in mice achieved increased transcript abundance and expression in cardiac tissue. Leveraging the functional versatility of engineered viral RNA elements may help improve the potency of AAV vector-based gene therapies.

IMPORTANCE

Viral RNA elements can hijack host cell machinery to control stability of transcripts and consequently, infection. Studies that help better understand such viral elements can provide insights into antiviral strategies and also potentially leverage these features for therapeutic applications. In this study, by incorporating structured flaviviral RNA elements into recombinant adeno-associated viral (AAV) vector genomes, we show improved AAV transcript stability and transgene expression can be achieved, with implications for gene transfer.

RNA tertiary structure and conformational dynamics revealed by BASH MaP

The functional effects of an RNA can arise from complex three-dimensional folds known as tertiary structures. However, predicting the tertiary structure of an RNA and whether an RNA adopts distinct tertiary conformations remains challenging. To address this, we developed BASH MaP, a single-molecule dimethyl sulfate (DMS) footprinting method and DAGGER, a computational pipeline, to identify alternative tertiary structures adopted by different molecules of RNA. BASH MaP utilizes potassium borohydride to reveal the chemical accessibility of the N7 position of guanosine, a key mediator of tertiary structures. We used BASH MaP to identify diverse conformational states and dynamics of RNA G-quadruplexes, an important RNA tertiary motif, in vitro and in cells. BASH MaP and DAGGER analysis of the fluorogenic aptamer Spinach reveals that it adopts alternative tertiary conformations which determine its fluorescence states. BASH MaP thus provides an approach for structural analysis of RNA by revealing previously undetectable tertiary structures.

KREH2 helicase represses ND7 mRNA editing in procyclic-stage Trypanosoma brucei by opposite modulation of canonical and 'moonlighting' gRNA utilization creating a proposed mRNA structure

Unknown factors regulate mitochondrial U-insertion/deletion (U-indel) RNA editing in procyclic-form (PCF) and bloodstream-form (BSF) T. brucei. This editing, directed by anti-sense gRNAs, creates canonical protein-encoding mRNAs and may developmentally control respiration. Canonical editing by gRNAs that specify protein-encoding mRNA sequences occurs amid massive non-canonical editing of unclear sources and biological significance. We found PCF-specific repression at a major early checkpoint in mRNA ND7, involving helicase KREH2-dependent opposite modulation of canonical and non-canonical 'terminator' gRNA utilization. Terminator-programmed editing derails canonical editing and installs proposed repressive structure in 30% of the ND7 transcriptome. BSF-to-PCF differentiation in vitro recreated this negative control. Remarkably, KREH2-RNAi knockdown relieved repression and increased editing progression by reverting canonical/terminator gRNA utilization. ND7 transcripts lacking early terminator-directed editing in PCF exhibited similar negative editing control along the mRNA sequence, suggesting global modulation of gRNA utilization fidelity. The terminator is a 'moonlighting' gRNA also associated with mRNA COX3 canonical editing, so the gRNA transcriptome seems multifunctional. Thus, KREH2 is the first identified repressor in developmental editing control. This and our prior work support a model whereby KREH2 activates or represses editing in a stage and substrate-specific manner. KREH2's novel dual role tunes mitochondrial gene expression in either direction during development.

Modeling the structure and DAP5-binding site of the FGF-9 5'-UTR RNA utilized in cap-independent translation

Cap-independent or eukaryotic initiation factor (eIF) 4E-independent, translation initiation in eukaryotes requires scaffolding protein eIF4G or its homolog, death-associated protein 5 (DAP5). eIF4G associates with the 40S ribosomal subunit, recruiting the ribosome to the RNA transcript. A subset of RNA transcripts, such as fibroblast growth factor 9 (FGF-9), contain 5' untranslated regions (5' UTRs) that directly bind DAP5 or eIF4GI. For viral mRNA, eIF recruitment usually utilizes RNA structure, such as a pseudoknot or stem-loops, and the RNA-helicase eIF4A is required for DAP5- or 4G-mediated translation, suggesting these 5' UTRs are structured. However, for cellular IRES-like translation, no consensus RNA structures or sequences have yet been identified for eIF binding. However, the DAP5-binding site within the FGF-9 5' UTR is unknown. Moreover, DAP5 binds to other, dissimilar 5' UTRs, some of which require an unpaired, accessible 5' end to stimulate cap-independent translation. Using SHAPE-seq, we modeled the 186 nt FGF-9 5'-UTR RNA's complex secondary structure in vitro. Further, DAP5 footprinting, toeprinting, and UV cross-linking experiments identify DAP5-RNA interactions. Modeling of FGF-9 5'-UTR tertiary structure aligns DAP5-interacting nucleotides on one face of the predicted structure. We propose that RNA structure involving tertiary folding, rather than a conserved sequence or secondary structure, acts as a DAP5-binding site. DAP5 appears to contact nucleotides near the start codon. Our findings offer a new perspective in the hunt for cap-independent translational enhancers. Structural, rather than sequence-specific, eIF-binding sites may act as attractive chemotherapeutic targets or as dosage tools for mRNA-based therapies.

Morphology, complete mitochondrial genome, and molecular phylogeny of Rhabdias macrocephalum n. sp. (Nematoda: Rhabdiasidae) from Diploderma splendidum (Reptilia: Agamidae)

Species of the genus Rhabdias Stiles & Hassall, 1905 are common parasitic nematodes occurring in the lungs of amphibians and reptiles worldwide. In the present study, Rhabdias macrocephalum n. sp. is described using integrated morphological methods (light and scanning electron microscopy) and molecular approaches (sequencing of the nuclear 28S and ITS regions, and mitochondrial cox1, cox2, and 12S genes) based on specimens collected from the green striped tree dragon Diploderma splendidum (Barbour & Dunn) (Reptilia: Agamidae) in China. The complete mitochondrial genome of R. macrocephalum n. sp. was sequenced and annotated: it is 14,819 bp in length, including 12 protein coding genes (missing atp8), 22 tRNA genes, 2 rRNA genes and three non-coding regions. The gene arrangement of R. macrocephalum n. sp. is different from all of the currently available mitogenomes of nematodes and represents a novel type of mitochondrial gene arrangement reported in Nematoda. Molecular phylogenetic results based on the ITS + 28S data support the monophyly of Entomelas, Pneumonema, Serpentirhabdias, and Rhabdias, and showed R. macrocephalum n. sp. forming a most basal lineage in Rhabdias.

Logical regulation of endogenous gene expression using programmable, multi-input processing CRISPR guide RNAs

The CRISPR-Cas system provides a versatile RNA-guided approach for a broad range of applications. Thanks to advances in RNA synthetic biology, the engineering of guide RNAs (gRNAs) has enabled the conditional control of the CRISPR-Cas system. However, achieving precise regulation of the CRISPR-Cas system for efficient modulation of internal metabolic processes remains challenging. In this work, we developed a robust dCas9 regulator with engineered conditional gRNAs to enable tight control of endogenous genes. Our conditional gRNAs in Escherichia coli can control gene expression upon specific interaction with trigger RNAs with a dynamic range as high as 130-fold, evaluating up to a three-input logic A OR (B AND C). The conditional gRNA-mediated targeting of endogenous metabolic genes, lacZ, malT and poxB, caused differential regulation of growth in Escherichia coli via metabolic flux control. Further, conditional gRNAs could regulate essential cytoskeleton genes, ftsZ and mreB, to control cell filamentation and division. Finally, three types of two-input logic gates could be applied for the conditional control of ftsZ regulation, resulting in morphological changes. The successful operation and application of conditional gRNAs based on programmable RNA interactions suggests that our system could be compatible with other Cas-effectors and implemented in other host organisms.

Regulation of TCA cycle genes by srbA sRNA: Impacts on Pseudomonas aeruginosa virulence and survival

Pseudomonas aeruginosa, an opportunistic bacterial pathogen of public health concern, is known for its metabolic versatility, adaptability in harsh environment, and pathogenic aggressiveness. P. aeruginosa relies on various regulatory networks modulated by small non-coding RNAs, which in turn influence different physiological traits such as metabolism, stress response, and pathogenesis. In this study, srbA sRNA has been shown to play a diverse role in regulating cellular metabolism and the production of different virulence factors in P. aeruginosa. srbA was found to control the TCA cycle, a key regulatory pathway for cellular metabolism and energy production, by regulating three main enzymes: citrate synthase (gltA), isocitrate dehydrogenase (icd), and α-ketoglutarate dehydrogenase E1 subunit (sucA) at both the transcriptional and translational levels. By modulating the TCA cycle, srbA could help the bacteria to adapt nutritional stress by lowering energy consumption. Additionally, srbA has been found to differentially regulate production of various virulence factors such as rhamnolipid, elastase, LasA protease, and pyocyanin under both nutrient-rich and nutrient-limiting conditions. It could also influence motilities in P. aeruginosa, linked to biofilm formation and pathogenicity. Thus, srbA might hold a promise in the research area for identifying virulence pathways and developing novel therapeutic targets to combat the global pathogenic threat of P. aeruginosa.

Discovery and Quantification of Long-Range RNA Base Pairs in Coronavirus Genomes with SEARCH-MaP and SEISMIC-RNA

RNA molecules perform a diversity of essential functions for which their linear sequences must fold into higher-order structures. Techniques including crystallography and cryogenic electron microscopy have revealed 3D structures of ribosomal, transfer, and other well-structured RNAs; while chemical probing with sequencing facilitates secondary structure modeling of any RNAs of interest, even within cells. Ongoing efforts continue increasing the accuracy, resolution, and ability to distinguish coexisting alternative structures. However, no method can discover and quantify alternative structures with base pairs spanning arbitrarily long distances - an obstacle for studying viral, messenger, and long noncoding RNAs, which may form long-range base pairs. Here, we introduce the method of Structure Ensemble Ablation by Reverse Complement Hybridization with Mutational Profiling (SEARCH-MaP) and software for Structure Ensemble Inference by Sequencing, Mutation Identification, and Clustering of RNA (SEISMIC-RNA). We use SEARCH-MaP and SEISMIC-RNA to discover that the frameshift stimulating element of SARS coronavirus 2 base-pairs with another element 1 kilobase downstream in nearly half of RNA molecules, and that this structure competes with a pseudoknot that stimulates ribosomal frameshifting. Moreover, we identify long-range base pairs involving the frameshift stimulating element in other coronaviruses including SARS coronavirus 1 and transmissible gastroenteritis virus, and model the full genomic secondary structure of the latter. These findings suggest that long-range base pairs are common in coronaviruses and may regulate ribosomal frameshifting, which is essential for viral RNA synthesis. We anticipate that SEARCH-MaP will enable solving many RNA structure ensembles that have eluded characterization, thereby enhancing our general understanding of RNA structures and their functions. SEISMIC-RNA, software for analyzing mutational profiling data at any scale, could power future studies on RNA structure and is available on GitHub and the Python Package Index.

eIF4E-independent translation is largely eIF3d-dependent

Translation initiation is a highly regulated step needed for protein synthesis. Most cell-based mechanistic work on translation initiation has been done using non-stressed cells growing in medium with sufficient nutrients and oxygen. This has yielded our current understanding of 'canonical' translation initiation, involving recognition of the mRNA cap by eIF4E1 followed by successive recruitment of initiation factors and the ribosome. Many cells, however, such as tumor cells, are exposed to stresses such as hypoxia, low nutrients or proteotoxic stress. This leads to inactivation of mTORC1 and thereby inactivation of eIF4E1. Hence the question arises how cells translate mRNAs under such stress conditions. We study here how mRNAs are translated in an eIF4E1-independent manner by blocking eIF4E1 using a constitutively active version of eIF4E-binding protein (4E-BP). Via ribosome profiling we identify a subset of mRNAs that are still efficiently translated when eIF4E1 is inactive. We find that these mRNAs preferentially release eIF4E1 when eIF4E1 is inactive and bind instead to eIF3d via its cap-binding pocket. eIF3d then enables these mRNAs to be efficiently translated due to its cap-binding activity. In sum, our work identifies eIF3d-dependent translation as a major mechanism enabling mRNA translation in an eIF4E-independent manner.

Targeting sgRNA N secondary structure as a way of inhibiting SARS-CoV-2 replication

SARS-CoV-2 is a betacoronavirus that causes COVID-19, a global pandemic that has resulted in many infections, deaths, and socio-economic challenges. The virus has a large positive-sense, single-stranded RNA genome of ∼30 kb, which produces subgenomic RNAs (sgRNAs) through discontinuous transcription. The most abundant sgRNA is sgRNA N, which encodes the nucleocapsid (N) protein. In this study, we probed the secondary structure of sgRNA N and a shorter model without a 3' UTR in vitro, using the SHAPE (selective 2'-hydroxyl acylation analyzed by a primer extension) method and chemical mapping with dimethyl sulfate and 1-cyclohexyl-(2-morpholinoethyl) carbodiimide metho-p-toluene sulfonate. We revealed the secondary structure of sgRNA N and its shorter variant for the first time and compared them with the genomic RNA N structure. Based on the structural information, we designed gapmers, siRNAs and antisense oligonucleotides (ASOs) to target the N protein coding region of sgRNA N. We also generated eukaryotic expression vectors containing the complete sequence of sgRNA N and used them to screen for new SARS-CoV-2 gene N expression inhibitors. Our study provides novel insights into the structure and function of sgRNA N and potential therapeutic tools against SARS-CoV-2.

Work distribution for unzipping processes

A simple zipper model is introduced, representing in a simplified way, e.g., the folded DNA double helix or hairpin structures in RNA. The double stranded hairpin is connected to a heat bath at temperature T and subject to an external force f, which couples to the free length L of the unzipped sequence. The leftmost zipped position can be seen as the position of a random walker in a special external field. Increasing the force leads to a zipping-unzipping first-order phase transition at a critical force f_{c}(T) in the thermodynamic limit of a very large chain. We compute analytically, as a function of temperature T and force f, the full distribution P(L) of free lengths in the thermodynamic limit and show that it is qualitatively very different for ff_{c}. Next we consider quasistatic work processes where the force is incremented according to a linear protocol. Having obtained P(L) already allows us to derive an analytical expression for the work distribution P(W) in the zipped phase f Collapse

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Affiliation(s)

Peter Werner Institut für Physik, Universität Oldenburg, 26111 Oldenburg, Germany
Alexander K Hartmann Institut für Physik, Universität Oldenburg, 26111 Oldenburg, Germany
Satya N Majumdar Laboratoire de Physique Théorique et Modèles Statistique, Université Paris-Saclay, 91405 Orsay CEDEX, France

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RNA nanotechnology on the horizon: Self-assembly, chemical modifications, and functional applications

RNA nanotechnology harnesses the unique chemical and structural properties of RNA to build nanoassemblies and supramolecular structures with dynamic and functional capabilities. This review focuses on design and assembly approaches to building RNA structures, the RNA chemical modifications used to enhance stability and functionality, and modern-day applications in therapeutics, biosensing, and bioimaging.

Discovery and Quantification of Long-Range RNA Base Pairs in Coronavirus Genomes with SEARCH-MaP and SEISMIC-RNA

RNA molecules perform a diversity of essential functions for which their linear sequences must fold into higher-order structures. Techniques including crystallography and cryogenic electron microscopy have revealed 3D structures of ribosomal, transfer, and other well-structured RNAs; while chemical probing with sequencing facilitates secondary structure modeling of any RNAs of interest, even within cells. Ongoing efforts continue increasing the accuracy, resolution, and ability to distinguish coexisting alternative structures. However, no method can discover and quantify alternative structures with base pairs spanning arbitrarily long distances - an obstacle for studying viral, messenger, and long noncoding RNAs, which may form long-range base pairs. Here, we introduce the method of Structure Ensemble Ablation by Reverse Complement Hybridization with Mutational Profiling (SEARCH-MaP) and software for Structure Ensemble Inference by Sequencing, Mutation Identification, and Clustering of RNA (SEISMIC-RNA). We use SEARCH-MaP and SEISMIC-RNA to discover that the frameshift stimulating element of SARS coronavirus 2 base-pairs with another element 1 kilobase downstream in nearly half of RNA molecules, and that this structure competes with a pseudoknot that stimulates ribosomal frameshifting. Moreover, we identify long-range base pairs involving the frameshift stimulating element in other coronaviruses including SARS coronavirus 1 and transmissible gastroenteritis virus, and model the full genomic secondary structure of the latter. These findings suggest that long-range base pairs are common in coronaviruses and may regulate ribosomal frameshifting, which is essential for viral RNA synthesis. We anticipate that SEARCH-MaP will enable solving many RNA structure ensembles that have eluded characterization, thereby enhancing our general understanding of RNA structures and their functions. SEISMIC-RNA, software for analyzing mutational profiling data at any scale, could power future studies on RNA structure and is available on GitHub and the Python Package Index.

A highly specific aptamer for the SARS-CoV-2 spike protein from the authentic strain

DNA aptamers are oligonucleotides that specifically bind to target molecules, similar to how antibodies bind to antigens. We identified an aptamer named MEZ that is highly specific to the receptor-binding domain, RBD, of the SARS-CoV-2 spike protein from the Wuhan-Hu-1 strain. The SELEX procedure was utilized to enrich the initial 31-mer oligonucleotide library with the target aptamer. The aptamer identification was performed using the novel protocol based on nanopore sequencing developed in this study. The MEZ aptamer was chemically synthesized and tested for binding with the SARS-CoV-2 RBD of the spike protein from different strains. The Kd is 6.5 nM for the complex with the RBD from the Wuhan-Hu-1 strain, which is comparable with known aptamers; the advantage is that the MEZ aptamer is smaller than known analogs. The proposed aptamer is highly selective for the RBD protein from the Wuhan-Hu-1 strain and does not form complexes with the RBD from Beta, Delta and Omicron strains. Experimental and theoretical studies together revealed the molecular mechanism of aptamer binding. The aptamer occupies the same binding site as ACE2 when bound to the RBD. The 3'-end of the MEZ aptamer is important for complex formation and is responsible for the discrimination of the RBD protein from a specific strain. The 5'-end is responsible for the formation of a loop in the 3D structure of the aptamer, which is important for proper binding.

Comparative analysis of RNA 3D structure prediction methods: towards enhanced modeling of RNA-ligand interactions

Accurate RNA structure models are crucial for designing small molecule ligands that modulate their functions. This study assesses six standalone RNA 3D structure prediction methods-DeepFoldRNA, RhoFold, BRiQ, FARFAR2, SimRNA and Vfold2, excluding web-based tools due to intellectual property concerns. We focus on reproducing the RNA structure existing in RNA-small molecule complexes, particularly on the ability to model ligand binding sites. Using a comprehensive set of RNA structures from the PDB, which includes diverse structural elements, we found that machine learning (ML)-based methods effectively predict global RNA folds but are less accurate with local interactions. Conversely, non-ML-based methods demonstrate higher precision in modeling intramolecular interactions, particularly with secondary structure restraints. Importantly, ligand-binding site accuracy can remain sufficiently high for practical use, even if the overall model quality is not optimal. With the recent release of AlphaFold 3, we included this advanced method in our tests. Benchmark subsets containing new structures, not used in the training of the tested ML methods, show that AlphaFold 3's performance was comparable to other ML-based methods, albeit with some challenges in accurately modeling ligand binding sites. This study underscores the importance of enhancing binding site prediction accuracy and the challenges in modeling RNA-ligand interactions accurately.

A novel regulatory interplay between atypical B12 riboswitches and uORF translation in Mycobacterium tuberculosis

Vitamin B12 is an essential cofactor in all domains of life and B12-sensing riboswitches are some of the most widely distributed riboswitches. Mycobacterium tuberculosis, the causative agent of tuberculosis, harbours two B12-sensing riboswitches. One controls expression of metE, encoding a B12-independent methionine synthase, the other controls expression of ppe2 of uncertain function. Here, we analysed ligand sensing, secondary structure and gene expression control of the metE and ppe2 riboswitches. Our results provide the first evidence of B12 binding by these riboswitches and show that they exhibit different preferences for individual isoforms of B12, use distinct regulatory and structural elements and act as translational OFF switches. Based on our results, we propose that the ppe2 switch represents a new variant of Class IIb B12-sensing riboswitches. Moreover, we have identified short translated open reading frames (uORFs) upstream of metE and ppe2, which modulate the expression of their downstream genes. Translation of the metE uORF suppresses MetE expression, while translation of the ppe2 uORF is essential for PPE2 expression. Our findings reveal an unexpected regulatory interplay between B12-sensing riboswitches and the translational machinery, highlighting a new level of cis-regulatory complexity in M. tuberculosis. Attention to such mechanisms will be critical in designing next-level intervention strategies.

The 5'-terminal stem-loop RNA element of SARS-CoV-2 features highly dynamic structural elements that are sensitive to differences in cellular pH

We present the nuclear magnetic resonance spectroscopy (NMR) solution structure of the 5'-terminal stem loop 5_SL1 (SL1) of the SARS-CoV-2 genome. SL1 contains two A-form helical elements and two regions with non-canonical structure, namely an apical pyrimidine-rich loop and an asymmetric internal loop with one and two nucleotides at the 5'- and 3'-terminal part of the sequence, respectively. The conformational ensemble representing the averaged solution structure of SL1 was validated using NMR residual dipolar coupling (RDC) and small-angle X-ray scattering (SAXS) data. We show that the internal loop is the major binding site for fragments of low molecular weight. This internal loop of SL1 can be stabilized by an A12-C28 interaction that promotes the transient formation of an A+•C base pair. As a consequence, the pKa of the internal loop adenosine A12 is shifted to 5.8, compared to a pKa of 3.63 of free adenosine. Furthermore, applying a recently developed pH-differential mutational profiling (PD-MaP) approach, we not only recapitulated our NMR findings of SL1 but also unveiled multiple sites potentially sensitive to pH across the 5'-UTR of SARS-CoV-2.

The human mitochondrial mRNA structurome reveals mechanisms of gene expression

The human mitochondrial genome encodes crucial oxidative phosphorylation system proteins, pivotal for aerobic energy transduction. They are translated from nine monocistronic and two bicistronic transcripts whose native structures remain unexplored, posing a gap in understanding mitochondrial gene expression. In this work, we devised the mitochondrial dimethyl sulfate mutational profiling with sequencing (mitoDMS-MaPseq) method and applied detection of RNA folding ensembles using expectation-maximization (DREEM) clustering to unravel the native mitochondrial messenger RNA (mt-mRNA) structurome in wild-type (WT) and leucine-rich pentatricopeptide repeat-containing protein (LRPPRC)-deficient cells. Our findings elucidate LRPPRC's role as a holdase contributing to maintaining mt-mRNA folding and efficient translation. mt-mRNA structural insights in WT mitochondria, coupled with metabolic labeling, unveil potential mRNA-programmed translational pausing and a distinct programmed ribosomal frameshifting mechanism. Our data define a critical layer of mitochondrial gene expression regulation. These mt-mRNA folding maps provide a reference for studying mt-mRNA structures in diverse physiological and pathological contexts.

Direct testing of natural twister ribozymes from over a thousand organisms reveals a broad tolerance for structural imperfections

Twister ribozymes are an extensively studied class of nucleolytic RNAs. Thousands of natural twisters have been proposed using sequence homology and structural descriptors. Yet, most of these candidates have not been validated experimentally. To address this gap, we developed CHiTA (Cleavage High-Throughput Assay), a high-throughput pipeline utilizing massively parallel oligonucleotide synthesis and next-generation sequencing to test putative ribozymes en masse in a scarless fashion. As proof of principle, we applied CHiTA to a small set of known active and mutant ribozymes. We then used CHiTA to test two large sets of naturally occurring twister ribozymes: over 1, 600 previously reported putative twisters and ∼1, 000 new candidate twisters. The new candidates were identified computationally in ∼1, 000 organisms, representing a massive increase in the number of ribozyme-harboring organisms. Approximately 94% of the twisters we tested were active and cleaved site-specifically. Analysis of their structural features revealed that many substitutions and helical imperfections can be tolerated. We repeated our computational search with structural descriptors updated from this analysis, whereupon we identified and confirmed the first intrinsically active twister ribozyme in mammals. CHiTA broadly expands the number of active twister ribozymes found in nature and provides a powerful method for functional analyses of other RNAs.

GRAPHICAL ABSTRACT

DNA aptamer-functionalized PDA nanoparticles: from colloidal chemistry to biosensor applications

Polydopamine nanoparticles (PDA NPs) are widely utilized in the field of biomedical science for surface functionalization because of their unique characteristics, such as simple and low-cost preparation methods, good adhesive properties, and ability to incorporate amine and oxygen-rich chemical groups. However, challenges in the application of PDA NPs as surface coatings on electrode surfaces and in conjugation with biomolecules for electrochemical sensors still exist. In this work, we aimed to develop an electrochemical interface based on PDA NPs conjugated with a DNA aptamer for the detection of glycated albumin (GA) and to study DNA aptamers on the surfaces of PDA NPs to understand the aptamer-PDA surface interactions using molecular dynamics (MD) simulation. PDA NPs were synthesized by the oxidation of dopamine in Tris buffer at pH 10.5, conjugated with DNA aptamers specific to GA at different concentrations (0.05, 0.5, and 5 μM), and deposited on screen-printed carbon electrodes (SPCEs). The charge transfer resistance of the PDA NP-coated SPCEs decreased, indicating that the PDA NP composite is a conductive bioorganic material. Transmission electron microscopy (TEM) and scanning electron microscopy (SEM) confirmed that the PDA NPs were spherical, and dynamic light scattering (DLS), Fourier transform infrared spectroscopy (FTIR), and Raman spectroscopy data indicated the successful conjugation of the aptamers on the PDA NPs. The as-prepared electrochemical interface was employed for the detection of GA. The detection limit was 0.17 μg/mL. For MD simulation, anti-GA aptamer through the 5'terminal end in a single-stranded DNA-aptamer structure and NH2 linker showed a stable structure with its axis perpendicular to the PDA surface. These findings provide insights into improved biosensor design and have demonstrated the potential for employing electrochemical PDA NP interfaces in point-of-care applications.

Developing, characterizing and modeling CRISPR-based point-of-use pathogen diagnostics

Recent years have seen intense interest in the development of point-of-care nucleic acid diagnostic technologies to address the scaling limitations of laboratory-based approaches. Chief among these are combinations of isothermal amplification approaches with CRISPR-based detection and readouts of target products. Here, we contribute to the growing body of rapid, programmable point-of-care pathogen tests by developing and optimizing a one-pot NASBA-Cas13a nucleic acid detection assay. This test uses the isothermal amplification technique NASBA to amplify target viral nucleic acids, followed by Cas13a-based detection of amplified sequences. We first demonstrate an in-house formulation of NASBA that enables optimization of individual NASBA components. We then present design rules for NASBA primer sets and LbuCas13a guide RNAs for fast and sensitive detection of SARS-CoV-2 viral RNA fragments, resulting in 20 - 200 aM sensitivity without any specialized equipment. Finally, we explore the combination of high-throughput assay condition screening with mechanistic ordinary differential equation modeling of the reaction scheme to gain a deeper understanding of the NASBA-Cas13a system. This work presents a framework for developing a mechanistic understanding of reaction performance and optimization that uses both experiments and modeling, which we anticipate will be useful in developing future nucleic acid detection technologies.

Detecting haplotype-specific transcript variation in long reads with FLAIR2

BACKGROUND

RNA-seq has brought forth significant discoveries regarding aberrations in RNA processing, implicating these RNA variants in a variety of diseases. Aberrant splicing and single nucleotide variants (SNVs) in RNA have been demonstrated to alter transcript stability, localization, and function. In particular, the upregulation of ADAR, an enzyme that mediates adenosine-to-inosine editing, has been previously linked to an increase in the invasiveness of lung adenocarcinoma cells and associated with splicing regulation. Despite the functional importance of studying splicing and SNVs, the use of short-read RNA-seq has limited the community's ability to interrogate both forms of RNA variation simultaneously.

RESULTS

We employ long-read sequencing technology to obtain full-length transcript sequences, elucidating cis-effects of variants on splicing changes at a single molecule level. We develop a computational workflow that augments FLAIR, a tool that calls isoform models expressed in long-read data, to integrate RNA variant calls with the associated isoforms that bear them. We generate nanopore data with high sequence accuracy from H1975 lung adenocarcinoma cells with and without knockdown of ADAR. We apply our workflow to identify key inosine isoform associations to help clarify the prominence of ADAR in tumorigenesis.

CONCLUSIONS

Ultimately, we find that a long-read approach provides valuable insight toward characterizing the relationship between RNA variants and splicing patterns.

RNATACs: Multispecific small molecules targeting RNA by induced proximity

RNA-targeting small molecules (rSMs) have become an attractive modality to tackle traditionally undruggable proteins and expand the druggable space. Among many innovative concepts, RNA-targeting chimeras (RNATACs) represent a new class of multispecific, induced proximity small molecules that act by chemically bringing RNA targets into proximity with an endogenous RNA effector, such as a ribonuclease (RNase). Depending on the RNA effector, RNATACs can alter the stability, localization, translation, or splicing of the target RNA. Although still in its infancy, this new modality has the potential for broad applications in the future to treat diseases with high unmet need. In this review, we discuss potential advantages of RNATACs, recent progress in the field, and challenges to this cutting-edge technology.

A new reagent for in vivo structure probing of RNA G and U residues that improves RNA structure prediction alone and combined with DMS

A key to understanding the roles of RNA in regulating gene expression is knowing their structures in vivo. One way to obtain this information is through probing the structures of RNA with chemicals. To probe RNA structure directly in cells, membrane-permeable reagents that modify the Watson-Crick (WC) face of unpaired nucleotides can be used. Although dimethyl sulfate (DMS) has led to substantial insight into RNA structure, it has limited nucleotide specificity in vivo, with WC face reactivity only at adenine (A) and cytosine (C) at neutral pH. The reagent 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC) was recently shown to modify the WC face of guanine (G) and uracil (U). Although useful at lower concentrations in experiments that measure chemical modifications by reverse transcription (RT) stops, at higher concentrations necessary for detection by mutational profiling (MaP), EDC treatment leads to degradation of RNA. Here, we demonstrate EDC-stimulated degradation of RNA in Gram-negative and Gram-positive bacteria. In an attempt to overcome these limitations, we developed a new carbodiimide reagent, 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide methiodide (ETC), which we show specifically modifies unpaired Gs and Us in vivo without substantial degradation of RNA. We establish ETC as a probe for MaP and optimize the RT conditions and computational analysis in Escherichia coli Importantly, we demonstrate the utility of ETC as a probe for improving RNA structure prediction both alone and with DMS.

Directed-evolution mutations enhance DNA-binding affinity and protein stability of the adenine base editor ABE8e

Adenine base editors (ABEs), consisting of CRISPR Cas nickase and deaminase, can chemically convert the A:T base pair to G:C. ABE8e, an evolved variant of the base editor ABE7.10, contains eight directed evolution mutations in its deaminase TadA8e that significantly increase its base editing activity. However, the functional implications of these mutations remain unclear. Here, we combined molecular dynamics (MD) simulations and experimental measurements to investigate the role of the directed-evolution mutations in the base editing catalysis. MD simulations showed that the DNA-binding affinity of TadA8e is higher than that of the original deaminase TadA7.10 in ABE7.10 and is mainly driven by electrostatic interactions. The directed-evolution mutations increase the positive charge density in the DNA-binding region, thereby enhancing the electrostatic attraction of TadA8e to DNA. We identified R111, N119 and N167 as the key mutations for the enhanced DNA binding and confirmed them by microscale thermophoresis (MST) and in vivo reversion mutation experiments. Unexpectedly, we also found that the directed mutations improved the thermal stability of TadA8e by ~ 12 °C (Tm, melting temperature) and that of ABE8e by ~ 9 °C, respectively. Our results demonstrate that the directed-evolution mutations improve the substrate-binding ability and protein stability of ABE8e, thus providing a rational basis for further editing optimisation of the system.

Structural Requirements for Reverse Transcription by a Diversity-generating Retroelement

Diversity-generating retroelements (DGRs) create massive protein sequence variation in ecologically diverse microbes. Variation occurs during reverse transcription of a protein-encoding RNA template coupled to misincorporation at adenosines. In the prototypical Bordetella bacteriophage DGR, the template must be surrounded by upstream and downstream RNA segments for cDNA synthesis by the reverse transcriptase bRT and associated protein Avd. The function of the surrounding RNA was unknown. Cryo-EM revealed that this RNA enveloped bRT and lay over barrel-shaped Avd, forming an intimate ribonucleoprotein (RNP). An abundance of essential interactions between RNA structural elements and bRT-Avd precisely positioned an RNA homoduplex for initiation of cDNA synthesis by cis -priming. Our results explain how the surrounding RNA primes cDNA synthesis, promotes processivity, terminates polymerization, and strictly limits mutagenesis to select proteins through mechanisms that are likely conserved in DGRs from distant taxa.

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.

Integrating cryo-OrbiSIMS with computational modelling and metadynamics simulations enhances RNA structure prediction at atomic resolution

The 3D architecture of RNAs governs their molecular interactions, chemical reactions, and biological functions. However, a large number of RNAs and their protein complexes remain poorly understood due to the limitations of conventional structural biology techniques in deciphering their complex structures and dynamic interactions. To address this limitation, we have benchmarked an integrated approach that combines cryogenic OrbiSIMS, a state-of-the-art solid-state mass spectrometry technique, with computational methods for modelling RNA structures at atomic resolution with enhanced precision. Furthermore, using 7SK RNP as a test case, we have successfully determined the full 3D structure of a native RNA in its apo, native and disease-remodelled states, which offers insights into the structural interactions and plasticity of the 7SK complex within these states. Overall, our study establishes cryo-OrbiSIMS as a valuable tool in the field of RNA structural biology as it enables the study of challenging, native RNA systems.

Homozygous EPRS1 missense variant causing hypomyelinating leukodystrophy-15 alters variant-distal mRNA m6A site accessibility

Hypomyelinating leukodystrophy (HLD) is an autosomal recessive disorder characterized by defective central nervous system myelination. Exome sequencing of two siblings with severe cognitive and motor impairment and progressive hypomyelination characteristic of HLD revealed homozygosity for a missense single-nucleotide variant (SNV) in EPRS1 (c.4444 C > A; p.Pro1482Thr), encoding glutamyl-prolyl-tRNA synthetase, consistent with HLD15. Patient lymphoblastoid cell lines express markedly reduced EPRS1 protein due to dual defects in nuclear export and cytoplasmic translation of variant EPRS1 mRNA. Variant mRNA exhibits reduced METTL3 methyltransferase-mediated writing of N6-methyladenosine (m6A) and reduced reading by YTHDC1 and YTHDF1/3 required for efficient mRNA nuclear export and translation, respectively. In contrast to current models, the variant does not alter the sequence of m6A target sites, but instead reduces their accessibility for modification. The defect was rescued by antisense morpholinos predicted to expose m6A sites on target EPRS1 mRNA, or by m6A modification of the mRNA by METTL3-dCas13b, a targeted RNA methylation editor. Our bioinformatic analysis predicts widespread occurrence of SNVs associated with human health and disease that similarly alter accessibility of distal mRNA m6A sites. These results reveal a new RNA-dependent etiologic mechanism by which SNVs can influence gene expression and disease, consequently generating opportunities for personalized, RNA-based therapeutics targeting these disorders.

Navigating the landscape of epitranscriptomics and host immunity

RNA modifications, also termed epitranscriptomic marks, encompass chemical alterations to individual nucleotides, including processes such as methylation and editing. These marks contribute to a wide range of biological processes, many of which are related to host immune system defense. The functions of immune-related RNA modifications can be categorized into three main groups: regulation of immunogenic RNAs, control of genes involved in innate immune response, and facilitation of adaptive immunity. Here, we provide an overview of recent research findings that elucidate the contributions of RNA modifications to each of these processes. We also discuss relevant methods for genome-wide identification of RNA modifications and their immunogenic substrates. Finally, we highlight recent advances in cancer immunotherapies that aim to reduce cancer cell viability by targeting the enzymes responsible for RNA modifications. Our presentation of these dynamic research avenues sets the stage for future investigations in this field.

Long non-coding RNA TUG1 is downregulated in Friedreich's ataxia

Friedreich's ataxia is a neurodegenerative disorder caused by reduced frataxin levels. It leads to motor and sensory impairments and has a median life expectancy of around 35 years. As the most common inherited form of ataxia, Friedreich's ataxia lacks reliable, non-invasive biomarkers, prolonging and inflating the cost of clinical trials. This study proposes TUG1, a long non-coding RNA, as a promising blood-based biomarker for Friedreich's ataxia, which is known to regulate various cellular processes. In a previous study using a frataxin knockdown mouse model, we observed several hallmark Friedreich's ataxia symptoms. Building on this, we hypothesized that a dual-source approach-comparing the data from peripheral blood samples from Friedreich's ataxia patients with tissue samples from affected areas in Friedreich's ataxia knockdown mice, tissues usually unattainable from patients-would effectively identify robust biomarkers. A comprehensive reanalysis was conducted on gene expression data from 183 age- and sex-matched peripheral blood samples of Friedreich's ataxia patients, carriers and controls and 192 tissue data sets from Friedreich's ataxia knockdown mice. Blood and tissue samples underwent RNA isolation and quantitative reverse transcription polymerase chain reaction, and frataxin knockdown was confirmed through enzyme-linked immunosorbent assays. Tug1 RNA interaction was explored via RNA pull-down assays. Validation was performed in serum samples on an independent set of 45 controls and 45 Friedreich's ataxia patients and in blood samples from 66 heterozygous carriers and 72 Friedreich's ataxia patients. Tug1 and Slc40a1 emerged as potential blood-based biomarkers, confirmed in the Friedreich's ataxia knockdown mouse model (one-way ANOVA, P ≤ 0.05). Tug1 was consistently downregulated after Fxn knockdown and correlated strongly with Fxn levels (R 2 = 0.71 during depletion, R 2 = 0.74 during rescue). Slc40a1 showed a similar but tissue-specific pattern. Further validation of Tug1's downstream targets strengthened its biomarker candidacy. In additional human samples, TUG1 levels were significantly downregulated in both whole blood and serum of Friedreich's ataxia patients compared with controls (Wilcoxon signed-rank test, P < 0.05). Regression analyses revealed a negative correlation between TUG1 fold-change and disease onset (P < 0.0037) and positive correlations with disease duration and functional disability stage score (P < 0.04). This suggests that elevated TUG1 levels correlate with earlier onset and more severe cases. This study identifies TUG1 as a potential blood-based biomarker for Friedreich's ataxia, showing consistent expression variance in human and mouse tissues related to disease severity and key Friedreich's ataxia pathways. It correlates with frataxin levels, indicating its promise as an early, non-invasive marker. TUG1 holds potential for Friedreich's ataxia monitoring and therapeutic development, meriting additional research.

DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning

It is now known that RNAs play more active roles in cellular pathways beyond simply serving as transcription templates. These biological mechanisms might be mediated by higher RNA stereo conformations, triggering the need to understand RNA secondary structures first. However, experimental protocols for solving RNA structures are unavailable for large-scale investigation due to their high costs and time-consuming nature. Various computational tools were thus developed to predict the RNA secondary structures from sequences. Recently, deep networks have been investigated to help predict RNA structures directly from their sequences. However, existing deep-learning-based tools are more or less suffering from model overfitting due to their complicated problem formulation and defective model training processes, limiting their applications across sequences from different structural families. In this research, we designed a two-stage RNA structure prediction strategy called DEBFold (deep ensemble boosting and folding) based on convolution encoding/decoding and self-attention mechanisms to enhance the existing thermodynamic structure models. Moreover, the model training process followed rigorous steps to achieve an acceptable prediction generalization. On the family-wise reserved test sets and the PDB-derived test set, DEBFold achieves better structure prediction performance over traditional tools and existing deep-learning methods. In summary, we obtained a cutting-edge deep-learning-based structure prediction tool with supreme across-family generalization performance. The DEBFold tool can be accessed at https://cobis.bme.ncku.edu.tw/DEBFold/.