A pseudouridine residue in the spliceosome core is part of the filamentous growth program in yeast

Definer: A computational method for accurate identification of RNA pseudouridine sites based on deep learning

Pseudouridine is an important modification site, which is widely present in a variety of non-coding RNAs and is involved in a variety of important biological processes. Studies have shown that pseudouridine is important in many biological functions such as gene expression, RNA structural stability, and various diseases. Therefore, accurate identification of pseudouridine sites can effectively explain the functional mechanism of this modification site. Due to the rapid increase of genomics data, traditional biological experimental methods to identify RNA modification sites can no longer meet the practical needs, and it is necessary to accurately identify pseudouridine sites from high-throughput RNA sequence data by computational methods. In this study, we propose a deep learning-based computational method, Definer, to accurately identify RNA pseudouridine loci in three species, Homo sapiens, Saccharomyces cerevisiae and Mus musculus. The method incorporates two sequence coding schemes, including NCP and One-hot, and then feeds the extracted RNA sequence features into a deep learning model constructed from CNN, GRU and Attention. The benchmark dataset contains data from three species, H. sapiens, S. cerevisiae and M. musculus, and the results using 10-fold cross-validation show that Definer significantly outperforms other existing methods. Meanwhile, the data sets of two species, H. sapiens and S. cerevisiae, were tested independently to further demonstrate the predictive ability of the model. In summary, our method, Definer, can accurately identify pseudouridine modification sites in RNA.

Schizosaccharomyces pombe pus1 mutants are temperature sensitive due to decay of tRNAIle(UAU) by the 5'-3' exonuclease Dhp1, primarily targeting the unspliced pre-tRNA

The pseudouridylase Pus1 catalyzes pseudouridine (Ψ) formation at multiple uridine residues in tRNAs, and in some snRNAs and mRNAs. Although Pus1 is highly conserved, and mutations are associated with human disease, little is known about eukaryotic Pus1 biology. Here, we show that Schizosaccharomyces pombe pus1Δ mutants are temperature sensitive due to decay of tRNAIle(UAU), as tRNAIle(UAU) levels are reduced, and its overexpression suppresses the defect. We show that tRNAIle(UAU) is degraded by the 5'-3' exonuclease Dhp1 (ortholog of Saccharomyces cerevisiae Rat1), as each of four spontaneous pus1Δ suppressors had dhp1 mutations and restored tRNAIle(UAU) levels, and two suppressors that also restored tRNAIle(UAU) levels had mutations in tol1 (S. cerevisiae MET22 ortholog), predicted to inhibit Dhp1. We show that Pus1 modifies U27, U34, and U36 of tRNAIle(UAU), raising the question about how these modifications prevent decay. Our results suggest that Dhp1 targets unspliced pre-tRNAIle(UAU), as a pus1Δ strain in which the only copy of tRNAIle(UAU) has no intron [tI(UAU)-iΔ] is temperature resistant and undergoes no detectable decay, and the corresponding pus1Δ tI(UAU)-WT strain accumulates unspliced pre-tRNAIle(UAU) Moreover, the predicted exon-intron structure of pre-tRNAIle(UAU) differs from the canonical bulge-helix-loop structure compatible with tRNA splicing, and a pus1Δ tI(UAU)i-var strain with intron mutations predicted to improve exon-intron structure is temperature resistant and undergoes little decay. These results suggest that decay of tRNAIle(UAU) by Dhp1 in pus1Δ strains occurs at the level of unspliced pre-tRNAIle(UAU), implying a substantial role for one or more of the Ψ residues in stabilizing the pre-tRNA structure for splicing.

Deciphering the pseudouridine nucleobase modification in human diseases: From molecular mechanisms to clinical perspectives

RNA pseudouridylation, a dynamic and reversible post-transcriptional modification found in diverse RNA species, is crucial for various biological processes, including tRNA homeostasis, tRNA transport, translation initiation regulation, pre-mRNA splicing, enhancement of mRNA translation, and translational fidelity. Disruption of pseudouridylation impairs cellular homeostasis, contributing to pathological alterations. Recent studies have highlighted its regulatory role in human diseases, particularly in tumourigenesis. Cellular stresses trigger RNA pseudouridylation in organisms, suggesting that pseudouridylation-mediated epigenetic reprogramming is essential for maintaining cellular viability and responding to stress. This review examines the regulatory mechanisms and pathological implications of pseudouridylation in human diseases, with a focus on its involvement in tumourigenesis. Additionally, it explores the therapeutic potential of targeting pseudouridylation, presenting novel strategies for disease treatment. HIGHLIGHTS: Methods to detect pseudouridine were introduced from classic mass spectrometry-based methods to newer approaches such as nanopore-based technologies and BID sequencing, each with its advantages and limitations. RNA pseudouridylation is crucial for various biological processes, including tRNA homeostasis, tRNA transport, translation initiation regulation, pre-mRNA splicing, enhancement of mRNA translation, and translational fidelity. Increased pseudouridylation is frequently associated with tumour initiation, progression, and poor prognosis, whereas its reduction is predominantly implicated in non-tumour diseases. A comprehensive understanding of the inducing factors for RNA pseudouridylation will be essential for elucidating its role in diseases. Such insights can provide robust evidence for how pseudouridylation influences disease progression and offer new avenues for therapeutic strategies targeting pseudouridylation dysregulation. The therapeutic potential of RNA pseudouridylation in diseases is enormous, including inhibitors targeting pseudouridine synthases, the application of RNA pseudouridylation in RNA therapeutics, and its role as a biological marker.

Quantitative detection of pseudouridine in RNA by mass spectrometry

Pseudouridine (Ψ) is one of the most prevalent and dynamic modification in RNA, and was shown to evade the host immune response in mRNA vaccines. Despite its significance, the biological role of Ψ remains poorly understood as certain key limitations and challenges in the detection of Ψ are yet to be overcome. In account of this, we report the usage of a chemical labelling strategy for the first quantitative detection of Ψ by mass spectrometry. We demonstrate a labelling efficiency exceeding 99% in isolated yeast tRNAs hosting multiple Ψs. LC-MS/MS analysis enables precise mapping of Ψ at single-base resolution, while simultaneously capturing a wide array of additional post-transcriptional modifications, which is not achieved with current sequencing technologies. This advancement may help unravel the dynamics and biological implications of Ψ, shedding light on its interplay with other modifications and deepening our understanding of its functional role.

PseU-KeMRF: A Novel Method for Identifying RNA Pseudouridine Sites

Pseudouridine is a type of abundant RNA modification that is seen in many different animals and is crucial for a variety of biological functions. Accurately identifying pseudouridine sites within the RNA sequence is vital for the subsequent study of various biological mechanisms of pseudouridine. However, the use of traditional experimental methods faces certain challenges. The development of fast and convenient computational methods is necessary to accurately identify pseudouridine sites from RNA sequence information. To address this, we introduce a novel pseudouridine site prediction model called PseU-KeMRF, which can identify pseudouridine sites in three species, H. sapiens, S. cerevisiae, and M. musculus. Through comprehensive analysis, we selected four RNA coding schemes, including binary feature, position-specific trinucleotide propensity based on single strand (PSTNPss), nucleotide chemical property (NCP) and pseudo k-tuple composition (PseKNC). Then the support vector machine-recursive feature elimination (SVM-RFE) method was used for feature selection and the feature subset was optimized. Finally, the best feature subsets are input into the kernel based on multinomial random forests (KeMRF) classifier for cross-validation and independent testing. As a new classification method, compared with the traditional random forest, KeMRF not only improves the node splitting process of decision tree construction based on multinomial distribution, but also combines the easy to interpret kernel method for prediction, which makes the classification performance better. Our results indicate superior predictive performance of PseU-KeMRF over other existing models, which can prove that PseU-KeMRF is a highly competitive predictive model that can successfully identify pseudouridine sites in RNA sequences.

U6 snRNA m6A modification is required for accurate and efficient splicing of C. elegans and human pre-mRNAs

pre-mRNA splicing is a critical feature of eukaryotic gene expression. Both cis- and trans-splicing rely on accurately recognising splice site sequences by spliceosomal U snRNAs and associated proteins. Spliceosomal snRNAs carry multiple RNA modifications with the potential to affect different stages of pre-mRNA splicing. Here, we show that the conserved U6 snRNA m6A methyltransferase METT-10 is required for accurate and efficient cis- and trans-splicing of C. elegans pre-mRNAs. The absence of METT-10 in C. elegans and METTL16 in humans primarily leads to alternative splicing at 5' splice sites with an adenosine at +4 position. In addition, METT-10 is required for splicing of weak 3' cis- and trans-splice sites. We identified a significant overlap between METT-10 and the conserved splicing factor SNRNP27K in regulating 5' splice sites with +4A. Finally, we show that editing endogenous 5' splice site +4A positions to +4U restores splicing to wild-type positions in a mett-10 mutant background, supporting a direct role for U6 snRNA m6A modification in 5' splice site recognition. We conclude that the U6 snRNA m6A modification is important for accurate and efficient pre-mRNA splicing.

Mechanisms and regulation of spliceosome-mediated pre-mRNA splicing in Saccharomyces cerevisiae

Pre-mRNA splicing, the removal of introns and ligation of flanking exons, is a crucial step in eukaryotic gene expression. The spliceosome, a macromolecular complex made up of five small nuclear RNAs (snRNAs) and dozens of proteins, assembles on introns via a complex pathway before catalyzing the two transesterification reactions necessary for splicing. All of these steps have the potential to be highly regulated to ensure correct mRNA isoform production for proper cellular function. While Saccharomyces cerevisiae (yeast) has a limited set of intron-containing genes, many of these genes are highly expressed, resulting in a large number of transcripts in a cell being spliced. As a result, splicing regulation is of critical importance for yeast. Just as in humans, yeast splicing can be influenced by protein components of the splicing machinery, structures and properties of the pre-mRNA itself, or by the action of trans-acting factors. It is likely that further analysis of the mechanisms and pathways of splicing regulation in yeast can reveal general principles applicable to other eukaryotes. This article is categorized under: RNA Processing > Splicing Mechanisms RNA Processing > Splicing Regulation/Alternative Splicing.

Truncated protein isoforms generate diversity of protein localization and function in yeast

Genome-wide measurement of ribosome occupancy on mRNAs has enabled empirical identification of translated regions, but high-confidence detection of coding regions that overlap annotated coding regions has remained challenging. Here, we report a sensitive and robust algorithm that revealed the translation of 388 N-terminally truncated proteins in budding yeast-more than 30-fold more than previously known. We extensively experimentally validated them and defined two classes. The first class lacks large portions of the annotated protein and tends to be produced from a truncated transcript. We show that two such cases, Yap5truncation and Pus1truncation, have condition-specific regulation and distinct functions from their respective annotated isoforms. The second class of truncated protein isoforms lacks only a small region of the annotated protein and is less likely to be produced from an alternative transcript isoform. Many display different subcellular localizations than their annotated counterpart, representing a common strategy for dual localization of otherwise functionally identical proteins. A record of this paper's transparent peer review process is included in the supplemental information.

Non-Coding RNAs: Regulators of Stress, Ageing, and Developmental Decisions in Yeast?

Cells must change their properties in order to adapt to a constantly changing environment. Most of the cellular sensing and regulatory mechanisms described so far are based on proteins that serve as sensors, signal transducers, and effectors of signalling pathways, resulting in altered cell physiology. In recent years, however, remarkable examples of the critical role of non-coding RNAs in some of these regulatory pathways have been described in various organisms. In this review, we focus on all classes of non-coding RNAs that play regulatory roles during stress response, starvation, and ageing in different yeast species as well as in structured yeast populations. Such regulation can occur, for example, by modulating the amount and functional state of tRNAs, rRNAs, or snRNAs that are directly involved in the processes of translation and splicing. In addition, long non-coding RNAs and microRNA-like molecules are bona fide regulators of the expression of their target genes. Non-coding RNAs thus represent an additional level of cellular regulation that is gradually being uncovered.

Fuzzy kernel evidence Random Forest for identifying pseudouridine sites

Pseudouridine is an RNA modification that is widely distributed in both prokaryotes and eukaryotes, and plays a critical role in numerous biological activities. Despite its importance, the precise identification of pseudouridine sites through experimental approaches poses significant challenges, requiring substantial time and resources.Therefore, there is a growing need for computational techniques that can reliably and quickly identify pseudouridine sites from vast amounts of RNA sequencing data. In this study, we propose fuzzy kernel evidence Random Forest (FKeERF) to identify pseudouridine sites. This method is called PseU-FKeERF, which demonstrates high accuracy in identifying pseudouridine sites from RNA sequencing data. The PseU-FKeERF model selected four RNA feature coding schemes with relatively good performance for feature combination, and then input them into the newly proposed FKeERF method for category prediction. FKeERF not only uses fuzzy logic to expand the original feature space, but also combines kernel methods that are easy to interpret in general for category prediction. Both cross-validation tests and independent tests on benchmark datasets have shown that PseU-FKeERF has better predictive performance than several state-of-the-art methods. This new method not only improves the accuracy of pseudouridine site identification, but also provides a certain reference for disease control and related drug development in the future.

Interpretable Multi-Scale Deep Learning for RNA Methylation Analysis across Multiple Species

RNA modification plays a crucial role in cellular regulation. However, traditional high-throughput sequencing methods for elucidating their functional mechanisms are time-consuming and labor-intensive, despite extensive research. Moreover, existing methods often limit their focus to specific species, neglecting the simultaneous exploration of RNA modifications across diverse species. Therefore, a versatile computational approach is necessary for interpretable analysis of RNA modifications across species. A multi-scale biological language-based deep learning model is proposed for interpretable, sequential-level prediction of diverse RNA modifications. Benchmark comparisons across species demonstrate the model's superiority in predicting various RNA methylation types over current state-of-the-art methods. The cross-species validation and attention weight visualization also highlight the model's capability to capture sequential and functional semantics from genomic backgrounds. Our analysis of RNA modifications helps us find the potential existence of "biological grammars" in each modification type, which could be effective for mapping methylation-related sequential patterns and understanding the underlying biological mechanisms of RNA modifications.

Pseudouridylation-mediated gene expression modulation

RNA-guided pseudouridylation, a widespread post-transcriptional RNA modification, has recently gained recognition for its role in cellular processes such as pre-mRNA splicing and the modulation of premature termination codon (PTC) readthrough. This review provides insights into its mechanisms, functions, and potential therapeutic applications. It examines the mechanisms governing RNA-guided pseudouridylation, emphasizing the roles of guide RNAs and pseudouridine synthases in catalyzing uridine-to-pseudouridine conversion. A key focus is the impact of RNA-guided pseudouridylation of U2 small nuclear RNA on pre-mRNA splicing, encompassing its influence on branch site recognition and spliceosome assembly. Additionally, the review discusses the emerging role of RNA-guided pseudouridylation in regulating PTC readthrough, impacting translation termination and genetic disorders. Finally, it explores the therapeutic potential of pseudouridine modifications, offering insights into potential treatments for genetic diseases and cancer and the development of mRNA vaccine.

RNA pseudouridine modification in plants

Pseudouridine is one of the well-known chemical modifications in various RNA species. Current advances to detect pseudouridine show that the pseudouridine landscape is dynamic and affects multiple cellular processes. Although our understanding of this post-transcriptional modification mainly depends on yeast and human models, the recent findings provide strong evidence for the critical role of pseudouridine in plants. Here, we review the current knowledge of pseudouridine in plant RNAs, including its synthesis, degradation, regulatory mechanisms, and functions. Moreover, we propose future areas of research on pseudouridine modification in plants.

The structural basis of mRNA recognition and binding by yeast pseudouridine synthase PUS1

The chemical modification of RNA bases represents a ubiquitous activity that spans all domains of life. Pseudouridylation is the most common RNA modification and is observed within tRNA, rRNA, ncRNA and mRNAs. Pseudouridine synthase or 'PUS' enzymes include those that rely on guide RNA molecules and others that function as 'stand-alone' enzymes. Among the latter, several have been shown to modify mRNA transcripts. Although recent studies have defined the structural requirements for RNA to act as a PUS target, the mechanisms by which PUS1 recognizes these target sequences in mRNA are not well understood. Here we describe the crystal structure of yeast PUS1 bound to an RNA target that we identified as being a hot spot for PUS1-interaction within a model mRNA at 2.4 Å resolution. The enzyme recognizes and binds both strands in a helical RNA duplex, and thus guides the RNA containing the target uridine to the active site for subsequent modification of the transcript. The study also allows us to show the divergence of related PUS1 enzymes and their corresponding RNA target specificities, and to speculate on the basis by which PUS1 binds and modifies mRNA or tRNA substrates.

The emerging role of epitranscriptome in shaping stress responses in plants

KEY MESSAGE

RNA modifications and editing changes constitute 'epitranscriptome' and are crucial in regulating the development and stress response in plants. Exploration of the epitranscriptome and associated machinery would facilitate the engineering of stress tolerance in crops. RNA editing and modifications post-transcriptionally decorate almost all classes of cellular RNAs, including tRNAs, rRNAs, snRNAs, lncRNAs and mRNAs, with more than 170 known modifications, among which m6A, Ψ, m5C, 8-OHG and C-to-U editing are the most abundant. Together, these modifications constitute the "epitranscriptome", and contribute to changes in several RNA attributes, thus providing an additional structural and functional diversification to the "cellular messages" and adding another layer of gene regulation in organisms, including plants. Numerous evidences suggest that RNA modifications have a widespread impact on plant development as well as in regulating the response of plants to abiotic and biotic stresses. High-throughput sequencing studies demonstrate that the landscapes of m6A, m5C, Am, Cm, C-to-U, U-to-G, and A-to-I editing are remarkably dynamic during stress conditions in plants. GO analysis of transcripts enriched in Ψ, m6A and m5C modifications have identified bonafide components of stress regulatory pathways. Furthermore, significant alterations in the expression pattern of genes encoding writers, readers, and erasers of certain modifications have been documented when plants are grown in challenging environments. Notably, manipulating the expression levels of a few components of RNA editing machinery markedly influenced the stress tolerance in plants. We provide updated information on the current understanding on the contribution of RNA modifications in shaping the stress responses in plants. Unraveling of the epitranscriptome has opened new avenues for designing crops with enhanced productivity and stress resilience in view of global climate change.

Prediction of Multiple Types of RNA Modifications via Biological Language Model

It has been demonstrated that RNA modifications play essential roles in multiple biological processes. Accurate identification of RNA modifications in the transcriptome is critical for providing insights into the biological functions and mechanisms. Many tools have been developed for predicting RNA modifications at single-base resolution, which employ conventional feature engineering methods that focus on feature design and feature selection processes that require extensive biological expertise and may introduce redundant information. With the rapid development of artificial intelligence technologies, end-to-end methods are favorably received by researchers. Nevertheless, each well-trained model is only suitable for a specific RNA methylation modification type for nearly all of these approaches. In this study, we present MRM-BERT by feeding task-specific sequences into the powerful BERT (Bidirectional Encoder Representations from Transformers) model and implementing fine-tuning, which exhibits competitive performance to the state-of-the-art methods. MRM-BERT avoids repeated de novo training of the model and can predict multiple RNA modifications such as pseudouridine, m6A, m5C, and m1A in Mus musculus, Arabidopsis thaliana, and Saccharomyces cerevisiae. In addition, we analyse the attention heads to provide high attention regions for the prediction, and conduct saturated in silico mutagenesis of the input sequences to discover potential changes of RNA modifications, which can better assist researchers in their follow-up research.

Truncated protein isoforms generate diversity of protein localization and function in yeast

Genome-wide measurements of ribosome occupancy on mRNA transcripts have enabled global empirical identification of translated regions. These approaches have revealed an unexpected diversity of protein products, but high-confidence identification of new coding regions that entirely overlap annotated coding regions - including those that encode truncated protein isoforms - has remained challenging. Here, we develop a sensitive and robust algorithm focused on identifying N-terminally truncated proteins genome-wide, identifying 388 truncated protein isoforms, a more than 30-fold increase in the number known in budding yeast. We perform extensive experimental validation of these truncated proteins and define two general classes. The first set lack large portions of the annotated protein sequence and tend to be produced from a truncated transcript. We show two such cases, Yap5 truncation and Pus1 truncation , to have condition-specific regulation and functions that appear distinct from their respective annotated isoforms. The second set of N-terminally truncated proteins lack only a small region of the annotated protein and are less likely to be regulated by an alternative transcript isoform. Many localize to different subcellular compartments than their annotated counterpart, representing a common strategy for achieving dual localization of otherwise functionally identical proteins.

PseU-pred: An ensemble model for accurate identification of pseudouridine sites

Pseudouridine (ψ) is reported to occur frequently in all types of RNA. This uridine modification has been shown to be essential for processes such as RNA stability and stress response. Also, it is linked to a few human diseases, such as prostate cancer, anemia, etc. A few laboratory techniques, such as Pseudo-seq and N3-CMC-enriched Pseudouridine sequencing (CeU-Seq) are used for detecting ψ sites. However, these are laborious and drawn-out methods. The convenience of sequencing data has enabled the development of computationally intelligent models for improving ψ site identification methods. The proposed work provides a prediction model for the identification of ψ sites through popular ensemble methods such as stacking, bagging, and boosting. Features were obtained through a novel feature extraction mechanism with the assimilation of statistical moments, which were used to train ensemble models. The cross-validation test and independent set test were used to evaluate the precision of the trained models. The proposed model outperformed the preexisting predictors and revealed 87% accuracy, 0.90 specificity, 0.85 sensitivity, and a 0.75 Matthews correlation coefficient. A web server has been built and is available publicly for the researchers at https://taseersuleman-y-test-pseu-pred-c2wmtj.streamlit.app/.

PseU-ST: A new stacked ensemble-learning method for identifying RNA pseudouridine sites

Background: Pseudouridine (Ψ) is one of the most abundant RNA modifications found in a variety of RNA types, and it plays a significant role in many biological processes. The key to studying the various biochemical functions and mechanisms of Ψ is to identify the Ψ sites. However, identifying Ψ sites using experimental methods is time-consuming and expensive. Therefore, it is necessary to develop computational methods that can accurately predict Ψ sites based on RNA sequence information. Methods: In this study, we proposed a new model called PseU-ST to identify Ψ sites in Homo sapiens (H. sapiens), Saccharomyces cerevisiae (S. cerevisiae), and Mus musculus (M. musculus). We selected the best six encoding schemes and four machine learning algorithms based on a comprehensive test of almost all of the RNA sequence encoding schemes available in the iLearnPlus software package, and selected the optimal features for each encoding scheme using chi-square and incremental feature selection algorithms. Then, we selected the optimal feature combination and the best base-classifier combination for each species through an extensive performance comparison and employed a stacking strategy to build the predictive model. Results: The results demonstrated that PseU-ST achieved better prediction performance compared with other existing models. The PseU-ST accuracy scores were 93.64%, 87.74%, and 89.64% on H_990, S_628, and M_944, respectively, representing increments of 13.94%, 6.05%, and 0.26%, respectively, higher than the best existing methods on the same benchmark training datasets. Conclusion: The data indicate that PseU-ST is a very competitive prediction model for identifying RNA Ψ sites in H. sapiens, M. musculus, and S. cerevisiae. In addition, we found that the Position-specific trinucleotide propensity based on single strand (PSTNPss) and Position-specific of three nucleotides (PS3) features play an important role in Ψ site identification. The source code for PseU-ST and the data are obtainable in our GitHub repository (https://github.com/jluzhangxinrubio/PseU-ST).

Predicting Pseudouridine Sites with Porpoise

Pseudouridine is a ubiquitous RNA modification and plays a crucial role in many biological processes. However, it remains a challenging task to identify pseudouridine sites using expensive and time-consuming experimental research. To this end, we present Porpoise, a computational approach to identify pseudouridine sites from RNA sequence data. Porpoise builds on a stacking ensemble learning framework with several informative features and achieves competitive performance compared with state-of-the-art approaches. This protocol elaborates on step-by-step use and execution of the local stand-alone version and the webserver of Porpoise. In addition, we also provide a general machine learning framework that can help identify the optimal stacking ensemble learning model using different combinations of feature-based features. This general machine learning framework can facilitate users to build their pseudouridine predictors using their in-house datasets.

m6A modification of U6 snRNA modulates usage of two major classes of pre-mRNA 5' splice site

Alternative splicing of messenger RNAs is associated with the evolution of developmentally complex eukaryotes. Splicing is mediated by the spliceosome, and docking of the pre-mRNA 5' splice site into the spliceosome active site depends upon pairing with the conserved ACAGA sequence of U6 snRNA. In some species, including humans, the central adenosine of the ACAGA box is modified by N6 methylation, but the role of this m6A modification is poorly understood. Here, we show that m6A modified U6 snRNA determines the accuracy and efficiency of splicing. We reveal that the conserved methyltransferase, FIONA1, is required for Arabidopsis U6 snRNA m6A modification. Arabidopsis fio1 mutants show disrupted patterns of splicing that can be explained by the sequence composition of 5' splice sites and cooperative roles for U5 and U6 snRNA in splice site selection. U6 snRNA m6A influences 3' splice site usage. We generalise these findings to reveal two major classes of 5' splice site in diverse eukaryotes, which display anti-correlated interaction potential with U5 snRNA loop 1 and the U6 snRNA ACAGA box. We conclude that U6 snRNA m6A modification contributes to the selection of degenerate 5' splice sites crucial to alternative splicing.

iPseU-TWSVM: Identification of RNA pseudouridine sites based on TWSVM

Biological sequence analysis is an important basic research work in the field of bioinformatics. With the explosive growth of data, machine learning methods play an increasingly important role in biological sequence analysis. By constructing a classifier for prediction, the input sequence feature vector is predicted and evaluated, and the knowledge of gene structure, function and evolution is obtained from a large amount of sequence information, which lays a foundation for researchers to carry out in-depth research. At present, many machine learning methods have been applied to biological sequence analysis such as RNA gene recognition and protein secondary structure prediction. As a biological sequence, RNA plays an important biological role in the encoding, decoding, regulation and expression of genes. The analysis of RNA data is currently carried out from the aspects of structure and function, including secondary structure prediction, non-coding RNA identification and functional site prediction. Pseudouridine (У) is the most widespread and rich RNA modification and has been discovered in a variety of RNAs. It is highly essential for the study of related functional mechanisms and disease diagnosis to accurately identify У sites in RNA sequences. At present, several computational approaches have been suggested as an alternative to experimental methods to detect У sites, but there is still potential for improvement in their performance. In this study, we present a model based on twin support vector machine (TWSVM) for У site identification. The model combines a variety of feature representation techniques and uses the max-relevance and min-redundancy methods to obtain the optimum feature subset for training. The independent testing accuracy is improved by 3.4% in comparison to current advanced У site predictors. The outcomes demonstrate that our model has better generalization performance and improves the accuracy of У site identification. iPseU-TWSVM can be a helpful tool to identify У sites.

The Arabidopsis Mitochondrial Pseudouridine Synthase Homolog FCS1 Plays Critical Roles in Plant Development

As the most abundant RNA modification, pseudouridylation has been shown to play critical roles in Escherichia coli, yeast and humans. However, its function in plants is still unclear. Here, we characterized leaf curly and small 1 (FCS1), which encodes a pseudouridine synthase in Arabidopsis. fcs1 mutants exhibited severe defects in plant growth, such as delayed development and reduced fertility, and were significantly smaller than the wild type at different developmental stages. FCS1 protein is localized in the mitochondrion. The absence of FCS1 significantly reduces pseudouridylation of mitochondrial 26S ribosomal RNA (rRNA) at the U1692 site, which sits in the peptidyl transferase center. This affection of mitochondrial 26S rRNA may lead to the disruption of mitochondrial translation in the fcs1-1 mutant, causing high accumulation of transcripts but low production of proteins. Dysfunctional mitochondria with abnormal structures were also observed in the fcs1-1 mutant. Overall, our results suggest that FCS1-mediated pseudouridylation of mitochondrial 26S rRNA is required for mitochondrial translation, which is critical for maintaining mitochondrial function and plant development.

The plant epitranscriptome: revisiting pseudouridine and 2'-O-methyl RNA modifications

There is growing evidence that post-transcriptional RNA modifications are highly dynamic and can be used to improve crop production. Although more than 172 unique types of RNA modifications have been identified throughout the kingdom of life, we are yet to leverage upon the understanding to optimize RNA modifications in crops to improve productivity. The contributions of internal mRNA modifications such as N6-methyladenosine (m⁶ A) and 5-methylcytosine (m⁵ C) methylations to embryonic development, root development, leaf morphogenesis, flowering, fruit ripening and stress response are sufficiently known, but the roles of the two most abundant RNA modifications, pseudouridine (Ψ) and 2'-O-methylation (Nm), in the cell remain unclear due to insufficient advances in high-throughput technologies in plant development. Therefore, in this review, we discuss the latest methods and insights gained in mapping internal Ψ and Nm and their unique properties in plants and other organisms. In addition, we discuss the limitations that remain in high-throughput technologies for qualitative and quantitative mapping of these RNA modifications and highlight future challenges in regulating the plant epitranscriptome.

Genome-Wide Identification and Expression Analysis of Pseudouridine Synthase Family in Arabidopsis and Maize

Pseudouridine (Ψ), the isomer of uridine (U), is the most abundant type of RNA modification, which is crucial for gene regulation in various cellular processes. Pseudouridine synthases (PUSs) are the key enzymes for the U-to-Ψ conversion. However, little is known about the genome-wide features and biological function of plant PUSs. In this study, we identified 20 AtPUSs and 22 ZmPUSs from Arabidopsis and maize (Zea mays), respectively. Our phylogenetic analysis indicated that both AtPUSs and ZmPUSs could be clustered into six known subfamilies: RluA, RsuA, TruA, TruB, PUS10, and TruD. RluA subfamily is the largest subfamily in both Arabidopsis and maize. It's noteworthy that except the canonical XXHRLD-type RluAs, another three conserved RluA variants, including XXNRLD-, XXHQID-, and XXHRLG-type were also identified in those key nodes of vascular plants. Subcellular localization analysis of representative AtPUSs and ZmPUSs in each subfamily revealed that PUS proteins were localized in different organelles including nucleus, cytoplasm and chloroplasts. Transcriptional expression analysis indicated that AtPUSs and ZmPUSs were differentially expressed in various tissues and diversely responsive to abiotic stresses, especially suggesting their potential roles in response to heat and salt stresses. All these results would facilitate the functional identification of these pseudouridylation in the future.

Gene Expression-Based Predication of RNA Pseudouridine Modification in Tumor Microenvironment and Prognosis of Glioma Patients

Aberrant expression of methyltransferases and demethylases may augment tumor initiation, proliferation and metastasis through RNA modification, such as m⁶A and m⁵C. However, activity of pseudouridine (Ψ) modification of RNA remains unknown in glioma, the most common malignant intracranial tumor. In this study, we explored the expression profiles of the Ψ synthase genes in glioma and constructed an efficient prediction model for glioma prognosis based on the CGGA and TCGA datasets. In addition, the risk-score signature was positively associated with malignancy of gliomas and the abundance of tumor-infiltrating immune cells such as macrophages M0 and regulatory T cells (Tregs), but negatively associated with the abundance of monocytes, NK cell activation and T cell CD4⁺ naive. In terms of mechanism, the risk-score signature was positively associated with the expression of inflammatory molecules such as S100A11 and CASP4 in glioma. Overall, this study provided evidence for the activity of RNA Ψ modification in glioma malignancy and local immunity.

A nuclear function for an oncogenic microRNA as a modulator of snRNA and splicing

BACKGROUND

miRNAs are regulatory transcripts established as repressors of mRNA stability and translation that have been functionally implicated in carcinogenesis. miR-10b is one of the key onco-miRs associated with multiple forms of cancer. Malignant gliomas exhibit particularly striking dependence on miR-10b. However, despite the therapeutic potential of miR-10b targeting, this miRNA's poorly investigated and largely unconventional properties hamper the clinical translation.

METHODS

We utilized Covalent Ligation of Endogenous Argonaute-bound RNAs and their high-throughput RNA sequencing to identify miR-10b interactome and a combination of biochemical and imaging approaches for target validation. They included Crosslinking and RNA immunoprecipitation with spliceosomal proteins, a combination of miRNA FISH with protein immunofluorescence in glioma cells and patient-derived tumors, native Northern blotting, and the transcriptome-wide analysis of alternative splicing.

RESULTS

We demonstrate that miR-10b binds to U6 snRNA, a core component of the spliceosomal machinery. We provide evidence of the direct binding between miR-10b and U6, in situ imaging of miR-10b and U6 co-localization in glioma cells and tumors, and biochemical co-isolation of miR-10b with the components of the spliceosome. We further demonstrate that miR-10b modulates U6 N-6-adenosine methylation and pseudouridylation, U6 binding to splicing factors SART3 and PRPF8, and regulates U6 stability, conformation, and levels. These effects on U6 result in global splicing alterations, exemplified by the altered ratio of the isoforms of a small GTPase CDC42, reduced overall CDC42 levels, and downstream CDC42 -mediated effects on cell viability.

CONCLUSIONS

We identified U6 snRNA, the key RNA component of the spliceosome, as the top miR-10b target in glioblastoma. We, therefore, present an unexpected intersection of the miRNA and splicing machineries and a new nuclear function for a major cancer-associated miRNA.

The epitranscriptome of small non-coding RNAs

Small non-coding RNAs are short RNA molecules and involved in many biological processes, including cell proliferation and differentiation, immune response, cell death, epigenetic regulation, metabolic control. A diversity of RNA modifications have been identified in these small non-coding RNAs, including transfer RNAs (tRNAs), microRNAs (miRNAs), PIWI-interacting RNAs (piRNAs), small nuclear RNA (snRNA), small nucleolar RNAs (snoRNAs), and tRNA-derived small RNAs (tsRNAs). These post-transcriptional modifications are involved in the biogenesis and function of these small non-coding RNAs. In this review, we will summarize the existence of RNA modifications in the small non-coding RNAs and the emerging roles of these epitranscriptomic marks.

SnoRNA guide activities: real and ambiguous

In eukaryotes, rRNAs and spliceosomal snRNAs are heavily modified post-transcriptionally. Pseudouridylation and 2'-O-methylation are the most abundant types of RNA modifications. They are mediated by modification guide RNAs, also known as small nucleolar (sno)RNAs and small Cajal body-specific (sca)RNAs. We used yeast and vertebrate cells to test guide activities predicted for a number of snoRNAs, based on their regions of complementarity with rRNAs. We showed that human SNORA24 is a genuine guide RNA for 18S-Ψ609, despite some noncanonical base-pairing with its target. At the same time, we found quite a few snoRNAs that have the ability to base-pair with rRNAs and can induce predicted modifications in artificial substrate RNAs, but do not modify the same target sequence within endogenous rRNA molecules. Furthermore, certain fragments of rRNAs can be modified by the endogenous yeast modification machinery when inserted into an artificial backbone RNA, even though the same sequences are not modified in endogenous yeast rRNAs. In Xenopus cells, a guide RNA generated from scaRNA, but not from snoRNA, could induce an additional pseudouridylation of U2 snRNA at position 60; both guide RNAs were equally active on a U2 snRNA-specific substrate in yeast cells. Thus, post-transcriptional modification of functionally important RNAs, such as rRNAs and snRNAs, is highly regulated and more complex than simply strong base-pairing between a guide RNA and substrate RNA. We discuss possible regulatory roles for these unexpected modifications.

The Complex Genetic Basis and Multilayered Regulatory Control of Yeast Pseudohyphal Growth

Eukaryotic cells are exquisitely responsive to external and internal cues, achieving precise control of seemingly diverse growth processes through a complex interplay of regulatory mechanisms. The budding yeast Saccharomyces cerevisiae provides a fascinating model of cell growth in its stress-responsive transition from planktonic single cells to a filamentous pseudohyphal growth form. During pseudohyphal growth, yeast cells undergo changes in morphology, polarity, and adhesion to form extended and invasive multicellular filaments. This pseudohyphal transition has been studied extensively as a model of conserved signaling pathways regulating cell growth and for its relevance in understanding the pathogenicity of the related opportunistic fungus Candida albicans, wherein filamentous growth is required for virulence. This review highlights the broad gene set enabling yeast pseudohyphal growth, signaling pathways that regulate this process, the role and regulation of proteins conferring cell adhesion, and interesting regulatory mechanisms enabling the pseudohyphal transition. Expected final online publication date for the Annual Review of Genetics, Volume 55 is November 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

The small Cajal body-specific RNA 15 (SCARNA15) directs p53 and redox homeostasis via selective splicing in cancer cells

Small Cajal body-specific RNAs (scaRNAs) guide post-transcriptional modification of spliceosomal RNA and, while commonly altered in cancer, have poorly defined roles in tumorigenesis. Here, we uncover that SCARNA15 directs alternative splicing (AS) and stress adaptation in cancer cells. Specifically, we find that SCARNA15 guides critical pseudouridylation (Ψ) of U2 spliceosomal RNA to fine-tune AS of distinct transcripts enriched for chromatin and transcriptional regulators in malignant cells. This critically impacts the expression and function of the key tumor suppressors ATRX and p53. Significantly, SCARNA15 loss impairs p53-mediated redox homeostasis and hampers cancer cell survival, motility and anchorage-independent growth. In sum, these findings highlight an unanticipated role for SCARNA15 and Ψ in directing cancer-associated splicing programs.

Porpoise: a new approach for accurate prediction of RNA pseudouridine sites

Pseudouridine is a ubiquitous RNA modification type present in eukaryotes and prokaryotes, which plays a vital role in various biological processes. Almost all kinds of RNAs are subject to this modification. However, it remains a great challenge to identify pseudouridine sites via experimental approaches, requiring expensive and time-consuming experimental research. Therefore, computational approaches that can be used to perform accurate in silico identification of pseudouridine sites from the large amount of RNA sequence data are highly desirable and can aid in the functional elucidation of this critical modification. Here, we propose a new computational approach, termed Porpoise, to accurately identify pseudouridine sites from RNA sequence data. Porpoise builds upon a comprehensive evaluation of 18 frequently used feature encoding schemes based on the selection of four types of features, including binary features, pseudo k-tuple composition, nucleotide chemical property and position-specific trinucleotide propensity based on single-strand (PSTNPss). The selected features are fed into the stacked ensemble learning framework to enable the construction of an effective stacked model. Both cross-validation tests on the benchmark dataset and independent tests show that Porpoise achieves superior predictive performance than several state-of-the-art approaches. The application of model interpretation tools demonstrates the importance of PSTNPs for the performance of the trained models. This new method is anticipated to facilitate community-wide efforts to identify putative pseudouridine sites and formulate novel testable biological hypothesis.

Pseudouridines on Trypanosoma brucei mRNAs are developmentally regulated: Implications to mRNA stability and protein binding

The parasite Trypanosoma brucei cycles between an insect and a mammalian host and is the causative agent of sleeping sickness. Here, we performed high-throughput mapping of pseudouridines (Ψs) on mRNA from two life stages of the parasite. The analysis revealed ~273 Ψs, including developmentally regulated Ψs that are guided by homologs of pseudouridine synthases (PUS1, 3, 5, and 7). Mutating the U that undergoes pseudouridylation in the 3' UTR of valyl-tRNA synthetase destabilized the mRNA level. To investigate the mechanism by which Ψ affects the stability of this mRNA, proteins that bind to the 3' UTR were identified, including the RNA binding protein RBSR1. The binding of RBSR1 protein to the 3' UTR was stronger when lacking Ψ compared to transcripts carrying the modification, suggesting that Ψ can inhibit the binding of proteins to their target and thus affect the stability of mRNAs. Consequently, Ψ modification on mRNA adds an additional level of regulation to the dominant post-transcriptional control in these parasites.

HSP90-dependent PUS7 overexpression facilitates the metastasis of colorectal cancer cells by regulating LASP1 abundance

BACKGROUND

Pseudouridine synthase (PUS) 7 is a member of the PUS family that catalyses pseudouridine formation. It has been shown to be involved in intellectual development and haematological malignancies. Nevertheless, the role and the underlying molecular mechanisms of PUS7 in solid tumours, such as colorectal cancer (CRC), remain unexplored. This study elucidated, for the first time, the role of PUS7 in CRC cell metastasis and the underlying mechanisms.

METHODS

We conducted immunohistochemistry, qPCR, and western blotting to quantify the expression of PUS7 in CRC tissues as well as cell lines. Besides, diverse in vivo and in vitro functional tests were employed to establish the function of PUS7 in CRC. RNA-seq and proteome profiling analysis were also applied to identify the targets of PUS7. PUS7-interacting proteins were further uncovered using immunoprecipitation and mass spectrometry.

RESULTS

Overexpression of PUS7 was observed in CRC tissues and was linked to advanced clinical stages and shorter overall survival. PUS7 silencing effectively repressed CRC cell metastasis, while its upregulation promoted metastasis, independently of the PUS7 catalytic activity. LASP1 was identified as a downstream effector of PUS7. Forced LASP1 expression abolished the metastasis suppression triggered by PUS7 silencing. Furthermore, HSP90 was identified as a client protein of PUS7, associated with the increased PUS7 abundance in CRC. NMS-E973, a specific HSP90 inhibitor, also showed higher anti-metastatic activity when combined with PUS7 repression. Importantly, in line with these results, in human CRC tissues, the expression of PUS7 was positively linked to the expression of HSP90 and LASP1, and patients co-expressing HSP90/PUS7/LASP1 showed a worse prognosis.

CONCLUSIONS

The HSP90-dependent PUS7 upregulation promotes CRC cell metastasis via the regulation of LASP1. Thus, targeting the HSP90/PUS7/LASP1 axis may be a novel approach for the treatment of CRC.

Spliceosomal snRNA Epitranscriptomics

Small nuclear RNAs (snRNAs) are critical components of the spliceosome that catalyze the splicing of pre-mRNA. snRNAs are each complexed with many proteins to form RNA-protein complexes, termed as small nuclear ribonucleoproteins (snRNPs), in the cell nucleus. snRNPs participate in pre-mRNA splicing by recognizing the critical sequence elements present in the introns, thereby forming active spliceosomes. The recognition is achieved primarily by base-pairing interactions (or nucleotide-nucleotide contact) between snRNAs and pre-mRNA. Notably, snRNAs are extensively modified with different RNA modifications, which confer unique properties to the RNAs. Here, we review the current knowledge of the mechanisms and functions of snRNA modifications and their biological relevance in the splicing process.

Identification of RNA pseudouridine sites using deep learning approaches

Pseudouridine(Ψ) is widely popular among various RNA modifications which have been confirmed to occur in rRNA, mRNA, tRNA, and nuclear/nucleolar RNA. Hence, identifying them has vital significance in academic research, drug development and gene therapies. Several laboratory techniques for Ψ identification have been introduced over the years. Although these techniques produce satisfactory results, they are costly, time-consuming and requires skilled experience. As the lengths of RNA sequences are getting longer day by day, an efficient method for identifying pseudouridine sites using computational approaches is very important. In this paper, we proposed a multi-channel convolution neural network using binary encoding. We employed k-fold cross-validation and grid search to tune the hyperparameters. We evaluated its performance in the independent datasets and found promising results. The results proved that our method can be used to identify pseudouridine sites for associated purposes. We have also implemented an easily accessible web server at http://103.99.176.239/ipseumulticnn/.

Regulation and Function of RNA Pseudouridylation in Human Cells

Recent advances in pseudouridine detection reveal a complex pseudouridine landscape that includes messenger RNA and diverse classes of noncoding RNA in human cells. The known molecular functions of pseudouridine, which include stabilizing RNA conformations and destabilizing interactions with varied RNA-binding proteins, suggest that RNA pseudouridylation could have widespread effects on RNA metabolism and gene expression. Here, we emphasize how much remains to be learned about the RNA targets of human pseudouridine synthases, their basis for recognizing distinct RNA sequences, and the mechanisms responsible for regulated RNA pseudouridylation. We also examine the roles of noncoding RNA pseudouridylation in splicing and translation and point out the potential effects of mRNA pseudouridylation on protein production, including in the context of therapeutic mRNAs.

Analysis and Comparison of RNA Pseudouridine Site Prediction Tools

Background:

Pseudouridine (Ψ) is the most abundant RNA modification and has important functions in a series of biological and cellular processes. Although experimental techniques have made great contributions to identify Ψ sites, they are still labor-intensive and costineffective. In the past few years, a series of computational approaches have been developed, which provided rapid and efficient approaches to identify Ψ sites.

Results:

To provide the readership with a clear landscape about the recent development in this important area, in this review, we summarized and compared the representative computational approaches developed for identifying Ψ sites. Moreover, future directions in computationally identifying Ψ sites were discussed as well.

Conclusion:

We anticipate that this review will provide novel insights into the researches on pseudouridine modification.

LARP7-Mediated U6 snRNA Modification Ensures Splicing Fidelity and Spermatogenesis in Mice

U6 snRNA, as an essential component of the catalytic core of the pre-mRNA processing spliceosome, is heavily modified post-transcriptionally, with 2'-O-methylation being most common. The role of these modifications in pre-mRNA splicing as well as their physiological function in mammals have remained largely unclear. Here we report that the La-related protein LARP7 functions as a critical cofactor for 2'-O-methylation of U6 in mouse male germ cells. Mechanistically, LARP7 promotes U6 loading onto box C/D snoRNP, facilitating U6 2'-O-methylation by box C/D snoRNP. Importantly, ablation of LARP7 in the male germline causes defective U6 2'-O-methylation, massive alterations in pre-mRNA splicing, and spermatogenic failure in mice, which can be rescued by ectopic expression of wild-type LARP7 but not an U6-loading-deficient mutant LARP7. Our data uncover a novel role of LARP7 in regulating U6 2'-O-methylation and demonstrate the functional requirement of such modification for splicing fidelity and spermatogenesis in mice.

iPseU-NCP: Identifying RNA pseudouridine sites using random forest and NCP-encoded features

Background

Pseudouridine modification is most commonly found among various kinds of RNA modification occurred in both prokaryotes and eukaryotes. This biochemical event has been proved to occur in multiple types of RNAs, including rRNA, mRNA, tRNA, and nuclear/nucleolar RNA. Hence, gaining a holistic understanding of pseudouridine modification can contribute to the development of drug discovery and gene therapies. Although some laboratory techniques have come up with moderately good outcomes in pseudouridine identification, they are costly and required skilled work experience. We propose iPseU-NCP – an efficient computational framework to predict pseudouridine sites using the Random Forest (RF) algorithm combined with nucleotide chemical properties (NCP) generated from RNA sequences. The benchmark dataset collected from Chen et al. (2016) was used to develop iPseU-NCP and fairly compare its performances with other methods.

Results

Under the same experimental settings, comparing with three state-of-the-art methods including iPseU-CNN, PseUI, and iRNA-PseU, the Matthew’s correlation coefficient (MCC) of our model increased by about 20.0%, 55.0%, and 109.0% when tested on the H. sapiens (H_200) dataset and by about 6.5%, 35.0%, and 150.0% when tested on the S. cerevisiae (S_200) dataset, respectively. This significant growth in MCC is very important since it ensures the stability and performance of our model. With those two independent test datasets, our model also presented higher accuracy with a success rate boosted by 7.0%, 13.0%, and 20.0% and 2.0%, 9.5%, and 25.0% when compared to iPseU-CNN, PseUI, and iRNA-PseU, respectively. For majority of other evaluation metrics, iPseU-NCP demonstrated superior performance as well.

Conclusions

iPseU-NCP combining the RF and NPC-encoded features showed better performances than other existing state-of-the-art methods in the identification of pseudouridine sites. This also shows an optimistic view in addressing biological issues related to human diseases.

mRNA structure determines modification by pseudouridine synthase 1

Pseudouridine (Ψ) is a post-transcriptional RNA modification that alters RNA-RNA and RNA-protein interactions that affect gene expression. mRNA pseudouridylation was recently discovered as a widespread and conserved phenomenon, but the mechanisms responsible for selective, regulated pseudouridylation of specific sequences within mRNAs were unknown. Here, we have revealed new mRNA targets for five pseudouridine synthases and probed the determinants of mRNA target recognition by the predominant mRNA pseudouridylating enzyme, Pus1, by developing high-throughput kinetic analysis of pseudouridylation in vitro. Combining computational prediction and rational mutational analysis revealed an RNA structural motif that is both necessary and sufficient for mRNA pseudouridylation. Applying this structural context information predicted hundreds of additional mRNA targets, that we showed were pseudouridylated in vivo. These results demonstrate a structure-dependent mode of mRNA target recognition by a conserved pseudouridine synthase and implicate modulation of RNA structure as the likely mechanism to regulate mRNA pseudouridylation.

Modifications in small nuclear RNAs and their roles in spliceosome assembly and function

Modifications in cellular RNAs have emerged as key regulators of all aspects of gene expression, including pre-mRNA splicing. During spliceosome assembly and function, the small nuclear RNAs (snRNAs) form numerous dynamic RNA-RNA and RNA-protein interactions, which are required for spliceosome assembly, correct positioning of the spliceosome on substrate pre-mRNAs and catalysis. The human snRNAs contain several base methylations as well as a myriad of pseudouridines and 2'-O-methylated nucleotides, which are largely introduced by small Cajal body-specific ribonucleoproteins (scaRNPs). Modified nucleotides typically cluster in functionally important regions of the snRNAs, suggesting that their presence could optimise the interactions of snRNAs with each other or with pre-mRNAs, or may affect the binding of spliceosomal proteins. snRNA modifications appear to play important roles in snRNP biogenesis and spliceosome assembly, and have also been proposed to influence the efficiency and fidelity of pre-mRNA splicing. Interestingly, alterations in the modification status of snRNAs have recently been observed in different cellular conditions, implying that some snRNA modifications are dynamic and raising the possibility that these modifications may fine-tune the spliceosome for particular functions. Here, we review the current knowledge on the snRNA modification machinery and discuss the timing, functions and dynamics of modifications in snRNAs.

iPseU-CNN: Identifying RNA Pseudouridine Sites Using Convolutional Neural Networks

Pseudouridine is the most prevalent RNA modification and has been found in both eukaryotes and prokaryotes. Currently, pseudouridine has been demonstrated in several kinds of RNAs, such as small nuclear RNA, rRNA, tRNA, mRNA, and small nucleolar RNA. Therefore, its significance to academic research and drug development is understandable. Through biochemical experiments, the pseudouridine site identification has produced good outcomes, but these lab exploratory methods and biochemical processes are expensive and time consuming. Therefore, it is important to introduce efficient methods for identification of pseudouridine sites. In this study, an intelligent method for pseudouridine sites using the deep-learning approach was developed. The proposed prediction model is called iPseU-CNN (identifying pseudouridine by convolutional neural networks). The existing methods used handcrafted features and machine-learning approaches to identify pseudouridine sites. However, the proposed predictor extracts the features of the pseudouridine sites automatically using a convolution neural network model. The iPseU-CNN model yields better outcomes than the current state-of-the-art models in all evaluation parameters. It is thus highly projected that the iPseU-CNN predictor will become a helpful tool for academic research on pseudouridine site prediction of RNA, as well as in drug discovery.

Whi2 signals low leucine availability to halt yeast growth and cell death

Cells are exquisitely tuned to environmental ques. Amino acid availability is rapidly sensed, allowing cells to adjust molecular processes and implement short or long-term metabolic shifts accordingly. How levels of most individual amino acids may be sensed and subsequently signaled to inform cells of their nutrient status is largely unknown. We made the unexpected observation that small changes in the levels of specific amino acids can have a profound effect on yeast cell growth, leading to the identification of yeast Whi2 as a negative regulator of cell growth in low amino acids. Although Whi2 was originally thought to be fungi-specific, Whi2 appears to share a conserved structural domain found in a family of 25 largely uncharacterized human genes encoding the KCTD (potassium channel tetramerization domain) protein family. Insights gained from yeast Whi2 are likely to be revealing about human KCTDs, many of which have been implicated or demonstrated to cause disease when mutated. Here we report new evidence that Whi2 responds to specific amino acids in the medium, particularly low leucine levels. We also discuss the known pathways of amino acid signaling and potential points of regulation by Whi2 in nutrient signaling in yeast and mammals.

Pre-mRNA modifications and their role in nuclear processing

Background

Cellular non-coding RNAs are extensively modified post-transcriptionally, with more than 100 chemically distinct nucleotides identified to date. In the past five years, new sequencing based methods have revealed widespread decoration of eukaryotic messenger RNA with diverse RNA modifications whose functions in mRNA metabolism are only beginning to be known.

Results

Since most of the identified mRNA modifying enzymes are present in the nucleus, these modifications have the potential to function in nuclear pre-mRNA processing including alternative splicing. Here we review recent progress towards illuminating the role of pre-mRNA modifications in splicing and highlight key areas for future investigation in this rapidly growing field.

Conclusions

Future studies to identify which modifications are added to nascent pre-mRNA and to interrogate the direct effects of individual modifications are likely to reveal new mechanisms by which nuclear pre-mRNA processing is regulated.

The life of U6 small nuclear RNA, from cradle to grave

Removal of introns from precursor messenger RNA (pre-mRNA) and some noncoding transcripts is an essential step in eukaryotic gene expression. In the nucleus, this process of RNA splicing is carried out by the spliceosome, a multi-megaDalton macromolecular machine whose core components are conserved from yeast to humans. In addition to many proteins, the spliceosome contains five uridine-rich small nuclear RNAs (snRNAs) that undergo an elaborate series of conformational changes to correctly recognize the splice sites and catalyze intron removal. Decades of biochemical and genetic data, along with recent cryo-EM structures, unequivocally demonstrate that U6 snRNA forms much of the catalytic core of the spliceosome and is highly dynamic, interacting with three snRNAs, the pre-mRNA substrate, and >25 protein partners throughout the splicing cycle. This review summarizes the current state of knowledge on how U6 snRNA is synthesized, modified, incorporated into snRNPs and spliceosomes, recycled, and degraded.

The Role of Noncoding RNA Pseudouridylation in Nuclear Gene Expression Events

Pseudouridine is the most abundant internal RNA modification in stable noncoding RNAs (ncRNAs). It can be catalyzed by both RNA-dependent and RNA-independent mechanisms. Pseudouridylation impacts both the biochemical and biophysical properties of RNAs and thus influences RNA-mediated cellular processes. The investigation of nuclear-ncRNA pseudouridylation has demonstrated that it is critical for the proper control of multiple stages of gene expression regulation. Here, we review how nuclear-ncRNA pseudouridylation contributes to transcriptional regulation and pre-mRNA splicing.