Transcription Regulation Through Nascent RNA Folding

To exert their functions, RNAs adopt diverse structures, ranging from simple secondary to complex tertiary and quaternary folds. In vivo, RNA folding starts with RNA transcription, and a wide variety of processes are coupled to co-transcriptional RNA folding events, including the regulation of fundamental transcription dynamics, gene regulation by mechanisms like attenuation, RNA processing or ribonucleoprotein particle formation. While co-transcriptional RNA folding and associated co-transcriptional processes are by now well accepted as pervasive regulatory principles in all organisms, investigations into the role of the transcription machinery in co-transcriptional folding processes have so far largely focused on effects of the order in which RNA regions are produced and of transcription kinetics. Recent structural and structure-guided functional analyses of bacterial transcription complexes increasingly point to an additional role of RNA polymerase and associated transcription factors in supporting co-transcriptional RNA folding by fostering or preventing strategic contacts to the nascent transcripts. In general, the results support the view that transcription complexes can act as RNA chaperones, a function that has been suggested over 30 years ago. Here, we discuss transcription complexes as RNA chaperones based on recent examples from bacterial transcription.

The transcriptional intermediates of RNAs fold into secondary structures with multiple regulatory roles, yet the details of such cotranscriptional RNA folding are largely unresolved in eukaryotes. Here, we present eSPET-seq (Structural Probing of Elongating Transcripts in eukaryotes), a method to assess the cotranscriptional RNA folding in Saccharomyces cerevisiae. Our study reveals pervasive structural transitions during cotranscriptional folding and overall structural similarities between nascent and mature RNAs. Furthermore, a combined analysis with genome-wide R-loop and mutation rate approximations provides quantitative evidence for the antimutator effect of nascent RNA folding through competitive inhibition of the R-loops, known to facilitate transcription-associated mutagenesis. Taken together, we present an experimental evaluation of cotranscriptional folding in eukaryotes and demonstrate the antimutator effect of nascent RNA folding. These results suggest genome-wide coupling between the processing and transmission of genetic information through RNA folding.

Computational modeling of cotranscriptional RNA folding.

RNA begins to fold as it is transcribed by an RNA polymerase. Consequently, RNA folding is constrained by the direction and rate of transcription. Understanding how RNA folds into secondary and tertiary structures therefore requires methods for determining the structure of cotranscriptional folding intermediates. Cotranscriptional RNA chemical probing methods accomplish this by systematically probing the structure of nascent RNA that is displayed from an RNA polymerase. Here, we describe a concise, high-resolution cotranscriptional RNA chemical probing procedure called variable length Transcription Elongation Complex RNA structure probing (TECprobe-VL). We demonstrate the accuracy and resolution of TECprobe-VL by replicating and extending previous analyses of ZTP and fluoride riboswitch folding and mapping the folding pathway of a ppGpp-sensing riboswitch. In each system, we show that TECprobe-VL identifies coordinated cotranscriptional folding events that mediate transcription antitermination. Our findings establish TECprobe-VL as an accessible method for mapping cotranscriptional RNA folding pathways.

RNA Chaperones and the RNA Folding Problem

Dynamic extended folding: Modeling the RNA secondary structures during co-transcriptional folding

Defining the in vivo folding pathway of cellular RNAs is essential to understand how they reach their final native conformation. We here introduce a novel method, named Structural Probing of Elongating Transcripts (SPET-seq), that permits single-base resolution analysis of transcription intermediates’ secondary structures on a transcriptome-wide scale, enabling base-resolution analysis of the RNA folding events. Our results suggest that cotranscriptional RNA folding in vivo is a mixture of cooperative folding events, in which local RNA secondary structure elements are formed as they get transcribed, and non-cooperative events, in which 5′-halves of long-range helices get sequestered into transient non-native interactions until their 3′ counterparts have been transcribed. Together our work provides the first transcriptome-scale overview of RNA cotranscriptional folding in a living organism.

MotivationUnderstanding RNA folding at the level of secondary structures can give important insights concerning the function of a molecule. We are interested to learn how secondary structures change dynamically during transcription, as well as whether particular secondary structures form already during or only after transcription. While different approaches exist to simulate cotranscriptional folding, the current strategies for visualization are lagging behind. New, more suitable approaches are necessary to help with exploring the generated data from cotranscriptional folding simulations.ResultsWe present DrForna, an interactive visualization app for viewing the time course of a cotranscriptional RNA folding simulation. Specifically, users can scroll along the time axis and see the population of structures that are present at any particular time point.Availability and implementationDrForna is a JavaScript project available on Github at https://github.com/ViennaRNA/drforna and deployed at https://viennarna.github.io/drforna

We develop a systematic helix-based computational method to predict RNA folding kinetics during transcription. In our method, the transcription is modeled as stepwise process, where each step is the transcription of a nucleotide. For each step, the kinetics algorithm predicts the population kinetics, transition pathways, folding intermediates, and the transcriptional folding products. The folding pathways, rate constants, and the conformational populations for cotranscription folding show contrastingly different features than the refolding kinetics for a fully transcribed chain. The competition between the transcription speed and rate constants for the transitions between the different nascent structures determines the RNA folding pathway and the end product of folding. For example, fast transcription favors the formation of branch-like structures than rod-like structures and chain elongation in the folding process may reduce the probability of the formation of misfolded structures. Furthermore, good theory-experiment agreements suggest that our method may provide a reliable tool for quantitative prediction for cotranscriptional RNA folding, including the kinetics for the population distribution for the whole conformational ensemble.

MotivationFolding during transcription can have an important influence on the structure and function of RNA molecules, as regions closer to the 5′ end can fold into metastable structures before potentially stronger interactions with the 3′ end become available. Thermodynamic RNA folding models are not suitable to predict structures that result from cotranscriptional folding, as they can only calculate properties of the equilibrium distribution. Other software packages that simulate the kinetic process of RNA folding during transcription exist, but they are mostly applicable for short sequences.ResultsWe present a new algorithm that tracks changes to the RNA secondary structure ensemble during transcription. At every transcription step, new representative local minima are identified, a neighborhood relation is defined and transition rates are estimated for kinetic simulations. After every simulation, a part of the ensemble is removed and the remainder is used to search for new representative structures. The presented algorithm is deterministic (up to numeric instabilities of simulations), fast (in comparison with existing methods), and it is capable of folding RNAs much longer than 200 nucleotides.Availability and implementationThis software is open-source and available at https://github.com/ViennaRNA/drtransformer.Supplementary informationSupplementary data are available at Bioinformatics online.

BackgroundMost of the existing RNA structure prediction programs fold a completely synthesized RNA molecule. However, within the cell, RNA molecules emerge sequentially during the directed process of transcription. Dedicated experiments with individual RNA molecules have shown that RNA folds while it is being transcribed and that its correct folding can also depend on the proper speed of transcription.MethodsThe main aim of this work is to study if and how co-transcriptional folding is encoded within the primary and secondary structure of RNA genes. In order to achieve this, we study the known primary and secondary structures of a comprehensive data set of 361 RNA genes as well as a set of 48 RNA sequences that are known to differ from the originally transcribed sequence units. We detect co-transcriptional folding by defining two measures of directedness which quantify the extend of asymmetry between alternative helices that lie 5' and those that lie 3' of the known helices with which they compete.ResultsWe show with statistical significance that co-transcriptional folding strongly influences RNA sequences in two ways: (1) alternative helices that would compete with the formation of the functional structure during co-transcriptional folding are suppressed and (2) the formation of transient structures which may serve as guidelines for the co-transcriptional folding pathway is encouraged.ConclusionsThese findings have a number of implications for RNA secondary structure prediction methods and the detection of RNA genes.

Reflections on 20 years of RNA folding, dynamics, and structure.

In eukaryotic cells, RNA processing is physically separate from protein synthesis. As nuclear export of unspliced RNA is restricted, only mature messages are normally exposed to the translational machinery. In bacteria, however, splicing must be coordinated with the translation of nascent transcripts. These two processes make very different demands on the RNA substrate: Splicing of autocatalytic introns requires that the 58 and 38 splice sites be brought together as part of an elaborate tertiary structure, whereas translation requires that the mRNA be relatively free of secondary structure. Nonetheless, introns have been found in highly expressed genes in eubacteria, bacteriophages, mitochondria, and chloroplasts (for review, see Burke 1988). Clearly, there must be some means of balancing splicing of bacterial introns with cotranscriptional translation. In this issue, Semrad and Schroeder (1998) provide the surprising answer that splicing of a group I intron from phage T4 is facilitated by translation of the upstream open reading frame (ORF). This enhancement of splicing is achieved by modulating the long-range conformation of the premRNA. Their results provide useful analogies for the coupling of eukaryotic pre-mRNA splicing with transcription. Bacterial mRNAs exclusively contain group I or group II introns, and the three group I introns that are present in phage T4 are all able to self-splice in vitro (for review, see Belfort 1990). The introns are found in genes encoding thymidylate synthase (td), ribonucleotide reductase (nrdB) (Belfort 1990), and anaerobic ribonucleotide reductase (sunY, or nrdD) (Young et al. 1994). In addition to sequences that provide the necessary functions of selfsplicing, td and sunY in trans contain internal ORFs that encode double-stranded DNA endonucleases (Belfort 1989). The endonucleases trigger homing, or site-specific movement of the intron sequences to intronless alleles. Self-splicing requires that the intron RNA fold into a unique secondary and tertiary structure (Cech and Herschlag 1996). The central core of this structure is highly conserved among group I introns and contains the active site where the transfer of phosphodiester bonds takes place. A helix containing the 58 splice site docks into the active site via hydrogen bonds with its ribose 28 hydroxyl groups (Pyle et al. 1992). Recognition of the 38 splice involves several weak interactions, including a 2-bp stem between the 38 end of the intron and nucleotides in the intron core (Burke et al. 1990). The folded structure of the RNA depends on coordination of magnesium ions, which are required for self-splicing (Cech and Herschlag 1996). Long-range interactions, such as base-pairing between hairpin loops, or tetraloop–helix receptor interactions, also stabilize the tertiary structure of the catalytic core (for review, see Brion and Westhof 1997). Recent experiments on the folding kinetics of group I introns, as well as experiments carried out in the 1970s on tRNA, have begun to tease out the mechanisms by which RNAs reach a biologically active conformation (for review, see Draper 1996; Brion and Westhof 1997). Small hairpins can form in 10–100 μsec, and tRNAs can fold within milliseconds (Draper 1996). In contrast, larger RNAs fold in stages over much longer periods of time. The tertiary structure of the P4–P6 domain of the Tetrahymena group I intron, which can fold independently, appears in a few seconds (Sclavi et al. 1997). The core of the intron, however, takes several minutes to become completely folded (Zarrinkar and Williamson 1994; Banerjee and Turner 1995). The very slow folding of longer RNAs arises, in part, from their tendency to form many alternative secondary structures. As RNA secondary structure is stable, incorrect base pairs have the potential to trap the molecule in inactive conformations that can persist for relatively long periods of time (for review, see Herschlag 1995; Thirumalai and Woodson 1996; Brion and Westhof 1997). These metastable states may be quite dissimilar to the final structure. Therefore, the folding process itself can result in RNA populations with different levels of biological activity. Misfolding of RNA has been shown to inhibit ribozyme activity and spliceosome assembly (Goguel et al. 1993; Zavanelli et al. 1994; Uhlenbeck 1995). As discussed below, competition between metastable RNA conformations can also serve as a normal mechanism by which gene activity is regulated. Estimates of in vivo splicing rates are 10to 50-fold faster than in vitro self-splicing (Brehm and Cech 1983; Zhang et al. 1995). This disjunction between in vitro and in vivo activity of catalytic RNAs implies that kinetic folding traps are normally overcome in the cell. Proteins E-MAIL sw74@umail.umd.edu; FAX (301) 314-9121.

As a co-transcriptional process, RNA processing, including alternative splicing and alternative polyadenylation, is crucial for the generation of multiple mRNA isoforms. RNA processing mechanisms are widespread across all higher eukaryotes and play critical roles in cell differentiation, organ development and disease response. Recently, significant progresses have been made in understanding the mechanism of RNA processing. RNA processing is regulated by trans-acting factors such as splicing factors, RNA-binding proteins and cis-sequences in pre-mRNA, and increasing evidence suggests that epigenetic mechanisms, which are important for the dynamic regulation and state of specific chromatic regions, are also involved in co-transcriptional RNA processing. In contrast, recent studies also suggest that alternative RNA processing also has a feedback regulation on epigenetic mechanisms. In this review, we discuss recent studies and summarize the current knowledge on the epigenetic regulation of alternative RNA processing. In addition, a feedback regulation of RNA processing on epigenetic regulators is also discussed.

Sequence analysis of the human T-cell leukemia virus type I (HTLV-I) long terminal repeat (LTR) does not reveal a polyadenylation consensus sequence, AAUAAA, close to the polyadenylation site at the 3' end of the viral RNA. Using site-directed mutagenesis, we demonstrated that two cis-acting signals are required for efficient RNA processing in HTLV-I LTR: (i) a remote AAUAAA hexamer at a distance of 276 nucleotides upstream of the polyadenylation site, and (ii) the 20-nucleotide GU-rich sequence immediately downstream from the poly(A) site. It has been postulated that the folding of RNA into a secondary structure juxtaposes the AAUAAA sequence, in a noncontiguous manner, to within 14 nucleotides of the polyadenylation site. To test this hypothesis, we introduced deletions and point mutations within the U3 and R regions of the LTR. RNA 3'-end processing occurred efficiently at the authentic HTLV-I poly(A) site after deletion of the sequences predicted to form the secondary structure. Thus, the genetic analysis supports the hypothesis that folding of the HTLV-I RNA in the U3 and R regions juxtaposes the AAUAAA sequence and the poly(A) site to the correct functional distance. This unique arrangement of RNA-processing signals is also found in the related retroviruses HTLV-II and bovine leukemia virus.