DRUID: a pipeline for transcriptome-wide measurements of mRNA stability

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.

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.

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.

3D chromatin architecture, BRD4, and Mediator have distinct roles in regulating genome-wide transcriptional bursting and gene network

Discontinuous transcription is evolutionarily conserved and a fundamental feature of gene regulation; yet, the exact mechanisms underlying transcriptional bursting are unresolved. Analyses of bursting transcriptome-wide have focused on the role of cis-regulatory elements, but other factors that regulate this process remain elusive. We applied mathematical modeling to single-cell RNA sequencing data to infer bursting dynamics transcriptome-wide under multiple conditions to identify possible molecular mechanisms. We found that Mediator complex subunit 26 (MED26) primarily regulates frequency, MYC regulates burst size, while cohesin and Bromodomain-containing protein 4 (BRD4) can modulate both. Despite comparable effects on RNA levels among these perturbations, acute depletion of MED26 had the most profound impact on the entire gene regulatory network, acting downstream of chromatin spatial architecture and without affecting TATA box-binding protein (TBP) recruitment. These results indicate that later steps in the initiation of transcriptional bursts are primary nodes for integrating gene networks in single cells.

Isoform and pathway-specific regulation of post-transcriptional RNA processing in human cells

Steady-state levels of RNA transcripts are controlled by their rates of synthesis and degradation. Here we used nascent RNA Bru-seq and BruChase-seq to profile RNA dynamics across 16 human cell lines as part of ENCODE4 Deeply Profiled Cell Lines collection. We show that RNA turnover dynamics differ widely between transcripts of different genes and between different classes of RNA. Gene set enrichment analysis (GSEA) revealed that transcripts encoding proteins belonging to the same pathway often show similar turnover dynamics. Furthermore, transcript isoforms show distinct dynamics suggesting that RNA turnover is important in regulating mRNA isoform choice. Finally, splicing across newly made transcripts appears to be cooperative with either all or none type splicing. These data sets generated as part of ENCODE4 illustrate the intricate and coordinated regulation of RNA dynamics in controlling gene expression to allow for the precise coordination of cellular functions.

Coordination of rhythmic RNA synthesis and degradation orchestrates 24- and 12-h RNA expression patterns in mouse fibroblasts

Circadian RNA expression is essential to ultimately regulate a plethora of downstream rhythmic biochemical, physiological, and behavioral processes. Both transcriptional and posttranscriptional mechanisms are considered important to drive rhythmic RNA expression; however, the extent to which each regulatory process contributes to the rhythmic RNA expression remains controversial. To systematically address this, we monitored RNA dynamics using metabolic RNA labeling technology during a circadian cycle in mouse fibroblasts. We find that rhythmic RNA synthesis is the primary contributor of 24-h RNA rhythms, while rhythmic degradation is more important for 12-h RNA rhythms. These rhythms were predominantly regulated by Bmal1 and/or the core clock mechanism, and the interplay between rhythmic synthesis and degradation has a significant impact in shaping rhythmic RNA expression patterns. Interestingly, core clock RNAs are regulated by multiple rhythmic processes and have the highest amplitude of synthesis and degradation, presumably critical to sustain robust rhythmicity of cell-autonomous circadian rhythms. Our study yields invaluable insights into the temporal dynamics of both 24- and 12-h RNA rhythms in mouse fibroblasts.

Coordination of rhythmic RNA synthesis and degradation orchestrates 24-hour and 12-hour RNA expression patterns in mouse fibroblasts

Circadian RNA expression is essential to ultimately regulate a plethora of downstream rhythmic biochemical, physiological, and behavioral processes. Both transcriptional and post-transcriptional mechanisms are considered important to drive rhythmic RNA expression, however, the extent to which each regulatory process contributes to the rhythmic RNA expression remains controversial. To systematically address this, we monitored RNA dynamics using metabolic RNA labeling technology during a circadian cycle in mouse fibroblasts. We find that rhythmic RNA synthesis is the primary contributor of 24 hr RNA rhythms, while rhythmic degradation is more important for 12 hr RNA rhythms. These rhythms were predominantly regulated by Bmal1 and/or the core clock mechanism, and interplay between rhythmic synthesis and degradation has a significant impact in shaping rhythmic RNA expression patterns. Interestingly, core clock RNAs are regulated by multiple rhythmic processes and have the highest amplitude of synthesis and degradation, presumably critical to sustain robust rhythmicity of cell-autonomous circadian rhythms. Our study yields invaluable insights into the temporal dynamics of both 24 hr and 12 hr RNA rhythms in mouse fibroblasts.

Low RNA stability signifies increased post-transcriptional regulation of cell identity genes

Cell identity genes are distinct from other genes with respect to the epigenetic mechanisms to activate their transcription, e.g. by super-enhancers and broad H3K4me3 domains. However, it remains unclear whether their post-transcriptional regulation is also unique. We performed a systematic analysis of transcriptome-wide RNA stability in nine cell types and found that unstable transcripts were enriched in cell identity-related pathways while stable transcripts were enriched in housekeeping pathways. Joint analyses of RNA stability and chromatin state revealed significant enrichment of super-enhancers and broad H3K4me3 domains at the gene loci of unstable transcripts. Intriguingly, the RNA m6A methyltransferase, METTL3, preferentially binds to chromatin at super-enhancers, broad H3K4me3 domains and their associated genes. METTL3 binding intensity is positively correlated with RNA m6A methylation and negatively correlated with RNA stability of cell identity genes, probably due to co-transcriptional m6A modifications promoting RNA decay. Nanopore direct RNA-sequencing showed that METTL3 knockdown has a stronger effect on RNA m6A and mRNA stability for cell identity genes. Our data suggest a run-and-brake model, where cell identity genes undergo both frequent transcription and fast RNA decay to achieve precise regulation of RNA expression.

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.

A computationally-enhanced hiCLIP atlas reveals Staufen1-RNA binding features and links 3' UTR structure to RNA metabolism

The structure of mRNA molecules plays an important role in its interactions with trans-acting factors, notably RNA binding proteins (RBPs), thus contributing to the functional consequences of this interplay. However, current transcriptome-wide experimental methods to chart these interactions are limited by their poor sensitivity. Here we extend the hiCLIP atlas of duplexes bound by Staufen1 (STAU1) ∼10-fold, through careful consideration of experimental assumptions, and the development of bespoke computational methods which we apply to existing data. We present Tosca, a Nextflow computational pipeline for the processing, analysis and visualisation of proximity ligation sequencing data generally. We use our extended duplex atlas to discover insights into the RNA selectivity of STAU1, revealing the importance of structural symmetry and duplex-span-dependent nucleotide composition. Furthermore, we identify heterogeneity in the relationship between transcripts with STAU1-bound 3' UTR duplexes and metabolism of the associated RNAs that we relate to RNA structure: transcripts with short-range proximal 3' UTR duplexes have high degradation rates, but those with long-range duplexes have low rates. Overall, our work enables the integrative analysis of proximity ligation data delivering insights into specific features and effects of RBP-RNA structure interactions.

Intron dynamics reveal principles of gene regulation during the maternal-to-zygotic transition

The maternal-to-zygotic transition (MZT) is a conserved embryonic process in animals where developmental control shifts from the maternal to zygotic genome. A key step in this transition is zygotic transcription, and deciphering the MZT requires classifying newly transcribed genes. However, due to current technological limitations, this starting point remains a challenge for studying many species. Here, we present an alternative approach that characterizes transcriptome changes based solely on RNA-seq data. By combining intron-mapping reads and transcript-level quantification, we characterized transcriptome dynamics during the Drosophila melanogaster MZT. Our approach provides an accessible platform to investigate transcriptome dynamics that can be applied to the MZT in nonmodel organisms. In addition to classifying zygotically transcribed genes, our analysis revealed that over 300 genes express different maternal and zygotic transcript isoforms due to alternative splicing, polyadenylation, and promoter usage. The vast majority of these zygotic isoforms have the potential to be subject to different regulatory control, and over two-thirds encode different proteins. Thus, our analysis reveals an additional layer of regulation during the MZT, where new zygotic transcripts can generate additional proteome diversity.

The genetic and biochemical determinants of mRNA degradation rates in mammals

BACKGROUND

Degradation rate is a fundamental aspect of mRNA metabolism, and the factors governing it remain poorly characterized. Understanding the genetic and biochemical determinants of mRNA half-life would enable more precise identification of variants that perturb gene expression through post-transcriptional gene regulatory mechanisms.

RESULTS

We establish a compendium of 39 human and 27 mouse transcriptome-wide mRNA decay rate datasets. A meta-analysis of these data identified a prevalence of technical noise and measurement bias, induced partially by the underlying experimental strategy. Correcting for these biases allowed us to derive more precise, consensus measurements of half-life which exhibit enhanced consistency between species. We trained substantially improved statistical models based upon genetic and biochemical features to better predict half-life and characterize the factors molding it. Our state-of-the-art model, Saluki, is a hybrid convolutional and recurrent deep neural network which relies only upon an mRNA sequence annotated with coding frame and splice sites to predict half-life (r=0.77). The key novel principle learned by Saluki is that the spatial positioning of splice sites, codons, and RNA-binding motifs within an mRNA is strongly associated with mRNA half-life. Saluki predicts the impact of RNA sequences and genetic mutations therein on mRNA stability, in agreement with functional measurements derived from massively parallel reporter assays.

CONCLUSIONS

Our work produces a more robust ground truth for transcriptome-wide mRNA half-lives in mammalian cells. Using these revised measurements, we trained Saluki, a model that is over 50% more accurate in predicting half-life from sequence than existing models. Saluki succinctly captures many of the known determinants of mRNA half-life and can be rapidly deployed to predict the functional consequences of arbitrary mutations in the transcriptome.

Internally controlled RNA sequencing comparisons using nucleoside recoding chemistry

Quantitative comparisons of RNA levels from different samples can lead to new biological understanding if they are able to distinguish biological variation from variable sample preparation. These challenges are pronounced in comparisons that require complex biochemical manipulations (e.g. isolating polysomes to study translation). Here, we present Transcript Regulation Identified by Labeling with Nucleoside Analogues in Cell Culture (TILAC), an internally controlled approach for quantitative comparisons of RNA content. TILAC uses two metabolic labels, 4-thiouridine (s4U) and 6-thioguanosine (s6G), to differentially label RNAs in cells, allowing experimental and control samples to be pooled prior to downstream biochemical manipulations. TILAC leverages nucleoside recoding chemistry to generate characteristic sequencing signatures for each label and uses statistical modeling to compare the abundance of RNA transcripts between samples. We verified the performance of TILAC in transcriptome-scale experiments involving RNA polymerase II inhibition and heat shock. We then applied TILAC to quantify changes in mRNA association with actively translating ribosomes during sodium arsenite stress and discovered a set of transcripts that are translationally upregulated, including MCM2 and DDX5. TILAC is broadly applicable to uncover differences between samples leading to improved biological insights.

Comprehensive analysis of the circadian nuclear and cytoplasmic transcriptome in mouse liver

In eukaryotes, RNA is synthesised in the nucleus, spliced, and exported to the cytoplasm where it is translated and finally degraded. Any of these steps could be subject to temporal regulation during the circadian cycle, resulting in daily fluctuations of RNA accumulation and affecting the distribution of transcripts in different subcellular compartments. Our study analysed the nuclear and cytoplasmic, poly(A) and total transcriptomes of mouse livers collected over the course of a day. These data provide a genome-wide temporal inventory of enrichment in subcellular RNA, and revealed specific signatures of splicing, nuclear export and cytoplasmic mRNA stability related to transcript and gene lengths. Combined with a mathematical model describing rhythmic RNA profiles, we could test the rhythmicity of export rates and cytoplasmic degradation rates of approximately 1400 genes. With nuclear export times usually much shorter than cytoplasmic half-lives, we found that nuclear export contributes to the modulation and generation of rhythmic profiles of 10% of the cycling nuclear mRNAs. This study contributes to a better understanding of the dynamic regulation of the transcriptome during the day-night cycle.

Nuclear and cytoplasmic poly(A) binding proteins (PABPs) favor distinct transcripts and isoforms

The poly(A)-tail appended to the 3'-end of most eukaryotic transcripts plays a key role in their stability, nuclear transport, and translation. These roles are largely mediated by Poly(A) Binding Proteins (PABPs) that coat poly(A)-tails and interact with various proteins involved in the biogenesis and function of RNA. While it is well-established that the nuclear PABP (PABPN) binds newly synthesized poly(A)-tails and is replaced by the cytoplasmic PABP (PABPC) on transcripts exported to the cytoplasm, the distribution of transcripts for different genes or isoforms of the same gene on these PABPs has not been investigated on a genome-wide scale. Here, we analyzed the identity, splicing status, poly(A)-tail size, and translation status of RNAs co-immunoprecipitated with endogenous PABPN or PABPC in human cells. At steady state, many protein-coding and non-coding RNAs exhibit strong bias for association with PABPN or PABPC. While PABPN-enriched transcripts more often were incompletely spliced and harbored longer poly(A)-tails and PABPC-enriched RNAs had longer half-lives and higher translation efficiency, there are curious outliers. Overall, our study reveals the landscape of RNAs bound by PABPN and PABPC, providing new details that support and advance the current understanding of the roles these proteins play in poly(A)-tail synthesis, maintenance, and function.

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.

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.

FMRP links optimal codons to mRNA stability in neurons

Fragile X syndrome (FXS) is caused by inactivation of the FMR1 gene and loss of encoded FMRP, an RNA binding protein that represses translation of some of its target transcripts. Here we use ribosome profiling and RNA sequencing to investigate the dysregulation of translation in the mouse brain cortex. We find that most changes in ribosome occupancy on hundreds of mRNAs are largely driven by dysregulation in transcript abundance. Many down-regulated mRNAs, which are mostly responsible for neuronal and synaptic functions, are highly enriched for FMRP binding targets. RNA metabolic labeling demonstrates that, in FMRP-deficient cortical neurons, mRNA down-regulation is caused by elevated degradation and is correlated with codon optimality. Moreover, FMRP preferentially binds mRNAs with optimal codons, suggesting that it stabilizes such transcripts through direct interactions via the translational machinery. Finally, we show that the paradigm of genetic rescue of FXS-like phenotypes in FMRP-deficient mice by deletion of the Cpeb1 gene is mediated by restoration of steady-state RNA levels and consequent rebalancing of translational homeostasis. Our data establish an essential role of FMRP in codon optimality-dependent mRNA stability as an important factor in FXS.

Principles of mRNA control by human PUM proteins elucidated from multimodal experiments and integrative data analysis

The human PUF-family proteins, PUM1 and PUM2, posttranscriptionally regulate gene expression by binding to a PUM recognition element (PRE) in the 3'-UTR of target mRNAs. Hundreds of PUM1/2 targets have been identified from changes in steady-state RNA levels; however, prior studies could not differentiate between the contributions of changes in transcription and RNA decay rates. We applied metabolic labeling to measure changes in RNA turnover in response to depletion of PUM1/2, showing that human PUM proteins regulate expression almost exclusively by changing RNA stability. We also applied an in vitro selection workflow to precisely identify the binding preferences of PUM1 and PUM2. By integrating our results with prior knowledge, we developed a "rulebook" of key contextual features that differentiate functional versus nonfunctional PREs, allowing us to train machine learning models that accurately predict the functional regulation of RNA targets by the human PUM proteins.

Cold-induced chromatin compaction and nuclear retention of clock mRNAs resets the circadian rhythm

Cooling patients to sub‐physiological temperatures is an integral part of modern medicine. We show that cold exposure induces temperature‐specific changes to the higher‐order chromatin and gene expression profiles of human cells. These changes are particularly dramatic at 18°C, a temperature synonymous with that experienced by patients undergoing controlled deep hypothermia during surgery. Cells exposed to 18°C exhibit largely nuclear‐restricted transcriptome changes. These include the nuclear accumulation of mRNAs encoding components of the negative limbs of the core circadian clock, most notably REV‐ERBα. This response is accompanied by compaction of higher‐order chromatin and hindrance of mRNPs from engaging nuclear pores. Rewarming reverses chromatin compaction and releases the transcripts into the cytoplasm, triggering a pulse of negative limb gene proteins that reset the circadian clock. We show that cold‐induced upregulation of REV‐ERBα is sufficient to trigger this reset. Our findings uncover principles of the cellular cold response that must be considered for current and future applications involving therapeutic deep hypothermia.

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.

Chromatin Hyperacetylation Impacts Chromosome Folding by Forming a Nuclear Subcompartment

Delineating how chromosomes fold at length scales beyond one megabase remains obscure relative to smaller-scale folding into TADs, loops, and nucleosomes. We find that rather than simply unfolding chromatin, histone hyperacetylation results in interactions between distant genomic loci separated by tens to hundreds of megabases, even in the absence of transcription. These hyperacetylated "megadomains" are formed by the BRD4-NUT fusion oncoprotein, interact both within and between chromosomes, and form a specific nuclear subcompartment that has elevated gene activity with respect to other subcompartments. Pharmacological degradation of BRD4-NUT results in collapse of megadomains and attenuation of the interactions between them. In contrast, these interactions persist and contacts between newly acetylated regions are formed after inhibiting RNA polymerase II initiation. Our structure-function approach thus reveals that broad chromatin domains of identical biochemical composition, independent of transcription, form nuclear subcompartments, and also indicates the potential of altering chromosome structure for treating human disease.

A Protocol for Transcriptome-Wide Inference of RNA Metabolic Rates in Mouse Embryonic Stem Cells

The relative ease of mouse Embryonic Stem Cells (mESCs) culture and the potential of these cells to differentiate into any of the three primary germ layers: ectoderm, endoderm and mesoderm (pluripotency), makes them an ideal and frequently used ex vivo system to dissect how gene expression changes impact cell state and differentiation. These efforts are further supported by the large number of constitutive and inducible mESC mutants established with the aim of assessing the contributions of different pathways and genes to cell homeostasis and gene regulation. Gene product abundance is controlled by the modulation of the rates of RNA synthesis, processing, and degradation. The ability to determine the relative contribution of these different RNA metabolic rates to gene expression control using standard RNA-sequencing approaches, which only capture steady state abundance of transcripts, is limited. In contrast, metabolic labeling of RNA with 4-thiouridine (4sU) coupled with RNA-sequencing, allows simultaneous and reproducible inference of transcriptome wide synthesis, processing, and degradation rates. Here we describe, a detailed protocol for 4sU metabolic labeling in mESCs that requires short 4sU labeling times at low concentration and minimally impacts cellular homeostasis. This approach presents a versatile method for in-depth characterization of the gene regulatory strategies governing gene steady state abundance in mESC.

Coding regions affect mRNA stability in human cells

A new paradigm has emerged that coding regions can regulate mRNA stability in model organisms. Here, due to differences in cognate tRNA abundance, synonymous codons are translated at different speeds, and slow codons then stimulate mRNA decay. To ask if this phenomenon also occurs in humans, we isolated RNA stability effects due to coding regions using the human ORFeome collection. We find that many open reading frame (ORF) characteristics, such as length and secondary structure, fail to provide explanations for how coding regions alter mRNA stability, and, instead, that the ORF relies on translation to impact mRNA stability. Consistent with what has been seen in other organisms, codon use is related to the effects of ORFs on transcript stability. Importantly, we found instability-associated codons have longer A-site dwell times, suggesting for the first time in humans a connection between elongation speed and mRNA decay. Thus, we propose that codon usage alters decoding speeds and so affects human mRNA stability.

A novel optical tracer for VMAT2 applied to live cell measurements of vesicle maturation in cultured human β-cells

The islet β-cells integrate external signals to modulate insulin secretion to better regulate blood glucose levels during periods of changing metabolic demand. The vesicular monoamine transporter type 2 (VMAT2), an important regulator of CNS neurotransmission, has an analogous role in the endocrine pancreas as a key control point of insulin secretion, with additional roles in regulating β-cell differentiation and proliferation. Here we report on the synthesis and biological characterisation of a fluorescent ligand for VMAT2 suitable for live cell imaging. Staining for VMAT2 and dopamine in live β-cell cultures show colocalisation in specific vesicles and reveal a heterogeneous population with respect to cell size, shape, vesicle number, size, and contents. Staining for VMAT2 and zinc ion, as a surrogate for insulin, reveals a wide range of vesicle sizes. Immunohistochemistry shows larger β-cell vesicles enriched for proinsulin, whereas smaller vesicles predominantly contain the processed mature insulin. In β-cell cultures obtained from nondiabetic donors, incubation at non-stimulatory glucose concentrations promotes a shift in vesicle diameter towards the more mature insulin vesicles at the expense of the larger immature insulin secretory vesicle population. We anticipate that this probe will be a useful reagent to identify living β-cells within complex mixtures for further manipulation and characterisation.

Evolutionarily informed deep learning methods for predicting relative transcript abundance from DNA sequence

Deep learning methodologies have revolutionized prediction in many fields and show potential to do the same in molecular biology and genetics. However, applying these methods in their current forms ignores evolutionary dependencies within biological systems and can result in false positives and spurious conclusions. We developed two approaches that account for evolutionary relatedness in machine learning models: (i) gene-family-guided splitting and (ii) ortholog contrasts. The first approach accounts for evolution by constraining model training and testing sets to include different gene families. The second approach uses evolutionarily informed comparisons between orthologous genes to both control for and leverage evolutionary divergence during the training process. The two approaches were explored and validated within the context of mRNA expression level prediction and have the area under the ROC curve (auROC) values ranging from 0.75 to 0.94. Model weight inspections showed biologically interpretable patterns, resulting in the hypothesis that the 3' UTR is more important for fine-tuning mRNA abundance levels while the 5' UTR is more important for large-scale changes.

Global analysis of RNA metabolism using bio-orthogonal labeling coupled with next-generation RNA sequencing

Many open questions in RNA biology relate to the kinetics of gene expression and the impact of RNA binding regulatory factors on processing or decay rates of particular transcripts. Steady state measurements of RNA abundance obtained from RNA-seq approaches are not able to separate the effects of transcription from those of RNA decay in the overall abundance of any given transcript, instead only giving information on the (presumed steady-state) abundances of transcripts. Through the combination of metabolic labeling and high-throughput sequencing, several groups have been able to measure both transcription rates and decay rates of the entire transcriptome of an organism in a single experiment. This review focuses on the methodology used to specifically measure RNA decay at a global level. By comparing and contrasting approaches and describing the experimental protocols in a modular manner, we intend to provide both experienced and new researchers to the field the ability to combine aspects of various protocols to fit the unique needs of biological questions not addressed by current methods.

Gaining insight into transcriptome-wide RNA population dynamics through the chemistry of 4-thiouridine

Cellular RNA levels are the result of a juggling act between RNA transcription, processing, and degradation. By tuning one or more of these parameters, cells can rapidly alter the available pool of transcripts in response to stimuli. While RNA sequencing (RNA-seq) is a vital method to quantify RNA levels genome-wide, it is unable to capture the dynamics of different RNA populations at steady-state or distinguish between different mechanisms that induce changes to the steady-state (i.e., altered rate of transcription vs. degradation). The dynamics of different RNA populations can be studied by targeted incorporation of noncanonical nucleosides. 4-Thiouridine (s⁴ U) is a commonly used and versatile RNA metabolic label that allows the study of many properties of RNA metabolism from synthesis to degradation. Numerous experimental strategies have been developed that leverage the power of s⁴ U to label newly transcribed RNA in whole cells, followed by enrichment with activated disulfides or chemistry to induce C mutations at sites of s⁴ U during sequencing. This review presents existing methods to study RNA population dynamics genome-wide using s⁴ U metabolic labeling, as well as a discussion of considerations and challenges when designing s⁴ U metabolic labeling experiments. This article is categorized under: RNA Methods > RNA Analyses in Cells RNA Turnover and Surveillance > Regulation of RNA Stability.

Contributions of regulated transcription and mRNA decay to the dynamics of gene expression

Organisms have acquired sophisticated regulatory networks that control gene expression in response to cellular perturbations. Understanding of the mechanisms underlying the coordinated changes in gene expression in response to external and internal stimuli is a fundamental issue in biology. Recent advances in high-throughput technologies have enabled the measurement of diverse biological information, including gene expression levels, kinetics of gene expression, and interactions among gene expression regulatory molecules. By coupling these technologies with quantitative modeling, we can now uncover the biological roles and mechanisms of gene regulation at the system level. This review consists of two parts. First, we focus on the methods using uridine analogs that measure synthesis and decay rates of RNAs, which demonstrate how cells dynamically change the regulation of gene expression in response to both internal and external cues. Second, we discuss the underlying mechanisms of these changes in kinetics, including the functions of transcription factors and RNA-binding proteins. Overall, this review will help to clarify a system-level view of gene expression programs in cells. This article is categorized under: Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs RNA Turnover and Surveillance > Regulation of RNA Stability RNA Methods > RNA Analyses in vitro and In Silico.