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.

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.

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.

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.

Dynamic interplay between RPL3- and RPL3L-containing ribosomes modulates mitochondrial activity in the mammalian heart.

The existence of naturally occurring ribosome heterogeneity is now a well-acknowledged phenomenon. However, whether this heterogeneity leads to functionally diverse 'specialized ribosomes' is still a controversial topic. Here, we explore the biological function of RPL3L (uL3L), a ribosomal protein (RP) paralogue of RPL3 (uL3) that is exclusively expressed in skeletal muscle and heart tissues, by generating a viable homozygous Rpl3l knockout mouse strain. We identify a rescue mechanism in which, upon RPL3L depletion, RPL3 becomes up-regulated, yielding RPL3-containing ribosomes instead of RPL3L-containing ribosomes that are typically found in cardiomyocytes. Using both ribosome profiling (Ribo-seq) and a novel orthogonal approach consisting of ribosome pulldown coupled to nanopore sequencing (Nano-TRAP), we find that RPL3L modulates neither translational efficiency nor ribosome affinity towards a specific subset of transcripts. In contrast, we show that depletion of RPL3L leads to increased ribosome-mitochondria interactions in cardiomyocytes, which is accompanied by a significant increase in ATP levels, potentially as a result of fine-tuning of mitochondrial activity. Our results demonstrate that the existence of tissue-specific RP paralogues does not necessarily lead to enhanced translation of specific transcripts or modulation of translational output. Instead, we reveal a complex cellular scenario in which RPL3L modulates the expression of RPL3, which in turn affects ribosomal subcellular localization and, ultimately, mitochondrial activity.

Ribosomes are macromolecular machines responsible for protein synthesis in all living beings. Recent studies have shown that ribosomes can be heterogeneous in their structure, possibly leading to a specialized function. Here, we focus on RPL3L, a ribosomal protein expressed exclusively in striated muscles. We find that the deletion of the Rpl3l gene in a mouse model triggers a compensation mechanism, in which the missing RPL3L protein is replaced by its paralogue, RPL3. Furthermore, we find that RPL3-containing ribosomes establish closer interactions with mitochondria, cellular organelles responsible for energy production, leading to higher energy production when compared with RPL3L-containing ribosomes. Finally, we show that the RPL3­RPL3L compensation mechanism is also triggered in heart disease conditions, such as hypertrophy and myocardial infarction.

Nanopore Direct RNA Sequencing Data Processing and Analysis Using MasterOfPores.

This chapter describes MasterOfPores v.2 (MoP2), an open-source suite of pipelines for processing and analyzing direct RNA Oxford Nanopore sequencing data. The MoP2 relies on the Nextflow DSL2 framework and Linux containers, thus enabling reproducible data analysis in transcriptomic and epitranscriptomic studies. We introduce the key concepts of MoP2 and provide a step-by-step fully reproducible and complete example of how to use the workflow for the analysis of S. cerevisiae total RNA samples sequenced using MinION flowcells. The workflow starts with the pre-processing of raw FAST5 files, which includes basecalling, read quality control, demultiplexing, filtering, mapping, estimation of per-gene/transcript abundances, and transcriptome assembly, with support of the GPU computing for the basecalling and read demultiplexing steps. The secondary analyses of the workflow focus on the estimation of RNA poly(A) tail lengths and the identification of RNA modifications. The MoP2 code is available at https://github.com/biocorecrg/MOP2 and is distributed under the MIT license.

Nano3P-seq: transcriptome-wide analysis of gene expression and tail dynamics using end-capture nanopore cDNA sequencing.

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.

Exploring the epitranscriptome by native RNA sequencing.

Chemical RNA modifications, collectively referred to as the "epitranscriptome," are essential players in fine-tuning gene expression. Our ability to analyze RNA modifications has improved rapidly in recent years, largely due to the advent of high-throughput sequencing methodologies, which typically consist of coupling modification-specific reagents, such as antibodies or enzymes, to next-generation sequencing. Recently, it also became possible to map RNA modifications directly by sequencing native RNAs using nanopore technologies, which has been applied for the detection of a number of RNA modifications, such as N6-methyladenosine (m6A), pseudouridine (Ψ), and inosine (I). However, the signal modulations caused by most RNA modifications are yet to be determined. A global effort is needed to determine the signatures of the full range of RNA modifications to avoid the technical biases that have so far limited our understanding of the epitranscriptome.

CHIKV infection reprograms codon optimality to favor viral RNA translation by altering the tRNA epitranscriptome.

Ample evidence indicates that codon usage bias regulates gene expression. How viruses, such as the emerging mosquito-borne Chikungunya virus (CHIKV), express their genomes at high levels despite an enrichment in rare codons remains a puzzling question. Using ribosome footprinting, we analyze translational changes that occur upon CHIKV infection. We show that CHIKV infection induces codon-specific reprogramming of the host translation machinery to favor the translation of viral RNA genomes over host mRNAs with an otherwise optimal codon usage. This reprogramming was mostly apparent at the endoplasmic reticulum, where CHIKV RNAs show high ribosome occupancy. Mechanistically, it involves CHIKV-induced overexpression of KIAA1456, an enzyme that modifies the wobble U34 position in the anticodon of tRNAs, which is required for proper decoding of codons that are highly enriched in CHIKV RNAs. Our findings demonstrate an unprecedented interplay of viruses with the host tRNA epitranscriptome to adapt the host translation machinery to viral production.

High-performance nano-flow liquid chromatography column combined with high- and low-collision energy data-independent acquisition enables targeted and discovery identification of modified ribonucleotides by mass spectrometry.

Over 170 post-transcriptional RNA modifications have been described and are common in all kingdoms of life. These modifications range from methylation to complex chemical structures, with methylation being the most abundant. RNA modifications play a key role in RNA folding and function and their dysregulation in humans has been linked to several diseases such as cancer, metabolic diseases or neurological disorder. Nowadays, liquid chromatography-tandem mass spectrometry is considered the gold standard method for the identification and quantification of these modifications due to its sensitivity and accuracy. However, the analysis of modified ribonucleosides by mass spectrometry is complex due to the presence of positional isomers. In this scenario, optimal separation of these compounds by highly sensitive liquid chromatography combined with the generation of high-information spectra is critical to unequivocally identify them, especially in high-complex mixtures. Here we present an analytical method that comprises a new type of mixed-mode nano-flow liquid chromatography column combined with high- and low-collision energy data-independent mass spectrometric acquisition for the identification and quantitation of modified ribonucleosides. The method produces content-rich spectra and combines targeted and screening capabilities thus enabling the identification of a variety of modified nucleosides in biological matrices by single-shot liquid chromatographic analysis coupled to mass spectrometry.

Computational methods for RNA modification detection from nanopore direct RNA sequencing data.

The covalent modification of RNA molecules is a pervasive feature of all classes of RNAs and has fundamental roles in the regulation of several cellular processes. Mapping the location of RNA modifications transcriptome-wide is key to unveiling their role and dynamic behaviour, but technical limitations have often hampered these efforts. Nanopore direct RNA sequencing is a third-generation sequencing technology that allows the sequencing of native RNA molecules, thus providing a direct way to detect modifications at single-molecule resolution. Despite recent advances, the analysis of nanopore sequencing data for RNA modification detection is still a complex task that presents many challenges. Many works have addressed this task using different approaches, resulting in a large number of tools with different features and performances. Here we review the diverse approaches proposed so far and outline the principles underlying currently available algorithms.

High-throughput 5' UTR engineering for enhanced protein production in non-viral gene therapies.

Despite significant clinical progress in cell and gene therapies, maximizing protein expression in order to enhance potency remains a major technical challenge. Here, we develop a high-throughput strategy to design, screen, and optimize 5' UTRs that enhance protein expression from a strong human cytomegalovirus (CMV) promoter. We first identify naturally occurring 5' UTRs with high translation efficiencies and use this information with in silico genetic algorithms to generate synthetic 5' UTRs. A total of ~12,000 5' UTRs are then screened using a recombinase-mediated integration strategy that greatly enhances the sensitivity of high-throughput screens by eliminating copy number and position effects that limit lentiviral approaches. Using this approach, we identify three synthetic 5' UTRs that outperform commonly used non-viral gene therapy plasmids in expressing protein payloads. In summary, we demonstrate that high-throughput screening of 5' UTR libraries with recombinase-mediated integration can identify genetic elements that enhance protein expression, which should have numerous applications for engineered cell and gene therapies.

ModPhred: an integrative toolkit for the analysis and storage of nanopore sequencing DNA and RNA modification data.

MOTIVATION: DNA and RNA modifications can now be identified using nanopore sequencing. However, we currently lack a flexible software to efficiently encode, store, analyze and visualize DNA and RNA modification data. RESULTS: Here, we present ModPhred, a versatile toolkit that facilitates DNA and RNA modification analysis from nanopore sequencing reads in a user-friendly manner. ModPhred integrates probabilistic DNA and RNA modification information within the FASTQ and BAM file formats, can be used to encode multiple types of modifications simultaneously, and its output can be easily coupled to genomic track viewers, facilitating the visualization and analysis of DNA and RNA modification information in individual reads in a simple and computationally efficient manner. AVAILABILITY AND IMPLEMENTATION: ModPhred is available at https://github.com/novoalab/modPhred, is implemented in Python3, and is released under an MIT license. Docker images with all dependencies preinstalled are also provided. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

EpiNano: Detection of m6A RNA Modifications Using Oxford Nanopore Direct RNA Sequencing.

RNA modifications play pivotal roles in the RNA life cycle and RNA fate, and are now appreciated as a major posttranscriptional regulatory layer in the cell. In the last few years, direct RNA nanopore sequencing (dRNA-seq) has emerged as a promising technology that can provide single-molecule resolution maps of RNA modifications in their native RNA context. While native RNA can be successfully sequenced using this technology, the detection of RNA modifications is still challenging. Here, we provide an upgraded version of EpiNano (version 1.2), an algorithm to predict m6A RNA modifications from dRNA-seq datasets. The latest version of EpiNano contains models for predicting m6A RNA modifications in dRNA-seq data that has been base-called with Guppy. Moreover, it can now train models with features extracted from both base-called dRNA-seq FASTQ data and raw FAST5 nanopore outputs. Finally, we describe how EpiNano can be used in stand-alone mode to extract base-calling "error" features and current intensity information from dRNA-seq datasets. In this chapter, we provide step-by-step instructions on how to produce in vitro transcribed constructs to train EpiNano, as well as detailed information on how to use EpiNano to train, test, and predict m6A RNA modifications in dRNA-seq data.

Human tRNAs with inosine 34 are essential to efficiently translate eukarya-specific low-complexity proteins.

The modification of adenosine to inosine at the wobble position (I34) of tRNA anticodons is an abundant and essential feature of eukaryotic tRNAs. The expansion of inosine-containing tRNAs in eukaryotes followed the transformation of the homodimeric bacterial enzyme TadA, which generates I34 in tRNAArg and tRNALeu, into the heterodimeric eukaryotic enzyme ADAT, which modifies up to eight different tRNAs. The emergence of ADAT and its larger set of substrates, strongly influenced the tRNA composition and codon usage of eukaryotic genomes. However, the selective advantages that drove the expansion of I34-tRNAs remain unknown. Here we investigate the functional relevance of I34-tRNAs in human cells and show that a full complement of these tRNAs is necessary for the translation of low-complexity protein domains enriched in amino acids cognate for I34-tRNAs. The coding sequences for these domains require codons translated by I34-tRNAs, in detriment of synonymous codons that use other tRNAs. I34-tRNA-dependent low-complexity proteins are enriched in functional categories related to cell adhesion, and depletion in I34-tRNAs leads to cellular phenotypes consistent with these roles. We show that the distribution of these low-complexity proteins mirrors the distribution of I34-tRNAs in the phylogenetic tree.

Quantitative profiling of pseudouridylation dynamics in native RNAs with nanopore sequencing.

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 N6-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.

Subcellular relocalization and nuclear redistribution of the RNA methyltransferases TRMT1 and TRMT1L upon neuronal activation.

RNA modifications are dynamic chemical entities that expand the RNA lexicon and regulate RNA fate. The most abundant modification present in mRNAs, N6-methyladenosine (m6A), has been implicated in neurogenesis and memory formation. However, whether additional RNA modifications may be playing a role in neuronal functions and in response to environmental queues is largely unknown. Here we characterize the biochemical function and cellular dynamics of two human RNA methyltransferases previously associated with neurological dysfunction, TRMT1 and its homolog, TRMT1-like (TRMT1L). Using a combination of next-generation sequencing, LC-MS/MS, patient-derived cell lines and knockout mouse models, we confirm the previously reported dimethylguanosine (m2,2G) activity of TRMT1 in tRNAs, as well as reveal that TRMT1L, whose activity was unknown, is responsible for methylating a subset of cytosolic tRNAAla(AGC) isodecoders at position 26. Using a cellular in vitro model that mimics neuronal activation and long term potentiation, we find that both TRMT1 and TRMT1L change their subcellular localization upon neuronal activation. Specifically, we observe a major subcellular relocalization from mitochondria and other cytoplasmic domains (TRMT1) and nucleoli (TRMT1L) to different small punctate compartments in the nucleus, which are as yet uncharacterized. This phenomenon does not occur upon heat shock, suggesting that the relocalization of TRMT1 and TRMT1L is not a general reaction to stress, but rather a specific response to neuronal activation. Our results suggest that subcellular relocalization of RNA modification enzymes may play a role in neuronal plasticity and transmission of information, presumably by addressing new targets.

Molecular barcoding of native RNAs using nanopore sequencing and deep learning.

Nanopore sequencing enables direct measurement of RNA molecules without conversion to cDNA, thus opening the gates to a new era for RNA biology. However, the lack of molecular barcoding of direct RNA nanopore sequencing data sets severely affects the applicability of this technology to biological samples, where RNA availability is often limited. Here, we provide the first experimental protocol and associated algorithm to barcode and demultiplex direct RNA nanopore sequencing data sets. Specifically, we present a novel and robust approach to accurately classify raw nanopore signal data by transforming current intensities into images or arrays of pixels, followed by classification using a deep learning algorithm. We demonstrate the power of this strategy by developing the first experimental protocol for barcoding and demultiplexing direct RNA sequencing libraries. Our method, DeePlexiCon, can classify 93% of reads with 95.1% accuracy or 60% of reads with 99.9% accuracy. The availability of an efficient and simple multiplexing strategy for native RNA sequencing will improve the cost-effectiveness of this technology, as well as facilitate the analysis of lower-input biological samples. Overall, our work exemplifies the power, simplicity, and robustness of signal-to-image conversion for nanopore data analysis using deep learning.