RNA最新文献

RNA binding proteins (RBPs) play essential roles in post-transcriptional gene regulation by interacting with a wide range of RNA targets. In addition to regulating RNA processing via individual RBP-RNA interactions, there is a growing appreciation of the regulatory impact of protein-associated RNA-RNA interactions that include both well-studied examples of small regulatory RNAs (e.g. microRNAs, snRNAs, snoRNAs, piRNAs) guiding ribonucleoprotein complexes to their targets as well as structured RNA elements defining the interaction landscape for an RBP. To elucidate the full scope of RBP-RNA interactions, CLIP ( crosslinking and immunoprecipitation)-based methods have emerged as powerful tools. Even with the wide application of CLIP and variant approaches, these methods are still under significant ongoing advancement to better accommodate diverse biological systems and experimental demands and improve scalability. In particular, recent years have seen an emergent focus on improved techniques to globally profile protein-associated RNA-RNA interactions. In this review, we provide a summary of recent improvements in traditional CLIP methods that improve the mapping of RBP-RNA interactions, with particular focus on those that specifically enable the profiling of protein-associated RNA-RNA interactions. We discuss the unique challenges involved in mapping protein-associated RNA-RNA interactions and highlight different ways current approaches address these challenges in order to offer a practical framework for researchers seeking to investigate RBP-associated RNA interactions.

Nanopore direct RNA sequencing (DRS) is revolutionizing our ability to analyze the epitranscriptome to evaluate nucleoside modifications in both cellular and synthetic RNA. The process involves minimal handling of fragile RNA strands, one round of reverse transcription to provide a DNA:RNA duplex, and library preparation to directly read nucleotides with their modifications as the pass through a protein nanopore embedded in a membrane. Simultaneous sequencing of hundreds of strands on a chip provides unprecedented access to whole transcriptome information. A key advantage is the long read length that permits, for example, operon-specific epitranscriptomics of ribosomal RNA modifications as a function of cellular stress. By analyzing the entire transcriptome, the interplay of different modifications on the same RNA, or the correlation of changes in different RNAs in the same cell type can be monitored. This review presents several recent examples of the types of experiments that are suitable for nanopore DRS as well as some of the current challenges and future expectations.

The translation of mRNA is a tightly regulated, energy-intensive process that drives cellular diversity. Understanding its control requires tools that can capture behavior across scales. Over the past two decades, two complementary techniques have emerged that have transformed our understanding of mRNA translation within cells: ribosome profiling (Ribo-Seq) and live, single-molecule imaging. Ribo-Seq provides genome-wide, codon-level maps of ribosome positions, revealing pause sites, novel open reading frames, and global translation efficiencies. In contrast, live, single-molecule imaging visualizes translation on individual mRNAs in living cells, uncovering heterogeneous initiation, elongation, pausing, and spatial organization in real time. Together, these methods offer complementary strengths - molecular breadth versus temporal and spatial precision - but are rarely applied in tandem. Here, we review their principles, key discoveries, and recent innovations that are bringing them closer together, including endogenous tagging, higher-throughput imaging, absolute calibration, and spatially resolved footprinting. Integrating these approaches promises a unified, multiscale view of translation that connects the dynamics of individual ribosomes to genome-wide patterns of protein synthesis.

The diverse regulatory functions, protein production capacity, and stability of natural and synthetic RNAs are closely tied to their ability to fold into intricate structures. Determining RNA structure is thus fundamental to RNA biology and bioengineering. Among existing approaches to structure determination, computational secondary structure prediction offers a rapid and low-cost strategy and is thus widely used, especially when seeking to identify functional RNA elements in large transcriptomes or screen massive libraries of novel designs. While traditional approaches rely on detailed measurements of folding energetics and/or probabilistic modeling of structural data, recent years have witnessed a surge in deep learning methods, inspired by their tremendous success in protein structure prediction. However, the limited diversity and volume of known RNA structures can impede their ability to accurately predict structures markedly different from the ones they have seen. This is known as the generalization gap and currently poses a major barrier to progress in the field. In this Perspective article, we gauge method generalizability using a new benchmark dataset of structured RNAs we curated from the Protein Data Bank. We also discuss the emergence of deep learning methods for predicting structure probing data and use a new dataset to underscore generalization challenges unique to this domain along with directions for future improvement. Expanding beyond improving predictive accuracy, we review how advances in deep learning have recently enabled scalable and accessible optimization of traditional structure prediction methods and their seamless integration with modern neural networks.

Single-cell RNA sequencing (scRNA-seq) has become a cornerstone experimental technique in cellular biology, with gene expression data for over 100 million sequenced cells available in public repositories. The high dimensionality, sparsity, and technical noise inherent to scRNA-seq data have motivated the development of a broad spectrum of representation learning approaches. These methods learn denoised, low-dimensional representations of single-cell transcriptomes that can then be used for clustering, visualization, trajectory inference, and other downstream analyses. Furthermore, methods have emerged that learn latent representations based on scRNA-seq data pooled across multiple experiments. In this review, we frame factor models, autoencoders, contrastive learning approaches, and transformer-based foundation models as distinct paradigms of representation learning for scRNA-seq. We provide a coherent taxonomy of these methods that articulates their conceptual foundations, shared assumptions, and key distinctions. We also discuss existing benchmarks and identify the major challenges and open questions that will shape the future of the field.

Pre-mRNA splicing plays a crucial role in maintaining cellular homeostasis, with strict regulation required for processes such as cell cycle progression. SF3B1, a core component of the spliceosome, has emerged as a key player in alternative splicing regulation and is frequently mutated in cancer. Among these mutations, SF3B1K700E disrupts normal splicing patterns and deregulates cell cycle control. Here we profiled K562 erythroleukaemia cells expressing either wild-type or SF3B1K700E by RNA-seq and uncovered 763 high-confidence splicing alterations enriched for G2/M regulators, including ARPP19, ENSA, STAG2 and ECT2. Notably, increased inclusion of ARPP19 exon 2 produces the ARPP19-long isoform, which sustains PP2A-B55 inhibition and promotes mitotic progression. A core subset of the K700E-linked splicing changes re-appeared after siRNA-mediated SF3B1 depletion in HeLa cells, underscoring a mutation-dependent spliceosomal signature that transcends cell type. Pharmacological inhibition of DYRK1A or broad serine/threonine phosphatases shifted ARPP19 exon 2 inclusion in the same direction as SF3B1K700E, pointing to a kinase-phosphatase signaling axis that influences these splice events. Functionally, ectopic expression of ARPP19-long accelerated mitotic exit, and high ARPP19-long abundance associated with poorer overall survival in the TCGA-AML cohort. Our findings highlight a connection between SF3B1-dependent splicing, cell cycle progression, and tumorigenesis, offering new insights into the molecular mechanisms underlying cancer-associated splicing dysregulation.

The members of the genus Pestivirus in the family Flaviviridae comprise economically important pathogens of livestock like classical swine fever (CSFV) and bovine viral diarrhea virus (BVDV). Intense research over the last years has revealed that at least 11 recognized and eight proposed pestivirus species exist. The single-stranded, positive-sense RNA genome encodes for one large polyprotein, which is processed by viral and cell-derived proteases into 12 mature proteins. Besides its protein-coding function, the RNA genome also contains RNA secondary structures with critical importance for various stages of the viral life cycle. Some of those RNA secondary structures, like the internal ribosome entry site (IRES) and a 3' stem-loop essential for genome replication, have already been studied for a few individual pestiviruses. In this study, we provide the first genome-wide multiple sequence alignment (MSA) including all known pestivirus species (accepted and tentative). Moreover, we performed a comprehensive analysis of RNA secondary structures phylogenetically conserved across the complete genus. While showing that well-described structures, like a 5' stem-loop structure, the IRES element, and the 3' stem loop SLI are conserved between all pestiviruses, other RNA secondary structures in the 3' untranslated region (UTR) were only conserved in subsets of the species. We identified 29 novel phylogenetically conserved RNA secondary structures in the protein-coding region, with thus far unresolved functional importance. The microRNA binding site for miR-17 was previously known in species A, B, and C; in this study, we identified it in ten additional species, but not in species K, S, Q, and R. Another interesting finding is the identification of a putative long-distance RNA interaction between the IRES and the 3' end of the genome. These results, together with the now available comprehensive multiple sequence alignment including all 19 pestivirus species, represent a valuable resource for future research and diagnostic purposes.

N6-methyladenosine (m6A) is the most prevalent internal mRNA modification in eukaryotes, yet whether m6A sites are functionally important or represent neutral byproducts remains unclear. Previous evolutionary analyses failed to detect consistent conservation signatures at m6A sites, and report conflicting patterns of conservation across genic regions, such as the coding sequence (CDS) and untranslated regions (UTRs). To reconcile these inconsistencies and definitively determine whether m6A sites are under selection, we develop novel motif-level conservation metrics that incorporate knowledge of m6A biogenesis to distinguish m6A-specific selection from other confounding sources. We analyze ~500,000 candidate sites with quantitative, single-nucleotide resolution m6A measurements across a phylogeny spanning 447 mammalian species. After controlling for proximity to exon-junctions, we observe a clear, dose-dependent relationship between m6A stoichiometry and evolutionary conservation in both CDS and UTRs. Highly methylated sites (>60%) exhibit significantly increased conservation compared to lowly methylated sites - with an effect size approximately one-third of the typical CDS-UTR difference - providing definitive evidence of purifying selection and supporting a model where highly modified sites contribute functionally to gene regulation. We establish a methodological framework for evolutionary analysis of RNA modifications, highlighting the necessity of quantitative measurements, comprehensive phylogenetic sampling, and careful consideration of modification biogenesis.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immune escape strategies include general inhibition of host gene expression referred to as host shutoff. Viral non-structural protein 1 (Nsp1) is the main host shutoff factor that blocks protein translation and induces messenger RNA (mRNA) cleavage and degradation. Viral mRNAs are resistant to the translation shutoff and cleavage induced by Nsp1, and the 5' leader sequence present in all viral mRNAs has been shown to confer resistance. However, the exact molecular mechanism for escape from Nsp1 host shutoff has not been demonstrated. In our previous work, we analyzed the effects of Nsp1 on the expression and function of cellular proteins important for stress granule formation. We discovered that the host transcript for the TIA1 cytotoxic granule-associated RNA-binding protein-like 1 (TIAL1, commonly referred to as TIAR) is resistant to SARS-CoV-2 Nsp1 host shutoff. In this work, using reporter shutoff assays, we examined sequence and structural features of the TIAR 5' untranslated region (UTR) and discovered that the first 23 nucleotides of the TIAR transcript are both necessary and sufficient to confer resistance to the Nsp1. Furthermore, our work revealed that the lack of guanosines within a window of 10 to 18 nucleotides downstream from the 5' end is a defining feature of Nsp1-resistant transcripts shared between the SARS-CoV-2 leader sequence and the TIAR 5' UTR. Our findings are consistent with the model in which sequence features of 5' UTRs, rather than their secondary structure, confer resistance to Nsp1 host shutoff to both viral and cellular mRNAs.

RNA-binding proteins (RBPs) and microRNAs (miRNAs) play crucial roles in regulating gene expression at the post-transcriptional level in tumorigenesis. They primarily target the 3'UTRs of mRNAs to control their translation and stability. However, their coregulatory effects on specific mRNAs in the pathogenesis of particular cancers are yet to be fully explored. CSDE1 is an RBP that promotes melanoma metastasis, and the mechanisms underlying its function in melanoma development are yet to be fully understood. Here, we report that CSDE1 enhances TMBIM6 protein expression without altering its mRNA levels in melanoma cells, indicating post-transcriptional regulation. CSDE1 and AGO2 competitively bind to TMBIM6 mRNA, and we identify that miR-20-5p, which represses TMBIM6 expression, regulates the binding of CSDE1 to TMBIM6 mRNA. Further, the RNA-binding mutant of CSDE1 showed reduced affinity toward TMBIM6 mRNA, thus allowing AGO2-mediated silencing of TMBIM6 expression. Our study highlights the pivotal role of CSDE1 in regulating miR-20a-5p function and the expression of TMBIM6 in melanoma cells, thus unveiling the potential of therapeutic strategies targeting this regulatory pathway in treating malignant skin cancers.