Maxwell R. Mumbach

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.

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.

Transcriptome-Wide Mapping of 5-methylcytidine RNA Modifications in Bacteria, Archaea, and Yeast Reveals m5C within Archaeal mRNAs

The presence of 5-methylcytidine (m5C) in tRNA and rRNA molecules of a wide variety of organisms was first observed more than 40 years ago. However, detection of this modification was limited to specific, abundant, RNA species, due to the usage of low-throughput methods. To obtain a high resolution, systematic, and comprehensive transcriptome-wide overview of m5C across the three domains of life, we used bisulfite treatment on total RNA from both gram positive (B. subtilis) and gram negative (E. coli) bacteria, an archaeon (S. solfataricus) and a eukaryote (S. cerevisiae), followed by massively parallel sequencing. We were able to recover most previously documented m5C sites on rRNA in the four organisms, and identified several novel sites in yeast and archaeal rRNAs. Our analyses also allowed quantification of methylated m5C positions in 64 tRNAs in yeast and archaea, revealing stoichiometric differences between the methylation patterns of these organisms. Molecules of tRNAs in which m5C was absent were also discovered. Intriguingly, we detected m5C sites within archaeal mRNAs, and identified a consensus motif of AUCGANGU that directs methylation in S. solfataricus. Our results, which were validated using m5C-specific RNA immunoprecipitation, provide the first evidence for mRNA modifications in archaea, suggesting that this mode of post-transcriptional regulation extends beyond the eukaryotic domain.

HiChIP: efficient and sensitive analysis of protein-directed genome architecture

Genome conformation is central to gene control but challenging to interrogate. Here we present HiChIP, a protein-centric chromatin conformation method. HiChIP improves the yield of conformation-informative reads by over 10-fold and lowers the input requirement over 100-fold relative to that of ChIA-PET. HiChIP of cohesin reveals multiscale genome architecture with greater signal-to-background ratios than those of in situ Hi-C.

Enhancer connectome in primary human cells identifies target genes of disease-associated DNA elements

The challenge of linking intergenic mutations to target genes has limited molecular understanding of human diseases. Here we show that H3K27ac HiChIP generates high-resolution contact maps of active enhancers and target genes in rare primary human T cell subtypes and coronary artery smooth muscle cells. Differentiation of naive T cells into T helper 17 cells or regulatory T cells creates subtype-specific enhancer–promoter interactions, specifically at regions of shared DNA accessibility. These data provide a principled means of assigning molecular functions to autoimmune and cardiovascular disease risk variants, linking hundreds of noncoding variants to putative gene targets. Target genes identified with HiChIP are further supported by CRISPR interference and activation at linked enhancers, by the presence of expression quantitative trait loci, and by allele-specific enhancer loops in patient-derived primary cells. The majority of disease-associated enhancers contact genes beyond the nearest gene in the linear genome, leading to a fourfold increase in the number of potential target genes for autoimmune and cardiovascular diseases.

Discovery of stimulation-responsive immune enhancers with CRISPR activation

The majority of genetic variants associated with common human diseases map to enhancers, non-coding elements that shape cell-type-specific transcriptional programs and responses to extracellular cues. Systematic mapping of functional enhancers and their biological contexts is required to understand the mechanisms by which variation in non-coding genetic sequences contributes to disease. Functional enhancers can be mapped by genomic sequence disruption, but this approach is limited to the subset of enhancers that are necessary in the particular cellular context being studied. We hypothesized that recruitment of a strong transcriptional activator to an enhancer would be sufficient to drive target gene expression, even if that enhancer was not currently active in the assayed cells. Here we describe a discovery platform that can identify stimulus-responsive enhancers for a target gene independent of stimulus exposure. We used tiled CRISPR activation (CRISPRa) to synthetically recruit a transcriptional activator to sites across large genomic regions (more than 100 kilobases) surrounding two key autoimmunity risk loci, CD69 and IL2RA. We identified several CRISPRa-responsive elements with chromatin features of stimulus-responsive enhancers, including an IL2RA enhancer that harbours an autoimmunity risk variant. Using engineered mouse models, we found that sequence perturbation of the disease-associated Il2ra enhancer did not entirely block Il2ra expression, but rather delayed the timing of gene activation in response to specific extracellular signals. Enhancer deletion skewed polarization of naive T cells towards a pro-inflammatory T helper (TH17) cell state and away from a regulatory T cell state. This integrated approach identifies functional enhancers and reveals how non-coding variation associated with human immune dysfunction alters context-specific gene programs.

Transcriptome-wide interrogation of RNA secondary structure in living cells with icSHAPE

icSHAPE (in vivo click selective 2-hydroxyl acylation and profiling experiment) captures RNA secondary structure at a transcriptome-wide level by measuring nucleotide flexibility at base resolution. Living cells are treated with the icSHAPE chemical NAI-N3 followed by selective chemical enrichment of NAI-N3–modified RNA, which provides an improved signal-to-noise ratio compared with similar methods leveraging deep sequencing. Purified RNA is then reverse-transcribed to produce cDNA, with SHAPE-modified bases leading to truncated cDNA. After deep sequencing of cDNA, computational analysis yields flexibility scores for every base across the starting RNA population. The entire experimental procedure can be completed in ∼5 d, and the sequencing and bioinformatics data analysis take an additional 4–5 d with no extensive computational skills required. Comparing in vivo and in vitro icSHAPE measurements can reveal in vivo RNA-binding protein imprints or facilitate the dissection of RNA post-transcriptional modifications. icSHAPE reactivities can additionally be used to constrain and improve RNA secondary structure prediction models.

An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues

We present Omni-ATAC, an improved ATAC-seq protocol for chromatin accessibility profiling that works across multiple applications with substantial improvement of signal-to-background ratio and information content. The Omni-ATAC protocol generates chromatin accessibility profiles from archival frozen tissue samples and 50-μm sections, revealing the activities of disease-associated DNA elements in distinct human brain structures. The Omni-ATAC protocol enables the interrogation of personal regulomes in tissue context and translational studies.

The chromatin accessibility landscape of primary human cancers

Cancer chromatin accessibility landscape The Cancer Genome Atlas (TCGA) provides a high-quality resource of molecular data on a large variety of human cancers. Corces et al. used a recently modified assay to profile chromatin accessibility to determine the accessible chromatin landscape in 410 TCGA samples from 23 cancer types (see the Perspective by Taipale). When the data were integrated with other omics data available for the same tumor samples, inherited risk loci for cancer predisposition were revealed, transcription factors and enhancers driving molecular subtypes of cancer with patient survival differences were identified, and noncoding mutations associated with clinical prognosis were discovered. Science, this issue p. eaav1898; see also p. 401 Chromatin accessibility profiling identifies principles of epigenetic regulation in 23 primary human cancers. INTRODUCTION Cancer is one of the leading causes of death worldwide. Although the 2% of the human genome that encodes proteins has been extensively studied, much remains to be learned about the noncoding genome and gene regulation in cancer. Genes are turned on and off in the proper cell types and cell states by transcription factor (TF) proteins acting on DNA regulatory elements that are scattered over the vast noncoding genome and exert long-range influences. The Cancer Genome Atlas (TCGA) is a global consortium that aims to accelerate the understanding of the molecular basis of cancer. TCGA has systematically collected DNA mutation, methylation, RNA expression, and other comprehensive datasets from primary human cancer tissue. TCGA has served as an invaluable resource for the identification of genomic aberrations, altered transcriptional networks, and cancer subtypes. Nonetheless, the gene regulatory landscapes of these tumors have largely been inferred through indirect means. RATIONALE A hallmark of active DNA regulatory elements is chromatin accessibility. Eukaryotic genomes are compacted in chromatin, a complex of DNA and proteins, and only the active regulatory elements are accessible by the cell’s machinery such as TFs. The assay for transposase-accessible chromatin using sequencing (ATAC-seq) quantifies DNA accessibility through the use of transposase enzymes that insert sequencing adapters at these accessible chromatin sites. ATAC-seq enables the genome-wide profiling of TF binding events that orchestrate gene expression programs and give a cell its identity. RESULTS We generated high-quality ATAC-seq data in 410 tumor samples from TCGA, identifying diverse regulatory landscapes across 23 cancer types. These chromatin accessibility profiles identify cancer- and tissue-specific DNA regulatory elements that enable classification of tumor subtypes with newly recognized prognostic importance. We identify distinct TF activities in cancer based on differences in the inferred patterns of TF-DNA interaction and gene expression. Genome-wide correlation of gene expression and chromatin accessibility predicts tens of thousands of putative interactions between distal regulatory elements and gene promoters, including key oncogenes and targets in cancer immunotherapy, such as MYC, SRC, BCL2, and PDL1. Moreover, these regulatory interactions inform known genetic risk loci linked to cancer predisposition, nominating biochemical mechanisms and target genes for many cancer-linked genetic variants. Lastly, integration with mutation profiling by whole-genome sequencing identifies cancer-relevant noncoding mutations that are associated with altered gene expression. A single-base mutation located 12 kilobases upstream of the FGD4 gene, a regulator of the actin cytoskeleton, generates a putative de novo binding site for an NKX TF and is associated with an increase in chromatin accessibility and a concomitant increase in FGD4 gene expression. CONCLUSION The accessible genome of primary human cancers provides a wealth of information on the susceptibility, mechanisms, prognosis, and potential therapeutic strategies of diverse cancer types. Prediction of interactions between DNA regulatory elements and gene promoters sets the stage for future integrative gene regulatory network analyses. The discovery of hundreds of noncoding somatic mutations that exhibit allele-specific regulatory effects suggests a pervasive mechanism for cancer cells to manipulate gene expression and increase cellular fitness. These data may serve as a foundational resource for the cancer research community. Cancer gene regulatory landscape. Chromatin accessibility profiling of 23 human cancer types (left) in 410 tumor samples from TCGA revealed 562,709 DNA regulatory elements. The activity of these DNA elements organized cancer subtypes, identified TF proteins and regulatory elements controlling cancer gene expression, and suggested molecular mechanisms for cancer-associated inherited variants and somatic mutations in the noncoding genome. See main article for abbreviations of cancer types. Ref., reference; Var., variant. We present the genome-wide chromatin accessibility profiles of 410 tumor samples spanning 23 cancer types from The Cancer Genome Atlas (TCGA). We identify 562,709 transposase-accessible DNA elements that substantially extend the compendium of known cis-regulatory elements. Integration of ATAC-seq (the assay for transposase-accessible chromatin using sequencing) with TCGA multi-omic data identifies a large number of putative distal enhancers that distinguish molecular subtypes of cancers, uncovers specific driving transcription factors via protein-DNA footprints, and nominates long-range gene-regulatory interactions in cancer. These data reveal genetic risk loci of cancer predisposition as active DNA regulatory elements in cancer, identify gene-regulatory interactions underlying cancer immune evasion, and pinpoint noncoding mutations that drive enhancer activation and may affect patient survival. These results suggest a systematic approach to understanding the noncoding genome in cancer to advance diagnosis and therapy.