Schraga Schwartz

Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq

An extensive repertoire of modifications is known to underlie the versatile coding, structural and catalytic functions of RNA, but it remains largely uncharted territory. Although biochemical studies indicate that N6-methyladenosine (m6A) is the most prevalent internal modification in messenger RNA, an in-depth study of its distribution and functions has been impeded by a lack of robust analytical methods. Here we present the human and mouse m6A modification landscape in a transcriptome-wide manner, using a novel approach, m6A-seq, based on antibody-mediated capture and massively parallel sequencing. We identify over 12,000 m6A sites characterized by a typical consensus in the transcripts of more than 7,000 human genes. Sites preferentially appear in two distinct landmarks—around stop codons and within long internal exons—and are highly conserved between human and mouse. Although most sites are well preserved across normal and cancerous tissues and in response to various stimuli, a subset of stimulus-dependent, dynamically modulated sites is identified. Silencing the m6A methyltransferase significantly affects gene expression and alternative splicing patterns, resulting in modulation of the p53 (also known as TP53) signalling pathway and apoptosis. Our findings therefore suggest that RNA decoration by m6A has a fundamental role in regulation of gene expression.

Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells

Recent molecular studies have shown that, even when derived from a seemingly homogenous population, individual cells can exhibit substantial differences in gene expression, protein levels and phenotypic output, with important functional consequences. Existing studies of cellular heterogeneity, however, have typically measured only a few pre-selected RNAs or proteins simultaneously, because genomic profiling methods could not be applied to single cells until very recently. Here we use single-cell RNA sequencing to investigate heterogeneity in the response of mouse bone-marrow-derived dendritic cells (BMDCs) to lipopolysaccharide. We find extensive, and previously unobserved, bimodal variation in messenger RNA abundance and splicing patterns, which we validate by RNA-fluorescence in situ hybridization for select transcripts. In particular, hundreds of key immune genes are bimodally expressed across cells, surprisingly even for genes that are very highly expressed at the population average. Moreover, splicing patterns demonstrate previously unobserved levels of heterogeneity between cells. Some of the observed bimodality can be attributed to closely related, yet distinct, known maturity states of BMDCs; other portions reflect differences in the usage of key regulatory circuits. For example, we identify a module of 137 highly variable, yet co-regulated, antiviral response genes. Using cells from knockout mice, we show that variability in this module may be propagated through an interferon feedback circuit, involving the transcriptional regulators Stat2 and Irf7. Our study demonstrates the power and promise of single-cell genomics in uncovering functional diversity between cells and in deciphering cell states and circuits.

Chromatin organization marks exon-intron structure.

An increasing body of evidence indicates that transcription and splicing are coupled, and it is accepted that chromatin organization regulates transcription. Little is known about the cross-talk between chromatin structure and exon-intron architecture. By analysis of genome-wide nucleosome-positioning data sets from humans, flies and worms, we found that exons show increased nucleosome-occupancy levels with respect to introns, a finding that we link to differential GC content and nucleosome-disfavoring elements between exons and introns. Analysis of genome-wide chromatin immunoprecipitation data in humans and mice revealed four specific post-translational histone modifications enriched in exons. Our findings indicate that previously described enrichment of H3K36me3 modifications in exons reflects a more fundamental phenomenon, namely increased nucleosome occupancy along exons. Our results suggest an RNA polymerase II–mediated cross-talk between chromatin structure and exon-intron architecture, implying that exon selection may be modulated by chromatin structure.

Single cell RNA Seq reveals dynamic paracrine control of cellular variation

High-throughput single-cell transcriptomics offers an unbiased approach for understanding the extent, basis and function of gene expression variation between seemingly identical cells. Here we sequence single-cell RNA-seq libraries prepared from over 1,700 primary mouse bone-marrow-derived dendritic cells spanning several experimental conditions. We find substantial variation between identically stimulated dendritic cells, in both the fraction of cells detectably expressing a given messenger RNA and the transcript’s level within expressing cells. Distinct gene modules are characterized by different temporal heterogeneity profiles. In particular, a ‘core’ module of antiviral genes is expressed very early by a few ‘precocious’ cells in response to uniform stimulation with a pathogenic component, but is later activated in all cells. By stimulating cells individually in sealed microfluidic chambers, analysing dendritic cells from knockout mice, and modulating secretion and extracellular signalling, we show that this response is coordinated by interferon-mediated paracrine signalling from these precocious cells. Notably, preventing cell-to-cell communication also substantially reduces variability between cells in the expression of an early-induced ‘peaked’ inflammatory module, suggesting that paracrine signalling additionally represses part of the inflammatory program. Our study highlights the importance of cell-to-cell communication in controlling cellular heterogeneity and reveals general strategies that multicellular populations can use to establish complex dynamic responses.

Transcriptome-wide Mapping Reveals Widespread Dynamic-Regulated Pseudouridylation of ncRNA and mRNA

Pseudouridine is the most abundant RNA modification, yet except for a few well-studied cases, little is known about the modified positions and their function(s). Here, we develop Ψ-seq for transcriptome-wide quantitative mapping of pseudouridine. We validate Ψ-seq with spike-ins and de novo identification of previously reported positions and discover hundreds of unique sites in human and yeast mRNAs and snoRNAs. Perturbing pseudouridine synthases (PUS) uncovers which pseudouridine synthase modifies each site and their target sequence features. mRNA pseudouridinylation depends on both site-specific and snoRNA-guided pseudouridine synthases. Upon heat shock in yeast, Pus7p-mediated pseudouridylation is induced at >200 sites, and PUS7 deletion decreases the levels of otherwise pseudouridylated mRNA, suggesting a role in enhancing transcript stability. rRNA pseudouridine stoichiometries are conserved but reduced in cells from dyskeratosis congenita patients, where the PUS DKC1 is mutated. Our work identifies an enhanced, transcriptome-wide scope for pseudouridine and methods to dissect its underlying mechanisms and function.

High-Resolution Mapping Reveals a Conserved, Widespread, Dynamic mRNA Methylation Program in Yeast Meiosis

N(6)-methyladenosine (m(6)A) is the most ubiquitous mRNA base modification, but little is known about its precise location, temporal dynamics, and regulation. Here, we generated genomic maps of m(6)A sites in meiotic yeast transcripts at nearly single-nucleotide resolution, identifying 1,308 putatively methylated sites within 1,183 transcripts. We validated eight out of eight methylation sites in different genes with direct genetic analysis, demonstrated that methylated sites are significantly conserved in a related species, and built a model that predicts methylated sites directly from sequence. Sites vary in their methylation profiles along a dense meiotic time course and are regulated both locally, via predictable methylatability of each site, and globally, through the core meiotic circuitry. The methyltransferase complex components localize to the yeast nucleolus, and this localization is essential for mRNA methylation. Our data illuminate a conserved, dynamically regulated methylation program in yeast meiosis and provide an important resource for studying the function of this epitranscriptomic modification.

Transcriptome-wide discovery of circular RNAs in Archaea

Circular RNA forms had been described in all domains of life. Such RNAs were shown to have diverse biological functions, including roles in the life cycle of viral and viroid genomes, and in maturation of permuted tRNA genes. Despite their potentially important biological roles, discovery of circular RNAs has so far been mostly serendipitous. We have developed circRNA-seq, a combined experimental/computational approach that enriches for circular RNAs and allows profiling their prevalence in a whole-genome, unbiased manner. Application of this approach to the archaeon Sulfolobus solfataricus P2 revealed multiple circular transcripts, a subset of which was further validated independently. The identified circular RNAs included expected forms, such as excised tRNA introns and rRNA processing intermediates, but were also enriched with non-coding RNAs, including C/D box RNAs and RNase P, as well as circular RNAs of unknown function. Many of the identified circles were conserved in Sulfolobus acidocaldarius, further supporting their functional significance. Our results suggest that circular RNAs, and particularly circular non-coding RNAs, are more prevalent in archaea than previously recognized, and might have yet unidentified biological roles. Our study establishes a specific and sensitive approach for identification of circular RNAs using RNA-seq, and can readily be applied to other organisms.

Dynamic profiling of the protein life cycle in response to pathogens

How the immune system readies for battle Although gene expression is tightly controlled at both the RNA and protein levels, the quantitative contribution of each step, especially during dynamic responses, remains largely unknown. Indeed, there has been much debate whether changes in RNA level contribute substantially to protein-level regulation. Jovanovic et al. built a genome-scale model of the temporal dynamics of differential protein expression during the stimulation of immunological dendritic cells (see the Perspective by Li and Biggin). Newly stimulated functions involved the up-regulation of specific RNAs and concomitant increases in the levels of the proteins they encode, whereas housekeeping functions were regulated posttranscriptionally at the protein level. Science, this issue 10.1126/science.1259038; see also p. 1066 Levels of “housekeeping” proteins are maintained directly, but those of immune response proteins depend on more transcription. [Also see Perspective by Li and Biggin] INTRODUCTION Mammalian gene expression is tightly controlled through the interplay between the RNA and protein life cycles. Although studies of individual genes have shown that regulation of each of these processes is important for correct protein expression, the quantitative contribution of each step to changes in protein expression levels remains largely unknown and much debated. Many studies have attempted to address this question in the context of steady-state protein levels, and comparing steady-state RNA and protein abundances has indicated a considerable discrepancy between RNA and protein levels. In contrast, only a few studies have attempted to shed light on how changes in each of these processes determine differential protein expression—either relative (ratios) or absolute (differences)—during dynamic responses, and only one recent report has attempted to quantitate each process. Understanding these contributions to a dynamic response on a systems scale is essential both for deciphering how cells deploy regulatory processes to accomplish physiological changes and for discovering key molecular regulators controlling each process. RATIONALE We developed an integrated experimental and computational strategy to quantitatively assess how protein levels are maintained in the context of a dynamic response and applied it to the model response of mouse immune bone marrow–derived dendritic cells (DCs) to stimulation with lipopolysaccharide (LPS). We used a modified pulsed-SILAC (stable isotope labeling with amino acids in cell culture) approach to track newly synthesized and previously labeled proteins over the first 12 hours of the response. In addition, we independently measured replicate RNA-sequencing profiles under the same conditions. We devised a computational strategy to infer per-mRNA translation rates and protein degradation rates at each time point from the temporal transcriptional profiles and pulsed-SILAC proteomics data. This allowed us to build a genome-scale quantitative model of the temporal dynamics of differential protein expression in DCs responding to LPS. RESULTS We found that before stimulation, mRNA levels contribute to overall protein expression levels more than double the combined contribution of protein translation and degradation rates. Upon LPS stimulation, changes in mRNA abundance play an even more dominant role in dynamic changes in protein levels, especially in immune response genes. Nevertheless, several protein modules—especially the preexisting proteome of proteins performing basic cellular functions—are predominantly regulated in stimulated cells at the level of protein translation or degradation, accounting for over half of the absolute change in protein molecules in the cell. In particular, despite the repression of their transcripts, the level of many proteins in the translational machinery is up-regulated upon LPS stimulation because of significantly increased translation rates, and elevated protein degradation of mitochondrial proteins plays a central role in remodeling cellular energy metabolism. CONCLUSIONS Our results support a model in which the induction of novel cellular functions is primarily driven through transcriptional changes, whereas regulation of protein production or degradation updates the levels of preexisting functions as required for an activated state. Our approach for building quantitative genome-scale models of the temporal dynamics of protein expression is broadly applicable to other dynamic systems. Dynamic protein expression regulation in dendritic cells upon stimulation with LPS. We developed an integrated experimental and computational strategy to quantitatively assess how protein levels are maintained in the context of a dynamic response. Our results support a model in which the induction of novel cellular functions is primarily driven through transcriptional changes, whereas regulation of protein production or degradation updates the levels of preexisting functions. Protein expression is regulated by the production and degradation of messenger RNAs (mRNAs) and proteins, but their specific relationships remain unknown. We combine measurements of protein production and degradation and mRNA dynamics so as to build a quantitative genomic model of the differential regulation of gene expression in lipopolysaccharide-stimulated mouse dendritic cells. Changes in mRNA abundance play a dominant role in determining most dynamic fold changes in protein levels. Conversely, the preexisting proteome of proteins performing basic cellular functions is remodeled primarily through changes in protein production or degradation, accounting for more than half of the absolute change in protein molecules in the cell. Thus, the proteome is regulated by transcriptional induction for newly activated cellular functions and by protein life-cycle changes for remodeling of preexisting functions.

Position-dependent alternative splicing activity revealed by global profiling of alternative splicing events regulated by PTB

To gain global insights into the role of the well-known repressive splicing regulator PTB, we analyzed the consequences of PTB knockdown in HeLa cells using high-density oligonucleotide splice-sensitive microarrays. The major class of identified PTB-regulated splicing event was PTB-repressed cassette exons, but there was also a substantial number of PTB-activated splicing events. PTB-repressed and PTB-activated exons showed a distinct arrangement of motifs with pyrimidine-rich motif enrichment within and upstream of repressed exons but downstream of activated exons. The N-terminal half of PTB was sufficient to activate splicing when recruited downstream of a PTB-activated exon. Moreover, insertion of an upstream pyrimidine tract was sufficient to convert a PTB-activated exon to a PTB-repressed exon. Our results show that PTB, an archetypal splicing repressor, has variable splicing activity that predictably depends upon its binding location with respect to target exons.

Chromatin density and splicing destiny: on the cross-talk between chromatin structure and splicing

How are short exonic sequences recognized within the vast intronic oceans in which they reside? Despite decades of research, this remains one of the most fundamental, yet enigmatic, questions in the field of pre‐mRNA splicing research. For many years, studies aiming to shed light on this process were focused at the RNA level, characterizing the manner by which splicing factors and auxiliary proteins interact with splicing signals, thereby enabling, facilitating and regulating splicing. However, we increasingly understand that splicing is not an isolated process; rather it occurs co‐transcriptionally and is presumably also regulated by transcription‐related processes. In fact, studies by our group and others over the past year suggest that DNA structure in terms of nucleosome positioning and specific histone modifications, which have a well established role in transcription, may also have a role in splicing. In this review we discuss evidence for the coupling between transcription and splicing, focusing on recent findings suggesting a link between chromatin structure and splicing, and highlighting challenges this emerging field is facing.