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Life times of metastable states guide regulatory signaling in transcriptional riboswitches.

ABSTRACT: Transcriptional riboswitches modulate downstream gene expression by a tight coupling of ligand-dependent RNA folding kinetics with the rate of transcription. RNA folding pathways leading to functional ON and OFF regulation involve the formation of metastable states within well-defined sequence intervals during transcription. The kinetic requirements for the formation and preservation of these metastable states in the context of transcription remain unresolved. Here, we reversibly trap the previously defined regulatory relevant metastable intermediate of the Mesoplasma florum 2'-deoxyguanosine (2'dG)-sensing riboswitch using a photocaging-ligation approach, and monitor folding to its native state by real-time NMR in both presence and absence of ligand. We further determine transcription rates for two different bacterial RNA polymerases. Our results reveal that the riboswitch functions only at transcription rates typical for bacterial polymerases (10-50?nt?s-1) and that gene expression is modulated by 40-50% only, while subtle differences in folding rates guide population ratios within the structural ensemble to a specific regulatory outcome.

SUBMITTER: Helmling C

PROVIDER: S-EPMC5838219 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Life times of metastable states guide regulatory signaling in transcriptional riboswitches.

Helmling Christina C Klötzner Dean-Paulos DP Sochor Florian F Mooney Rachel Anne RA Wacker Anna A Landick Robert R Fürtig Boris B Heckel Alexander A Schwalbe Harald H

Nature communications 20180305 1

Transcriptional riboswitches modulate downstream gene expression by a tight coupling of ligand-dependent RNA folding kinetics with the rate of transcription. RNA folding pathways leading to functional ON and OFF regulation involve the formation of metastable states within well-defined sequence intervals during transcription. The kinetic requirements for the formation and preservation of these metastable states in the context of transcription remain unresolved. Here, we reversibly trap the previo ...[more]

PMID: 29507289

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Project description:BackgroundHigh throughput gene expression experiments yield large amounts of data that can augment our understanding of disease processes, in addition to classifying samples. Here we present new paradigms of data Separation based on construction of transcriptional regulatory networks for normal and abnormal cells using sequence predictions, literature based data and gene expression studies. We analyzed expression datasets from a number of diseased and normal cells, including different types of acute leukemia, and breast cancer with variable clinical outcome.ResultsWe constructed sample-specific regulatory networks to identify links between transcription factors (TFs) and regulated genes that differentiate between healthy and diseased states. This approach carries the advantage of identifying key transcription factor-gene pairs with differential activity between healthy and diseased states rather than merely using gene expression profiles, thus alluding to processes that may be involved in gene deregulation. We then generalized this approach by studying simultaneous changes in functionality of multiple regulatory links pointing to a regulated gene or emanating from one TF (or changes in gene centrality defined by its in-degree or out-degree measures, respectively). We found that samples can often be separated based on these measures of gene centrality more robustly than using individual links. We examined distributions of distances (the number of links needed to traverse the path between each pair of genes) in the transcriptional networks for gene subsets whose collective expression profiles could best separate each dataset into predefined groups. We found that genes that optimally classify samples are concentrated in neighborhoods in the gene regulatory networks. This suggests that genes that are deregulated in diseased states exhibit a remarkable degree of connectivity.ConclusionTranscription factor-regulated gene links and centrality of genes on transcriptional networks can be used to differentiate between cell types. Transcriptional network blueprints can be used as a basis for further research into gene deregulation in diseased states.

Project description:Riboswitches are messenger RNA (mRNA) domains that regulate gene function in response to the intracellular concentration of a variety of metabolites and second messengers. They control essential genes in many pathogenic bacteria, thus representing an inviting new class of biomolecular target for the development of antibiotics and chemical-biological tools. In this Account, we briefly review the discovery of riboswitches in the first years of the 21st century and their ensuing characterization over the past decade. We then discuss the progress achieved so far in using riboswitches as a focus for drug discovery, considering both the value of past serendipity and the particular challenges that confront current researchers. Five mechanisms of gene regulation have been determined for riboswitches. Most bacterial riboswitches modulate either transcription termination or translation initiation in response to ligand binding. All known examples of eukaryotic riboswitches, and some bacterial riboswitches, control gene expression by alternative splicing. The glmS riboswitch, which is widespread in Gram-positive bacteria, is a catalytic RNA activated by ligand binding: its self-cleavage destabilizes the mRNA of which it is part. Finally, one example of a trans-acting riboswitch is known. Three-dimensional structures have been determined for representatives of 13 structurally distinct riboswitch classes, providing atomic-level insight into their mechanisms of ligand recognition. While cellular and viral RNAs have attracted widespread interest as potential drug targets, riboswitches show special promise due to the diversity of small-molecule recognition strategies that are on display in their ligand-binding pockets. Moreover, riboswitches have evolved to recognize small-molecule ligands, which is unique among known structured RNA domains. Structural and biochemical advances in the study of riboswitches provide an impetus for the development of methods for the discovery of novel riboswitch activators and inhibitors. Recent rational drug design efforts focused on select riboswitch classes have yielded a small number of candidate antibiotic compounds, including one active in a mouse model of Staphylococcus aureus infection. The development of high-throughput methods suitable for riboswitch-specific drug discovery is ongoing. A fragment-based screening approach employing equilibrium dialysis that may be generically useful has demonstrated early success. Riboswitch-mediated gene regulation is widely employed by bacteria; however, only the thiamine pyrophosphate-responsive riboswitch has thus far been found in eukaryotes. Thus, riboswitches are particularly attractive as targets for antibacterials. Indeed, antimicrobials with previously unknown mechanisms have been found to function by binding riboswitches and causing aberrant gene expression.

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Life times of metastable states guide regulatory signaling in transcriptional riboswitches.

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Life times of metastable states guide regulatory signaling in transcriptional riboswitches.

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