Nanodynamo quantifies subcellular RNA dynamics revealing extensive coupling between steps of the RNA life cycle.

The coordinated action of transcriptional and post-transcriptional machineries shapes gene expression programs at steady state and determines their concerted response to perturbations. We have developed Nanodynamo, an experimental and computational workflow for quantifying the kinetic rates of nuclear and cytoplasmic steps of the RNA life cycle. Nanodynamo is based on mathematical modelling following sequencing of native RNA from cellular fractions and polysomes. We have applied this workflow to triple-negative breast cancer cells, revealing widespread post-transcriptional RNA processing that is mutually exclusive with its co-transcriptional counterpart. We used Nanodynamo to unravel the coupling between transcription, processing, export, decay and translation machineries. We have identified a number of coupling interactions within and between the nucleus and cytoplasm that largely contribute to coordinating how cells respond to perturbations that affect gene expression programs. Nanodynamo will be instrumental in unravelling the determinants and regulatory processes involved in the coordination of gene expression responses.

Depletion of Mettl3 in cholinergic neurons causes adult-onset neuromuscular degeneration.

Motor neuron (MN) demise is a hallmark of several neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). Post-transcriptional gene regulation can control RNA's fate, and defects in RNA processing are critical determinants of MN degeneration. N6-methyladenosine (m6A) is a post-transcriptional RNA modification that controls diverse aspects of RNA metabolism. To assess the m6A requirement in MNs, we depleted the m6A methyltransferase-like 3 (METTL3) in cells and mice. METTL3 depletion in embryonic stem cell-derived MNs has profound and selective effects on survival and neurite outgrowth. Mice with cholinergic neuron-specific METTL3 depletion display a progressive decline in motor behavior, accompanied by MN loss and muscle denervation, culminating in paralysis and death. Reader proteins convey m6A effects, and their silencing phenocopies METTL3 depletion. Among the m6A targets, we identified transactive response DNA-binding protein 43 (TDP-43) and discovered that its expression is under epitranscriptomic control. Thus, impaired m6A signaling disrupts MN homeostasis and triggers neurodegeneration conceivably through TDP-43 deregulation.

Comprehensive transcriptome analysis reveals altered mRNA splicing and post-transcriptional changes in the aged mouse brain.

A comprehensive understanding of molecular changes during brain aging is essential to mitigate cognitive decline and delay neurodegenerative diseases. The interpretation of mRNA alterations during brain aging is influenced by the health and age of the animal cohorts studied. Here, we carefully consider these factors and provide an in-depth investigation of mRNA splicing and dynamics in the aging mouse brain, combining short- and long-read sequencing technologies with extensive bioinformatic analyses. Our findings encompass a spectrum of age-related changes, including differences in isoform usage, decreased mRNA dynamics and a module showing increased expression of neuronal genes. Notably, our results indicate a reduced abundance of mRNA isoforms leading to nonsense-mediated RNA decay and suggest a regulatory role for RNA-binding proteins, indicating that their regulation may be altered leading to the reshaping of the aged brain transcriptome. Collectively, our study highlights the importance of studying mRNA splicing events during brain aging.

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.

Identification, semantic annotation and comparison of combinations of functional elements in multiple biological conditions.

MOTIVATION: Approaches such as chromatin immunoprecipitation followed by sequencing (ChIP-seq) represent the standard for the identification of binding sites of DNA-associated proteins, including transcription factors and histone marks. Public repositories of omics data contain a huge number of experimental ChIP-seq data, but their reuse and integrative analysis across multiple conditions remain a daunting task. RESULTS: We present the Combinatorial and Semantic Analysis of Functional Elements (CombSAFE), an efficient computational method able to integrate and take advantage of the valuable and numerous, but heterogeneous, ChIP-seq data publicly available in big data repositories. Leveraging natural language processing techniques, it integrates omics data samples with semantic annotations from selected biomedical ontologies; then, using hidden Markov models, it identifies combinations of static and dynamic functional elements throughout the genome for the corresponding samples. CombSAFE allows analyzing the whole genome, by clustering patterns of regions with similar functional elements and through enrichment analyses to discover ontological terms significantly associated with them. Moreover, it allows comparing functional states of a specific genomic region to analyze their different behavior throughout the various semantic annotations. Such findings can provide novel insights by identifying unexpected combinations of functional elements in different biological conditions. AVAILABILITY AND IMPLEMENTATION: The Python implementation of the CombSAFE pipeline is freely available for non-commercial use at: https://github.com/DEIB-GECO/CombSAFE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

New insight into the catalytic -dependent and -independent roles of METTL3 in sustaining aberrant translation in chronic myeloid leukemia.

Chronic myeloid leukemia (CML) is a myeloproliferative neoplasm caused by the presence of tyrosine kinase BCR-ABL1 fusion protein, which deregulate transcription and mRNA translation. Tyrosine kinase inhibitors (TKIs) are the first-choice treatment. However, resistance to TKIs remains a challenge to cure CML patients. Here, we reveal that the m6A methyltransferase complex METTL3/METTL14 is upregulated in CML patients and that is required for proliferation of primary CML cells and CML cell lines sensitive and resistant to the TKI imatinib. We demonstrate that depletion of METTL3 strongly impairs global translation efficiency. In particular, our data show that METTL3 is crucial for the expression of genes involved in ribosome biogenesis and translation. Specifically, we found that METTL3 directly regulates the level of PES1 protein identified as an oncogene in several tumors. We propose a model in which nuclear METTL3/METTL14 methyltransferase complex modified nascent transcripts whose translation is enhanced by cytoplasmic localization of METTL3, independently from its catalytic activity. In conclusion, our results point to METTL3 as a novel relevant oncogene in CML and as a promising therapeutic target for TKI resistant CML.

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.

Identification of Genes Post-Transcriptionally Regulated from RNA-seq: The Case Study of Liver Hepatocellular Carcinoma.

The field of transcriptional regulation generally assumes that changes in transcripts levels reflect changes in transcriptional status of the corresponding gene. While this assumption might hold true for a large population of transcripts, a considerable and still unrecognized fraction of the variation might involve other steps of the RNA lifecycle, that is the processing of the premature RNA, and degradation of the mature RNA. Discrimination between these layers requires complementary experimental techniques, such as RNA metabolic labeling or block of transcription experiments. Nonetheless, the analysis of the premature and mature RNA, derived from intronic and exonic read counts in RNA-seq data, allows distinguishing between transcriptionally and post-transcriptionally regulated genes, although not recognizing the specific step involved in the post-transcriptional response, that is processing, degradation, or a combination of the two. We illustrate how the INSPEcT R/Bioconductor package could be used to infer post-transcriptional regulation in TCGA RNA-seq samples for Hepatocellular Carcinoma.

Dynamics of transcriptional and post-transcriptional regulation.

Despite gene expression programs being notoriously complex, RNA abundance is usually assumed as a proxy for transcriptional activity. Recently developed approaches, able to disentangle transcriptional and post-transcriptional regulatory processes, have revealed a more complex scenario. It is now possible to work out how synthesis, processing and degradation kinetic rates collectively determine the abundance of each gene's RNA. It has become clear that the same transcriptional output can correspond to different combinations of the kinetic rates. This underscores the fact that markedly different modes of gene expression regulation exist, each with profound effects on a gene's ability to modulate its own expression. This review describes the development of the experimental and computational approaches, including RNA metabolic labeling and mathematical modeling, that have been disclosing the mechanisms underlying complex transcriptional programs. Current limitations and future perspectives in the field are also discussed.

Translation is required for miRNA-dependent decay of endogenous transcripts.

Post-transcriptional repression of gene expression by miRNAs occurs through transcript destabilization or translation inhibition. mRNA decay is known to account for most miRNA-dependent repression. However, because transcript decay occurs co-translationally, whether target translation is a requirement for miRNA-dependent transcript destabilization remains unknown. To decouple these two molecular processes, we used cytosolic long noncoding RNAs (lncRNAs) as models for endogenous transcripts that are not translated. We show that, despite interacting with the miRNA-loaded RNA-induced silencing complex, the steady-state abundance and decay rates of these transcripts are minimally affected by miRNA loss. To further validate the apparent requirement of translation for miRNA-dependent decay, we fused two lncRNA candidates to the 3'-end of a protein-coding gene reporter and found this results in their miRNA-dependent destabilization. Further analysis revealed that the few natural lncRNAs whose levels are regulated by miRNAs in mESCs tend to associate with translating ribosomes, and possibly represent misannotated micropeptides, further substantiating the necessity of target translation for miRNA-dependent transcript decay. In summary, our analyses suggest that translation is required for miRNA-dependent transcript destabilization, and demonstrate that the levels of coding and noncoding transcripts are differently affected by miRNAs.

Genome-wide dynamics of RNA synthesis, processing, and degradation without RNA metabolic labeling.

The quantification of the kinetic rates of RNA synthesis, processing, and degradation are largely based on the integrative analysis of total and nascent transcription, the latter being quantified through RNA metabolic labeling. We developed INSPEcT-, a computational method based on the mathematical modeling of premature and mature RNA expression that is able to quantify kinetic rates from steady-state or time course total RNA-seq data without requiring any information on nascent transcripts. Our approach outperforms available solutions, closely recapitulates the kinetic rates obtained through RNA metabolic labeling, improves the ability to detect changes in transcript half-lives, reduces the cost and complexity of the experiments, and can be adopted to study experimental conditions in which nascent transcription cannot be readily profiled. Finally, we applied INSPEcT- to the characterization of post-transcriptional regulation landscapes in dozens of physiological and disease conditions. This approach was included in the INSPEcT Bioconductor package, which can now unveil RNA dynamics from steady-state or time course data, with or without the profiling of nascent RNA.

INSPEcT-GUI Reveals the Impact of the Kinetic Rates of RNA Synthesis, Processing, and Degradation, on Premature and Mature RNA Species.

The abundance of RNA species and their response to perturbations are set by the kinetics rates of RNA synthesis, processing, and degradation. However, the visualization, interpretation, and manipulation of these data require familiarity with mathematical modeling and command line tools. INSPEcT-GUI is an R-Shiny interface that allows researchers without specific training to effortlessly explore how the fine kinetic regulation of the RNA life cycle can shape gene expression programs. In particular, it allows to: (i) interactively visualize gene-level RNA dynamics; (ii) refine the model fit of experimental data; (iii) test alternative regulatory models; (iv) explore, independently from the availability of data, how the combined action of the RNA kinetic rates impacts on premature and mature RNA. INSPEcT-GUI is freely available within the R/Bioconductor package INSPEcT at http://bioconductor.org/packages/INSPEcT/. An HTML vignette including documentation on the tool startup and usage, executable examples, and a video demonstration, are available at: http://bioconductor.org/packages/release/bioc/vignettes/INSPEcT/inst/doc/INSPEcT_GUI.html.

Direct RNA Sequencing for the Study of Synthesis, Processing, and Degradation of Modified Transcripts.

It has been known for a few decades that transcripts can be marked by dozens of different modifications. Yet, we are just at the beginning of charting these marks and understanding their functional impact. High-quality methods were developed for the profiling of some of these marks, and approaches to finely study their impact on specific phases of the RNA life-cycle are available, including RNA metabolic labeling. Thanks to these improvements, the most abundant marks, including N6-methyladenosine, are emerging as important determinants of the fate of marked RNAs. However, we still lack approaches to directly study how the set of marks for a given RNA molecule shape its fate. In this perspective, we first review current leading approaches in the field. Then, we propose an experimental and computational setup, based on direct RNA sequencing and mathematical modeling, to decipher the functional consequences of RNA modifications on the fate of individual RNA molecules and isoforms.

Ontology-driven integrative analysis of omics data through Onassis.

Public repositories of large-scale omics datasets represent a valuable resource for researchers. In fact, data re-analysis can either answer novel questions or provide critical data able to complement in-house experiments. However, despite the development of standards for the compilation of metadata, the identification and organization of samples still constitutes a major bottleneck hampering data reuse. We introduce Onassis, an R package within the Bioconductor environment providing key functionalities of Natural Language Processing (NLP) tools. Leveraging biomedical ontologies, Onassis greatly simplifies the association of samples from large-scale repositories to their representation in terms of ontology-based annotations. Moreover, through the use of semantic similarity measures, Onassis hierarchically organizes the datasets of interest, thus supporting the semantically aware analysis of the corresponding omics data. In conclusion, Onassis leverages NLP techniques, biomedical ontologies, and the R statistical framework, to identify, relate, and analyze datasets from public repositories. The tool was tested on various large-scale datasets, including compendia of gene expression, histone marks, and DNA methylation, illustrating how it can facilitate the integrative analysis of various omics data.

A dual role of dLsd1 in oogenesis: regulating developmental genes and repressing transposons.

The histone demethylase LSD1 is a key chromatin regulator that is often deregulated in cancer. Its ortholog, dLsd1 plays a crucial role in Drosophila oogenesis; however, our knowledge of dLsd1 function is insufficient to explain its role in the ovary. Here, we have performed genome-wide analysis of dLsd1 binding in the ovary, and we document that dLsd1 is preferentially associated to the transcription start site of developmental genes. We uncovered an unanticipated interplay between dLsd1 and the GATA transcription factor Serpent and we report an unexpected role for Serpent in oogenesis. Besides, our transcriptomic data show that reducing dLsd1 levels results in ectopic transposable elements (TE) expression correlated with changes in H3K4me2 and H3K9me2 at TE loci. In addition, our results suggest that dLsd1 is required for Piwi dependent TE silencing. Hence, we propose that dLsd1 plays crucial roles in establishing specific gene expression programs and in repressing transposons during oogenesis.

Macrophages fine tune satellite cell fate in dystrophic skeletal muscle of mdx mice.

Satellite cells (SCs) are muscle stem cells that remain quiescent during homeostasis and are activated in response to acute muscle damage or in chronic degenerative conditions such as Duchenne Muscular Dystrophy. The activity of SCs is supported by specialized cells which either reside in the muscle or are recruited in regenerating skeletal muscles, such as for instance macrophages (MΦs). By using a dystrophic mouse model of transient MΦ depletion, we describe a shift in identity of muscle stem cells dependent on the crosstalk between MΦs and SCs. Indeed MΦ depletion determines adipogenic conversion of SCs and exhaustion of the SC pool leading to an exacerbated dystrophic phenotype. The reported data could also provide new insights into therapeutic approaches targeting inflammation in dystrophic muscles.

An early Myc-dependent transcriptional program orchestrates cell growth during B-cell activation.

Upon activation, lymphocytes exit quiescence and undergo substantial increases in cell size, accompanied by activation of energy-producing and anabolic pathways, widespread chromatin decompaction, and elevated transcriptional activity. These changes depend upon prior induction of the Myc transcription factor, but how Myc controls them remains unclear. We addressed this issue by profiling the response to LPS stimulation in wild-type and c-myc-deleted primary mouse B-cells. Myc is rapidly induced, becomes detectable on virtually all active promoters and enhancers, but has no direct impact on global transcriptional activity. Instead, Myc contributes to the swift up- and down-regulation of several hundred genes, including many known regulators of the aforementioned cellular processes. Myc-activated promoters are enriched for E-box consensus motifs, bind Myc at the highest levels, and show enhanced RNA Polymerase II recruitment, the opposite being true at down-regulated loci. Remarkably, the Myc-dependent signature identified in activated B-cells is also enriched in Myc-driven B-cell lymphomas: hence, besides modulation of new cancer-specific programs, the oncogenic action of Myc may largely rely on sustained deregulation of its normal physiological targets.

m6A RNA modification and its writer/reader VIRMA/YTHDF3 in testicular germ cell tumors: a role in seminoma phenotype maintenance.

BACKGROUND: Covalent RNA modifications, such as N-6-methyladenosine (m6A), have been associated with various biological processes, but their role in cancer remains largely unexplored. m6A dynamics depends on specific enzymes whose deregulation may also impact in tumorigenesis. Herein, we assessed the differential abundance of m6A, its writer VIRMA and its reader YTHDF3, in testicular germ cell tumors (TGCTs), looking for clinicopathological correlates. METHODS: In silico analysis of TCGA data disclosed altered expression of VIRMA (52%) and YTHDF3 (48%), prompting subsequent validation. Formalin-fixed paraffin-embedded tissues from 122 TGCTs (2005-2016) were selected. RNA extraction, cDNA synthesis and real-time qPCR (Taqman assays) for VIRMA and YTHDF3 were performed, as well as immunohistochemistry for VIRMA, YTHDF3 and m6A, for staining intensity assessment. Associations between categorical variables were assessed using Chi square and Fisher's exact test. Distribution of continuous variables between groups was compared using the nonparametric Mann-Whitney and Kruskal-Wallis tests. Biomarker performance was assessed through receiver operating characteristics (ROC) curve construction and a cut-off was established by Youden's index method. Statistical significance was set at p < 0.05. RESULTS: In our cohort, VIRMA and YTHDF3 mRNA expression levels differed among TGCT subtypes, with Seminomas (SEs) depicting higher levels than Non-Seminomatous tumors (NSTs) (p < 0.01 for both). A positive correlation was found between VIRMA and YTHDF3 expression levels. VIRMA discriminated SEs from NSTs with AUC = 0.85 (Sensitivity 77.3%, Specificity 81.1%, PPV 71.6%, NPV 85.3%, Accuracy 79.7%). Immunohistochemistry paralleled transcript findings, as patients with strong m6A immunostaining intensity depicted significantly higher VIRMA mRNA expression levels and stronger VIRMA immunoexpression intensity (p < 0.001 and p < 0.01, respectively). CONCLUSION: Abundance of m6A and expression of VIRMA/YTHDF3 were different among TGCT subtypes, with higher levels in SEs, suggesting a contribution to SE phenotype maintenance. VIRMA and YTHDF3 might cooperate in m6A establishment in TGCTs, and their transcript levels accurately discriminate between SEs and NSTs, constituting novel candidate biomarkers for patient management.

m6A-Dependent RNA Dynamics in T Cell Differentiation.

N6-methyladenosine (m6A) is the most abundant RNA modification. It has been involved in the regulation of RNA metabolism, including degradation and translation, in both physiological and disease conditions. A recent study showed that m6A-mediated degradation of key transcripts also plays a role in the control of T cells homeostasis and IL-7 induced differentiation. We re-analyzed the omics data from that study and, through the integrative analysis of total and nascent RNA-seq data, we were able to comprehensively quantify T cells RNA dynamics and how these are affected by m6A depletion. In addition to the expected impact on RNA degradation, we revealed a broader effect of m6A on RNA dynamics, which included the alteration of RNA synthesis and processing. Altogether, the combined action of m6A on all major steps of the RNA life-cycle closely re-capitulated the observed changes in the abundance of premature and mature RNA species. Ultimately, our re-analysis extended the findings of the initial study, focused on RNA stability, and proposed a yet unappreciated role for m6A in RNA synthesis and processing dynamics.