Accurate detection of m6A RNA modifications in native RNA sequences

Nanopore Current Events Magnifier (nanoCEM): a novel tool for visualizing current events at modification sites of nanopore sequencing

Nanopore sequencing technologies have enabled the direct detection of base modifications in DNA or RNA molecules. Despite these advancements, the tools for visualizing electrical current, essential for analyzing base modifications, are often lacking in clarity and compatibility with diverse nanopore pipelines. Here, we present Nanopore Current Events Magnifier (nanoCEM, https://github.com/lrslab/nanoCEM), a Python command-line tool designed to facilitate the identification of DNA/RNA modification sites through enhanced visualization and statistical analysis. Compatible with the four preprocessing methods including 'f5c resquiggle', 'f5c eventalign', 'Tombo' and 'move table', nanoCEM is applicable to RNA and DNA analysis across multiple flow cell types. By utilizing rescaling techniques and calculating various statistical features, nanoCEM provides more accurate and comparable visualization of current events, allowing researchers to effectively observe differences between samples and showcase the modified sites.

NanoMUD: Profiling of pseudouridine and N1-methylpseudouridine using Oxford Nanopore direct RNA sequencing

Nanopore direct RNA sequencing provided a promising solution for unraveling the landscapes of modifications on single RNA molecules. Here, we proposed NanoMUD, a computational framework for predicting the RNA pseudouridine modification (Ψ) and its methylated analog N1-methylpseudouridine (m1Ψ), which have critical application in mRNA vaccination, at single-base and single-molecule resolution from direct RNA sequencing data. Electric signal features were fed into a bidirectional LSTM neural network to achieve improved accuracy and predictive capabilities. Motif-specific models (NNUNN, N = A, C, U or G) were trained based on features extracted from designed dataset and achieved superior performance on molecule-level modification prediction (Ψ models: min AUC = 0.86, max AUC = 0.99; m1Ψ models: min AUC = 0.87, max AUC = 0.99). We then aggregated read-level predictions for site stoichiometry estimation. Given the observed sequence-dependent bias in model performance, we trained regression models based on the distribution of modification probabilities for sites with known stoichiometry. The distribution-based site stoichiometry estimation method allows unbiased comparison between different contexts. To demonstrate the feasibility of our work, three case studies on both in vitro and in vivo transcribed RNAs were presented. NanoMUD will make a powerful tool to facilitate the research on modified therapeutic IVT RNAs and provides useful insight to the landscape and stoichiometry of pseudouridine and N1-pseudouridine on in vivo transcribed RNA species.

Transfer learning enables identification of multiple types of RNA modifications using nanopore direct RNA sequencing

Nanopore direct RNA sequencing (DRS) has emerged as a powerful tool for RNA modification identification. However, concurrently detecting multiple types of modifications in a single DRS sample remains a challenge. Here, we develop TandemMod, a transferable deep learning framework capable of detecting multiple types of RNA modifications in single DRS data. To train high-performance TandemMod models, we generate in vitro epitranscriptome datasets from cDNA libraries, containing thousands of transcripts labeled with various types of RNA modifications. We validate the performance of TandemMod on both in vitro transcripts and in vivo human cell lines, confirming its high accuracy for profiling m6A and m5C modification sites. Furthermore, we perform transfer learning for identifying other modifications such as m7G, Ψ, and inosine, significantly reducing training data size and running time without compromising performance. Finally, we apply TandemMod to identify 3 types of RNA modifications in rice grown in different environments, demonstrating its applicability across species and conditions. In summary, we provide a resource with ground-truth labels that can serve as benchmark datasets for nanopore-based modification identification methods, and TandemMod for identifying diverse RNA modifications using a single DRS sample.

Prediction of m6A and m5C at single-molecule resolution reveals a transcriptome-wide co-occurrence of RNA modifications

The epitranscriptome embodies many new and largely unexplored functions of RNA. A significant roadblock hindering progress in epitranscriptomics is the identification of more than one modification in individual transcript molecules. We address this with CHEUI (CH3 (methylation) Estimation Using Ionic current). CHEUI predicts N6-methyladenosine (m6A) and 5-methylcytosine (m5C) in individual molecules from the same sample, the stoichiometry at transcript reference sites, and differential methylation between any two conditions. CHEUI processes observed and expected nanopore direct RNA sequencing signals to achieve high single-molecule, transcript-site, and stoichiometry accuracies in multiple tests using synthetic RNA standards and cell line data. CHEUI's capability to identify two modification types in the same sample reveals a co-occurrence of m6A and m5C in individual mRNAs in cell line and tissue transcriptomes. CHEUI provides new avenues to discover and study the function of the epitranscriptome.

Unveiling microbial diversity: harnessing long-read sequencing technology

Long-read sequencing has recently transformed metagenomics, enhancing strain-level pathogen characterization, enabling accurate and complete metagenome-assembled genomes, and improving microbiome taxonomic classification and profiling. These advancements are not only due to improvements in sequencing accuracy, but also happening across rapidly changing analysis methods. In this Review, we explore long-read sequencing's profound impact on metagenomics, focusing on computational pipelines for genome assembly, taxonomic characterization and variant detection, to summarize recent advancements in the field and provide an overview of available analytical methods to fully leverage long reads. We provide insights into the advantages and disadvantages of long reads over short reads and their evolution from the early days of long-read sequencing to their recent impact on metagenomics and clinical diagnostics. We further point out remaining challenges for the field such as the integration of methylation signals in sub-strain analysis and the lack of benchmarks.

Detection of ribonucleotides embedded in DNA by Nanopore sequencing

Ribonucleotides represent the most common non-canonical nucleotides found in eukaryotic genomes. The sources of chromosome-embedded ribonucleotides and the mechanisms by which unrepaired rNMPs trigger genome instability and human pathologies are not fully understood. The available sequencing technologies only allow to indirectly deduce the genomic location of rNMPs. Oxford Nanopore Technologies (ONT) may overcome such limitation, revealing the sites of rNMPs incorporation in genomic DNA directly from raw sequencing signals. We synthesized two types of DNA molecules containing rNMPs at known or random positions and we developed data analysis pipelines for DNA-embedded ribonucleotides detection by ONT. We report that ONT can identify all four ribonucleotides incorporated in DNA by capturing rNMPs-specific alterations in nucleotide alignment features, current intensity, and dwell time. We propose that ONT may be successfully employed to directly map rNMPs in genomic DNA and we suggest a strategy to build an ad hoc basecaller to analyse native genomes.

Challenges to mapping and defining m6A function in viral RNA

Viral RNA molecules contain multiple layers of regulatory information. This includes features beyond the primary sequence, such as RNA structures and RNA modifications, including N6-methyladenosine (m6A). Many recent studies have identified the presence and location of m6A in viral RNA and have found diverse regulatory roles for this modification during viral infection. However, to date, viral m6A mapping strategies have limitations that prevent a complete understanding of the function of m6A on individual viral RNA molecules. While m6A sites have been profiled on bulk RNA from many viruses, the resulting m6A maps of viral RNAs described to date present a composite picture of m6A across viral RNA molecules in the infected cell. Thus, for most viruses, it is unknown if unique viral m6A profiles exist throughout infection, nor if they regulate specific viral life cycle stages. Here, we describe several challenges to defining the function of m6A in viral RNA molecules and provide a framework for future studies to help in the understanding of how m6A regulates viral infection.

Comprehensive map of ribosomal 2'-O-methylation and C/D box snoRNAs in Drosophila melanogaster

During their maturation, ribosomal RNAs (rRNAs) are decorated by hundreds of chemical modifications that participate in proper folding of rRNA secondary structures and therefore in ribosomal function. Along with pseudouridine, methylation of the 2'-hydroxyl ribose moiety (Nm) is the most abundant modification of rRNAs. The majority of Nm modifications in eukaryotes are placed by Fibrillarin, a conserved methyltransferase belonging to a ribonucleoprotein complex guided by C/D box small nucleolar RNAs (C/D box snoRNAs). These modifications impact interactions between rRNAs, tRNAs and mRNAs, and some are known to fine tune translation rates and efficiency. In this study, we built the first comprehensive map of Nm sites in Drosophila melanogaster rRNAs using two complementary approaches (RiboMethSeq and Nanopore direct RNA sequencing) and identified their corresponding C/D box snoRNAs by whole-transcriptome sequencing. We de novo identified 61 Nm sites, from which 55 are supported by both sequencing methods, we validated the expression of 106 C/D box snoRNAs and we predicted new or alternative rRNA Nm targets for 31 of them. Comparison of methylation level upon different stresses show only slight but specific variations, indicating that this modification is relatively stable in D. melanogaster. This study paves the way to investigate the impact of snoRNA-mediated 2'-O-methylation on translation and proteostasis in a whole organism.

GLORI for absolute quantification of transcriptome-wide m6A at single-base resolution

N6-methyladenosine (m6A) is the most abundant posttranscriptional chemical modification in mRNA, involved in regulating various physiological and pathological processes throughout mRNA metabolism. Recently, we developed GLORI, a sequencing method that enables the production of a globally absolute-quantitative m6A map at single-base resolution. Our technique utilizes the glyoxal- and nitrite-based chemical reaction, which selectively deaminates unmethylated adenosines while leaving m6A intact. The RNA library can then be prepared using a modified library construction protocol from enhanced UV crosslinking and immunoprecipitation (eCLIP) or commercial kits. Here we provide a detailed protocol for proper RNA sample handling and provide further guidelines for the use of a tailored bioinformatics pipeline (GLORI-tools) for subsequent data analysis. Compared with current methods, this new method is both exceptionally sensitive and robust, capable of identifying ~80,000 m6A sites with 50 Gb sequencing data in mammalian cells. It also provides a quantitative readout for m6A sites at single-base resolution. We hope the technique will provide a precise and unbiased tool for investigating m6A biology across various fields. Basic expertise in molecular biology and knowledge of bioinformatics are required for the protocol. The entire procedure can be completed within a week, with the library construction process taking ~4 d.

Discovering Consensus Regions for Interpretable Identification of RNA N6-Methyladenosine Modification Sites via Graph Contrastive Clustering

As a pivotal post-transcriptional modification of RNA, N6-methyladenosine (m6A) has a substantial influence on gene expression modulation and cellular fate determination. Although a variety of computational models have been developed to accurately identify potential m6A modification sites, few of them are capable of interpreting the identification process with insights gained from consensus knowledge. To overcome this problem, we propose a deep learning model, namely M6A-DCR, by discovering consensus regions for interpretable identification of m6A modification sites. In particular, M6A-DCR first constructs an instance graph for each RNA sequence by integrating specific positions and types of nucleotides. The discovery of consensus regions is then formulated as a graph clustering problem in light of aggregating all instance graphs. After that, M6A-DCR adopts a motif-aware graph reconstruction optimization process to learn high-quality embeddings of input RNA sequences, thus achieving the identification of m6A modification sites in an end-to-end manner. Experimental results demonstrate the superior performance of M6A-DCR by comparing it with several state-of-the-art identification models. The consideration of consensus regions empowers our model to make interpretable predictions at the motif level. The analysis of cross validation through different species and tissues further verifies the consistency between the identification results of M6A-DCR and the evolutionary relationships among species.

The Application of Long-Read Sequencing to Cancer

Cancer is a multifaceted disease arising from numerous genomic aberrations that have been identified as a result of advancements in sequencing technologies. While next-generation sequencing (NGS), which uses short reads, has transformed cancer research and diagnostics, it is limited by read length. Third-generation sequencing (TGS), led by the Pacific Biosciences and Oxford Nanopore Technologies platforms, employs long-read sequences, which have marked a paradigm shift in cancer research. Cancer genomes often harbour complex events, and TGS, with its ability to span large genomic regions, has facilitated their characterisation, providing a better understanding of how complex rearrangements affect cancer initiation and progression. TGS has also characterised the entire transcriptome of various cancers, revealing cancer-associated isoforms that could serve as biomarkers or therapeutic targets. Furthermore, TGS has advanced cancer research by improving genome assemblies, detecting complex variants, and providing a more complete picture of transcriptomes and epigenomes. This review focuses on TGS and its growing role in cancer research. We investigate its advantages and limitations, providing a rigorous scientific analysis of its use in detecting previously hidden aberrations missed by NGS. This promising technology holds immense potential for both research and clinical applications, with far-reaching implications for cancer diagnosis and treatment.

N6-methyladenosine modification is not a general trait of viral RNA genomes

Despite the nuclear localization of the m6A machinery, the genomes of multiple exclusively-cytoplasmic RNA viruses, such as chikungunya (CHIKV) and dengue (DENV), are reported to be extensively m6A-modified. However, these findings are mostly based on m6A-Seq, an antibody-dependent technique with a high rate of false positives. Here, we address the presence of m6A in CHIKV and DENV RNAs. For this, we combine m6A-Seq and the antibody-independent SELECT and nanopore direct RNA sequencing techniques with functional, molecular, and mutagenesis studies. Following this comprehensive analysis, we find no evidence of m6A modification in CHIKV or DENV transcripts. Furthermore, depletion of key components of the host m6A machinery does not affect CHIKV or DENV infection. Moreover, CHIKV or DENV infection has no effect on the m6A machinery's localization. Our results challenge the prevailing notion that m6A modification is a general feature of cytoplasmic RNA viruses and underscore the importance of validating RNA modifications with orthogonal approaches.

The lncRNA Snhg11, a new candidate contributing to neurogenesis, plasticity, and memory deficits in Down syndrome

Down syndrome (DS) stands as the prevalent genetic cause of intellectual disability, yet comprehensive understanding of its cellular and molecular underpinnings remains limited. In this study, we explore the cellular landscape of the hippocampus in a DS mouse model, the Ts65Dn, through single-nuclei transcriptional profiling. Our findings demonstrate that trisomy manifests as a highly specific modification of the transcriptome within distinct cell types. Remarkably, we observed a significant shift in the transcriptomic profile of granule cells in the dentate gyrus (DG) associated with trisomy. We identified the downregulation of a specific small nucleolar RNA host gene, Snhg11, as the primary driver behind this observed shift in the trisomic DG. Notably, reduced levels of Snhg11 in this region were also observed in a distinct DS mouse model, the Dp(16)1Yey, as well as in human postmortem brain tissue, indicating its relevance in Down syndrome. To elucidate the function of this long non-coding RNA (lncRNA), we knocked down Snhg11 in the DG of wild-type mice. Intriguingly, this intervention alone was sufficient to impair synaptic plasticity and adult neurogenesis, resembling the cognitive phenotypes associated with trisomy in the hippocampus. Our study uncovers the functional role of Snhg11 in the DG and underscores the significance of this lncRNA in intellectual disability. Furthermore, our findings highlight the importance of DG in the memory deficits observed in Down syndrome.

Advances in the Structural and Functional Understanding of m1A RNA Modification

ConspectusRNA modification is a co- or post-transcriptional process by which specific nucleotides are chemically altered by enzymes after their initial incorporation into the RNA chain, expanding the chemical and functional diversity of RNAs. Our understanding of RNA modifications has changed dramatically in recent years. In the past decade, RNA methyltransferases (MTases) have been highlighted in numerous clinical studies and disease models, modifications have been found to be dynamically regulated by demodification enzymes, and significant technological advances have been made in the fields of RNA sequencing, mass spectrometry, and structural biology. Among RNAs, transfer RNAs (tRNAs) exhibit the greatest diversity and density of post-transcriptional modifications, which allow for potential cross-talks and regulation during their incorporation. N1-methyladenosine (m1A) modification is found in tRNAs at positions 9, 14, 16, 22, 57, and 58, depending on the tRNA and organism.Our laboratory has used and developed a large panel of tools to decipher the different mechanisms used by m1A tRNA MTases to recognize and methylate tRNA. We have solved the structures of TrmI from Thermus thermophilus (m1A58), TrmK from Bacillus subtilis (m1A22), and human TRMT10C (m1A9). These MTases do not share the same structure or organization to recognize tRNAs, but they all modify an adenosine, forming a non-Watson-Crick (WC) interaction. For TrmK, nuclear magnetic resonance (NMR) chemical shift mapping of the binding interface between TrmK and tRNASer was invaluable to build a TrmK/tRNA model, where both domains of TrmK participate in the binding of a full-length L-shaped tRNA and where the non-WC purine 13-A22 base pair positions the A22 N1-atom close to the methyl of the S-adenosyl-l-methionine (SAM) TrmK cofactor. For TRMT10C, cryoEM structures showed the MTase poised to N1-methylate A9 or G9 in tRNA and revealed different steps of tRNA maturation, where TRMT10C acts as a tRNA binding platform for sequential docking of each maturation enzyme. This work confers a role for TRMT10C in tRNA quality control and provides a framework to understand the link between mitochondrial tRNA maturation dysfunction and diseases.Methods to directly detect the incorporation of modifications during tRNA biosynthesis are rare and do not provide easy access to the temporality of their introduction. To this end, we have introduced time-resolved NMR to monitor tRNA maturation in the cellular environment. Combined with genetic and biochemical approaches involving the synthesis of specifically modified tRNAs, our methodology revealed that some modifications are incorporated in a defined sequential order, controlled by cross-talks between modification events. In particular, a strong modification circuit, namely Ψ55 → m5U54 → m1A58, controls the modification process in the T-arm of yeast elongator tRNAs. Conversely, we showed that m1A58 is efficiently introduced on unmodified initiator tRNAiMet without the need of any prior modification. Two distinct pathways are therefore followed for m1A58 incorporation in elongator and initiator tRNAs.We are undoubtedly entering an exciting period for the elucidation of the functions of RNA modifications and the intricate mechanisms by which modification enzymes identify and alter their RNA substrates. These are promising directions for the field of epitranscriptomics.

Simultaneous nanopore profiling of mRNA m6A and pseudouridine reveals translation coordination

N6-methyladenosine (m6A) and pseudouridine (Ψ) are the two most abundant modifications in mammalian messenger RNA, but the coordination of their biological functions remains poorly understood. We develop a machine learning-based nanopore direct RNA sequencing method (NanoSPA) that simultaneously analyzes m6A and Ψ in the human transcriptome. Applying NanoSPA to polysome profiling, we reveal opposing transcriptomic co-occurrence of m6A and Ψ and synergistic, hierarchical effects of m6A and Ψ on the polysome.

YTHDF1's Regulatory Involvement in Breast Cancer Prognosis, Immunity, and the ceRNA Network

YTH N6-methyladenosine RNA binding protein 1 (YTHDF1), an m6A reader, has a role in the development and progression of breast cancer as well as the immunological microenvironment. The networks of competing endogenous RNA in cancer have received much attention in research. In tumor gene therapy, the regulatory networks of m6A and competing endogenous RNA are increasingly emerging as a new route. We evaluated the relationship between the YTHDF1 expression, overall survival, and clinicopathology of breast cancer using TCGA, PrognoScan, and other datasets. We used Western blot to demonstrate that YTHDF1 is substantially expressed in breast cancer tissues. Furthermore, we explored YTHDF1's functions in the tumor mutational burden, microsatellite instability, and tumor microenvironment. Our findings indicate that YTHDF1 is a critical component of the m6A regulatory proteins in breast cancer and may have a particular function in the immunological microenvironment. Crucially, we investigated the relationship between YTHDF1 and the associated competitive endogenous RNA regulatory networks, innovatively creating three such networks (Dehydrogenase/Reductase 4-Antisense RNA 1-miR-378g-YTHDF1, HLA Complex Group 9-miR-378g-YTHDF1, Taurine Up-regulated 1-miR-378g-YTHDF1). Furthermore, we showed that miR-378g could inhibit the expression of YTHDF1, and that miR-378g/YTHDF1 could impact MDA-MB-231 proliferation. We speculate that YTHDF1 may serve as a biomarker for poor prognosis and differential diagnosis, impact the growth of breast cancer cells via the ceRNA network axis, and be a target for immunotherapy against breast cancer.

NanoCon: contrastive learning-based deep hybrid network for nanopore methylation detection

MOTIVATION

5-Methylcytosine (5mC), a fundamental element of DNA methylation in eukaryotes, plays a vital role in gene expression regulation, embryonic development, and other biological processes. Although several computational methods have been proposed for detecting the base modifications in DNA like 5mC sites from Nanopore sequencing data, they face challenges including sensitivity to noise, and ignoring the imbalanced distribution of methylation sites in real-world scenarios.

RESULTS

Here, we develop NanoCon, a deep hybrid network coupled with contrastive learning strategy to detect 5mC methylation sites from Nanopore reads. In particular, we adopted a contrastive learning module to alleviate the issues caused by imbalanced data distribution in nanopore sequencing, offering a more accurate and robust detection of 5mC sites. Evaluation results demonstrate that NanoCon outperforms existing methods, highlighting its potential as a valuable tool in genomic sequencing and methylation prediction. In addition, we also verified the effectiveness of our representation learning ability on two datasets by visualizing the dimension reduction of the features of methylation and nonmethylation sites from our NanoCon. Furthermore, cross-species and cross-5mC methylation motifs experiments indicated the robustness and the ability to perform transfer learning of our model. We hope this work can contribute to the community by providing a powerful and reliable solution for 5mC site detection in genomic studies.

AVAILABILITY AND IMPLEMENTATION

The project code is available at https://github.com/Challis-yin/NanoCon.

T-S2Inet: Transformer-based sequence-to-image network for accurate nanopore sequence recognition

MOTIVATION

Nanopore sequencing is a new macromolecular recognition and perception technology that enables high-throughput sequencing of DNA, RNA, even protein molecules. The sequences generated by nanopore sequencing span a large time frame, and the labor and time costs incurred by traditional analysis methods are substantial. Recently, research on nanopore data analysis using machine learning algorithms has gained unceasing momentum, but there is often a significant gap between traditional and deep learning methods in terms of classification results. To analyze nanopore data using deep learning technologies, measures such as sequence completion and sequence transformation can be employed. However, these technologies do not preserve the local features of the sequences. To address this issue, we propose a sequence-to-image (S2I) module that transforms sequences of unequal length into images. Additionally, we propose the Transformer-based T-S2Inet model to capture the important information and improve the classification accuracy.

RESULTS

Quantitative and qualitative analysis shows that the experimental results have an improvement of around 2% in accuracy compared to previous methods. The proposed method is adaptable to other nanopore platforms, such as the Oxford nanopore. It is worth noting that the proposed method not only aims to achieve the most advanced performance, but also provides a general idea for the analysis of nanopore sequences of unequal length.

AVAILABILITY AND IMPLEMENTATION

The main program is available at https://github.com/guanxiaoyu11/S2Inet.

Common analysis of direct RNA sequencinG CUrrently leads to misidentification of m5C at GCU motifs

RNA modifications, such as methylation, can be detected with Oxford Nanopore Technologies direct RNA sequencing. One commonly used tool for detecting 5-methylcytosine (m5C) modifications is Tombo, which uses an "Alternative Model" to detect putative modifications from a single sample. We examined direct RNA sequencing data from diverse taxa including viruses, bacteria, fungi, and animals. The algorithm consistently identified a m5C at the central position of a GCU motif. However, it also identified a m5C in the same motif in fully unmodified in vitro transcribed RNA, suggesting that this is a frequent false prediction. In the absence of further validation, several published predictions of m5C in a GCU context should be reconsidered, including those from human coronavirus and human cerebral organoid samples.

RNA modifications in physiology and disease: towards clinical applications

The ability of chemical modifications of single nucleotides to alter the electrostatic charge, hydrophobic surface and base pairing of RNA molecules is exploited for the clinical use of stable artificial RNAs such as mRNA vaccines and synthetic small RNA molecules - to increase or decrease the expression of therapeutic proteins. Furthermore, naturally occurring biochemical modifications of nucleotides regulate RNA metabolism and function to modulate crucial cellular processes. Studies showing the mechanisms by which RNA modifications regulate basic cell functions in higher organisms have led to greater understanding of how aberrant RNA modification profiles can cause disease in humans. Together, these basic science discoveries have unravelled the molecular and cellular functions of RNA modifications, have provided new prospects for therapeutic manipulation and have led to a range of innovative clinical approaches.

Exploring N6-methyladenosine (m6A) modification in tree species: opportunities and challenges

N 6-methyladenosine (m6A) in eukaryotes is the most common and widespread internal modification in mRNA. The modification regulates mRNA stability, translation efficiency, and splicing, thereby fine-tuning gene regulation. In plants, m6A is dynamic and critical for various growth stages, embryonic development, morphogenesis, flowering, stress response, crop yield, and biomass. Although recent high-throughput sequencing approaches have enabled the rapid identification of m6A modification sites, the site-specific mechanism of this modification remains unclear in trees. In this review, we discuss the functional significance of m6A in trees under different stress conditions and discuss recent advancements in the quantification of m6A. Quantitative and functional insights into the dynamic aspect of m6A modification could assist researchers in engineering tree crops for better productivity and resistance to various stress conditions.

Nanopore Direct RNA Sequencing Reveals the Short-Term Salt Stress Response in Maize Roots

Transcriptome analysis, relying on the cutting-edge sequencing of cDNA libraries, has become increasingly prevalent within functional genome studies. However, the dependence on cDNA in most RNA sequencing technologies restricts their ability to detect RNA base modifications. To address this limitation, the latest Oxford Nanopore Direct RNA Sequencing (ONT DRS) technology was employed to investigate the transcriptome of maize seedling roots under salt stress. This approach aimed to unveil both the RNA transcriptional profiles and alterations in base modifications. The analysis of the differential expression revealed a total of 1398 genes and 2223 transcripts that exhibited significant variation within the maize root system following brief exposure to salt stress. Enrichment analyses, such as the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway assessments, highlighted the predominant involvement of these differentially expressed genes (DEGs) in regulating ion homeostasis, nitrogen metabolism, amino acid metabolism, and the phytohormone signaling pathways. The protein-protein interaction (PPI) analysis showed the participation of various proteins related to glycolytic metabolism, nitrogen metabolism, amino acid metabolism, abscisic acid signaling, and the jasmonate signaling pathways. It was through this intricate molecular network that these proteins collaborated to safeguard root cells against salt-induced damage. Moreover, under salt stress conditions, the occurrence of variable shear events (AS) in RNA modifications diminished, the average length of poly(A) tails underwent a slight decrease, and the number of genes at the majority of the variable polyadenylation (APA) sites decreased. Additionally, the levels of N5-methylcytosine (m5C) and N6-methyladenosine (m6A) showed a reduction. These results provide insights into the mechanisms of early salt tolerance in maize.

Effects of N6-methyladenosine modification on metabolic reprogramming in digestive tract tumors

N6-methyladenosine (m6A), the most abundant RNA modification within cells, participates in various biological and pathological processes, including self-renewal, invasion and proliferation, drug resistance, and stem cell characteristics. The m6A methylation plays a crucial role in tumors by regulating multiple RNA processes such as transcription, processing, and translation. Three protein types are primarily involved in m6A methylation: methyltransferases (such as METTL3, METTL14, ZC3H13, and KIAA1429), demethylases (such as FTO, ALKBH5), and RNA-binding proteins (such as the family of YTHDF, YTHDC1, YTHDC2, and IGF2BPs). Various metabolic pathways are reprogrammed in digestive tumors to meet the heightened growth demands and sustain cellular functionality. Recent studies have highlighted the extensive impact of m6A on the regulation of digestive tract tumor metabolism, further modulating tumor initiation and progression. Our review aims to provide a comprehensive understanding of the expression patterns, functional roles, and regulatory mechanisms of m6A in digestive tract tumor metabolism-related molecules and pathways. The characterization of expression profiles of m6A regulatory factors and in-depth studies on m6A methylation in digestive system tumors may provide new directions for clinical prediction and innovative therapeutic interventions.

In silico λ-dynamics predicts protein binding specificities to modified RNAs

RNA modifications shape gene expression through a smorgasbord of chemical changes to canonical RNA bases. Although numbering in the hundreds, only a few RNA modifications are well characterized, in part due to the absence of methods to identify modification sites. Antibodies remain a common tool to identify modified RNA and infer modification sites through straightforward applications. However, specificity issues can result in off-target binding and confound conclusions. This work utilizes in silico λ-dynamics to efficiently estimate binding free energy differences of modification-targeting antibodies between a variety of naturally occurring RNA modifications. Crystal structures of inosine and N6-methyladenosine (m6A) targeting antibodies bound to their modified ribonucleosides were determined and served as structural starting points. λ-Dynamics was utilized to predict RNA modifications that permit or inhibit binding to these antibodies. In vitro RNA-antibody binding assays supported the accuracy of these in silico results. High agreement between experimental and computed binding propensities demonstrated that λ-dynamics can serve as a predictive screen for antibody specificity against libraries of RNA modifications. More importantly, this strategy is an innovative way to elucidate how hundreds of known RNA modifications interact with biological molecules without the limitations imposed by in vitro or in vivo methodologies.

Benchmarking of computational methods for m6A profiling with Nanopore direct RNA sequencing

N6-methyladenosine (m6A) is the most abundant internal eukaryotic mRNA modification, and is involved in the regulation of various biological processes. Direct Nanopore sequencing of native RNA (dRNA-seq) emerged as a leading approach for its identification. Several software were published for m6A detection and there is a strong need for independent studies benchmarking their performance on data from different species, and against various reference datasets. Moreover, a computational workflow is needed to streamline the execution of tools whose installation and execution remains complicated. We developed NanOlympicsMod, a Nextflow pipeline exploiting containerized technology for comparing 14 tools for m6A detection on dRNA-seq data. NanOlympicsMod was tested on dRNA-seq data generated from in vitro (un)modified synthetic oligos. The m6A hits returned by each tool were compared to the m6A position known by design of the oligos. In addition, NanOlympicsMod was used on dRNA-seq datasets from wild-type and m6A-depleted yeast, mouse and human, and each tool's hits were compared to reference m6A sets generated by leading orthogonal methods. The performance of the tools markedly differed across datasets, and methods adopting different approaches showed different preferences in terms of precision and recall. Changing the stringency cut-offs allowed for tuning the precision-recall trade-off towards user preferences. Finally, we determined that precision and recall of tools are markedly influenced by sequencing depth, and that additional sequencing would likely reveal additional m6A sites. Thanks to the possibility of including novel tools, NanOlympicsMod will streamline the benchmarking of m6A detection tools on dRNA-seq data, improving future RNA modification characterization.

Modifying the antiviral innate immune response by selective writing, erasing, and reading of m6A on viral and cellular RNA

Viral infection and the antiviral innate immune response are regulated by the RNA modification m6A. m6A directs nearly all aspects of RNA metabolism by recruiting RNA-binding proteins that mediate the fate of m6A-containing RNA. m6A controls the antiviral innate immune response in diverse ways, including shielding viral RNA from detection by antiviral sensors and influencing the expression of cellular mRNAs encoding antiviral signaling proteins, cytokines, and effector proteins. While m6A and the m6A machinery are important for the antiviral response, the precise mechanisms that determine how the m6A machinery selects specific viral or cellular RNA molecules for modification during infection are not fully understood. In this review, we highlight recent findings that shed light on how viral infection redirects the m6A machinery during the antiviral response. A better understanding of m6A targeting during viral infection could lead to new immunomodulatory and therapeutic strategies against viral infection.

MODOMICS: a database of RNA modifications and related information. 2023 update

The MODOMICS database was updated with recent data and now includes new data types related to RNA modifications. Changes to the database include an expanded modification catalog, encompassing both natural and synthetic residues identified in RNA structures. This addition aids in representing RNA sequences from the RCSB PDB database more effectively. To manage the increased number of modifications, adjustments to the nomenclature system were made. Updates in the RNA sequences section include the addition of new sequences and the reintroduction of sequence alignments for tRNAs and rRNAs. The protein section was updated and connected to structures from the RCSB PDB database and predictions by AlphaFold. MODOMICS now includes a data annotation system, with 'Evidence' and 'Estimated Reliability' features, offering clarity on data support and accuracy. This system is open to all MODOMICS entries, enhancing the accuracy of RNA modification data representation. MODOMICS is available at https://iimcb.genesilico.pl/modomics/.

Direct Analysis of HIV mRNA m6A Methylation by Nanopore Sequencing

The post-transcriptional processing and chemical modification of HIV RNA are understudied aspects of HIV virology, primarily due to the limited ability to accurately map and quantify RNA modifications. Modification-specific antibodies or modification-sensitive endonucleases coupled with short-read RNA sequencing technologies have allowed for low-resolution or limited mapping of important regulatory modifications of HIV RNA such as N6-methyladenosine (m6A). However, a high-resolution map of where these sites occur on HIV transcripts is needed for detailed mechanistic understanding. This has recently become possible with new sequencing technologies. Here, we describe the direct RNA sequencing of HIV transcripts using an Oxford Nanopore Technologies sequencer and the use of this technique to map m6A at near single nucleotide resolution. This technology also provides the ability to identify splice variants with long RNA reads and thus, can provide high-resolution RNA modification maps that distinguish between overlapping splice variants. The protocols outlined here for m6A also provide a powerful paradigm for studying any other RNA modifications that can be detected on the nanopore platform.

Quantitative analysis of tRNA abundance and modifications by nanopore RNA sequencing

Transfer RNAs (tRNAs) play a central role in protein translation. Studying them has been difficult in part because a simple method to simultaneously quantify their abundance and chemical modifications is lacking. Here we introduce Nano-tRNAseq, a nanopore-based approach to sequence native tRNA populations that provides quantitative estimates of both tRNA abundances and modification dynamics in a single experiment. We show that default nanopore sequencing settings discard the vast majority of tRNA reads, leading to poor sequencing yields and biased representations of tRNA abundances based on their transcript length. Re-processing of raw nanopore current intensity signals leads to a 12-fold increase in the number of recovered tRNA reads and enables recapitulation of accurate tRNA abundances. We then apply Nano-tRNAseq to Saccharomyces cerevisiae tRNA populations, revealing crosstalks and interdependencies between different tRNA modification types within the same molecule and changes in tRNA populations in response to oxidative stress.

Analyzing RNA posttranscriptional modifications to decipher the epitranscriptomic code

The discovery of RNA silencing has revealed that non-protein-coding sequences (ncRNAs) can cover essential roles in regulatory networks and their malfunction may result in severe consequences on human health. These findings have prompted a general reassessment of the significance of RNA as a key player in cellular processes. This reassessment, however, will not be complete without a greater understanding of the distribution and function of the over 170 variants of the canonical ribonucleotides, which contribute to the breathtaking structural diversity of natural RNA. This review surveys the analytical approaches employed for the identification, characterization, and detection of RNA posttranscriptional modifications (rPTMs). The merits of analyzing individual units after exhaustive hydrolysis of the initial biopolymer are outlined together with those of identifying their position in the sequence of parent strands. Approaches based on next generation sequencing and mass spectrometry technologies are covered in depth to provide a comprehensive view of their respective merits. Deciphering the epitranscriptomic code will require not only mapping the location of rPTMs in the various classes of RNAs, but also assessing the variations of expression levels under different experimental conditions. The fact that no individual platform is currently capable of meeting all such demands implies that it will be essential to capitalize on complementary approaches to obtain the desired information. For this reason, the review strived to cover the broadest possible range of techniques to provide readers with the fundamental elements necessary to make informed choices and design the most effective possible strategy to accomplish the task at hand.

Unraveling C-to-U RNA editing events from direct RNA sequencing

In mammals, RNA editing events involve the conversion of adenosine (A) in inosine (I) by ADAR enzymes or the hydrolytic deamination of cytosine (C) in uracil (U) by the APOBEC family of enzymes, mostly APOBEC1. RNA editing has a plethora of biological functions, and its deregulation has been associated with various human disorders. While the large-scale detection of A-to-I is quite straightforward using the Illumina RNAseq technology, the identification of C-to-U events is a non-trivial task. This difficulty arises from the rarity of such events in eukaryotic genomes and the challenge of distinguishing them from background noise. Direct RNA sequencing by Oxford Nanopore Technology (ONT) permits the direct detection of Us on sequenced RNA reads. Surprisingly, using ONT reads from wild-type (WT) and APOBEC1-knock-out (KO) murine cell lines as well as in vitro synthesized RNA without any modification, we identified a systematic error affecting the accuracy of the Cs call, thereby leading to incorrect identifications of C-to-U events. To overcome this issue in direct RNA reads, here we introduce a novel machine learning strategy based on the isolation Forest (iForest) algorithm in which C-to-U editing events are considered as sequencing anomalies. Using in vitro synthesized and human ONT reads, our model optimizes the signal-to-noise ratio improving the detection of C-to-U editing sites with high accuracy, over 90% in all samples tested. Our results suggest that iForest, known for its rapid implementation and minimal memory requirements, is a promising tool to denoise ONT reads and reliably identify RNA modifications.

Adapting Nanopore Sequencing Basecalling Models for Modification Detection via Incremental Learning and Anomaly Detection

We leverage machine learning approaches to adapt nanopore sequencing basecallers for nucleotide modification detection. We first apply the incremental learning technique to improve the basecalling of modification-rich sequences, which are usually of high biological interests. With sequence backbones resolved, we further run anomaly detection on individual nucleotides to determine their modification status. By this means, our pipeline promises the single-molecule, single-nucleotide and sequence context-free detection of modifications. We benchmark the pipeline using control oligos, further apply it in the basecalling of densely-modified yeast tRNAs and E.coli genomic DNAs, the cross-species detection of N6-methyladenosine (m6A) in mammalian mRNAs, and the simultaneous detection of N1-methyladenosine (m1A) and m6A in human mRNAs. Our IL-AD workflow is available at: https://github.com/wangziyuan66/IL-AD.

Genome-Wide Profiling of tRNA Using an Unexplored Reverse Transcriptase with High Processivity

Monitoring the dynamic changes of cellular tRNA pools is challenging, due to the extensive post-transcriptional modifications of individual species. The most critical component in tRNAseq is a processive reverse transcriptase (RT) that can read through each modification with high efficiency. Here we show that the recently developed group-II intron RT Induro has the processivity and efficiency necessary to profile tRNA dynamics. Using our Induro-tRNAseq, simpler and more comprehensive than the best methods to date, we show that Induro progressively increases readthrough of tRNA over time and that the mechanism of increase is selective removal of RT stops, without altering the misincorporation frequency. We provide a parallel dataset of the misincorporation profile of Induro relative to the related TGIRT RT to facilitate the prediction of non-annotated modifications. We report an unexpected modification profile among human proline isoacceptors, absent from mouse and lower eukaryotes, that indicates new biology of decoding proline codons.

Identification and comparison of m6A modifications in glioblastoma non-coding RNAs with MeRIP-seq and Nanopore dRNA-seq

The most prominent RNA modification - N6-methyladenosine (m6A) - affects gene regulation and cancer progression. The extent and effect of m6A on long non-coding RNAs (lncRNAs) is, however, still not clear. The most established method for m6A detection is methylated RNA immunoprecipitation and sequencing (MeRIP-seq). However, Oxford Nanopore Technologies recently developed direct RNA-seq (dRNA-seq) method, allowing m6A identification at higher resolution and in its native form. We performed whole transcriptome sequencing of the glioblastoma cell line U87-MG with both MeRIP-seq and dRNA-seq. For MeRIP-seq, m6A peaks were identified using nf-core/chipseq, and for dRNA-seq - EpiNano pipeline. MeRIP-seq analysis revealed 5086 lncRNAs transcripts, while dRNA-seq identified 336 lncRNAs transcripts from which 556 and 198 were found to be m6A modified, respectively. While 24 lncRNAs with m6A overlapped between two methods. Gliovis database analysis revealed that the expression of the major part of identified overlapping lncRNAs was associated with glioma grade or patient survival prognosis. We found that the frequency of m6A occurrence in lncRNAs varied more than 9-fold throughout the provided list of 24 modified lncRNAs. The highest m6A frequency was detected in MIR1915HG, THAP9-AS1, MALAT1, NORAD1, and NEAT1 (49-88nt), while MIR99AHG, SNHG3, LOXL1-AS1, ILF3-DT showed the lowest m6A frequency (445-261nt). Taken together, (1) we provide a high accuracy list of 24 m6A modified lncRNAs of U87-MG cells; (2) we conclude that MeRIP-seq is more suitable for an initial m6A screening study, due to its higher lncRNA coverage, whereas dRNA-seq is most useful when more in-depth analysis of m6A quantity and precise location is of interest.Abbreviations: (dRNA-seq) direct RNA-seq, (GBM) glioblastoma, (LGG) low-grade glioma, (lncRNAs) long non-coding RNAs, (m6A) N6-methyladenosine, (MeRIP-seq) methylated RNA immunoprecipitation and sequencing, (ncRNA) non-coding RNA, (ONT) Oxford Nanopore Technologi; Lietuvos Mokslo Taryba.

Adaptive sampling for nanopore direct RNA-sequencing

Nanopore long-read sequencing enables real-time monitoring and controlling of individual nanopores. This allows us to enrich or deplete specific sequences in DNA sequencing in a process called "adaptive sampling." So far, adaptive sampling (AS) was not applicable to the direct sequencing of RNA. Here, we show that AS is feasible and useful for direct RNA sequencing (DRS), which has its specific technical and biological challenges. Using a well-controlled in vitro transcript-based model system, we identify essential characteristics and parameter settings for AS in DRS, as the superior performance of depletion over enrichment. Here, the efficiency of depletion is close to the theoretical maximum. Additionally, we demonstrate that AS efficiently depletes specific transcripts in transcriptome-wide sequencing applications. Specifically, we applied our AS approach to poly(A)-enriched RNA samples from human-induced pluripotent stem cell-derived cardiomyocytes and mouse whole heart tissue and show efficient 2.5- to 2.8-fold depletion of highly abundant mitochondrial-encoded transcripts. Finally, we characterize depletion and enrichment performance for complex transcriptome subsets, that is, at the level of the entire Chromosome 11, proving the general applicability of direct RNA AS. Our analyses provide evidence that AS is especially useful to enable the detection of lowly expressed transcripts and reduce the sequencing of highly abundant disturbing transcripts.

m6A RNA demethylase AtALKBH9B promotes mobilization of a heat-activated long terminal repeat retrotransposon in Arabidopsis

Transposons are mobile and ubiquitous DNA molecules that can cause vast genomic alterations. In plants, it is well documented that transposon mobilization is strongly repressed by DNA methylation; however, its regulation at the posttranscriptional level remains relatively uninvestigated. Here, we suggest that transposon RNA is marked by m6A RNA methylation and can be localized in stress granules (SGs). Intriguingly, SG-localized AtALKBH9B selectively demethylates a heat-activated retroelement, Onsen, and thereby releases it from spatial confinement, allowing for its mobilization. In addition, we show evidence that m6A RNA methylation contributes to transpositional suppression by inhibiting virus-like particle assembly and extrachromosomal DNA production. In summary, this study unveils a previously unknown role for m6A in the suppression of transposon mobility and provides insight into how transposons counteract the m6A-mediated repression mechanism by hitchhiking the RNA demethylase of the host.

Deep learning and direct sequencing of labeled RNA captures transcriptome dynamics

Quantification of the dynamics of RNA metabolism is essential for understanding gene regulation in health and disease. Existing methods rely on metabolic labeling of nascent RNAs and physical separation or inference of labeling through PCR-generated mutations, followed by short-read sequencing. However, these methods are limited in their ability to identify transient decay intermediates or co-analyze RNA decay with cis-regulatory elements of RNA stability such as poly(A) tail length and modification status, at single molecule resolution. Here we use 5-ethynyl uridine (5EU) to label nascent RNA followed by direct RNA sequencing with nanopores. We developed RNAkinet, a deep convolutional and recurrent neural network that processes the electrical signal produced by nanopore sequencing to identify 5EU-labeled nascent RNA molecules. RNAkinet demonstrates generalizability to distinct cell types and organisms and reproducibly quantifies RNA kinetic parameters allowing the combined interrogation of RNA metabolism and cis-acting RNA regulatory elements.

Detection of queuosine and queuosine precursors in tRNAs by direct RNA sequencing

Queuosine (Q) is a complex tRNA modification found in bacteria and eukaryotes at position 34 of four tRNAs with a GUN anticodon, and it regulates the translational efficiency and fidelity of the respective codons that differ at the Wobble position. In bacteria, the biosynthesis of Q involves two precursors, preQ0 and preQ1, whereas eukaryotes directly obtain Q from bacterial sources. The study of queuosine has been challenging due to the limited availability of high-throughput methods for its detection and analysis. Here, we have employed direct RNA sequencing using nanopore technology to detect the modification of tRNAs with Q and Q precursors. These modifications were detected with high accuracy on synthetic tRNAs as well as on tRNAs extracted from Schizosaccharomyces pombe and Escherichia coli by comparing unmodified to modified tRNAs using the tool JACUSA2. Furthermore, we present an improved protocol for the alignment of raw sequence reads that gives high specificity and recall for tRNAs ex cellulo that, by nature, carry multiple modifications. Altogether, our results show that 7-deazaguanine-derivatives such as queuosine are readily detectable using direct RNA sequencing. This advancement opens up new possibilities for investigating these modifications in native tRNAs, furthering our understanding of their biological function.

Advantages and challenges associated with bisulfite-assisted nanopore direct RNA sequencing for modifications

Nanopore direct RNA sequencing is a technology that allows sequencing for epitranscriptomic modifications with the possibility of a quantitative assessment. In the present work, pseudouridine (Ψ) was sequenced with the nanopore before and after the pH 7 bisulfite reaction that yields stable ribose adducts at C1' of Ψ. The adducted sites produced greater base call errors in the form of deletion signatures compared to Ψ. Sequencing studies on E. coli rRNA and tmRNA before and after the pH 7 bisulfite reaction demonstrated that using chemically-assisted nanopore sequencing has distinct advantages for minimization of false positives and false negatives in the data. The rRNA from E. coli has 19 known U/C sequence variations that give similar base call signatures as Ψ, and therefore, are false positives when inspecting base call data; however, these sites are refractory to reacting with bisulfite as is easily observed in nanopore data. The E. coli tmRNA has a low occupancy Ψ in a pyrimidine-rich sequence context that is called a U representing a false negative; partial occupancy by Ψ is revealed after the bisulfite reaction. In a final study, 5-methylcytidine (m5C) in RNA can readily be observed after the pH 5 bisulfite reaction in which the parent C deaminates to U and the modified site does not react. This locates m5C when using bisulfite-assisted nanopore direct RNA sequencing, which is otherwise challenging to observe. The advantages and challenges of the overall approach are discussed.

Direct Nanopore Sequencing for the 17 RNA Modification Types in 36 Locations in the E. coli Ribosome Enables Monitoring of Stress-Dependent Changes

The bacterium Escherichia coli possesses 16S and 23S rRNA strands that have 36 chemical modification sites with 17 different structures. Nanopore direct RNA sequencing using a protein nanopore sensor and helicase brake, which is also a sensor, was applied to the rRNAs. Nanopore current levels, base calling profile, and helicase dwell times for the modifications relative to unmodified synthetic rRNA controls found signatures for nearly all modifications. Signatures for clustered modifications were determined by selective sequencing of writer knockout E. coli and sequencing of synthetic RNAs utilizing some custom-synthesized nucleotide triphosphates for their preparation. The knowledge of each modification's signature, apart from 5-methylcytidine, was used to determine how metabolic and cold-shock stress impact rRNA modifications. Metabolic stress resulted in either no change or a decrease, and one site increased in modification occupancy, while cold-shock stress led to either no change or a decrease. The double modification m4Cm1402 resides in 16S rRNA, and it decreased with both stressors. Using the helicase dwell time, it was determined that the N4 methyl group is lost during both stressors, and the 2'-OMe group remained. In the ribosome, this modification stabilizes binding to the mRNA codon at the P-site resulting in increased translational fidelity that is lost during stress. The E. coli genome has seven rRNA operons (rrn), and the earlier studies aligned the nanopore reads to a single operon (rrnA). Here, the reads were aligned to all seven operons to identify operon-specific changes in the 11 pseudouridines. This study demonstrates that direct sequencing for >16 different RNA modifications in a strand is achievable.

SWAMNA: a comprehensive platform for analysis of nucleic acid modifications

The interest in MS-based analysis of modified nucleic acids is increasing due to the application of nucleic acids in therapeutics. However, there are few available integrated platforms for characterizing nucleic acid modifications. Herein, we report a general mass spectrometry-based SWATH platform to identify and quantify both RNA and DNA modifications, which we call SWATH analysis of modified nucleic acids (SWAMNA). SWAMNA incorporates the search engine, NuMo finder, enabling the analysis of modifications in native and permethylated form. SWAMNA will aid discoveries that provide new insights into nucleic acid modifications.

The lncRNA Snhg11, a new candidate contributing to neurogenesis, plasticity and memory deficits in Down syndrome

Down syndrome (DS) stands as the prevalent genetic cause of intellectual disability, yet comprehensive understanding of its cellular and molecular underpinnings remains limited. In this study, we explore the cellular landscape of the hippocampus in a DS mouse model through single-nuclei transcriptional profiling. Our findings demonstrate that trisomy manifests as a highly specific modification of the transcriptome within distinct cell types. Remarkably, we observed a significant shift in the transcriptomic profile of granule cells in the dentate gyrus (DG) associated with trisomy. We identified the downregulation of a specific small nucleolar RNA host gene, Snhg11, as the primary driver behind this observed shift in the trisomic DG. Notably, reduced levels of Snhg11 in this region were also observed in a distinct DS mouse model, the Dp(16)1Yey, as well as in human postmortem tissue, indicating its relevance in Down syndrome. To elucidate the function of this long non-coding RNA (lncRNA), we knocked down Snhg11 in the DG of wild-type mice. Intriguingly, this intervention alone was sufficient to impair synaptic plasticity and adult neurogenesis, resembling the cognitive phenotypes associated with trisomy in the hippocampus. Our study uncovers the functional role of Snhg11 in the DG and underscores the significance of this lncRNA in intellectual disability. Furthermore, our findings highlight the importance of the DG in the memory deficits observed in Down syndrome.

Epitranscriptomic subtyping, visualization, and denoising by global motif visualization

Advances in sequencing technologies have empowered epitranscriptomic profiling at the single-base resolution. Putative RNA modification sites identified from a single high-throughput experiment may contain one type of modification deposited by different writers or different types of modifications, along with false positive results because of the challenge of distinguishing signals from noise. However, current tools are insufficient for subtyping, visualization, and denoising these signals. Here, we present iMVP, which is an interactive framework for epitranscriptomic analysis with a nonlinear dimension reduction technique and density-based partition. As exemplified by the analysis of mRNA m5C and ModTect variant data, we show that iMVP allows the identification of previously unknown RNA modification motifs and writers and the discovery of false positives that are undetectable by traditional methods. Using putative m6A/m6Am sites called from 8 profiling approaches, we illustrate that iMVP enables comprehensive comparison of different approaches and advances our understanding of the difference and pattern of true positives and artifacts in these methods. Finally, we demonstrate the ability of iMVP to analyze an extremely large human A-to-I editing dataset that was previously unmanageable. Our work provides a general framework for the visualization and interpretation of epitranscriptomic data.

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

pre-mRNA splicing is a critical feature of eukaryotic gene expression. Many eukaryotes use cis-splicing to remove intronic sequences from pre-mRNAs. In addition to cis-splicing, many organisms use trans-splicing to replace the 5' ends of mRNAs with a non-coding spliced-leader RNA. 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 m6A modification of U6 snRNA A43 by the RNA methyltransferase METT-10 is required for accurate and efficient cis- and trans-splicing of C. elegans pre-mRNAs. The absence of U6 snRNA m6A modification primarily leads to alternative splicing at 5' splice sites. Furthermore, weaker 5' splice site recognition by the unmodified U6 snRNA A43 affects splicing at 3' splice sites. U6 snRNA m6A43 and the splicing factor SNRNP27K function to recognise an overlapping set of 5' splice sites with an adenosine at +4 position. Finally, we show that U6 snRNA m6A43 is required for efficient SL trans-splicing at weak 3' trans-splice sites. We conclude that the U6 snRNA m6A modification is important for accurate and efficient cis- and trans-splicing in C. elegans.

Inhibition of DNA and RNA methylation disturbs root development of moso bamboo

DNA methylation (5mC) and N6-methyladenosine (m6A) are two important epigenetics regulators, which have a profound impact on plant growth development. Phyllostachys edulis (P. edulis) is one of the fastest spreading plants due to its well-developed root system. However, the association between 5mC and m6A has seldom been reported in P. edulis. In particular, the connection between m6A and several post-transcriptional regulators remains uncharacterized in P. edulis. Here, our morphological and electron microscope observations showed the phenotype of increased lateral root under RNA methylation inhibitor (DZnepA) and DNA methylation inhibitor (5-azaC) treatment. RNA epitranscriptome based on Nanopore direct RNA sequencing revealed that DZnepA treatment exhibits significantly decreased m6A level in the 3'-untranslated region (3'-UTR), which was accompanied by increased gene expression, full-length ratio, higher proximal poly(A) site usage and shorter poly(A) tail length. DNA methylation levels of CG and CHG were reduced in both coding sequencing and transposable element upon 5-azaC treatment. Cell wall synthesis was impaired under methylation inhibition. In particular, differentially expressed genes showed a high percentage of overlap between DZnepA and 5-azaC treatment, which suggested a potential correlation between two methylations. This study provides preliminary information for a better understanding of the link between m6A and 5mC in root development of moso bamboo.

Ghost authors revealed: The structure and function of human N6 -methyladenosine RNA methyltransferases

Despite the discovery of modified nucleic acids nearly 75 years ago, their biological functions are still being elucidated. N6 -methyladenosine (m6 A) is the most abundant modification in eukaryotic messenger RNA (mRNA) and has also been detected in non-coding RNAs, including long non-coding RNA, ribosomal RNA, and small nuclear RNA. In general, m6 A marks can alter RNA secondary structure and initiate unique RNA-protein interactions that can alter splicing, mRNA turnover, and translation, just to name a few. Although m6 A marks in human RNAs have been known to exist since 1974, the structures and functions of methyltransferases responsible for writing m6 A marks have been established only recently. Thus far, there are four confirmed human methyltransferases that catalyze the transfer of a methyl group from S-adenosylmethionine (SAM) to the N6 position of adenosine, producing m6 A: methyltransferase-like protein (METTL) 3/METTL14 complex, METTL16, METTL5, and zinc-finger CCHC-domain-containing protein 4. Though the methyltransferases have unique RNA targets, all human m6 A RNA methyltransferases contain a Rossmann fold with a conserved SAM-binding pocket, suggesting that they utilize a similar catalytic mechanism for methyl transfer. For each of the human m6 A RNA methyltransferases, we present the biological functions and links to human disease, RNA targets, catalytic and kinetic mechanisms, and macromolecular structures. We also discuss m6 A marks in human viruses and parasites, assigning m6 A marks in the transcriptome to specific methyltransferases, small molecules targeting m6 A methyltransferases, and the enzymes responsible for hypermodified m6 A marks and their biological functions in humans. Understanding m6 A methyltransferases is a critical steppingstone toward establishing the m6 A epitranscriptome and more broadly the RNome. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications.

Genomics in the long-read sequencing era

Long-read sequencing (LRS) technologies have provided extremely powerful tools to explore genomes. While in the early years these methods suffered technical limitations, they have recently made significant progress in terms of read length, throughput, and accuracy and bioinformatics tools have strongly improved. Here, we aim to review the current status of LRS technologies, the development of novel methods, and the impact on genomics research. We will explore the most impactful recent findings made possible by these technologies focusing on high-resolution sequencing of genomes and transcriptomes and the direct detection of DNA and RNA modifications. We will also discuss how LRS methods promise a more comprehensive understanding of human genetic variation, transcriptomics, and epigenetics for the coming years.

Mapping epigenetic modifications by sequencing technologies

The "epigenetics" concept was first described in 1942. Thus far, chemical modifications on histones, DNA, and RNA have emerged as three important building blocks of epigenetic modifications. Many epigenetic modifications have been intensively studied and found to be involved in most essential biological processes as well as human diseases, including cancer. Precisely and quantitatively mapping over 100 [1], 17 [2], and 160 [3] different known types of epigenetic modifications in histone, DNA, and RNA is the key to understanding the role of epigenetic modifications in gene regulation in diverse biological processes. With the rapid development of sequencing technologies, scientists are able to detect specific epigenetic modifications with various quantitative, high-resolution, whole-genome/transcriptome approaches. Here, we summarize recent advances in epigenetic modification sequencing technologies, focusing on major histone, DNA, and RNA modifications in mammalian cells.

N6-adenosine methylation controls the translation of insulin mRNA

Control of insulin mRNA translation is crucial for energy homeostasis, but the mechanisms remain largely unknown. We discovered that insulin mRNAs across invertebrates, vertebrates and mammals feature the modified base N6-methyladenosine (m6A). In flies, this RNA modification enhances insulin mRNA translation by promoting the association of the transcript with polysomes. Depleting m6A in Drosophila melanogaster insulin 2 mRNA (dilp2) directly through specific 3' untranslated region (UTR) mutations, or indirectly by mutating the m6A writer Mettl3, decreases dilp2 protein production, leading to aberrant energy homeostasis and diabetic-like phenotypes. Together, our findings reveal adenosine mRNA methylation as a key regulator of insulin protein synthesis with notable implications for energy balance and metabolic disease.

m6A-TSHub: Unveiling the Context-specific m6A Methylation and m6A-affecting Mutations in 23 Human Tissues

As the most pervasive epigenetic marker present on mRNAs and long non-coding RNAs (lncRNAs), N6-methyladenosine (m6A) RNA methylation has been shown to participate in essential biological processes. Recent studies have revealed the distinct patterns of m6A methylome across human tissues, and a major challenge remains in elucidating the tissue-specific presence and circuitry of m6A methylation. We present here a comprehensive online platform, m6A-TSHub, for unveiling the context-specific m6A methylation and genetic mutations that potentially regulate m6A epigenetic mark. m6A-TSHub consists of four core components, including (1) m6A-TSDB, a comprehensive database of 184,554 functionally annotated m6A sites derived from 23 human tissues and 499,369 m6A sites from 25 tumor conditions, respectively; (2) m6A-TSFinder, a web server for high-accuracy prediction of m6A methylation sites within a specific tissue from RNA sequences, which was constructed using multi-instance deep neural networks with gated attention; (3) m6A-TSVar, a web server for assessing the impact of genetic variants on tissue-specific m6A RNA modifications; and (4) m6A-CAVar, a database of 587,983 The Cancer Genome Atlas (TCGA) cancer mutations (derived from 27 cancer types) that were predicted to affect m6A modifications in the primary tissue of cancers. The database should make a useful resource for studying the m6A methylome and the genetic factors of epitranscriptome disturbance in a specific tissue (or cancer type). m6A-TSHub is accessible at www.xjtlu.edu.cn/biologicalsciences/m6ats.