Genome-wide probing of eukaryotic nascent RNA structure elucidates cotranscriptional folding and its antimutagenic effect

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.

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.

Computational modeling of cotranscriptional RNA folding.

RNAs can begin to fold immediately after emerging from RNA polymerase during transcription. Interactions between nascent RNAs and ligands during cotranscriptional folding can direct the formation of alternative RNA structures, a feature exploited by non-coding RNAs called riboswitches to make gene regulatory decisions. Despite their importance, cotranscriptional folding pathways have yet to be uncovered with sufficient resolution to reveal how cotranscriptional folding governs RNA structure and function. To access cotranscriptional folding at nucleotide resolution, we extend selective 2’-hydroxyl acylation analyzed by primer extension sequencing (SHAPE-Seq) to measure structural information of nascent RNAs during transcription. With cotranscriptional SHAPE-Seq, we determine how the B. cereus crcB fluoride riboswitch cotranscriptional folding pathway undergoes a ligand-dependent bifurcation that delays or promotes terminator formation via a series of coordinated structural transitions. Our results directly link cotranscriptional RNA folding to a genetic decision and establish a framework for cotranscriptional analysis of RNA structure at nucleotide resolution.

Cotranscriptional RNA folding pathways typically involve the sequential formation of folding intermediates. Existing methods for cotranscriptional RNA structure probing map the structure of nascent RNA in the context of a terminally arrested transcription elongation complex. Consequently, the rearrangement of RNA structures as nucleotides are added to the transcript can be inferred but is not assessed directly. Here, we describe linked-multipoint Transcription Elongation Complex RNA structure probing (TECprobe-LM), which assesses the cotranscriptional rearrangement of RNA structures by sequentially positioning E. coli RNAP at two or more points within a DNA template so that nascent RNA can be chemically probed. We validate TECprobe-LM by measuring known folding events that occur within the E. coli signal recognition particle RNA, Clostridium beijerinckii pfl ZTP riboswitch, and Bacillus cereus crcB fluoride riboswitch folding pathways. Our findings establish TECprobe-LM as a strategy for observing cotranscriptional RNA folding events directly using chemical probing.

Cotranscriptional RNA folding pathways typically involve the sequential formation of folding intermediates. Existing methods for cotranscriptional RNA structure probing map the structure of nascent RNA in the context of a terminally arrested transcription elongation complex. Consequently, the rearrangement of RNA structures as nucleotides are added to the transcript can be inferred but is not assessed directly. To address this limitation, we have developed linked-multipoint Transcription Elongation Complex RNA structure probing (TECprobe-LM), which assesses the cotranscriptional rearrangement of RNA structures by sequentially positioning E. coli RNAP at two or more points within a DNA template so that nascent RNA can be chemically probed. We validated TECprobe-LM by measuring known folding events that occur within the E. coli signal recognition particle RNA, Clostridium beijerinckii pfl ZTP riboswitch, and Bacillus cereus crcB fluoride riboswitch folding pathways. Our findings establish TECprobe-LM as a strategy for detecting cotranscriptional RNA folding events directly using chemical probing.

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.

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

Transcription Regulation Through Nascent RNA Folding

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.

Cotranscriptional folding is an obligate step of RNA biogenesis that can guide RNA structure formation and function through transient intermediate folds. This process is particularly important for transcriptional riboswitches in which the formation of ligand-dependent structures during transcription regulates downstream gene expression. However, the intermediate structures that comprise cotranscriptional RNA folding pathways and the mechanisms that enable transit between them remain largely unknown. Here we determine the series of cotranscriptional folds and rearrangements that mediate antitermination by the Clostridium beijerinckii pfl ZTP riboswitch in response to the purine biosynthetic intermediate ZMP. We uncover sequence and structural determinants that modulate an internal RNA strand displacement process and identify biases within natural ZTP riboswitch sequences that promote on-pathway folding. Our findings establish a mechanism for pfl riboswitch antitermination and suggest general strategies by which nascent RNA molecules navigate cotranscriptional folding pathways.

MotivationRNA molecules can undergo complex structural dynamics, especially during transcription, which influence their biological functions. Recently developed high-throughput chemical probing experiments that study RNA cotranscriptional folding generate nucleotide-resolution ‘reactivities’ for each length of a growing nascent RNA that reflect structural dynamics. However, the manual annotation and qualitative interpretation of reactivity across these large datasets can be nuanced, laborious, and difficult for new practitioners. We developed a quantitative and systematic approach to automatically detect RNA folding events from these datasets to reduce human bias/error, standardize event discovery and generate hypotheses about RNA folding trajectories for further analysis and experimental validation.ResultsDetection of Unknown Events with Tunable Thresholds (DUETT) identifies RNA structural transitions in cotranscriptional RNA chemical probing datasets. DUETT employs a feedback control-inspired method and a linear regression approach and relies on interpretable and independently tunable parameter thresholds to match qualitative user expectations with quantitatively identified folding events. We validate the approach by identifying known RNA structural transitions within the cotranscriptional folding pathways of the Escherichia coli signal recognition particle RNA and the Bacillus cereus crcB fluoride riboswitch. We identify previously overlooked features of these datasets such as heightened reactivity patterns in the signal recognition particle RNA about 12 nt lengths before base-pair rearrangement. We then apply a sensitivity analysis to identify tradeoffs when choosing parameter thresholds. Finally, we show that DUETT is tunable across a wide range of contexts, enabling flexible application to study broad classes of RNA folding mechanisms.Availability and implementation https://github.com/BagheriLab/DUETT.Supplementary information Supplementary 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.

RNA molecules fold as they are transcribed. Cotranscriptional folding of RNA plays a critical role in RNA functions in vivo. Present computational strategies focus on simulations where large structural changes may not be completely sampled. Here, we describe an alternative approach to predicting cotranscriptional RNA folding by zooming in and out of the RNA folding energy landscape. By classifying the RNA structural ensemble into "partitions" based on long, stable helices, we zoom out of the landscape and predict the overall slow folding kinetics from the interpartition kinetic network, and for each interpartition transition, we zoom in on the landscape to simulate the kinetics. Applications of the model to the 117-nucleotide E. coli SRP RNA and the 59-nucleotide HIV-1 TAR RNA show agreements with the experimental data and new structural and kinetic insights into biologically significant conformational switches and pathways for these important systems. This approach, by zooming in/out of an RNA folding landscape at different resolutions, might allow us to treat large RNAs in vivo with transcriptional pause, transcription speed, and other in vivo effects.