Alternative splicing is a highly intricate process that plays a crucial role in post-transcriptional regulation and significantly expands the functional proteome of a limited number of coding genes in eukaryotes. Its regulation is multifactorial, with RNA structure exerting a significant impact. Aberrant RNA conformations lead to dysregulation of splicing patterns, which directly affects the manifestation of disease symptoms. In this review, the molecular mechanisms of RNA secondary structure-mediated splicing regulation are summarized, with a focus on the complex interplay between aberrant RNA conformations and disease phenotypes resulted from splicing defects. This study also explores additional factors that reshape structural conformations, enriching our understanding of the mechanistic network underlying structure-mediated splicing regulation. In addition, an emphasis has been placed on the clinical role of targeting aberrant splicing corrections in human diseases. The principal mechanisms of action behind this phenomenon are described, followed by a discussion of prospective development strategies and pertinent challenges.

Gapmer antisense oligonucleotides (ASOs) hold therapeutic promise for allele-specific silencing, but face challenges in distinguishing between mutant and wild-type transcripts. This study explores new design strategies to enhance ASO specificity, focusing on a common dominant mutation in COL6A3 gene associated with Ullrich congenital muscular dystrophy. Initial gapmer ASO design exhibited high efficiency but poor specificity for the mutant allele. We then adopted a mixmer design, incorporating additional RNA bases based on computational predictions of secondary structures for both mutant and wild-type alleles, aiming to enhance ASO accessibility to mutant transcripts. The mixmer ASO design demonstrated up to a 3-fold increase in specificity compared with the classical gapmer design. Further refinement involved introducing a nucleotide mismatch as a structural modification, resulting in a 10-fold enhancement in specificity compared with the gapmer design and a 3-fold over the mixmer design. Additionally, we identified for the first time a potential role of the RNA-induced silencing complex (RISC), alongside RNase H1, in gapmer-mediated silencing, in contrast with what was observed with mixmer ASOs, where only RNase H1 was involved. In conclusion, this study presents a novel design concept for allele-specific ASOs leveraging mRNA secondary structures and nucleotide mismatching and suggests a potential involvement of RISC in gapmer-mediated silencing.

Extrinsic, experimental information can be incorporated into thermodynamics-based RNA folding algorithms in the form of pseudo-energies. Evolutionary conservation of RNA secondary structure elements is detectable in alignments of phylogenetically related sequences and provides evidence for the presence of certain base pairs that can also be converted into pseudo-energy contributions. We show that the centroid base pairs computed from a consensus folding model such as RNAalifold result in a substantial improvement of the prediction accuracy for single sequences. Evidence for specific base pairs turns out to be more informative than a position-wise profile for the conservation of the pairing status. A comparison with chemical probing data, furthermore, strongly suggests that phylogenetic base pairing data are more informative than position-specific data on (un)pairedness as obtained from chemical probing experiments. In this context we demonstrate, in addition, that the conversion of signal from probing data into pseudo-energies is possible using thermodynamic structure predictions as a reference instead of known RNA structures.

Hepatitis A virus (HAV), a member of the genus Hepatovirus (Picornaviridae HepV), remains a significant viral pathogen, frequently causing enterically transmitted hepatitis worldwide. In this study, we conducted an epidemiological survey of HepVs carried by small terrestrial mammals in the wild in Yunnan Province, China. Utilizing HepV-specific broad-spectrum RT-PCR, next-generation sequencing (NGS), and QNome nanopore sequencing (QNS) techniques, we identified and characterized two novel HepVs provisionally named EpMa-HAV and EpLe-HAV, discovered in the long-tailed mountain shrew (Episoriculus macrurus) and long-tailed brown-toothed shrew (Episoriculus leucops), respectively. Our sequence and phylogenetic analyses of EpMa-HAV and EpLe-HAV indicated that they belong to the species Hepatovirus I (HepV-I) clade II, also known as the Chinese shrew HepV clade. Notably, the codon usage bias pattern of novel shrew HepVs is consistent with that of previously identified Chinese shrew HepV. Furthermore, our structural analysis demonstrated that shrew HepVs differ from other mammalian HepVs in RNA secondary structure and exhibit variances in key protein sites. Overall, the discovery of two novel HepVs in shrews expands the host range of HepV and underscores the existence of genetically diverse animal homologs of human HAV within the genus HepV.

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.

The structure of RNA molecules is absolutely critical to their functions in a biological system. RNA structure is dynamic and changes in response to cellular needs. Within the last few decades, there has been an increased interest in studying the structure of RNA molecules and how they change to support the needs of the cell in different conditions. Selective 2\'-hydroxyl acylation-based mutational profiling using high-throughput sequencing is a powerful method to predict the secondary structure of RNA molecules both in vivo and in immunopurified samples. Selective 2\'-hydroxyl acylation-based mutational profiling using high-throughput sequencing works by adding bulky groups onto accessible \"flexible\" bases in an RNA molecule that are not involved in any base-pairing or RNA-protein interactions. When the RNA is reverse transcribed into cDNA, the bulky groups are incorporated as base mutations, which can be compared to an unmodified control to identify the locations of flexible bases. The comparison of sequence data between modified and unmodified samples allows the computer software program (developed to generate reactivity profiles) to generate RNA secondary structure models. These models can be compared in a variety of conditions to determine how specific stimuli influence RNA secondary structures.

BACKGROUND: 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.

Over the last quarter of a century it has become clear that RNA is much more than just a boring intermediate in protein expression. Ancient RNAs still appear in the core information metabolism and comprise a surprisingly large component in bacterial gene regulation. A common theme with these types of mostly small RNAs is their reliance of conserved secondary structures. Large-scale sequencing projects, on the other hand, have profoundly changed our understanding of eukaryotic genomes. Pervasively transcribed, they give rise to a plethora of large and evolutionarily extremely flexible non-coding RNAs that exert a vastly diverse array of molecule functions. In this chapter we provide a-necessarily incomplete-overview of the current state of comparative analysis of non-coding RNAs, emphasizing computational approaches as a means to gain a global picture of the modern RNA world.

Although RNA molecules are synthesized via transcription, little is known about the general impact of cotranscriptional folding in vivo. We present different computational approaches for the simulation of changing structure ensembles during transcription, including interpretations with respect to experimental data from literature. Specifically, we analyze different mutations of the E. coli SRP RNA, which has been studied comparatively well in previous literature, yet the details of which specific metastable structures form as well as when they form are still under debate. Here, we combine thermodynamic and kinetic, deterministic, and stochastic models with automated and visual inspection of those systems to derive the most likely scenario of which substructures form at which point during transcription. The simulations do not only provide explanations for present experimental observations but also suggest previously unnoticed conformations that may be verified through future experimental studies.

The structure of an RNA sequence encodes information about its biological function. Dynamic programming algorithms are often used to predict the conformation of an RNA molecule from its sequence alone, and adding experimental data as auxiliary information improves prediction accuracy. This auxiliary data is typically incorporated into the nearest neighbor thermodynamic model22 by converting the data into pseudoenergies. Here, we look at how much of the space of possible structures auxiliary data allows prediction methods to explore. We find that for a large class of RNA sequences, auxiliary data shifts the predictions significantly. Additionally, we find that predictions are highly sensitive to the parameters which define the auxiliary data pseudoenergies. In fact, the parameter space can typically be partitioned into regions where different structural predictions predominate.