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Nanopore RNA sequencing shows promise as a method for discriminating and identifying different RNA modifications in native RNA. Expanding on the ability of nanopore sequencing to detect N 6 -methyladenosine, we show that other modifications, in particular pseudouridine (Ψ) and 2′- O -methylation (Nm), also result in characteristic base-calling ‘error’ signatures in the nanopore data. Focusing on Ψ modification sites, we detected known and uncovered previously unreported Ψ sites in mRNAs, non-coding RNAs and rRNAs, including a Pus4-dependent Ψ modification in yeast mitochondrial rRNA. To explore the dynamics of pseudouridylation, we treated yeast cells with oxidative, cold and heat stresses and detected heat-sensitive Ψ-modified sites in small nuclear RNAs, small nucleolar RNAs and mRNAs. Finally, we developed a software, nanoRMS, that estimates per-site modification stoichiometries by identifying single-molecule reads with altered current intensity and trace profiles. This work demonstrates that Nm and Ψ RNA modifications can be detected in cellular RNAs and that their modification stoichiometry can be quantified by nanopore sequencing of native RNA. Nanopore sequencing detects pseudouridine and 2′- O -methylation modifications in cellular RNAs.

The epitranscriptomics field has undergone an enormous expansion in the last few years; however, a major limitation is the lack of generic methods to map RNA modifications transcriptome-wide. Here, we show that using direct RNA sequencing, N 6 -methyladenosine (m 6 A) RNA modifications can be detected with high accuracy, in the form of systematic errors and decreased base-calling qualities. Specifically, we find that our algorithm, trained with m 6 A-modified and unmodified synthetic sequences, can predict m 6 A RNA modifications with ~90% accuracy. We then extend our findings to yeast data sets, finding that our method can identify m 6 A RNA modifications in vivo with an accuracy of 87%. Moreover, we further validate our method by showing that these ‘errors’ are typically not observed in yeast ime4 -knockout strains, which lack m 6 A modifications. Our results open avenues to investigate the biological roles of RNA modifications in their native RNA context. We currently lack generic methods to map RNA modifications across the entire transcriptome. Here, the authors demonstrate that m 6 A RNA modifications can be detected with high accuracy using nanopore direct RNA sequencing.

The biological relevance and dynamics of mRNA modifications have been extensively studied; however, whether rRNA modifications are dynamically regulated, and under which conditions, remains unclear. Here, we systematically characterize bacterial rRNA modifications upon exposure to diverse antibiotics using native RNA nanopore sequencing. To identify significant rRNA modification changes, we develop NanoConsensus , a novel pipeline that is robust across RNA modification types, stoichiometries and coverage, with very low false positive rates, outperforming all individual algorithms tested. We then apply NanoConsensus to characterize the rRNA modification landscape upon antibiotic exposure, finding that rRNA modification profiles are altered in the vicinity of A and P-sites of the ribosome, in an antibiotic-specific manner, possibly contributing to antibiotic resistance. Our work demonstrates that rRNA modification profiles can be rapidly altered in response to environmental exposures, and provides a robust workflow to study rRNA modification dynamics in any species, in a scalable and reproducible manner. It remains unclear whether rRNA modifications can be naturally altered in response to antibiotics in bacteria. Here, the authors analyzed direct RNA nanopore sequencing data with an analytical pipeline Nanoconsensus , to investigate whether bacterial rRNA modifications are modulated upon exposure to various antibiotics.

Background RNA modifications play central roles in cellular fate and differentiation. However, the machinery responsible for placing, removing, and recognizing more than 170 RNA modifications remains largely uncharacterized and poorly annotated, and we currently lack integrative studies that identify which RNA modification-related proteins (RMPs) may be dysregulated in each cancer type. Results Here, we perform a comprehensive annotation and evolutionary analysis of human RMPs, as well as an integrative analysis of their expression patterns across 32 tissues, 10 species, and 13,358 paired tumor-normal human samples. Our analysis reveals an unanticipated heterogeneity of RMP expression patterns across mammalian tissues, with a vast proportion of duplicated enzymes displaying testis-specific expression, suggesting a key role for RNA modifications in sperm formation and possibly intergenerational inheritance. We uncover many RMPs that are dysregulated in various types of cancer, and whose expression levels are predictive of cancer progression. Surprisingly, we find that several commonly studied RNA modification enzymes such as METTL3 or FTO are not significantly upregulated in most cancer types, whereas several less-characterized RMPs, such as LAGE3 and HENMT1, are dysregulated in many cancers. Conclusions Our analyses reveal an unanticipated heterogeneity in the expression patterns of RMPs across mammalian tissues and uncover a large proportion of dysregulated RMPs in multiple cancer types. We provide novel targets for future cancer research studies targeting the human epitranscriptome, as well as foundations to understand cell type-specific behaviors that are orchestrated by RNA modifications.

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

RNA polyadenylation plays a central role in RNA maturation, fate, and stability. In response to developmental cues, polyA tail lengths can vary, affecting the translation efficiency and stability of mRNAs. Here we develop Nanopore 3′ end-capture sequencing (Nano3P-seq), a method that relies on nanopore cDNA sequencing to simultaneously quantify RNA abundance, tail composition, and tail length dynamics at per-read resolution. By employing a template-switching-based sequencing protocol, Nano3P-seq can sequence RNA molecule from its 3′ end, regardless of its polyadenylation status, without the need for PCR amplification or ligation of RNA adapters. We demonstrate that Nano3P-seq provides quantitative estimates of RNA abundance and tail lengths, and captures a wide diversity of RNA biotypes. We find that, in addition to mRNA and long non-coding RNA, polyA tails can be identified in 16S mitochondrial ribosomal RNA in both mouse and zebrafish models. Moreover, we show that mRNA tail lengths are dynamically regulated during vertebrate embryogenesis at an isoform-specific level, correlating with mRNA decay. Finally, we demonstrate the ability of Nano3P-seq in capturing non-A bases within polyA tails of various lengths, and reveal their distribution during vertebrate embryogenesis. Overall, Nano3P-seq is a simple and robust method for accurately estimating transcript levels, tail lengths, and tail composition heterogeneity in individual reads, with minimal library preparation biases, both in the coding and non-coding transcriptome. Nano3P-seq presents a nanopore-based sequencing tool to profile polyA-tailed and non-polyA-tailed transcripts, as well as capture polyA tail length and composition.

Despite the nuclear localization of the m 6 A machinery, the genomes of multiple exclusively-cytoplasmic RNA viruses, such as chikungunya (CHIKV) and dengue (DENV), are reported to be extensively m 6 A-modified. However, these findings are mostly based on m 6 A-Seq, an antibody-dependent technique with a high rate of false positives. Here, we address the presence of m 6 A in CHIKV and DENV RNAs. For this, we combine m 6 A-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 m 6 A modification in CHIKV or DENV transcripts. Furthermore, depletion of key components of the host m 6 A machinery does not affect CHIKV or DENV infection. Moreover, CHIKV or DENV infection has no effect on the m 6 A machinery’s localization. Our results challenge the prevailing notion that m 6 A modification is a general feature of cytoplasmic RNA viruses and underscore the importance of validating RNA modifications with orthogonal approaches. A comprehensive analysis found no evidence of m6A modifications in the genome of the cytoplasmic RNA viruses CHIKV and DENV, challenging the current notion that m6A modification is a general feature of cytoplasmic RNA viruses.

RNA modifications, collectively referred to as the 'epitranscriptome', are not mere decorations of RNA molecules, but can be dynamically regulated upon environmental queues and changes in cellular conditions. This dynamic behaviour is achieved through the RNA modification machinery, which comprises \"writer\", \"reader\" and \"eraser\" proteins that modify, recognize and remove the modification, respectively.Chapter 2 presents a comprehensive analysis of the RNA modification machinery (readers, writers and erasers) across species, tissues and cancer types, revealing gene duplications during eukaryotic evolution, changes in substrate specificity and tissue- and cancer-specific expression patterns.Chapters 3 and 4 present the exploration and development of novel methods to map and analyze RNA modifications transcriptome-wide. Nanopore direct-RNA sequencing technology was used to provide RNA modification maps in full- length native RNA molecules. Firstly, it is shown that RNA modifications can be detected in the form of base-calling 'errors', thus allowing us to train Support Vector Machine models that can distinguish m6A-modified from unmodified sites, both in vitro and in vivo. Secondly, it is demonstrated that distinct RNA modification types have unique base-calling 'error' signatures, allowing us to exploit these signatures to distinguish different RNA modification types. It is found that pseudouridine has one of the most distinct signatures, appearing in the form of C-to-U mismatches. Finally, this information was used to predict novel pseudouridine sites on ncRNAs and mRNAs transcriptome-wide, as well as to obtain quantitative measurements of the stoichiometry of modified sites.Chapter 5 presents the development of a novel nanopore-based method, which is termed 'Nano3P-seq', to simultaneously quantify RNA abundance and tail length dynamics in individual molecules in both the coding and non- coding transcriptome, from cDNA reads. It is demonstrated that Nano3P-seq offers a simple approach to study the coding and non-coding transcriptome at single molecule resolution regardless of the tail ends.Together, this work provides a comprehensive framework for the study of RNA modifications and polyA tail dynamics using third generation sequencing technologies, opening novel avenues for future works that aim to characterize their dynamics and biological roles both in health and in disease.

The epitranscriptomics field has undergone an enormous expansion in the last few years; however, a major limitation is the lack of generic methods to map RNA modifications transcriptome-wide. Here, we show that using direct RNA sequencing, N -methyladenosine (m A) RNA modifications can be detected with high accuracy, in the form of systematic errors and decreased base-calling qualities. Specifically, we find that our algorithm, trained with m A-modified and unmodified synthetic sequences, can predict m A RNA modifications with ~90% accuracy. We then extend our findings to yeast data sets, finding that our method can identify m A RNA modifications in vivo with an accuracy of 87%. Moreover, we further validate our method by showing that these 'errors' are typically not observed in yeast ime4-knockout strains, which lack m A modifications. Our results open avenues to investigate the biological roles of RNA modifications in their native RNA context.