Dissecting newly transcribed and old RNA using GRAND-SLAM

m6A alters ribosome dynamics to initiate mRNA degradation

Degradation of mRNA containing N6-methyladenosine (m6A) is essential for cell growth, differentiation, and stress responses. Here, we show that m6A markedly alters ribosome dynamics and that these alterations mediate the degradation effect of m6A on mRNA. We find that m6A is a potent inducer of ribosome stalling, and these stalls lead to ribosome collisions that form a unique conformation unlike those seen in other contexts. We find that the degree of ribosome stalling correlates with m6A-mediated mRNA degradation, and increasing the persistence of collided ribosomes correlates with enhanced m6A-mediated mRNA degradation. Ribosome stalling and collision at m6A is followed by recruitment of YTHDF m6A reader proteins to promote mRNA degradation. We show that mechanisms that reduce ribosome stalling and collisions, such as translation suppression during stress, stabilize m6A-mRNAs and increase their abundance, enabling stress responses. Overall, our study reveals the ribosome as the initial m6A sensor for beginning m6A-mRNA degradation.

tRNA modifications tune m6A-dependent mRNA decay

Chemically modified nucleotides in mRNA are critical regulators of gene expression, primarily through interactions with reader proteins that bind to these modifications. Here, we present a mechanism by which the epitranscriptomic mark N6-methyladenosine (m6A) is read by tRNAs during translation. Codons that are modified with m6A are decoded inefficiently by the ribosome, rendering them "non-optimal" and inducing ribosome collisions on cellular transcripts. This couples mRNA translation to decay. 5-Methoxycarbonylmethyl-2-thiouridine (mcm5s2U) in the tRNA anticodon loop counteracts this effect. This unanticipated link between the mRNA and tRNA epitranscriptomes enables the coordinated decay of mRNA regulons, including those encoding oncogenic signaling pathways. In cancer, dysregulation of the m6A and mcm5s2U biogenesis pathways-marked by a shift toward more mcm5s2U-is associated with more aggressive tumors and poor prognosis. Overall, this pan-epitranscriptomic interaction represents a novel mechanism of post-transcriptional gene regulation with implications for human health.

Tristetraprolin-mediated mRNA destabilization regulates basophil inflammatory responses

BACKGROUND

Basophils, despite being the least common granulocytes, play crucial roles in type 2 immune responses, such as chronic allergic inflammation and protective immunity against parasites. However, the molecular mechanisms regulating basophil activation and inflammatory molecule production remain poorly understood. Therefore, we investigated the role of RNA-binding proteins, specifically tristetraprolin (TTP), in regulating inflammatory molecule production in basophils.

METHODS

Using antigen/IgE-stimulated basophils from wild-type (WT) and TTP-knockout (TTP-KO) mice, we performed bulk RNA sequencing, transcriptome-wide mRNA stability assays, and protein analyses. We also examined mRNA expression and protein production of inflammatory molecules in TTP-KO basophils under stimulation with IL-33 or LPS. Furthermore, we evaluated the in vivo significance of TTP in basophils using basophil-specific TTP-deficient mice and a hapten oxazolone-induced atopic dermatitis model.

RESULTS

TTP expression was upregulated in basophils following stimulation with antigen/IgE, IL-33, or LPS. Under these stimuli, TTP-KO basophils exhibited elevated mRNA expression of inflammatory molecules, such as Il4, Areg, Ccl3, and Cxcl2, compared to WT basophils. Transcriptome-wide mRNA stability assays revealed that TTP deficiency prolonged the mRNA half-life of these inflammatory mediators. Notably, the production of these inflammatory proteins was significantly increased in TTP-KO basophils. Moreover, basophil-specific TTP-deficient mice showed exacerbated oxazolone-induced atopic dermatitis-like skin allergic inflammation.

CONCLUSIONS

TTP is a key regulator of basophil activation, controlling the production of inflammatory mediators through mRNA destabilization. Our in vivo findings demonstrate that the absence of TTP in basophils significantly aggravates allergic skin inflammation, highlighting its potential as a therapeutic target for allergic diseases.

Uncovering the isoform-resolution kinetic landscape of nonsense-mediated mRNA decay with EZbakR

Cellular RNA levels are a product of synthesis and degradation kinetics, which can differ among transcripts of the same gene. An important cause of isoform-specific decay is the nonsense-mediated mRNA decay (NMD) pathway, which degrades transcripts with premature termination codons (PTCs) and other features. Understanding NMD functions requires strategies to quantify isoform kinetics; however, current approaches remain limited. Methods like nucleotide-recoding RNA-seq (NR-seq) enable insights into RNA kinetics, but existing bioinformatic tools do not provide robust, isoform-specific degradation rate constant estimates. We extend the EZbakR-suite by implementing a strategy to infer isoform-level kinetics from short-read NR-seq data. This approach uncovers unexpected variability in NMD efficiency among transcripts with conserved PTC-containing exons and rapid decay of a subset of mRNAs lacking PTCs. Our findings highlight the effects of competition between NMD and other decay pathways, provide mechanistic insights into established NMD efficiency correlates, and identify transcript features promoting efficient decay.

A convenient single-cell newly synthesized transcriptome assay reveals FLI1 downregulation during T-cell activation

Sequencing newly synthesized transcriptome alongside regular transcriptome in single cells enables the study of gene expression temporal dynamics during rapid chromatin and gene regulation processes. Existing assays for profiling single-cell newly synthesized transcriptome often require specialized technical expertise to achieve high cellular throughput, limiting their accessibility. Here, we developed NOTE-seq, a method for simultaneous profiling of regular and newly synthesized transcriptomes in single cells with high cellular throughput. NOTE-seq integrates 4-thiouridine labeling of newly synthesized RNA, thiol-alkylation-based chemical conversion, and a streamlined 10X Genomics workflow, making it accessible and convenient for biologists without extensive single-cell expertise. Using NOTE-seq, we investigated the temporal dynamics of gene expression during early-stage T-cell activation, identified transcription factors and regulons in Jurkat and naïve T cells, and uncovered the down-regulation of FLI1 as a master transcription factor upon T-cell stimulation. Notably, topoisomerase inhibition led to the depletion of both topoisomerases and FLI1 in T cells through a proteasome-dependent mechanism driven by topoisomerase cleavage complexes, highlighting potential complications topoisomerase-targeting cancer chemotherapies could pose to the immune system.

Halfpipe: a tool for analyzing metabolic labeling RNA-seq data to quantify RNA half-lives

We introduce Halfpipe, a tool for analyzing RNA-seq data from metabolic RNA labeling experiments. Its main features are the absolute quantification of 4-thiouridine-labeling-induced T>C conversions in the data as generated by SLAM-seq, calculating the proportion of newly synthesized transcripts, and estimating subcellular RNA half-lives. Halfpipe excels at correcting critical biases caused by typically low labeling efficiency. We measure and compare the RNA metabolism in the G1 phase and during the mitosis of synchronized human cells. We find that RNA half-lives of constantly expressed RNAs are similar in mitosis and G1 phase, suggesting that RNA stability of those genes is constant throughout the cell cycle. Our estimates correlate well with literature values and with known RNA sequence features. Halfpipe is freely available at https://github.com/IMSBCompBio/Halfpipe.

Phenotypic screens identify SCAF1 as critical activator of RNAPII elongation and global transcription

Transcripts produced by RNA polymerase II (RNAPII) are fundamental for cellular responses to environmental changes. It is therefore no surprise that there exist multiple avenues for the regulation of this process. To explore the regulation mediated by RNAPII-interacting proteins, we used a small interfering RNA (siRNA)-based screen to systematically evaluate their influence on RNA synthesis. We identified several proteins that strongly affected RNAPII activity. We evaluated one of the top hits, SCAF1 (SR-related C-terminal domain-associated factor 1), using an auxin-inducible degradation system and sequencing approaches. In agreement with our screen results, acute depletion of SCAF1 decreased RNA synthesis, and showed an increase of Serine-2 phosphorylated-RNAPII (pS2-RNAPII). We found that the accumulation of pS2-RNAPII within the gene body occurred at GC-rich regions and was indicative of stalled RNAPII complexes. The accumulation of stalled RNAPII complexes was accompanied by reduced recruitment of initiating RNAPII, explaining the observed global decrease in transcriptional output. Furthermore, upon SCAF1 depletion, RNAPII complexes showed increased association with components of the proteasomal-degradation machinery. We concluded that in cells lacking SCAF1, RNAPII undergoes a rather interrupted passage, resulting in intervention by the proteasomal-degradation machinery to clear stalled RNAPII. While cells survive the compromised transcription caused by absence of SCAF1, further inhibition of proteasomal-degradation machinery is synthetically lethal.

SLAM-seq reveals independent contributions of RNA processing and stability to gene expression in African trypanosomes

Gene expression is a multi-step process that converts DNA-encoded information into proteins, involving RNA transcription, maturation, degradation, and translation. While transcriptional control is a major regulator of protein levels, the role of post-transcriptional processes such as RNA processing and degradation is less well understood due to the challenge of measuring their contributions individually. To address this challenge, we investigated the control of gene expression in Trypanosoma brucei, a unicellular parasite assumed to lack transcriptional control. Instead, mRNA levels in T. brucei are controlled by post-transcriptional processes, which enabled us to disentangle the contribution of both processes to total mRNA levels. In this study, we developed an efficient metabolic RNA labeling approach and combined ultra-short metabolic labeling with transient transcriptome sequencing (TT-seq) to confirm the long-standing assumption that RNA polymerase II transcription is unregulated in T. brucei. In addition, we established thiol (SH)-linked alkylation for metabolic sequencing of RNA (SLAM-seq) to globally quantify RNA processing rates and half-lives. Our data, combined with scRNA-seq data, indicate that RNA processing and stability independently affect total mRNA levels and contribute to the variability seen between individual cells in African trypanosomes.

m6A sites in the coding region trigger translation-dependent mRNA decay

N6-Methyladenosine (m6A) is the predominant internal RNA modification in eukaryotic messenger RNAs (mRNAs) and plays a crucial role in mRNA stability. Here, using human cells, we reveal that m6A sites in the coding sequence (CDS) trigger CDS-m6A decay (CMD), a pathway that is distinct from previously reported m6A-dependent degradation mechanisms. Importantly, CDS m6A sites act considerably faster and more efficiently than those in the 3' untranslated region, which to date have been considered the main effectors. Mechanistically, CMD depends on translation, whereby m6A deposition in the CDS triggers ribosome pausing and transcript destabilization. The subsequent decay involves the translocation of the CMD target transcripts to processing bodies (P-bodies) and recruitment of the m6A reader protein YT521-B homology domain family protein 2 (YTHDF2). Our findings highlight CMD as a previously unknown pathway, which is particularly important for controlling the expression of developmental regulators and retrogenes.

Subcellular mRNA kinetic modeling reveals nuclear retention as rate-limiting

Eukaryotic mRNAs are transcribed, processed, translated, and degraded in different subcellular compartments. Here, we measured mRNA flow rates between subcellular compartments in mouse embryonic stem cells. By combining metabolic RNA labeling, biochemical fractionation, mRNA sequencing, and mathematical modeling, we determined the half-lives of nuclear pre-, nuclear mature, cytosolic, and membrane-associated mRNAs from over 9000 genes. In addition, we estimated transcript elongation rates. Many matured mRNAs have long nuclear half-lives, indicating nuclear retention as the rate-limiting step in the flow of mRNAs. In contrast, mRNA transcripts coding for transcription factors show fast kinetic rates, and in particular short nuclear half-lives. Differentially localized mRNAs have distinct rate constant combinations, implying modular regulation. Membrane stability is high for membrane-localized mRNA and cytosolic stability is high for cytosol-localized mRNA. mRNAs encoding target signals for membranes have low cytosolic and high membrane half-lives with minor differences between signals. Transcripts of nuclear-encoded mitochondrial proteins have long nuclear retention and cytoplasmic kinetics that do not reflect co-translational targeting. Our data and analyses provide a useful resource to study spatiotemporal gene expression regulation.

Transcriptome and Temporal Transcriptome Analyses in Single Cells

Transcriptome analysis in single cells, enabled by single-cell RNA sequencing, has become a prevalent approach in biomedical research, ranging from investigations of gene regulation to the characterization of tissue organization. Over the past decade, advances in single-cell RNA sequencing technology, including its underlying chemistry, have significantly enhanced its performance, marking notable improvements in methodology. A recent development in the field, which integrates RNA metabolic labeling with single-cell RNA sequencing, has enabled the profiling of temporal transcriptomes in individual cells, offering new insights into dynamic biological processes involving RNA kinetics and cell fate determination. In this review, we explore the chemical principles and design improvements that have enhanced single-molecule capture efficiency, improved RNA quantification accuracy, and increased cellular throughput in single-cell transcriptome analysis. We also illustrate the concept of RNA metabolic labeling for detecting newly synthesized transcripts and summarize recent advancements that enable single-cell temporal transcriptome analysis. Additionally, we examine data analysis strategies for the precise quantification of newly synthesized transcripts and highlight key applications of transcriptome and temporal transcriptome analyses in single cells.

Storm: Incorporating transient stochastic dynamics to infer the RNA velocity with metabolic labeling information

The time-resolved scRNA-seq (tscRNA-seq) provides the possibility to infer physically meaningful kinetic parameters, e.g., the transcription, splicing or RNA degradation rate constants with correct magnitudes, and RNA velocities by incorporating temporal information. Previous approaches utilizing the deterministic dynamics and steady-state assumption on gene expression states are insufficient to achieve favorable results for the data involving transient process. We present a dynamical approach, Storm (Stochastic models of RNA metabolic-labeling), to overcome these limitations by solving stochastic differential equations of gene expression dynamics. The derivation reveals that the new mRNA sequencing data obeys different types of cell-specific Poisson distributions when jointly considering both biological and cell-specific technical noise. Storm deals with measured counts data directly and extends the RNA velocity methodology based on metabolic labeling scRNA-seq data to transient stochastic systems. Furthermore, we relax the constant parameter assumption over genes/cells to obtain gene-cell-specific transcription/splicing rates and gene-specific degradation rates, thus revealing time-dependent and cell-state-specific transcriptional regulations. Storm will facilitate the study of the statistical properties of tscRNA-seq data, eventually advancing our understanding of the dynamic transcription regulation during development and disease.

Non-redundant roles for the human mRNA decapping cofactor paralogs DCP1a and DCP1b

Eukaryotic gene expression is regulated at the transcriptional and post-transcriptional levels, with disruption of regulation contributing significantly to human diseases. The 5' m7G mRNA cap is a central node in post-transcriptional regulation, participating in both mRNA stabilization and translation efficiency. In mammals, DCP1a and DCP1b are paralogous cofactor proteins of the mRNA cap hydrolase DCP2. As lower eukaryotes have a single DCP1 cofactor, the functional advantages gained by this evolutionary divergence remain unclear. We report the first functional dissection of DCP1a and DCP1b, demonstrating that they are non-redundant cofactors of DCP2 with unique roles in decapping complex integrity and specificity. DCP1a is essential for decapping complex assembly and interactions between the decapping complex and mRNA cap-binding proteins. DCP1b is essential for decapping complex interactions with protein degradation and translational machinery. DCP1a and DCP1b impact the turnover of distinct mRNAs. The observation that different ontological groups of mRNA molecules are regulated by DCP1a and DCP1b, along with their non-redundant roles in decapping complex integrity, provides the first evidence that these paralogs have qualitatively distinct functions.

Metabolic RNA Labeling and Translating Ribosome Affinity Purification for Measurement of Nascent RNA Translation

Regulation of gene expression in response to various biological processes, including extracellular stimulation and environmental adaptation, requires nascent mRNA synthesis and translation. Simultaneous analysis of the coordinated regulation of dynamic mRNA synthesis and translation using the same experiment remains a major challenge in the field. Here, we describe a step-by-step protocol for the simultaneous measurement of transcription of nascent mRNA and its translation at the gene level during the acute unfolded protein response (UPR) in HEK293 cells by combining 4-thiouridine metabolic mRNA labeling with translational ribosome affinity purification (TRAP) using a monoclonal antibody against evolutionarily conserved ribosomal P-stalk proteins (P-TRAP). Since P-TRAP captures full-length RNAs bound to ribosomes, it is compatible with 3' mRNA-seq, which analyzes the uridine-rich 3' UTRs of polyadenylated RNAs, allowing robust quantification of T>C conversions. Our nascent P-TRAP (nP-TRAP) method, in which P-TRAP is combined with metabolic mRNA labeling, can serve as a simple and powerful tool to analyze the coordinated regulation of transcription and translation of individual genes in cultured cells. Key features • Simple and retriable analysis of nascent mRNA synthesis and its translation in cultured cells • Combination of 4-thiouridine metabolic RNA labeling with translating ribosome affinity purification (TRAP) • Ribosomal P-stalk-mediated TRAP (P-TRAP) allows single-step and efficient purification of non-tagged ribosomes and translated mRNAs.

Expanding and improving analyses of nucleotide recoding RNA-seq experiments with the EZbakR suite

Nucleotide recoding RNA sequencing methods (NR-seq; TimeLapse-seq, SLAM-seq, TUC-seq, etc.) are powerful approaches for assaying transcript population dynamics. In addition, these methods have been extended to probe a host of regulated steps in the RNA life cycle. Current bioinformatic tools significantly constrain analyses of NR-seq data. To address this limitation, we developed EZbakR, an R package to facilitate a more comprehensive set of NR-seq analyses, and fastq2EZbakR, a Snakemake pipeline for flexible preprocessing of NR-seq datasets, collectively referred to as the EZbakR suite. Together, these tools generalize many aspects of the NR-seq analysis workflow. The fastq2EZbakR pipeline can assign reads to a diverse set of genomic features (e.g., genes, exons, splice junctions, etc.), and EZbakR can perform analyses on any combination of these features. EZbakR extends standard NR-seq mutational modeling to support multi-label analyses (e.g., s4U and s6G dual labeling), and implements an improved hierarchical model to better account for transcript-to-transcript variance in metabolic label incorporation. EZbakR also generalizes dynamical systems modeling of NR-seq data to support analyses of premature mRNA processing and flow between subcellular compartments. Finally, EZbakR implements flexible and well-powered comparative analyses of all estimated parameters via design matrix-specified generalized linear modeling. The EZbakR suite will thus allow researchers to make full, effective use of NR-seq data.

Exploring the expanding universe of host-virus interactions mediated by viral RNA

RNA is a central molecule in RNA virus biology; however, the interactions that it establishes with the host cell are only starting to be elucidated. In recent years, a methodology revolution has dramatically expanded the scope of host-virus interactions involving the viral RNA (vRNA). A second wave of method development has enabled the precise study of these protein-vRNA interactions in a life cycle stage-dependent manner, as well as providing insights into the interactome of specific vRNA species. This review discusses these technical advances and describes the new regulatory mechanisms that have been identified through their use. Among these, we discuss the importance of vRNA in regulating protein function through a process known as riboregulation. We envision that the elucidation of vRNA interactomes will open new avenues of research, including pathways to the discovery of host factors with therapeutic potential against viruses.

Single-cell new RNA sequencing reveals principles of transcription at the resolution of individual bursts

Analyses of transcriptional bursting from single-cell RNA-sequencing data have revealed patterns of variation and regulation in the kinetic parameters that could be inferred. Here we profiled newly transcribed (4-thiouridine-labelled) RNA across 10,000 individual primary mouse fibroblasts to more broadly infer bursting kinetics and coordination. We demonstrate that inference from new RNA profiles could separate the kinetic parameters that together specify the burst size, and that the synthesis rate (and not the transcriptional off rate) controls the burst size. Importantly, transcriptome-wide inference of transcriptional on and off rates provided conclusive evidence that RNA polymerase II transcribes genes in bursts. Recent reports identified examples of transcriptional co-bursting, yet no global analyses have been performed. The deep new RNA profiles we generated with allelic resolution demonstrated that co-bursting rarely appears more frequently than expected by chance, except for certain gene pairs, notably paralogues located in close genomic proximity. Altogether, new RNA single-cell profiling critically improves the inference of transcriptional bursting and provides strong evidence for independent transcriptional bursting of mammalian genes.

Global transcription regulation revealed from dynamical correlations in time-resolved single-cell RNA sequencing

Single-cell transcriptomics reveals significant variations in transcriptional activity across cells. Yet, it remains challenging to identify mechanisms of transcription dynamics from static snapshots. It is thus still unknown what drives global transcription dynamics in single cells. We present a stochastic model of gene expression with cell size- and cell cycle-dependent rates in growing and dividing cells that harnesses temporal dimensions of single-cell RNA sequencing through metabolic labeling protocols and cel lcycle reporters. We develop a parallel and highly scalable approximate Bayesian computation method that corrects for technical variation and accurately quantifies absolute burst frequency, burst size, and degradation rate along the cell cycle at a transcriptome-wide scale. Using Bayesian model selection, we reveal scaling between transcription rates and cell size and unveil waves of gene regulation across the cell cycle-dependent transcriptome. Our study shows that stochastic modeling of dynamical correlations identifies global mechanisms of transcription regulation. A record of this paper's transparent peer review process is included in the supplemental information.

Genome-wide quantification of RNA flow across subcellular compartments reveals determinants of the mammalian transcript life cycle

Dissecting the regulatory mechanisms controlling mammalian transcripts from production to degradation requires quantitative measurements of mRNA flow across the cell. We developed subcellular TimeLapse-seq to measure the rates at which RNAs are released from chromatin, exported from the nucleus, loaded onto polysomes, and degraded within the nucleus and cytoplasm in human and mouse cells. These rates varied substantially, yet transcripts from genes with related functions or targeted by the same transcription factors and RNA-binding proteins flowed across subcellular compartments with similar kinetics. Verifying these associations uncovered a link between DDX3X and nuclear export. For hundreds of RNA metabolism genes, most transcripts with retained introns were degraded by the nuclear exosome, while the remaining molecules were exported with stable cytoplasmic lifespans. Transcripts residing on chromatin for longer had extended poly(A) tails, whereas the reverse was observed for cytoplasmic mRNAs. Finally, machine learning identified molecular features that predicted the diverse life cycles of mRNAs.

Hypoxia and inflammation induce synergistic transcriptome turnover in macrophages

Macrophages are effector immune cells that experience substantial changes to oxygenation when transiting through tissues, especially when entering tumors or infected wounds. How hypoxia alters gene expression and macrophage effector function at the post-transcriptional level remains poorly understood. Here, we use TimeLapse-seq to measure how inflammatory activation modifies the hypoxic response in primary macrophages. Nucleoside recoding sequencing allows the derivation of steady-state transcript levels, degradation rates, and transcriptional synthesis rates from the same dataset. We find that hypoxia produces distinct responses from resting and inflammatory macrophages. Hypoxia induces destabilization of mRNA transcripts, though inflammatory macrophages substantially increase mRNA degradation compared to resting macrophages. Increased RNA turnover results in the upregulation of ribosomal protein genes and downregulation of extracellular matrix components in inflammatory macrophages. Pathways regulated by mRNA decay in vitro are differentially regulated in tumor-associated macrophages implying that mixed stimuli could induce post-transcriptional regulation of macrophage function in solid tumors.

Multimodal HLA-I genotype regulation by human cytomegalovirus US10 and resulting surface patterning

Human leucocyte antigen class I (HLA-I) molecules play a central role for both NK and T-cell responses that prevent serious human cytomegalovirus (HCMV) disease. To create opportunities for viral spread, several HCMV-encoded immunoevasins employ diverse strategies to target HLA-I. Among these, the glycoprotein US10 is so far insufficiently studied. While it was reported that US10 interferes with HLA-G expression, its ability to manipulate classical HLA-I antigen presentation remains unknown. In this study, we demonstrate that US10 recognizes and binds to all HLA-I (HLA-A, -B, -C, -E, -G) heavy chains. Additionally, impaired recruitment of HLA-I to the peptide loading complex was observed. Notably, the associated effects varied significantly dependending on HLA-I genotype and allotype: (i) HLA-A molecules evaded downregulation by US10, (ii) tapasin-dependent HLA-B molecules showed impaired maturation and cell surface expression, and (iii) β2m-assembled HLA-C, in particular HLA-C*05:01 and -C*12:03, and HLA-G were strongly retained in complex with US10 in the endoplasmic reticulum. These genotype-specific effects on HLA-I were confirmed through unbiased HLA-I ligandome analyses. Furthermore, in HCMV-infected fibroblasts inhibition of overlapping US10 and US11 transcription had little effect on HLA-A, but induced HLA-B antigen presentation. Thus, the US10-mediated impact on HLA-I results in multiple geno- and allotypic effects in a so far unparalleled and multimodal manner.

Reconstructing developmental trajectories using latent dynamical systems and time-resolved transcriptomics

The snapshot nature of single-cell transcriptomics presents a challenge for studying the dynamics of cell fate decisions. Metabolic labeling and splicing can provide temporal information at single-cell level, but current methods have limitations. Here, we present a framework that overcomes these limitations: experimentally, we developed sci-FATE2, an optimized method for metabolic labeling with increased data quality, which we used to profile 45,000 embryonic stem (ES) cells differentiating into neural tube identities. Computationally, we developed a two-stage framework for dynamical modeling: VelvetVAE, a variational autoencoder (VAE) for velocity inference that outperforms all other tools tested, and VelvetSDE, a neural stochastic differential equation (nSDE) framework for simulating trajectory distributions. These recapitulate underlying dataset distributions and capture features such as decision boundaries between alternative fates and fate-specific gene expression. These methods recast single-cell analyses from descriptions of observed data to models of the dynamics that generated them, providing a framework for investigating developmental fate decisions.

Nuclear export is a limiting factor in eukaryotic mRNA metabolism

The eukaryotic mRNA life cycle includes transcription, nuclear mRNA export and degradation. To quantify all these processes simultaneously, we perform thiol-linked alkylation after metabolic labeling of RNA with 4-thiouridine (4sU), followed by sequencing of RNA (SLAM-seq) in the nuclear and cytosolic compartments of human cancer cells. We develop a model that reliably quantifies mRNA-specific synthesis, nuclear export, and nuclear and cytosolic degradation rates on a genome-wide scale. We find that nuclear degradation of polyadenylated mRNA is negligible and nuclear mRNA export is slow, while cytosolic mRNA degradation is comparatively fast. Consequently, an mRNA molecule generally spends most of its life in the nucleus. We also observe large differences in the nuclear export rates of different 3'UTR transcript isoforms. Furthermore, we identify genes whose expression is abruptly induced upon metabolic labeling. These transcripts are exported substantially faster than average mRNAs, suggesting the existence of alternative export pathways. Our results highlight nuclear mRNA export as a limiting factor in mRNA metabolism and gene regulation.

Correcting 4sU induced quantification bias in nucleotide conversion RNA-seq data

Nucleoside analogues like 4-thiouridine (4sU) are used to metabolically label newly synthesized RNA. Chemical conversion of 4sU before sequencing induces T-to-C mismatches in reads sequenced from labelled RNA, allowing to obtain total and labelled RNA expression profiles from a single sequencing library. Cytotoxicity due to extended periods of labelling or high 4sU concentrations has been described, but the effects of extensive 4sU labelling on expression estimates from nucleotide conversion RNA-seq have not been studied. Here, we performed nucleotide conversion RNA-seq with escalating doses of 4sU with short-term labelling (1h) and over a progressive time course (up to 2h) in different cell lines. With high concentrations or at later time points, expression estimates were biased in an RNA half-life dependent manner. We show that bias arose by a combination of reduced mappability of reads carrying multiple conversions, and a global, unspecific underrepresentation of labelled RNA emerging during library preparation and potentially global reduction of RNA synthesis. We developed a computational tool to rescue unmappable reads, which performed favourably compared to previous read mappers, and a statistical method, which could fully remove remaining bias. All methods developed here are freely available as part of our GRAND-SLAM pipeline and grandR package.

A kinetic dichotomy between mitochondrial and nuclear gene expression processes

Oxidative phosphorylation (OXPHOS) complexes, encoded by both mitochondrial and nuclear DNA, are essential producers of cellular ATP, but how nuclear and mitochondrial gene expression steps are coordinated to achieve balanced OXPHOS subunit biogenesis remains unresolved. Here, we present a parallel quantitative analysis of the human nuclear and mitochondrial messenger RNA (mt-mRNA) life cycles, including transcript production, processing, ribosome association, and degradation. The kinetic rates of nearly every stage of gene expression differed starkly across compartments. Compared with nuclear mRNAs, mt-mRNAs were produced 1,100-fold more, degraded 7-fold faster, and accumulated to 160-fold higher levels. Quantitative modeling and depletion of mitochondrial factors LRPPRC and FASTKD5 identified critical points of mitochondrial regulatory control, revealing that the mitonuclear expression disparities intrinsically arise from the highly polycistronic nature of human mitochondrial pre-mRNA. We propose that resolving these differences requires a 100-fold slower mitochondrial translation rate, illuminating the mitoribosome as a nexus of mitonuclear co-regulation.

Stress-induced nuclear speckle reorganization is linked to activation of immediate early gene splicing

Current models posit that nuclear speckles (NSs) serve as reservoirs of splicing factors and facilitate posttranscriptional mRNA processing. Here, we discovered that ribotoxic stress induces a profound reorganization of NSs with enhanced recruitment of factors required for splice-site recognition, including the RNA-binding protein TIAR, U1 snRNP proteins and U2-associated factor 65, as well as serine 2 phosphorylated RNA polymerase II. NS reorganization relies on the stress-activated p38 mitogen-activated protein kinase (MAPK) pathway and coincides with splicing activation of both pre-existing and newly synthesized pre-mRNAs. In particular, ribotoxic stress causes targeted excision of retained introns from pre-mRNAs of immediate early genes (IEGs), whose transcription is induced during the stress response. Importantly, enhanced splicing of the IEGs ZFP36 and FOS is accompanied by relocalization of the corresponding nuclear mRNA foci to NSs. Our study reveals NSs as a dynamic compartment that is remodeled under stress conditions, whereby NSs appear to become sites of IEG transcription and efficient cotranscriptional splicing.

SND1 binds SARS-CoV-2 negative-sense RNA and promotes viral RNA synthesis through NSP9

Regulation of viral RNA biogenesis is fundamental to productive SARS-CoV-2 infection. To characterize host RNA-binding proteins (RBPs) involved in this process, we biochemically identified proteins bound to genomic and subgenomic SARS-CoV-2 RNAs. We find that the host protein SND1 binds the 5' end of negative-sense viral RNA and is required for SARS-CoV-2 RNA synthesis. SND1-depleted cells form smaller replication organelles and display diminished virus growth kinetics. We discover that NSP9, a viral RBP and direct SND1 interaction partner, is covalently linked to the 5' ends of positive- and negative-sense RNAs produced during infection. These linkages occur at replication-transcription initiation sites, consistent with NSP9 priming viral RNA synthesis. Mechanistically, SND1 remodels NSP9 occupancy and alters the covalent linkage of NSP9 to initiating nucleotides in viral RNA. Our findings implicate NSP9 in the initiation of SARS-CoV-2 RNA synthesis and unravel an unsuspected role of a cellular protein in orchestrating viral RNA production.

SLAM-Drop-seq reveals mRNA kinetic rates throughout the cell cycle

RNA abundance is tightly regulated in eukaryotic cells by modulating the kinetic rates of RNA production, processing, and degradation. To date, little is known about time‐dependent kinetic rates during dynamic processes. Here, we present SLAM‐Drop‐seq, a method that combines RNA metabolic labeling and alkylation of modified nucleotides in methanol‐fixed cells with droplet‐based sequencing to detect newly synthesized and preexisting mRNAs in single cells. As a first application, we sequenced 7280 HEK293 cells and calculated gene‐specific kinetic rates during the cell cycle using the novel package Eskrate. Of the 377 robust‐cycling genes that we identified, only a minor fraction is regulated solely by either dynamic transcription or degradation (6 and 4%, respectively). By contrast, the vast majority (89%) exhibit dynamically regulated transcription and degradation rates during the cell cycle. Our study thus shows that temporally regulated mRNA degradation is fundamental for the correct expression of a majority of cycling genes. SLAM‐Drop‐seq, combined with Eskrate, is a powerful approach to understanding the underlying mRNA kinetics of single‐cell gene expression dynamics in continuous biological processes.

The International Virus Bioinformatics Meeting 2023

The 2023 International Virus Bioinformatics Meeting was held in Valencia, Spain, from 24-26 May 2023, attracting approximately 180 participants worldwide. The primary objective of the conference was to establish a dynamic scientific environment conducive to discussion, collaboration, and the generation of novel research ideas. As the first in-person event following the SARS-CoV-2 pandemic, the meeting facilitated highly interactive exchanges among attendees. It served as a pivotal gathering for gaining insights into the current status of virus bioinformatics research and engaging with leading researchers and emerging scientists. The event comprised eight invited talks, 19 contributed talks, and 74 poster presentations across eleven sessions spanning three days. Topics covered included machine learning, bacteriophages, virus discovery, virus classification, virus visualization, viral infection, viromics, molecular epidemiology, phylodynamic analysis, RNA viruses, viral sequence analysis, viral surveillance, and metagenomics. This report provides rewritten abstracts of the presentations, a summary of the key research findings, and highlights shared during the meeting.

A kinetic dichotomy between mitochondrial and nuclear gene expression drives OXPHOS biogenesis

Oxidative phosphorylation (OXPHOS) complexes, encoded by both mitochondrial and nuclear DNA, are essential producers of cellular ATP, but how nuclear and mitochondrial gene expression steps are coordinated to achieve balanced OXPHOS biogenesis remains unresolved. Here, we present a parallel quantitative analysis of the human nuclear and mitochondrial messenger RNA (mt-mRNA) life cycles, including transcript production, processing, ribosome association, and degradation. The kinetic rates of nearly every stage of gene expression differed starkly across compartments. Compared to nuclear mRNAs, mt-mRNAs were produced 700-fold higher, degraded 5-fold faster, and accumulated to 170-fold higher levels. Quantitative modeling and depletion of mitochondrial factors, LRPPRC and FASTKD5, identified critical points of mitochondrial regulatory control, revealing that the mitonuclear expression disparities intrinsically arise from the highly polycistronic nature of human mitochondrial pre-mRNA. We propose that resolving these differences requires a 100-fold slower mitochondrial translation rate, illuminating the mitoribosome as a nexus of mitonuclear co-regulation.

bakR: uncovering differential RNA synthesis and degradation kinetics transcriptome-wide with Bayesian hierarchical modeling

Differential expression analysis of RNA sequencing (RNA-seq) data can identify changes in cellular RNA levels, but provides limited information about the kinetic mechanisms underlying such changes. Nucleotide recoding RNA-seq methods (NR-seq; e.g., TimeLapse-seq, SLAM-seq, etc.) address this shortcoming and are widely used approaches to identify changes in RNA synthesis and degradation kinetics. While advanced statistical models implemented in user-friendly software (e.g., DESeq2) have ensured the statistical rigor of differential expression analyses, no such tools that facilitate differential kinetic analysis with NR-seq exist. Here, we report the development of Bayesian analysis of the kinetics of RNA (bakR; https:// github.com/simonlabcode/bakR), an R package to address this need. bakR relies on Bayesian hierarchical modeling of NR-seq data to increase statistical power by sharing information across transcripts. Analyses of simulated data confirmed that bakR implementations of the hierarchical model outperform attempts to analyze differential kinetics with existing models. bakR also uncovers biological signals in real NR-seq data sets and provides improved analyses of existing data sets. This work establishes bakR as an important tool for identifying differential RNA synthesis and degradation kinetics.

The Cytomegalovirus M35 Protein Directly Binds to the Interferon-β Enhancer and Modulates Transcription of Ifnb1 and Other IRF3-Driven Genes

Induction of type I interferon (IFN) gene expression is among the first lines of cellular defense a virus encounters during primary infection. We previously identified the tegument protein M35 of murine cytomegalovirus (MCMV) as an essential antagonist of this antiviral system, showing that M35 interferes with type I IFN induction downstream of pattern-recognition receptor (PRR) activation. Here, we report structural and mechanistic details of M35's function. Determination of M35's crystal structure combined with reverse genetics revealed that homodimerization is a key feature for M35's immunomodulatory activity. In electrophoretic mobility shift assays (EMSAs), purified M35 protein specifically bound to the regulatory DNA element that governs transcription of the first type I IFN gene induced in nonimmune cells, Ifnb1. DNA-binding sites of M35 overlapped with the recognition elements of interferon regulatory factor 3 (IRF3), a key transcription factor activated by PRR signaling. Chromatin immunoprecipitation (ChIP) showed reduced binding of IRF3 to the host Ifnb1 promoter in the presence of M35. We furthermore defined the IRF3-dependent and the type I IFN signaling-responsive genes in murine fibroblasts by RNA sequencing of metabolically labeled transcripts (SLAM-seq) and assessed M35's global effect on gene expression. Stable expression of M35 broadly influenced the transcriptome in untreated cells and specifically downregulated basal expression of IRF3-dependent genes. During MCMV infection, M35 impaired expression of IRF3-responsive genes aside of Ifnb1. Our results suggest that M35-DNA binding directly antagonizes gene induction mediated by IRF3 and impairs the antiviral response more broadly than formerly recognized. IMPORTANCE Replication of the ubiquitous human cytomegalovirus (HCMV) in healthy individuals mostly goes unnoticed but can impair fetal development or cause life-threatening symptoms in immunosuppressed or -deficient patients. Like other herpesviruses, CMV extensively manipulates its hosts and establishes lifelong latent infections. Murine CMV (MCMV) presents an important model system as it allows the study of CMV infection in the host organism. We previously showed that during entry into host cells, MCMV virions release the evolutionary conserved protein M35 protein to immediately dampen the antiviral type I interferon (IFN) response induced by pathogen detection. Here, we show that M35 dimers bind to regulatory DNA elements and interfere with recruitment of interferon regulatory factor 3 (IRF3), a key cellular factor for antiviral gene expression. Thereby, M35 interferes with expression of type I IFNs and other IRF3-dependent genes, reflecting the importance for herpesviruses to avoid IRF3-mediated gene induction.

Synergising single-cell resolution and 4sU labelling boosts inference of transcriptional bursting

Despite the recent rise of RNA-seq datasets combining single-cell (sc) resolution with 4-thiouridine (4sU) labelling, analytical methods exploiting their power to dissect transcriptional bursting are lacking. Here, we present a mathematical model and Bayesian inference implementation to facilitate genome-wide joint parameter estimation and confidence quantification (R package: burstMCMC). We demonstrate that, unlike conventional scRNA-seq, 4sU scRNA-seq resolves temporal parameters and furthermore boosts inference of dimensionless parameters via a synergy between single-cell resolution and 4sU labelling. We apply our method to published 4sU scRNA-seq data and linked with ChIP-seq data, we uncover previously obscured associations between different parameters and histone modifications.

grandR: a comprehensive package for nucleotide conversion RNA-seq data analysis

Metabolic labeling of RNA is a powerful technique for studying the temporal dynamics of gene expression. Nucleotide conversion approaches greatly facilitate the generation of data but introduce challenges for their analysis. Here we present grandR, a comprehensive package for quality control, differential gene expression analysis, kinetic modeling, and visualization of such data. We compare several existing methods for inference of RNA synthesis rates and half-lives using progressive labeling time courses. We demonstrate the need for recalibration of effective labeling times and introduce a Bayesian approach to study the temporal dynamics of RNA using snapshot experiments.

Improving the study of RNA dynamics through advances in RNA-seq with metabolic labeling and nucleotide-recoding chemistry

RNA metabolic labeling using 4-thiouridine (s4U) captures the dynamics of RNA synthesis and decay. The power of this approach is dependent on appropriate quantification of labeled and unlabeled sequencing reads, which can be compromised by the apparent loss of s4U-labeled reads in a process we refer to as dropout. Here we show that s4U-containing transcripts can be selectively lost when RNA samples are handled under sub-optimal conditions, but that this loss can be minimized using an optimized protocol. We demonstrate a second cause of dropout in nucleotide recoding and RNA sequencing (NR-seq) experiments that is computational and downstream of library preparation. NR-seq experiments involve chemically converting s4U from a uridine analog to a cytidine analog and using the apparent T-to-C mutations to identify the populations of newly synthesized RNA. We show that high levels of T-to-C mutations can prevent read alignment with some computational pipelines, but that this bias can be overcome using improved alignment pipelines. Importantly, kinetic parameter estimates are affected by dropout independent of the NR chemistry employed, and all chemistries are practically indistinguishable in bulk, short-read RNA-seq experiments. Dropout is an avoidable problem that can be identified by including unlabeled controls, and mitigated through improved sample handing and read alignment that together improve the robustness and reproducibility of NR-seq experiments.

Decoding murine cytomegalovirus

The genomes of both human cytomegalovirus (HCMV) and murine cytomegalovirus (MCMV) were first sequenced over 20 years ago. Similar to HCMV, the MCMV genome had initially been proposed to harbor ≈170 open reading frames (ORFs). More recently, omics approaches revealed HCMV gene expression to be substantially more complex comprising several hundred viral ORFs. Here, we provide a state-of-the art reannotation of lytic MCMV gene expression based on integrative analysis of a large set of omics data. Our data reveal 365 viral transcription start sites (TiSS) that give rise to 380 and 454 viral transcripts and ORFs, respectively. The latter include >200 small ORFs, some of which represented the most highly expressed viral gene products. By combining TiSS profiling with metabolic RNA labelling and chemical nucleotide conversion sequencing (dSLAM-seq), we provide a detailed picture of the expression kinetics of viral transcription. This not only resulted in the identification of a novel MCMV immediate early transcript encoding the m166.5 ORF, which we termed ie4, but also revealed a group of well-expressed viral transcripts that are induced later than canonical true late genes and contain an initiator element (Inr) but no TATA- or TATT-box in their core promoters. We show that viral upstream ORFs (uORFs) tune gene expression of longer viral ORFs expressed in cis at translational level. Finally, we identify a truncated isoform of the viral NK-cell immune evasin m145 arising from a viral TiSS downstream of the canonical m145 mRNA. Despite being ≈5-fold more abundantly expressed than the canonical m145 protein it was not required for downregulating the NK cell ligand, MULT-I. In summary, our work will pave the way for future mechanistic studies on previously unknown cytomegalovirus gene products in an important virus animal model.

Well-TEMP-seq as a microwell-based strategy for massively parallel profiling of single-cell temporal RNA dynamics

Single-cell RNA sequencing (scRNA-seq) reveals the transcriptional heterogeneity of cells, but the static snapshots fail to reveal the time-resolved dynamics of transcription. Herein, we develop Well-TEMP-seq, a high-throughput, cost-effective, accurate, and efficient method for massively parallel profiling the temporal dynamics of single-cell gene expression. Well-TEMP-seq combines metabolic RNA labeling with scRNA-seq method Well-paired-seq to distinguish newly transcribed RNAs marked by T-to-C substitutions from pre-existing RNAs in each of thousands of single cells. The Well-paired-seq chip ensures a high single cell/barcoded bead pairing rate (~80%) and the improved alkylation chemistry on beads greatly alleviates chemical conversion-induced cell loss (~67.5% recovery). We further apply Well-TEMP-seq to profile the transcriptional dynamics of colorectal cancer cells exposed to 5-AZA-CdR, a DNA-demethylating drug. Well-TEMP-seq unbiasedly captures the RNA dynamics and outperforms the splicing-based RNA velocity method. We anticipate that Well-TEMP-seq will be broadly applicable to unveil the dynamics of single-cell gene expression in diverse biological processes.

Revealing the History and Mystery of RNA-Seq

Advances in RNA-sequencing technologies have led to the development of intriguing experimental setups, a massive accumulation of data, and high demand for tools to analyze it. To answer this demand, computational scientists have developed a myriad of data analysis pipelines, but it is less often considered what the most appropriate one is. The RNA-sequencing data analysis pipeline can be divided into three major parts: data pre-processing, followed by the main and downstream analyses. Here, we present an overview of the tools used in both the bulk RNA-seq and at the single-cell level, with a particular focus on alternative splicing and active RNA synthesis analysis. A crucial part of data pre-processing is quality control, which defines the necessity of the next steps; adapter removal, trimming, and filtering. After pre-processing, the data are finally analyzed using a variety of tools: differential gene expression, alternative splicing, and assessment of active synthesis, the latter requiring dedicated sample preparation. In brief, we describe the commonly used tools in the sample preparation and analysis of RNA-seq data.

DNA storage-from natural biology to synthetic biology

Natural DNA storage allows cellular differentiation, evolution, the growth of our children and controls all our ecosystems. Here, we discuss the fundamental aspects of DNA storage and recent advances in this field, with special emphasis on natural processes and solutions that can be exploited. We point out new ways of efficient DNA and nucleotide storage that are inspired by nature. Within a few years DNA-based information storage may become an attractive and natural complementation to current electronic data storage systems. We discuss rapid and directed access (e.g. DNA elements such as promotors, enhancers), regulatory signals and modulation (e.g. lncRNA) as well as integrated high-density storage and processing modules (e.g. chromosomal territories). There is pragmatic DNA storage for use in biotechnology and human genetics. We examine DNA storage as an approach for synthetic biology (e.g. light-controlled nucleotide processing enzymes). The natural polymers of DNA and RNA offer much for direct storage operations (read-in, read-out, access control). The inbuilt parallelism (many molecules at many places working at the same time) is important for fast processing of information. Using biology concepts from chromosomal storage, nucleic acid processing as well as polymer material sciences such as electronical effects in enzymes, graphene, nanocellulose up to DNA macramé , DNA wires and DNA-based aptamer field effect transistors will open up new applications gradually replacing classical information storage methods in ever more areas over time (decades).

Rapid nuclear deadenylation of mammalian messenger RNA

Poly(A) tails protect RNAs from degradation and their deadenylation rates determine RNA stability. Although poly(A) tails are generated in the nucleus, deadenylation of tails has mostly been investigated within the cytoplasm. Here, we combined long-read sequencing with metabolic labeling, splicing inhibition and cell fractionation experiments to quantify, separately, the genesis and trimming of nuclear and cytoplasmic tails in vitro and in vivo. We present evidence for genome-wide, nuclear synthesis of tails longer than 200 nt, which are rapidly shortened after transcription. Our data suggests that rapid deadenylation is a nuclear process, and that different classes of transcripts and even transcript isoforms have distinct nuclear tail lengths. For example, many long-noncoding RNAs retain long poly(A) tails. Modeling deadenylation dynamics predicts nuclear deadenylation about 10 times faster than cytoplasmic deadenylation. In summary, our data suggests that nuclear deadenylation might be a key mechanism for regulating mRNA stability, abundance, and subcellular localization.

Two murine cytomegalovirus microRNAs target the major viral immediate early 3 gene

Human cytomegalovirus is responsible for morbidity and mortality in immune compromised patients and is the leading viral cause of congenital infection. Virus-encoded microRNAs (miRNAs) represent interesting targets for novel antiviral agents. While many cellular targets that augment productive infection have been identified in recent years, regulation of viral genes such as the major viral immediate early protein 72 (IE72) by hcmv-miR-UL112-1 may contribute to both the establishment and the maintenance of latent infection. We employed photoactivated ribonucleotide-enhanced individual nucleotide resolution crosslinking (PAR-iCLIP) to identify murine cytomegalovirus (MCMV) miRNA targets during lytic infection. While the PAR-iCLIP data were of insufficient quality to obtain a comprehensive list of cellular and viral miRNA targets, the most prominent PAR-iCLIP peak in the MCMV genome mapped to the 3′ untranslated region of the major viral immediate early 3 (ie3) transcript. We show that this results from two closely positioned binding sites for the abundant MCMV miRNAs miR-M23-2-3p and miR-m01-2-3p. Their pre-expression significantly impaired viral plaque formation. However, mutation of the respective binding sites did not alter viral fitness during acute or subacute infection in vivo. Furthermore, no differences in the induction of virus-specific CD8+ T cells were observed. Future studies will probably need to go beyond studying immunocompetent laboratory mice housed in pathogen-free conditions to reveal the functional relevance of viral miRNA-mediated regulation of key viral immediate early genes.

High quality mapping of chromatin at or near the nuclear lamina from small numbers of cells reveals cell cycle and developmental changes of chromatin at the nuclear periphery

The chromatin associated with the nuclear lamina (NL) is referred to as lamina-associated domains (LADs). Here, we present an adaptation of the tyramide-signal amplification sequencing (TSA-seq) protocol, which we call chromatin pull down-based TSA-seq (cTSA-seq), that can be used to map chromatin regions at or near the NL from as little as 50 000 cells. The cTSA-seq mapped regions are composed of previously defined LADs and smaller chromatin regions that fall within the Hi-C defined B-compartment containing nuclear peripheral heterochromatin. We used cTSA-seq to map chromatin at or near the assembling NL in cultured cells progressing through early G1. cTSA-seq revealed that the distal ends of chromosomes are near or at the reassembling NL during early G1, a feature similar to those found in senescent cells. We expand the use of cTSA-seq to the mapping of chromatin at or near the NL from fixed-frozen mouse cerebellar tissue sections. This mapping reveals a general conservation of NL-associated chromatin and identifies global and local changes during cerebellar development. The cTSA-seq method reported here is useful for analyzing chromatin at or near the NL from small numbers of cells derived from both in vitro and in vivo sources.

Regulation of both transcription and RNA turnover contribute to germline specification

The nuanced mechanisms driving primordial germ cells (PGC) specification remain incompletely understood since genome-wide transcriptional regulation in developing PGCs has previously only been defined indirectly. Here, using SLAMseq analysis, we determined genome-wide transcription rates during the differentiation of embryonic stem cells (ESCs) to form epiblast-like (EpiLC) cells and ultimately PGC-like cells (PGCLCs). This revealed thousands of genes undergoing bursts of transcriptional induction and rapid shut-off not detectable by RNAseq analysis. Our SLAMseq datasets also allowed us to infer RNA turnover rates, which revealed thousands of mRNAs stabilized and destabilized during PGCLC specification. mRNAs tend to be unstable in ESCs and then are progressively stabilized as they differentiate. For some classes of genes, mRNA turnover regulation collaborates with transcriptional regulation, but these processes oppose each other in a surprisingly high frequency of genes. To test whether regulated mRNA turnover has a physiological role in PGC development, we examined three genes that we found were regulated by RNA turnover: Sox2, Klf2 and Ccne1. Circumvention of their regulated RNA turnover severely impaired the ESC-to-EpiLC and EpiLC-to-PGCLC transitions. Our study demonstrates the functional importance of regulated RNA stability in germline development and provides a roadmap of transcriptional and post-transcriptional regulation during germline specification.

Parsing the role of NSP1 in SARS-CoV-2 infection

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) leads to shutoff of protein synthesis, and nsp1, a central shutoff factor in coronaviruses, inhibits cellular mRNA translation. However, the diverse molecular mechanisms employed by nsp1 as well as its functional importance are unresolved. By overexpressing various nsp1 mutants and generating a SARS-CoV-2 mutant, we show that nsp1, through inhibition of translation and induction of mRNA degradation, targets translated cellular mRNA and is the main driver of host shutoff during infection. The propagation of nsp1 mutant virus is inhibited exclusively in cells with intact interferon (IFN) pathway as well as in vivo, in hamsters, and this attenuation is associated with stronger induction of type I IFN response. Therefore, although nsp1's shutoff activity is broad, it plays an essential role, specifically in counteracting the IFN response. Overall, our results reveal the multifaceted approach nsp1 uses to shut off cellular protein synthesis and uncover nsp1's explicit role in blocking the IFN response.

Transcription feedback dynamics in the wake of cytoplasmic mRNA degradation shutdown

In the last decade, multiple studies demonstrated that cells maintain a balance of mRNA production and degradation, but the mechanisms by which cells implement this balance remain unknown. Here, we monitored cells' total and recently-transcribed mRNA profiles immediately following an acute depletion of Xrn1-the main 5'-3' mRNA exonuclease-which was previously implicated in balancing mRNA levels. We captured the detailed dynamics of the adaptation to rapid degradation of Xrn1 and observed a significant accumulation of mRNA, followed by a delayed global reduction in transcription and a gradual return to baseline mRNA levels. We found that this transcriptional response is not unique to Xrn1 depletion; rather, it is induced earlier when upstream factors in the 5'-3' degradation pathway are perturbed. Our data suggest that the mRNA feedback mechanism monitors the accumulation of inputs to the 5'-3' exonucleolytic pathway rather than its outputs.

Functional RNA Dynamics Are Progressively Governed by RNA Destabilization during the Adaptation to Chronic Hypoxia

Previous studies towards reduced oxygen availability have mostly focused on changes in total mRNA expression, neglecting underlying transcriptional and post-transcriptional events. Therefore, we generated a comprehensive overview of hypoxia-induced changes in total mRNA expression, global de novo transcription, and mRNA stability in monocytic THP-1 cells. Since hypoxic episodes often persist for prolonged periods, we further compared the adaptation to acute and chronic hypoxia. While total mRNA changes correlated well with enhanced transcription during short-term hypoxia, mRNA destabilization gained importance under chronic conditions. Reduced mRNA stability not only added to a compensatory attenuation of immune responses, but also, most notably, to the reduction in nuclear-encoded mRNAs associated with various mitochondrial functions. These changes may prevent the futile production of new mitochondria under conditions where mitochondria cannot exert their full metabolic function and are indeed actively removed by mitophagy. The post-transcriptional mode of regulation might further allow for the rapid recovery of mitochondrial capacities upon reoxygenation. Our results provide a comprehensive resource of functional mRNA expression dynamics and underlying transcriptional and post-transcriptional regulatory principles during the adaptation to hypoxia. Furthermore, we uncover that RNA stability regulation controls mitochondrial functions in the context of hypoxia.

Estimating RNA dynamics using one time point for one sample in a single-pulse metabolic labeling experiment

Background

Over the past decade, experimental procedures such as metabolic labeling for determining RNA turnover rates at the transcriptome-wide scale have been widely adopted and are now turning to single cell measurements. Several computational methods to estimate RNA synthesis, processing and degradation rates from such experiments have been suggested, but they all require several RNA sequencing samples. Here we present a method that can estimate those three rates from a single sample.

Methods

Our method relies on the analytical solution to the Zeisel model of RNA dynamics. It was validated on metabolic labeling experiments performed on mouse embryonic stem cells. Resulting degradation rates were compared both to previously published rates on the same system and to a state-of-the-art method applied to the same data.

Results

Our method is computationally efficient and outputs rates that correlate well with previously published data sets. Using it on a single sample, we were able to reproduce the observation that dynamic biological processes tend to involve genes with higher metabolic rates, while stable processes involve genes with lower rates. This supports the hypothesis that cells control not only the mRNA steady-state abundance, but also its responsiveness, i.e., how fast steady state is reached. Moreover, degradation rates obtained with our method compare favourably with the other tested method.

Conclusions

In addition to saving experimental work and computational time, estimating rates for a single sample has several advantages. It does not require an error-prone normalization across samples and enables the use of replicates to estimate uncertainty and assess sample quality. Finally the method and theoretical results described here are general enough to be useful in other contexts such as nucleotide conversion methods and single cell metabolic labeling experiments.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04672-4.

Parsing the role of NSP1 in SARS-CoV-2 infection

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the ongoing coronavirus disease 19 (COVID-19) pandemic. Despite its urgency, we still do not fully understand the molecular basis of SARS-CoV-2 pathogenesis and its ability to antagonize innate immune responses. SARS-CoV-2 leads to shutoff of cellular protein synthesis and over-expression of nsp1, a central shutoff factor in coronaviruses, inhibits cellular gene translation. However, the diverse molecular mechanisms nsp1 employs as well as its functional importance in infection are still unresolved. By overexpressing various nsp1 mutants and generating a SARS-CoV-2 mutant in which nsp1 does not bind ribosomes, we untangle the effects of nsp1. We uncover that nsp1, through inhibition of translation and induction of mRNA degradation, is the main driver of host shutoff during SARS-CoV-2 infection. Furthermore, we find the propagation of nsp1 mutant virus is inhibited specifically in cells with intact interferon (IFN) response as well as in-vivo , in infected hamsters, and this attenuation is associated with stronger induction of type I IFN response. This illustrates that nsp1 shutoff activity has an essential role mainly in counteracting the IFN response. Overall, our results reveal the multifaceted approach nsp1 uses to shut off cellular protein synthesis and uncover the central role it plays in SARS-CoV-2 pathogenesis, explicitly through blockage of the IFN response.

Mapping transcriptomic vector fields of single cells

Single-cell (sc)-RNA-seq, together with RNA-velocity and metabolic labeling, reveals cellular states and transitions at unprecedented resolution. Fully exploiting these data, however, requires kinetic models capable of unveiling governing regulatory functions. Here, we introduce an analytical framework dynamo, that infers absolute RNA velocity, reconstructs continuous vector-field functions that predict cell fates, employs differential geometry to extract underlying regulations, and ultimately predicts optimal reprogramming paths and perturbation outcomes. We highlight dynamo’s power to overcome fundamental limitations of conventional splicing-based RNA velocity analyses to enable accurate velocity estimations on a metabolically-labeled human hematopoiesis scRNA-seq dataset. Furthermore, differential geometry analyses reveal mechanisms driving early megakaryocyte appearance and elucidate asymmetrical regulation within the PU.1–GATA1 circuit. Leveraging the Least-Action-Path method, dynamo accurately predicts drivers of numerous hematopoietic transitions. Finally, in silico perturbations predict cell-fate diversions induced by gene perturbations. Dynamo thus represents an important step in advancing quantitative and predictive theories of cell-state transitions.

Interrogating the transcriptome with metabolically incorporated ribonucleosides

RNA is a central player in biological processes, but there remain major gaps in our understanding of transcriptomic processes and the underlying biochemical mechanisms regulating RNA in cells. A powerful strategy to facilitate molecular analysis of cellular RNA is the metabolic incorporation of chemical probes. In this review, we discuss current approaches for RNA metabolic labeling with modified ribonucleosides and their integration with Next-Generation Sequencing, mass spectrometry-based proteomics, and fluorescence microscopy in order to interrogate RNA behavior in its native context.