Explore the vast range of titles available.

RNA molecules undergo a vast array of chemical post-transcriptional modifications (PTMs) that can affect their structure and interaction properties. In recent years, a growing number of PTMs have been successfully mapped to the transcriptome using experimental approaches relying on high-throughput sequencing. Oxford Nanopore direct-RNA sequencing has been shown to be sensitive to RNA modifications. We developed and validated Nanocompore, a robust analytical framework that identifies modifications from these data. Our strategy compares an RNA sample of interest against a non-modified control sample, not requiring a training set and allowing the use of replicates. We show that Nanocompore can detect different RNA modifications with position accuracy in vitro, and we apply it to profile m 6 A in vivo in yeast and human RNAs, as well as in targeted non-coding RNAs. We confirm our results with orthogonal methods and provide novel insights on the co-occurrence of multiple modified residues on individual RNA molecules. Nanopore direct RNA Sequencing data contain information about the presence of RNA modifications, but their detection poses substantial challenges. Here the authors introduce Nanocompore, a new methodology for modification detection from Nanopore data.

Background Crosslinking and immunoprecipitation (CLIP) is a method used to identify in vivo RNA–protein binding sites on a transcriptome-wide scale. With the increasing amounts of available data for RNA-binding proteins (RBPs), it is important to understand to what degree the enriched motifs specify the RNA-binding profiles of RBPs in cells. Results We develop positionally enriched k-mer analysis (PEKA), a computational tool for efficient analysis of enriched motifs from individual CLIP datasets, which minimizes the impact of technical and regional genomic biases by internal data normalization. We cross-validate PEKA with mCross and show that the use of input control for background correction is not required to yield high specificity of enriched motifs. We identify motif classes with common enrichment patterns across eCLIP datasets and across RNA regions, while also observing variations in the specificity and the extent of motif enrichment across eCLIP datasets, between variant CLIP protocols, and between CLIP and in vitro binding data. Thereby, we gain insights into the contributions of technical and regional genomic biases to the enriched motifs, and find how motif enrichment features relate to the domain composition and low-complexity regions of the studied proteins. Conclusions Our study provides insights into the overall contributions of regional binding preferences, protein domains, and low-complexity regions to the specificity of protein-RNA interactions, and shows the value of cross-motif and cross-RBP comparison for data interpretation. Our results are presented for exploratory analysis via an online platform in an RBP-centric and motif-centric manner ( https://imaps.goodwright.com/apps/peka/ ).

Protein–RNA interactions are central to the regulation of gene expression. Emerging technologies for pinpointing these interactions, both in large complexes and between individual proteins and RNA, are discussed. Methods for analysing these data are also considered. RNA-binding proteins are key players in the regulation of gene expression. In this Progress article, we discuss state-of-the-art technologies that can be used to study individual RNA-binding proteins or large complexes such as the ribosome. We also describe how these approaches can be used to study interactions with different types of RNAs, including nascent transcripts, mRNAs, microRNAs and ribosomal RNAs, in order to investigate transcription, RNA processing and translation. Finally, we highlight current challenges in data analysis and the future steps that are needed to obtain a quantitative and high-resolution picture of protein–RNA interactions on a genome-wide scale.

A method, termed hiCLIP, has been developed to determine the RNA duplexes bound by RNA-binding proteins, revealing an unforeseen prevalence of long-range duplexes in 3′ untranslated regions (UTRs), and a decreased incidence of SNPs in duplex-forming regions; the results also show that RNA structure is able to regulate gene expression. Probing native RNA structure The single-stranded nature of cellular RNAs allows them flexibility to adopt different secondary structures that can affect their function. However, current methods of measuring RNA structure in vivo are limited. Two papers published in this week's issue of Nature present new techniques to address this gap. Howard Chang and colleagues have exploited a click methodology that enables the first global view of RNA secondary structures in living cells for all four bases. While some structures are stable and seem to be programmed by sequence, others are dynamic, reflecting the binding of proteins or modification of the bases. This method may allow RNA to be analysed in vivo from a structural genomics perspective. In the second study, Jernej Ule and colleagues have developed a method, hiCLIP, to specifically measure RNA structures bound by proteins. Various features are observed, such as a preference for intramolecular interactions and an under-representation of structures in coding regions. The results confirm that RNA structure is able to regulate gene expression. While the functional significance is not known, it is notable that SNPs are not present at the expected frequency in coding regions. The structure of messenger RNA is important for post-transcriptional regulation, mainly because it affects binding of trans -acting factors 1 . However, little is known about the in vivo structure of full-length mRNAs. Here we present hiCLIP, a biochemical technique for transcriptome-wide identification of RNA secondary structures interacting with RNA-binding proteins (RBPs). Using this technique to investigate RNA structures bound by Staufen 1 (STAU1) in human cells, we uncover a dominance of intra-molecular RNA duplexes, a depletion of duplexes from coding regions of highly translated mRNAs, an unexpected prevalence of long-range duplexes in 3′ untranslated regions (UTRs), and a decreased incidence of single nucleotide polymorphisms in duplex-forming regions. We also discover a duplex spanning 858 nucleotides in the 3′ UTR of the X-box binding protein 1 ( XBP1 ) mRNA that regulates its cytoplasmic splicing and stability. Our study reveals the fundamental role of mRNA secondary structures in gene expression and introduces hiCLIP as a widely applicable method for discovering new, especially long-range, RNA duplexes.

Replication-dependent histones (RDH) are required for packaging of newly synthetized DNA into nucleosomes during the S phase when their expression is highly upregulated. However, the mechanisms of this upregulation in metazoan cells remain poorly understood. Using iCLIP and ChIP–seq, we found that human cyclin-dependent kinase 11 (CDK11) associates with RNA and chromatin of RDH genes primarily in the S phase. Moreover, its amino-terminal region binds FLASH, an RDH-specific 3′-end processing factor, which keeps the kinase on the chromatin. CDK11 phosphorylates serine 2 (Ser2) of the carboxy-terminal domain of RNA polymerase II (RNAPII), which is initiated when RNAPII reaches the middle of RDH genes and is required for further RNAPII elongation and 3′-end processing. CDK11 depletion leads to decreased number of cells in S phase, likely owing to the function of CDK11 in RDH gene expression. Thus, the reliance of RDH expression on CDK11 could explain why CDK11 is essential for the growth of many cancers.CDK11 functions with histone pre-mRNA-processing factor FLASH to upregulate expression of replication-dependent histone genes in S phase and to couple transcription and processing of nascent histone transcripts in mammalian cells.

The PKCε-regulated genome protective pathway provides transformed cells a failsafe to successfully complete mitosis. Despite the necessary role for Aurora B in this programme, it is unclear whether its requirement is sufficient or if other PKCε cell cycle targets are involved. To address this, we developed a trapping strategy using UV-photocrosslinkable amino acids encoded in the PKCε kinase domain. The validation of the mRNA binding protein SERBP1 as a PKCε substrate revealed a series of mitotic events controlled by the catalytic form of PKCε. PKCε represses protein translation, altering SERBP1 binding to the 40 S ribosomal subunit and promoting the assembly of ribonucleoprotein granules containing SERBP1, termed M-bodies. Independent of Aurora B, SERBP1 is shown to be necessary for chromosome segregation and successful cell division, correlating with M-body formation. This requirement for SERBP1 demonstrates that Aurora B acts in concert with translational regulation in the PKCε-controlled pathway exerting genome protection. PKCε is known to exert a role in genome protection by directly phosphorylating and switching the specificity of Aurora B. Here the authors identify SERBP1 as a parallel mitotic PKCε substrate controlling translation and ensuring the integrity of chromosome segregation and successful cell division.

We present RBPNet, a novel deep learning method, which predicts CLIP-seq crosslink count distribution from RNA sequence at single-nucleotide resolution. By training on up to a million regions, RBPNet achieves high generalization on eCLIP, iCLIP and miCLIP assays, outperforming state-of-the-art classifiers. RBPNet performs bias correction by modeling the raw signal as a mixture of the protein-specific and background signal. Through model interrogation via Integrated Gradients, RBPNet identifies predictive sub-sequences that correspond to known and novel binding motifs and enables variant-impact scoring via in silico mutagenesis. Together, RBPNet improves imputation of protein-RNA interactions, as well as mechanistic interpretation of predictions.

TDP-43 regulates alternative splicing of the cystic fibrosis transmembrane regulator (CFTR) and is found in cytosolic granules associated with several neurodegenerative disorders. A new solution structure of the tandem RNA-recognition motifs (RRMs) that mediate interactions with its UG-rich RNA targets reveals a new RRM arrangement critical for TDP-43 function. TDP-43 encodes an alternative-splicing regulator with tandem RNA-recognition motifs (RRMs). The protein regulates cystic fibrosis transmembrane regulator ( CFTR ) exon 9 splicing through binding to long UG-rich RNA sequences and is found in cytoplasmic inclusions of several neurodegenerative diseases. We solved the solution structure of the TDP-43 RRMs in complex with UG-rich RNA. Ten nucleotides are bound by both RRMs, and six are recognized sequence specifically. Among these, a central G interacts with both RRMs and stabilizes a new tandem RRM arrangement. Mutations that eliminate recognition of this key nucleotide or crucial inter-RRM interactions disrupt RNA binding and TDP-43–dependent splicing regulation. In contrast, point mutations that affect base-specific recognition in either RRM have weaker effects. Our findings reveal not only how TDP-43 recognizes UG repeats but also how RNA binding–dependent inter-RRM interactions are crucial for TDP-43 function.

Exercise has a wide range of systemic effects. In animal models, repeated exertion reduces malignant tumor progression, and clinically, exercise can improve outcome for cancer patients. The etiology of the effects of exercise on tumor progression are unclear, as are the cellular actors involved. We show here that in mice, exercise-induced reduction in tumor growth is dependent on CD8+ T cells, and that metabolites produced in skeletal muscle and excreted into plasma at high levels during exertion in both mice and humans enhance the effector profile of CD8+ T-cells. We found that activated murine CD8+ T cells alter their central carbon metabolism in response to exertion in vivo, and that immune cells from trained mice are more potent antitumor effector cells when transferred into tumor-bearing untrained animals. These data demonstrate that CD8+ T cells are metabolically altered by exercise in a manner that acts to improve their antitumoral efficacy. Exercise affects almost all tissues in the body, and scientists have found that being physically active can reduce the risk of several types of cancer as well as improving outcomes for cancer patients. However, it is still unknown how exercise exerts its protective effects. One of the hallmarks of cancer is the ability of cancer cells to evade detection by the immune system, which can in some cases stop the body from eliminating tumor cells. Rundqvist et al. used mice to investigate how exercise helps the immune system act against tumor progression. They found that when mice exercised, tumor growth was reduced, and this decrease in growth depended on the levels of a specific type of immune cell, the CD8+ T cell, circulating in the blood. Additionally, Rundqvist et al. found that CD8+ T cells were made more effective by molecules that muscles released into the blood during exercise. Isolating immune cells after intense exercise showed that these super-effective CD8+ T cells alter how they use molecules for energy production after exertion. Next, immune cells from mice that had exercised frequently were transferred into mice that had not exercised, where they were more effective against tumor cells than the immune cells from untrained mice. These results demonstrate that CD8+ T cells are altered by exercise to improve their effectiveness against tumors. The ability of T cells to identify and eliminate cancer cells is essential to avoid tumor growth, and is one of the foundations of current immune therapy treatments. Exercise could improve the outcome of these treatments by increasing the activation of the immune system, making tumor-fighting cells more effective.