Regulation by gene-distal enhancers is critical for cell type-specific and condition-specific patterns of gene expression. Thus, to understand the basis of gene activity in a given cell type or ...tissue, we must identify the precise locations of enhancers and functionally characterize their behaviors. Here, we demonstrate that transcription is a nearly universal feature of enhancers in
and mammalian cells and that nascent RNA sequencing strategies are optimal for identification of both enhancers and superenhancers. We dissect the mechanisms governing enhancer transcription and discover remarkable similarities to transcription at protein-coding genes. We show that RNA polymerase II (RNAPII) undergoes regulated pausing and release at enhancers. However, as compared with mRNA genes, RNAPII at enhancers is less stable and more prone to early termination. Furthermore, we found that the level of histone H3 Lys4 (H3K4) methylation at enhancers corresponds to transcriptional activity such that highly active enhancers display H3K4 trimethylation rather than the H3K4 monomethylation considered a hallmark of enhancers. Finally, our work provides insights into the unique characteristics of superenhancers, which stimulate high-level gene expression through rapid pause release; interestingly, this property renders associated genes resistant to the loss of factors that stabilize paused RNAPII.
Single-molecule correlated chemical probing of RNA Homan, Philip J.; Favorov, Oleg V.; Lavender, Christopher A. ...
Proceedings of the National Academy of Sciences - PNAS,
09/2014, Volume:
111, Issue:
38
Journal Article
Peer reviewed
Open access
Significance RNA molecules function as the central conduit of information transfer in biology. To do this, they encode information both in their sequences and in their higher-order structures. ...Understanding the higher-order structure of RNA remains challenging. In this work we devise a simple, experimentally concise, and accurate approach for examining higher-order RNA structure by converting widely used massively parallel sequencing into an easily implemented single-molecule experiment for detecting through-space interactions and multiple conformations. We then use this experiment to analyze higher-order RNA structure, detect biologically important hidden states, and refine accurate three-dimensional structure models.
Complex higher-order RNA structures play critical roles in all facets of gene expression; however, the through-space interaction networks that define tertiary structures and govern sampling of multiple conformations are poorly understood. Here we describe single-molecule RNA structure analysis in which multiple sites of chemical modification are identified in single RNA strands by massively parallel sequencing and then analyzed for correlated and clustered interactions. The strategy thus identifies RNA interaction groups by mutational profiling (RING-MaP) and makes possible two expansive applications. First, we identify through-space interactions, create 3D models for RNAs spanning 80–265 nucleotides, and characterize broad classes of intramolecular interactions that stabilize RNA. Second, we distinguish distinct conformations in solution ensembles and reveal previously undetected hidden states and large-scale structural reconfigurations that occur in unfolded RNAs relative to native states. RING-MaP single-molecule nucleic acid structure interrogation enables concise and facile analysis of the global architectures and multiple conformations that govern function in RNA.
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Acquired human mitochondrial genome (mtDNA) deletions are symptoms and drivers of focal mitochondrial respiratory deficiency, a pathological hallmark of aging and late-onset mitochondrial disease.
To ...decipher connections between these processes, we create LostArc, an ultrasensitive method for quantifying deletions in circular mtDNA molecules. LostArc reveals 35 million deletions (~ 470,000 unique spans) in skeletal muscle from 22 individuals with and 19 individuals without pathogenic variants in POLG. This nuclear gene encodes the catalytic subunit of replicative mitochondrial DNA polymerase γ. Ablation, the deleted mtDNA fraction, suffices to explain skeletal muscle phenotypes of aging and POLG-derived disease. Unsupervised bioinformatic analyses reveal distinct age- and disease-correlated deletion patterns.
These patterns implicate replication by DNA polymerase γ as the deletion driver and suggest little purifying selection against mtDNA deletions by mitophagy in postmitotic muscle fibers. Observed deletion patterns are best modeled as mtDNA deletions initiated by replication fork stalling during strand displacement mtDNA synthesis.
Faithful transcription initiation is critical for accurate gene expression, yet the mechanisms underlying specific transcription start site (TSS) selection in mammals remain unclear. Here, we show ...that the histone-fold domain protein NF-Y, a ubiquitously expressed transcription factor, controls the fidelity of transcription initiation at gene promoters in mouse embryonic stem cells. We report that NF-Y maintains the region upstream of TSSs in a nucleosome-depleted state while simultaneously protecting this accessible region against aberrant and/or ectopic transcription initiation. We find that loss of NF-Y binding in mammalian cells disrupts the promoter chromatin landscape, leading to nucleosomal encroachment over the canonical TSS. Importantly, this chromatin rearrangement is accompanied by upstream relocation of the transcription pre-initiation complex and ectopic transcription initiation. Further, this phenomenon generates aberrant extended transcripts that undergo translation, disrupting gene expression profiles. These results suggest NF-Y is a central player in TSS selection in metazoans and highlight the deleterious consequences of inaccurate transcription initiation.
This article reports on the synthesis of thermo- and light-sensitive hydrophilic block copolymers, poly(ethylene oxide)-b-poly(ethoxytri(ethylene glycol) acrylate-co-o-nitrobenzyl acrylate), and the ...study of their micellization/dissociation transitions in water in response to temperature changes and UV irradiation. The block copolymers with controlled molecular weights and narrow polydispersities were synthesized by atom transfer radical polymerization of a mixture of ethoxytri(ethylene glycol) acrylate and o-nitrobenzyl acrylate with a molar ratio of 100:10 from a PEO macroinitiator. Dynamic light scattering and fluorescence spectroscopy studies showed that these copolymers were molecularly dissolved in water at lower temperatures and self-assembled into micelles with the thermosensitive block associating into the core and the PEO block forming the corona when the temperature was above the lower critical solution temperature (LCST) of the thermosensitive block. Upon UV irradiation, the o-nitrobenzyl group was cleaved and the LCST of the thermosensitive block was increased, causing the dissociation of micelles into unimers and the release of encapsulated fluorescent dye Nile Red into water. Further increasing the temperature induced the formation of micelles again and the re-encapsulation of Nile Red. The thermo-induced formation and dissociation of micelles were reversible.
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Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results ...across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems—patient classification, fundamental biological processes and treatment of patients—and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.
HIV and related primate lentiviruses possess single-stranded RNA genomes. Multiple regions of these genomes participate in critical steps in the viral replication cycle, and the functions of many RNA ...elements are dependent on the formation of defined structures. The structures of these elements are still not fully understood, and additional functional elements likely exist that have not been identified. In this work, we compared three full-length HIV-related viral genomes: HIV-1NL4-3, SIVcpz, and SIVmac (the latter two strains are progenitors for all HIV-1 and HIV-2 strains, respectively). Model-free RNA structure comparisons were performed using whole-genome structure information experimentally derived from nucleotide-resolution SHAPE reactivities. Consensus secondary structures were constructed for strongly correlated regions by taking into account both SHAPE probing structural data and nucleotide covariation information from structure-based alignments. In these consensus models, all known functional RNA elements were recapitulated with high accuracy. In addition, we identified multiple previously unannotated structural elements in the HIV-1 genome likely to function in translation, splicing and other replication cycle processes; these are compelling targets for future functional analyses. The structure-informed alignment strategy developed here will be broadly useful for efficient RNA motif discovery.
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Molecular modeling guided by experimentally derived structural information is an attractive approach for three-dimensional structure determination of complex RNAs that are not amenable to study by ...high-resolution methods. Hydroxyl radical probing (HRP), which is performed routinely in many laboratories, provides a measure of solvent accessibility at individual nucleotides. HRP measurements have, to date, only been used to evaluate RNA models qualitatively. Here we report the development of a quantitative structure refinement approach using HRP measurements to drive discrete molecular dynamics simulations for RNAs ranging in size from 80 to 230 nucleotides. We first used HRP reactivities to identify RNAs that form extensive helical packing interactions. For these RNAs, we achieved highly significant structure predictions given the inputs of RNA sequence and base pairing. This HRP-directed tertiary structure refinement approach generates robust structural hypotheses that are useful for guiding explorations of structure-function inter-relationships in RNA.
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It was shown decades ago that purified 30S ribosome subunits readily interconvert between “active” and “inactive” conformations in a switch that involves changes in the functionally important neck ...and decoding regions. However, the physiological significance of this conformational change had remained unknown. In exponentially growingEscherichia colicells, RNA SHAPE probing revealed that 16S rRNA largely adopts the inactive conformation in stably assembled, mature 30S subunits and the active conformation in translating (70S) ribosomes. Inactive 30S subunits bind mRNA as efficiently as active subunits but initiate translation more slowly. Mutations that inhibited interconversion between states compromised translation in vivo. Binding by the small antibiotic paromomycin induced the inactive-to-active conversion, consistent with a low-energy barrier between the two states. Despite the small energetic barrier between states, but consistent with slow translation initiation and a functional role in vivo, interconversion involved large-scale changes in structure in the neck region that likely propagate across the 30S body via helix 44. These findings suggest the inactive state is a biologically relevant alternate conformation that regulates ribosome function as a conformational switch.
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Discovery and characterization of functional RNA structures remains challenging due to deficiencies in de novo secondary structure modeling. Here we describe a dynamic programming approach for ...model-free sequence comparison that incorporates high-throughput chemical probing data. Based on SHAPE probing data alone, ribosomal RNAs (rRNAs) from three diverse organisms--the eubacteria E. coli and C. difficile and the archeon H. volcanii--could be aligned with accuracies comparable to alignments based on actual sequence identity. When both base sequence identity and chemical probing reactivities were considered together, accuracies improved further. Derived sequence alignments and chemical probing data from protein-free RNAs were then used as pseudo-free energy constraints to model consensus secondary structures for the 16S and 23S rRNAs. There are critical differences between these experimentally-informed models and currently accepted models, including in the functionally important neck and decoding regions of the 16S rRNA. We infer that the 16S rRNA has evolved to undergo large-scale changes in base pairing as part of ribosome function. As high-quality RNA probing data become widely available, structurally-informed sequence alignment will become broadly useful for de novo motif and function discovery.
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