Dissecting newly transcribed and old RNA using GRAND-SLAM

Mitogen-activated protein kinase activity drives cell trajectories in colorectal cancer

In colorectal cancer, oncogenic mutations transform a hierarchically organized and homeostatic epithelium into invasive cancer tissue lacking visible organization. We sought to define transcriptional states of colorectal cancer cells and signals controlling their development by performing single-cell transcriptome analysis of tumors and matched non-cancerous tissues of twelve colorectal cancer patients. We defined patient-overarching colorectal cancer cell clusters characterized by differential activities of oncogenic signaling pathways such as mitogen-activated protein kinase and oncogenic traits such as replication stress. RNA metabolic labeling and assessment of RNA velocity in patient-derived organoids revealed developmental trajectories of colorectal cancer cells organized along a mitogen-activated protein kinase activity gradient. This was in contrast to normal colon organoid cells developing along graded Wnt activity. Experimental targeting of EGFR-BRAF-MEK in cancer organoids affected signaling and gene expression contingent on predictive KRAS/BRAF mutations and induced cell plasticity overriding default developmental trajectories. Our results highlight directional cancer cell development as a driver of non-genetic cancer cell heterogeneity and re-routing of trajectories as a response to targeted therapy.

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.

A comparison of metabolic labeling and statistical methods to infer genome-wide dynamics of RNA turnover

Metabolic labeling of newly transcribed RNAs coupled with RNA-seq is being increasingly used for genome-wide analysis of RNA dynamics. Methods including standard biochemical enrichment and recent nucleotide conversion protocols each require special experimental and computational treatment. Despite their immediate relevance, these technologies have not yet been assessed and benchmarked, and no data are currently available to advance reproducible research and the development of better inference tools. Here, we present a systematic evaluation and comparison of four RNA labeling protocols: 4sU-tagging biochemical enrichment, including spike-in RNA controls, SLAM-seq, TimeLapse-seq and TUC-seq. All protocols are evaluated based on practical considerations, conversion efficiency and wet lab requirements to handle hazardous substances. We also compute decay rate estimates and confidence intervals for each protocol using two alternative statistical frameworks, pulseR and GRAND-SLAM, for over 11 600 human genes and evaluate the underlying computational workflows for their robustness and ease of use. Overall, we demonstrate a high inter-method reliability across eight use case scenarios. Our results and data will facilitate reproducible research and serve as a resource contributing to a fuller understanding of RNA biology.

Targeted protein degradation reveals a direct role of SPT6 in RNAPII elongation and termination

SPT6 is a histone chaperone that tightly binds RNA polymerase II (RNAPII) during transcription elongation. However, its primary role in transcription is uncertain. We used targeted protein degradation to rapidly deplete SPT6 in human cells and analyzed defects in RNAPII behavior by a multi-omics approach and mathematical modeling. Our data indicate that SPT6 is a crucial factor for RNAPII processivity and is therefore required for the productive transcription of protein-coding genes. Unexpectedly, SPT6 also has a vital role in RNAPII termination, as acute depletion induced readthrough transcription for thousands of genes. Long-term depletion of SPT6 induced cryptic intragenic transcription, as observed earlier in yeast. However, this phenotype was not observed upon acute SPT6 depletion and therefore can be attributed to accumulated epigenetic perturbations in the prolonged absence of SPT6. In conclusion, targeted degradation of SPT6 allowed the temporal discrimination of its function as an epigenetic safeguard and RNAPII elongation factor.

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Auxin-inducible degradation discriminates direct roles of human SPT6 in transcription

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Acute loss of SPT6 globally impairs RNAPII processivity and speed

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SPT6 is required for efficient transcription termination on protein-coding genes

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Long-term loss of SPT6 ultimately results in cryptic intragenic transcription

Time-resolved structured illumination microscopy reveals key principles of Xist RNA spreading

X-inactive specific transcript (Xist) RNA directs the process of X chromosome inactivation in mammals by spreading in cis along the chromosome from which it is transcribed and recruiting chromatin modifiers to silence gene transcription. To elucidate mechanisms of Xist RNA cis-confinement, we established a sequential dual-color labeling, super-resolution imaging approach to trace individual Xist RNA molecules over time, which enabled us to define fundamental parameters of spreading. We demonstrate a feedback mechanism linking Xist RNA synthesis and degradation and an unexpected physical coupling between preceding and newly synthesized Xist RNA molecules. Additionally, we find that the protein SPEN, a key factor for Xist-mediated gene silencing, has a distinct function in Xist RNA localization, stability, and coupling behaviors. Our results provide insights toward understanding the distinct dynamic properties of Xist RNA.

SARS-CoV-2 uses a multipronged strategy to impede host protein synthesis

The coronavirus SARS-CoV-2 is the cause of the ongoing pandemic of COVID-19¹. Coronaviruses have developed a variety of mechanisms to repress host mRNA translation to allow the translation of viral mRNA, and concomitantly block the cellular innate immune response^2,3. Although several different proteins of SARS-CoV-2 have previously been implicated in shutting off host expression^4-7, a comprehensive picture of the effects of SARS-CoV-2 infection on cellular gene expression is lacking. Here we combine RNA sequencing, ribosome profiling and metabolic labelling of newly synthesized RNA to comprehensively define the mechanisms that are used by SARS-CoV-2 to shut off cellular protein synthesis. We show that infection leads to a global reduction in translation, but that viral transcripts are not preferentially translated. Instead, we find that infection leads to the accelerated degradation of cytosolic cellular mRNAs, which facilitates viral takeover of the mRNA pool in infected cells. We reveal that the translation of transcripts that are induced in response to infection (including innate immune genes) is impaired. We demonstrate this impairment is probably mediated by inhibition of nuclear mRNA export, which prevents newly transcribed cellular mRNA from accessing ribosomes. Overall, our results uncover a multipronged strategy that is used by SARS-CoV-2 to take over the translation machinery and to suppress host defences.

The Zinc Finger Antiviral Protein ZAP Restricts Human Cytomegalovirus and Selectively Binds and Destabilizes Viral UL4/UL5 Transcripts

Interferon-stimulated gene products (ISGs) play a crucial role in early infection control. The ISG zinc finger CCCH-type antiviral protein 1 (ZAP/ZC3HAV1) antagonizes several RNA viruses by binding to CG-rich RNA sequences, whereas its effect on DNA viruses is less well understood. Here, we decipher the role of ZAP in the context of human cytomegalovirus (HCMV) infection, a β-herpesvirus that is associated with high morbidity in immunosuppressed individuals and newborns. We show that expression of the two major isoforms of ZAP, ZAP-S and ZAP-L, is induced during HCMV infection and that both negatively affect HCMV replication. Transcriptome and proteome analyses demonstrated that the expression of ZAP results in reduced viral mRNA and protein levels and decelerates the progression of HCMV infection. Metabolic RNA labeling combined with high-throughput sequencing (SLAM-seq) revealed that most of the gene expression changes late in infection result from the general attenuation of HCMV. Furthermore, at early stages of infection, ZAP restricts HCMV by destabilizing a distinct subset of viral mRNAs, particularly those from the previously uncharacterized UL4-UL6 HCMV gene locus. Through enhanced cross-linking immunoprecipitation and sequencing analysis (eCLIP-seq), we identified the transcripts expressed from this HCMV locus as the direct targets of ZAP. Moreover, our data show that ZAP preferentially recognizes not only CG, but also other cytosine-rich sequences, thereby expanding its target specificity. In summary, this report is the first to reveal direct targets of ZAP during HCMV infection, which strongly indicates that transcripts from the UL4-UL6 locus may play an important role for HCMV replication.IMPORTANCE Viral infections have a large impact on society, leading to major human and economic losses and even global instability. So far, many viral infections, including human cytomegalovirus (HCMV) infection, are treated with a small repertoire of drugs, often accompanied by the occurrence of resistant mutants. There is no licensed HCMV vaccine in sight to protect those most at risk, particularly immunocompromised individuals or pregnant women who might otherwise transmit the virus to the fetus. Thus, the identification of novel intervention strategies is urgently required. In this study, we show that ZAP decelerates the viral gene expression cascade, presumably by selectively handpicking a distinct set of viral transcripts for degradation. Our study illustrates the potent role of ZAP as an HCMV restriction factor and sheds light on a possible role for UL4 and/or UL5 early during infection, paving a new avenue for the exploration of potential targets for novel therapies.

ITN-VIROINF: Understanding (Harmful) Virus-Host Interactions by Linking Virology and Bioinformatics

Many recent studies highlight the fundamental importance of viruses. Besides their important role as human and animal pathogens, their beneficial, commensal or harmful functions are poorly understood. By developing and applying tailored bioinformatical tools in important virological models, the Marie Skłodowska-Curie Initiative International Training Network VIROINF will provide a better understanding of viruses and the interaction with their hosts. This will open the door to validate methods of improving viral growth, morphogenesis and development, as well as to control strategies against unwanted microorganisms. The key feature of VIROINF is its interdisciplinary nature, which brings together virologists and bioinformaticians to achieve common goals.

Kinetic modeling of stem cell transcriptome dynamics to identify regulatory modules of normal and disturbed neuroectodermal differentiation

Thousands of transcriptome data sets are available, but approaches for their use in dynamic cell response modelling are few, especially for processes affected simultaneously by two orthogonal influencing variables. We approached this problem for neuroepithelial development of human pluripotent stem cells (differentiation variable), in the presence or absence of valproic acid (signaling variable). Using few basic assumptions (sequential differentiation states of cells; discrete on/off states for individual genes in these states), and time-resolved transcriptome data, a comprehensive model of spontaneous and perturbed gene expression dynamics was developed. The model made reliable predictions (average correlation of 0.85 between predicted and subsequently tested expression values). Even regulations predicted to be non-monotonic were successfully validated by PCR in new sets of experiments. Transient patterns of gene regulation were identified from model predictions. They pointed towards activation of Wnt signaling as a candidate pathway leading to a redirection of differentiation away from neuroepithelial cells towards neural crest. Intervention experiments, using a Wnt/beta-catenin antagonist, led to a phenotypic rescue of this disturbed differentiation. Thus, our broadly applicable model allows the analysis of transcriptome changes in complex time/perturbation matrices.

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.

Massively parallel and time-resolved RNA sequencing in single cells with scNT-seq

Single-cell RNA sequencing offers snapshots of whole transcriptomes but obscures the temporal RNA dynamics. Here we present single-cell metabolically labeled new RNA tagging sequencing (scNT-seq), a method for massively parallel analysis of newly transcribed and pre-existing mRNAs from the same cell. This droplet microfluidics-based method enables high-throughput chemical conversion on barcoded beads, efficiently marking newly transcribed mRNAs with T-to-C substitutions. Using scNT-seq, we jointly profiled new and old transcriptomes in ~55,000 single cells. These data revealed time-resolved transcription factor activities and cell-state trajectories at the single-cell level in response to neuronal activation. We further determined rates of RNA biogenesis and decay to uncover RNA regulatory strategies during stepwise conversion between pluripotent and rare totipotent two-cell embryo (2C)-like stem cell states. Finally, integrating scNT-seq with genetic perturbation identifies DNA methylcytosine dioxygenase as an epigenetic barrier into the 2C-like cell state. Time-resolved single-cell transcriptomic analysis thus opens new lines of inquiry regarding cell-type-specific RNA regulatory mechanisms.

Identification of the Cryptic HLA-I Immunopeptidome

The success of cancer immunotherapy relies on the ability of cytotoxic T cells to specifically recognize and eliminate tumor cells based on peptides presented by HLA-I. Although the peptide epitopes that elicit the corresponding immune response often remain unidentified, it is generally assumed that neoantigens, due to tumor-specific mutations, are the most common targets. Here, we used a mass spectrometric approach to show an underappreciated class of epitopes that accounts for up to 15% of HLA-I peptides for certain HLA alleles in various tumors and patients. These peptides are translated from cryptic open reading frames in supposedly noncoding regions in the genome and are mostly unidentifiable with conventional computational analyses of mass spectrometry (MS) data. Our approach, Peptide-PRISM, identified thousands of such cryptic peptides in tumor immunopeptidomes. About 20% of these HLA-I peptides represented the C-terminus of the corresponding translation product, suggesting frequent proteasome-independent processing. Our data also revealed HLA-I allele-dependent presentation of cryptic peptides, with HLA-A*03 and HLA-A*11 presenting the highest percentage of cryptic peptides. Our analyses refute the reported frequent presentation of HLA peptides generated by proteasome-catalyzed peptide splicing. Thus, Peptide-PRISM represents an important step toward comprehensive identification of HLA-I immunopeptidomes and reveals cryptic peptides as an abundant class of epitopes with potential relevance for novel immunotherapeutic approaches.

Gene Architecture and Sequence Composition Underpin Selective Dependency of Nuclear Export of Long RNAs on NXF1 and the TREX Complex

The core components of the nuclear RNA export pathway are thought to be required for export of virtually all polyadenylated RNAs. Here, we depleted different proteins that act in nuclear export in human cells and quantified the transcriptome-wide consequences on RNA localization. Different genes exhibited substantially variable sensitivities, with depletion of NXF1 and TREX components causing some transcripts to become strongly retained in the nucleus while others were not affected. Specifically, NXF1 is preferentially required for export of single- or few-exon transcripts with long exons or high A/U content, whereas depletion of TREX complex components preferentially affects spliced and G/C-rich transcripts. Using massively parallel reporter assays, we identified short sequence elements that render transcripts dependent on NXF1 for their export and identified synergistic effects of splicing and NXF1. These results revise the current model of how nuclear export shapes the distribution of RNA within human cells.

Nascent RNA analyses: tracking transcription and its regulation

The programmes that direct an organism's development and maintenance are encoded in its genome. Decoding of this information begins with regulated transcription of genomic DNA into RNA. Although transcription and its control can be tracked indirectly by measuring stable RNAs, it is only by directly measuring nascent RNAs that the immediate regulatory changes in response to developmental, environmental, disease and metabolic signals are revealed. Multiple complementary methods have been developed to quantitatively track nascent transcription genome-wide at nucleotide resolution, all of which have contributed novel insights into the mechanisms of gene regulation and transcription-coupled RNA processing. Here we critically evaluate the array of strategies used for investigating nascent transcription and discuss the recent conceptual advances they have provided.

Chemo-enzymatic treatment of RNA to facilitate analyses

Labeling RNA is a recurring problem to make RNA compatible with state-of-the-art methodology and comes in many flavors. Considering only cellular applications, the spectrum still ranges from site-specific labeling of individual transcripts, for example, for live-cell imaging of mRNA trafficking, to metabolic labeling in combination with next generation sequencing to capture dynamic aspects of RNA metabolism on a transcriptome-wide scale. Combining the specificity of RNA-modifying enzymes with non-natural substrates has emerged as a valuable strategy to modify RNA site- or sequence-specifically with functional groups suitable for subsequent bioorthogonal reactions and thus label RNA with reporter moieties such as affinity or fluorescent tags. In this review article, we will cover chemo-enzymatic approaches (a) for in vitro labeling of RNA for application in cells, (b) for treatment of total RNA, and (c) for metabolic labeling of RNA. This article is categorized under: RNA Processing < RNA Editing and Modification RNA Methods < RNA Analyses in vitro and In Silico RNA Methods < RNA Analyses in Cells.

On the optimal design of metabolic RNA labeling experiments

Massively parallel RNA sequencing (RNA-seq) in combination with metabolic labeling has become the de facto standard approach to study alterations in RNA transcription, processing or decay. Regardless of advances in the experimental protocols and techniques, every experimentalist needs to specify the key aspects of experimental design: For example, which protocol should be used (biochemical separation vs. nucleotide conversion) and what is the optimal labeling time? In this work, we provide approximate answers to these questions using the asymptotic theory of optimal design. Specifically, we investigate, how the variance of degradation rate estimates depends on the time and derive the optimal time for any given degradation rate. Subsequently, we show that an increase in sample numbers should be preferred over an increase in sequencing depth. Lastly, we provide some guidance on use cases when laborious biochemical separation outcompetes recent nucleotide conversion based methods (such as SLAMseq) and show, how inefficient conversion influences the precision of estimates. Code and documentation can be found at https://github.com/dieterich-lab/DesignMetabolicRNAlabeling.

Massively parallel RNA sequencing (RNA-seq) in combination with metabolic labeling has become the de facto standard approach to study alterations in RNA transcription, processing or decay. In our manuscript, we address several key aspects of experimental design: 1) The optimal labeling time, 2) the number of replicate samples over sequencing depth and 3) the choice of experimental protocol. We provide approximate answers to these questions using asymptotic theory of optimal design.

NASC-seq monitors RNA synthesis in single cells

Sequencing of newly synthesised RNA can monitor transcriptional dynamics with great sensitivity and high temporal resolution, but is currently restricted to populations of cells. Here, we develop new transcriptome alkylation-dependent single-cell RNA sequencing (NASC-seq), to monitor newly synthesised and pre-existing RNA simultaneously in single cells. We validate the method on pre-labelled RNA, and by demonstrating that more newly synthesised RNA was detected for genes with known high mRNA turnover. Monitoring RNA synthesis during Jurkat T-cell activation with NASC-seq reveals both rapidly up- and down-regulated genes, and that induced genes are almost exclusively detected as newly transcribed. Moreover, the newly synthesised and pre-existing transcriptomes after T-cell activation are distinct, confirming that NASC-seq simultaneously measures gene expression corresponding to two time points in single cells. Altogether, NASC-seq enables precise temporal monitoring of RNA synthesis at single-cell resolution during homoeostasis, perturbation responses and cellular differentiation.

Quantification of experimentally induced nucleotide conversions in high-throughput sequencing datasets

BACKGROUND

Methods to read out naturally occurring or experimentally introduced nucleic acid modifications are emerging as powerful tools to study dynamic cellular processes. The recovery, quantification and interpretation of such events in high-throughput sequencing datasets demands specialized bioinformatics approaches.

RESULTS

Here, we present Digital Unmasking of Nucleotide conversions in K-mers (DUNK), a data analysis pipeline enabling the quantification of nucleotide conversions in high-throughput sequencing datasets. We demonstrate using experimentally generated and simulated datasets that DUNK allows constant mapping rates irrespective of nucleotide-conversion rates, promotes the recovery of multimapping reads and employs Single Nucleotide Polymorphism (SNP) masking to uncouple true SNPs from nucleotide conversions to facilitate a robust and sensitive quantification of nucleotide-conversions. As a first application, we implement this strategy as SLAM-DUNK for the analysis of SLAMseq profiles, in which 4-thiouridine-labeled transcripts are detected based on T > C conversions. SLAM-DUNK provides both raw counts of nucleotide-conversion containing reads as well as a base-content and read coverage normalized approach for estimating the fractions of labeled transcripts as readout.

CONCLUSION

Beyond providing a readily accessible tool for analyzing SLAMseq and related time-resolved RNA sequencing methods (TimeLapse-seq, TUC-seq), DUNK establishes a broadly applicable strategy for quantifying nucleotide conversions.