RNA最新文献

Epithelial cells exhibit a highly polarized organization along their apico-basal axis, a feature that is critical to their function and is frequently perturbed in cancer. One less explored process modulating epithelial cell polarity is the subcellular localization of mRNA molecules. In this study, we report that several mRNAs encoding evolutionarily conserved epithelial polarity regulatory proteins, including Zo-1, Afdn and Scrib, are localized to cell junction regions in Drosophila epithelial tissues and human epithelial cells. Targeting of these mRNAs coincides with robust junctional distribution of their encoded proteins, and these transcripts are translated in proximity to cell junction regions. Through systematic immuno-labeling, we identify a collection of RNA binding proteins with cell junction distribution patterns, several of which associate with junctional transcripts and are functionally required for proper targeting of ZO-1 and SCRIB proteins. Loss-of-function of two candidate factors, MAGOH and PCBP3, differentially impacts junctional mRNA, with MAGOH knock-down reducing Zo-1 and Scrib transcript targeting and localized translation, while PCBP3 knock-down only perturbs local translation. Depletion of Drosophila MAGO in vivo in follicular epithelial cells also disrupts the distribution of junctional transcripts and proteins. Finally, through tissue microarray analysis of ovarian cancer tumor specimens, we find that the expression of MAGOH and ZO-1 is positively correlated and that both proteins are potential biomarkers of good prognosis. We conclude that localized mRNA regulation at cell junction regions is important for modulating epithelial cell integrity.

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.

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.

Proteins have traditionally been understood through their tertiary structures, with well-defined conformations considered essential for biological function. This classical structure-function paradigm implies that proteins with high intrinsic disorder would be less critical for cellular survival. Recent discoveries have suggested that some intrinsically disordered proteins or even fully disordered proteins without any apparent tertiary structures are essential. However, the biological significance of such disordered proteins is not comprehensively understood. Here, using genome-wide CRISPR screening, we demonstrated that highly or fully disordered proteins show comparable essentiality to well-folded proteins. We found that the proportion of essential proteins is comparable across proteins of varying disorder levels, although structured proteins are more prevalent among essential genes. Focusing on FAM32A, one of the essential, fully disordered proteins identified in our screen, we show that its depletion leads to increased intron retention and downregulation of many other essential genes. These findings reshape our understanding of the structure-function paradigm, highlighting that fully disordered proteins can be essential for cellular viability.

Maturation of protein-coding precursor messenger RNA (pre-mRNA) is closely linked to RNA polymerase II (Pol II) transcription. However, the mechanistic understanding of how pre-mRNA processing is coordinated with transcription remains incomplete. Conserved proteins interacting with the C-terminal domain of the largest catalytic subunit of Pol II and nascent RNA (CID-RRM factors) were demonstrated to play a role in pre-mRNA 3'-end processing and termination of Pol II transcription. Here, we use a fully reconstituted system to demonstrate that the fission yeast CID-RRM factor Seb1 acts as a bona fide elongation factor. Our analyses show that Seb1 exhibits context-dependent regulation of Pol II pausing, capable of either promoting or inhibiting pause site entry. We propose that CID-RRM factors coordinate Pol II transcription and pre-mRNA 3'-end processing by modulating the rate of Pol II transcription.