The multiple roles of both viral and cellular RNAs have become increasingly apparent in recent years, and techniques to model them have become significantly more powerful, enabling faster and more accurate visualization of RNA structures.Techniques such as SHAPE  have revolutionized the field, and have been used to examine RNAs belonging to many and diverse retroviruses. Secondary structure probing reagents such as these have been aided by the development of faster methods of analysis either via capillary or next-generation sequencing, allowing the analysis of entire genomes, and of retroviral RNA structures within virions. Techniques to model the three-dimensional structures of these large RNAs have also recently developed.The flexibility of retroviral RNAs, both structural and functional, is clear from the results of these new experimental techniques. Retroviral RNA structures and structural changes control many stages of the lifecycle, and both the RNA structures themselves and their interactions with ligands are potential new drug targets. In addition, our growing understanding of retroviral RNA structures is aiding our knowledge of cellular RNA form and function. A recent wave of RNA-structure related techniques has brought new and different ways in which we can view retroviral RNAs in two and three dimensions, both inside and outside cells. Such technical power has shown us that retroviral RNAs are physically and functionally flexible and multidimensional, and insights that emerged from these studies initially are now enabling wider understanding of cellular RNA form and function.Less than a decade ago most RNA structure probing was performed using enzymes and chemicals with limited reactivity, specific for particular bases and structural motifs. These reagents were used to cleave or modify RNAs that were either radioactively end-labelled, or subsequently reverse transcribed into radiolabeled cDNAs, to map the modification sites Fig.  1, 2]. , 2. 1, 2SHAPE  reagents selectively acylate the ribose 2’hydroxyl, where the backbone is flexible . This coResolving techniques for cDNAs made during primer extension assays have also changed the field. Users have moved from manually pouring sequencing gels and running radioactively labeled samples, followed by analysis by densitometry or by eye, to separation by capillary using fluorophore-labelled cDNAs and more integrated analysis . This alThe functionality of both SHAPE and capillary electrophoresis have enabled analyses of many distinct retroviral RNA structures, from lentiviruses like HIV-1, HIV-2 , SIV 1515 and FIPerhaps the most important and surprising property of retroviral RNA structures that has been uncovered by the new wave of RNA secondary structure probing techniques is the degree of their flexibility. Where RNA sequences are duplicated within the genome or between genomic and spliced RNAs, it is becoming apparent that the different RNA structures formed by the same RNA sequence give it completely different functional properties. For example, polyadenylation and splice donor site choice between 5’ and 3’ ends of the retroviral genome have been observed to be due to the formation of different RNA structures for foamy viruses . RNA traThe flexibility of retroviral RNA does not necessarily limit it to a finite number of distinct structures, but also enables it to remodel during interactions with proteins or other ligands. The fast-acting SHAPE reagent benzoyl cyanide has been used to study the folding pathway of the RRE as it interacts with increasing amounts of the Rev protein . ConversThe study of RNA structures is incomplete without an understanding of their functions. Retroviral RNAs interact with host and viral RNAs and proteins, and several techniques to map and model interaction sites have been recently developed and applied to retroviruses. CLIP data will be discussed elsewhere in this edition and hence will not be discussed here. MIME  is an in vitro approach, using protein ligand to capture mutated RNAs and Illumina sequencing to determine which mutated sequences retain the ability to bind to the ligand, and which mutations prevent binding . This apRNAs, and particularly those of retroviruses, often have the ability to switch structure in order to interact with different cognate ligands. One disadvantage of using chemical or enzymatic probing agents to ‘footprint’ RNA-ligand interactions is that the disappearance of signal could indicate the presence of ligand blocking the access of the reagent, or it could indicate an RNA secondary or tertiary structural change, which may be distant from the ligand binding site. XL-SHAPE combines identification of RNA structural changes using N-methylisatoic anhydride (NMIA), with protein binding site identification using cross-linking . The tecSHAPE has also been combined with evolutionary analyses to identify conserved RNA structures of unknown function: low acylation sensitivity of a region indicates that it is likely to be structured. Zanini et al.  used pubRNA structures identified can be examined functionally by mutation and viral replication assay. In many cases, such mutations have provided confirmation of their importance to the viral lifecycle. For example, a stable RNA stem-loop observed by SHAPE in Rex-dependent but not in Rex-independent human T-lymphotropic virus RNAs was subsequently found to promote nuclear retention and mRNA degradation, aiding the virus to temporally control its gene expression . HoweverSHAPE reagents, alongside some of their longer-established counterparts, diffuse across membranes and can thus be used to probe RNA structures inside living cells. Such data are then examined by various next-generation sequencing methods, and have begun to show fascinating insights into the cellular regulation of RNAs, and cellular regulation by RNAs. The structures of retroviral RNAs inside living cells have not yet been established, perhaps because of their enormous structural flexibility and low abundance, but technology to enable this has developed so rapidly that it is likely to be a relatively short time before we begin to see these results –42.in virio). An early study using RNAs prepared in different ways found little structural difference between in vitro transcribed, ex virio and in virio structures of the HIV-1 leader [Several studies exist however that examine the structures of retroviral RNAs inside virions  have recently been linked to control of RNA function. In HIV-1, m6A deposition was shown to be vital for viral RNA expression and function , althougTrans-splicing, in which the splice donor and acceptor sites are initially on separate RNAs which are subsequently joined occurs naturally within cells, albeit to a much lesser extent than the constitutive cis-splicing process. Once again, the authors used published SHAPE reactivity information to identify RNA sequences whose structures would be most likely to promote trans-splicing. This approach resulted in a high degree of success, with proof of principle that multiple sites in the retroviral genome are targetable using this approach.The ability to examine RNA structure and structural changes so quickly has had knock-on effects on the development of therapeutics. Recently, SHAPE has been used to identify structural effects of small molecules that bind specifically to HIV-1 TAR RNA  and to SStructural analysis of retroviral RNA is a compelling necessity while designing synthetic drugs against specific molecular targets within it. And with steady RNA structural changes adopted to suit different functions throughout the viral life cycle it is important to understand the spatial orientation of the various RNA structural domains, and their structural flexibility. Single molecule FRET (smFRET) can be performed on long RNAs but requires only small quantities of sample to predict native sequences in their physiological conditions . It reliA SAXS study of the HIV-1 5’ UTR revealed that the PBS fold displays molecular mimicry of tRNA . SAXS ha1H–13C correlated HMQC NMR, whereby short stretches of 13C-edited RNA are ligated to non-edited RNA, allowing the observation of signals that can be assigned to the 13C edited RNA segment. Using this technique along with Adenosine interaction detection, the Summers lab proposed the monomer–dimer structural switch that was subsequently seen using in-gel SHAPE [2H-edited NMR was also used by the same laboratory to assign NMR signals to specific residues and identify a three-way junction that the RNA is able to adopt, flattening SL2 [2H editing was also recently used to show that the dimerization contacts between strands are much more extensive than previously thought, extending beyond SL1 and to the U5: AUG region [Of the methods that provide the greatest structural detail, enhancements in experimental NMR techniques have recently expanded the size of retroviral RNA structures able to be visualized. This is partly due to the implementation of el SHAPE . Althougning SL2 . 2H editG region . This raG region . AlthougG region  the incrG region  suggestsSHAPE reagents will all react with the ribose 2’OH according to backbone flexibility, and are similarly insensitive to the base composition. The reaction kinetics vary between reagents, with the time the reagent is active for, before it is hydrolysed from milliseconds (benzoyl cyanide ) to hourAlthough SHAPE reagents provide detailed information on the flexibility of the RNA backbone, interpretation of which nucleotide base pairs with which is generally inferred computationally, using minimal free energy modeling and programs such as RNAstructure . To gainThe limiting factor in capillary electrophoresis is likely to be the availability of an appropriate capillary sequencing machine and experiments may have to be tailored around the make and model to be used. Several programs are available for analysis; some of these necessitate the use of particular fluorophores. The first of the analysis programmes to be established, SHAPEfinder software is Mac-specific and is rigid in its interpretation of each wavelength, necessitating the use of sets such as 6FAM, VIC, NED and PET on an Applied Biosystems instrument, to run experimental and control samples and two sequencing ladders respectively . HoweverCapillary electrophoresis is commonly used to study molecules of over 50 nt in length. This is particularly if using primer extension, as is used to map acylation sites of SHAPE reagents. However, techniques exist to map SHAPE reagent binding to smaller RNAs also, such as SHAMS, which uses mass spectrometry to pinpoint acylation sites on small RNAs  and seleThe understanding of both viral and cellular RNA structures is becoming more vital for our understanding of retrovirology in general, as the viral roles of RNA structure become more apparent. These disruptive technologies give retrovirologists the power to investigate and visualize such structures with relative ease. From distinct types of retrovirus it is clear that RNA structures are as vital to the lifecycle as protein structures, with viral protein function often dependent on viral RNA structure. The TAR-Tat interaction of HIV-1 has been understood for many years, but the disruptive technologies discussed in this article have established the importance of further interactions across retroviral genomes and genera.Moreover, as the multiple roles of RNA in both viral and cellular processes become apparent, our growing knowledge of retroviral RNA behaviour helps to understand their cellular counterparts. For example, an RNA stability element (RSE) in Rous sarcoma virus that maps to a region downstream of the Gag stop codon was examined by SHAPE and by mutational analyses, to determine the mechanism by which it prevents nonsense mediated decay (NMD) of the viral genomic RNA. The authors found that the RNA structures mediating this effect were separated at defined distances from one another within a 155 nt region of the RNA, and proposed that this overall RSE structure acts as an insulator from the NMD machinery. Their results added weight to the argument that the exon junction complex is not required in order to identify a premature stop codon , and wer


It has been suggested that the mechanism of action of 5-FU involves both DNA- and RNA-directed processes, but this has remained controversial. In this study, using a series of in vivo reporter constructs capable of measuring translational recoding, we demonstrate that cells exposed to 5-FU display a reduced capacity to engage in a variety of translational recoding events, including +1 programmed frameshifting (PRF) and −1 PRF. In addition, 5-FU-treated cells are much less accurate at stop codon recognition, resulting in a significant increase in stop codon-readthrough. Remarkably, while the efficiency of cap-dependent translation appears to be unaffected by 5-FU, 5-FU-treated cells display a decreased ability to initiate cap-independent translation. We further show that knockdown of thymidylate synthase, an enzyme believed to be at the center of 5-FU-induced DNA damage, has no effect on the observed alterations in translational recoding. On the other hand, ribosomal RNA (rRNA) pseudouridylation, which plays an important role in translational recoding, is significantly inhibited. Taken together, our results suggest that the observed effect of 5-FU on recoding is an RNA-directed effect. Our results are the first to show definitely and quantitatively that translational recoding is affected by exposure to 5-FU. Thus, it is possible that a substantial portion of 5-FU cytotoxicity might possibly be the result of alterations in translational recoding efficiency. Fluoropyrimidine 5-fluorouracil (5-FU) was initially developed in the 1950’s following the observation that rat hepatomas metabolized uracil more rapidly than other nucleotides, implicating uracil metabolism as a potential antimetabolic target . Althoug5-FU, which enters cells through the same facilitated transport mechanism used by uracil, is intracellularly converted into several active metabolites, including fluorodeoxyuridine monophosphate (FdUMP), fluorodeoxyuridine triphosphate (FdUTP), and fluorouridine triphosphate (FUTP) ,4. The cSaccharomyces cerevisiae and mutants lacking nonessential transfer RNA (tRNA) modification enzymes are temperature sensitive and hypersensitive to 5-FU, respectively -orthophosphate  was added to the medium to a final concentration of 20 μCi/mL and incubated for 24 h at a 37 °C in a CO2 humidified cell culture incubator. The incubations were terminated by removing medium, and the cells were washed twice with ice-cold PBS to remove residual radioactive phosphate. Total RNA was purified using the TRIzol reagent (Invitrogen) following the manufacturer’s recommendation.SW-480 cells were grown in 100-mm culture dishes to near confluence. One day before labeling, cells were incubated with 5-FU or uracil. One hour before labeling, growth media were removed and replaced with prewarmed phosphate-free DMEM containing 5-FU or uracil. -labeled antisense U2 probe (complementary to nucleotides 25–45) in 50 mL of hybridization buffer , 5× Denhardt’s solution , 25 mM sodium phosphate buffer, pH 6.5, 0.5% SDS) at 37 °C for 1 h or longer using 30 cm hybridization bottles and a hybridization oven . The membrane was then washed twice with 30 mL of 1× SSC, 0.1% SDS at 37 °C for 15 min and analyzed by phosphorimaging.Total RNA was resolved by electrophoresis on 0.8% agarose gel containing 2.2 M formaldehyde both in the gel and in the 1× MOPS running buffer for 3–4 h at 25 V. After electrophoresis, gels were photographed. To analyze the levels of U2 (as a loading control), gels were blotted overnight onto nylon membranes by capillary action. Membranes were recovered by rinsing in 2× saline-sodium citrate buffer (SSC), cross-linked using a ultra violet (UV) Stratalinker . U2 small nuclear RNA (snRNA)was detected by standard northern blotting techniques. The membrane was hybridized with 20 pmol of -orthophosphate was added to the medium to a final concentration of 20 μCi/mL. rRNA was purified and subjected to nuclease P1 cleavage and 2D TLC (In order to quantitatively determine pseudouridine (Ψ) levels, SW-480 cells were treated for 24 h with either 10 μM 5-FU or 10 μM uracil. One day prior to harvesting cells, [d 2D TLC A. We detd 2D TLC B,C. ThusTaken together, these results are consistent with the notion that 5-FU, when included in the medium, is able to inhibit rRNA pseudouridylation, resulting in alterations in translational recoding.E. coli [The mechanism of 5-FU cytotoxicity has been in debate since its initial development as a chemotherapeutic agent. Previous studies have shown that 5-FU treatment alters several RNA-mediated processes, including snRNA pseudouridylation, pre-mRNA splicing  and rRNAE. coli . Here, wE. coli . Our resIt is known that 5-FU, when included in medium, can be incorporated into various types of RNA. However, given that rRNA plays an important role in translation and recoding, we believe that the defects in recoding observed are at least partially the result of the incorporation of 5-FU into rRNA. Although it is possible that 5-FU, a modified nucleotide analog that is distinct from any other known nucleotides, affects RNA-RNA and/or RNA-protein interactions, thus directly contributing to translational recoding, for at least three reasons, we argue that it is more likely the subsequent inhibition of rRNA pseudouridylation that exerts an indirect effect on translational recoding. First, this argument is consistent with numerous reports indicating RNA pseudouridylation is critical to a number of cellular processes, including pre-mRNA splicing and protein translation ,40,42,43Regarding the molecular mechanism of how RNA pseudouridylation is inhibited, it has been reported that 5-FU, when included in the medium, can incorporate into U2  at naturally occurring pseudouridylation sites. The 5-FU-incorporated U2 can in turn block pseudouridylation of newly synthesized U2 snRNA in a site-specific manner. This is achieved through tight binding between the 5-FU-incorporated U2 and the U2-specific pseudouridylases, and hence sequestration of U2-specific pseudouridylases that would otherwise modify U2 under normal conditions ,42,44. WMalignant transformation of cells is a coordinated multi-step process requiring the misregulation of several genes and gene products. While the extent to which oncogenes and tumor suppressors engage in translational recoding is only beginning to emerge, whether the cytotoxicity of 5-FU is the result of a particular misregulation is uncertain and more work is necessary. Culturing cells in medium containing 5-FU results in reduction in rRNA pseudouridylation and alterations in translational recoding, which may contribute at least in part to 5-FU cytotoxicity.


Vibrio vulnificus add adenine riboswitch that reveals five classes of structures. In the presence of adenine, projection of the SHAPE-directed sampling correctly identified the on-conformation; without the ligand, only off-conformations were visualized. We also collected the whole-transcript in vitro and in vivo SHAPE-MaP for human β-actin messenger RNA that revealed similar global folds in both conditions. Nonetheless, a comparison of in vitro and in vivo data revealed that specific regions exhibited significantly different SHAPE-MaP profiles indicative of structural rearrangements, including rearrangement consistent with binding of the zipcode protein in a region distal to the stop codon.When a ribonucleic acid (RNA) molecule folds, it often does not adopt a single, well-defined conformation. The folding energy landscape of an RNA is highly dependent on its nucleotide sequence and molecular environment. Cellular molecules sometimes alter the energy landscape, thereby changing the ensemble of likely low-energy conformations. The effects of these energy landscape changes on the conformational ensemble are particularly challenging to visualize for large RNAs. We have created a robust approach for visualizing the conformational ensemble of RNAs that is well suited for in vitro versus in vivo comparisons. Our method creates a stable map of conformational space for a given RNA sequence. We first identify single point mutations in the RNA that maximally sample suboptimal conformational space based on the ensemble’s partition function. Then, we cluster these diverse ensembles to identify the most diverse partition functions for Boltzmann stochastic sampling. By using, to our knowledge, a novel nestedness distance metric, we iteratively add mutant suboptimal ensembles to converge on a stable 2D map of conformational space. We then compute the selective 2′ hydroxyl acylation by primer extension (SHAPE)-directed ensemble for the RNA folding under different conditions, and we project these ensembles on the map to visualize. To validate our approach, we established a conformational map of the  Ribonucleic acid (RNA) 3D structures are the result of remarkably complex interaction networks that together create emergent biological functions , 2, 3, 4Vibrio vulnificus adenosine deaminase (add) adenine riboswitch (A)  . These titch (A) , 18, 20.itch (A) , 22, 23.add riboswitch (B), which was stochastically sampled from the Boltzmann ensemble. In B, we illustrate a map of conformational space, in which each square represents a cluster of similar conformations based on a nested feature vector that we define below. This representation is particularly interesting as it reveals several aspects of the add riboswitch conformational ensemble that are not apparent when considering only two structures (A). First, this visualization suggests that there are more than two classes of conformations in the add riboswitch conformational ensemble. Second, the on- and off-conformational change is conveniently captured along dimension 1. The methods we describe below provide a robust approach for identifying specific dimensions that capture biologically informative structural differences, such as those in B.Defining structural similarity requires a representation that captures biologically important structural features of the RNA to facilitate clustering of highly similar conformations. From these clusters, it is then possible to determine the relative abundance of the conformations, which reflects their relative thermodynamic weights in the Boltzmann ensemble. We therefore aim to create a visualization based on a sampling of conformational space like the one illustrated for the boswitch B, whichructures A. FirstB, we purposely did not indicate the relative abundance of conformations in each conformational cluster; each square is equal in size. The relative weight of these clusters depends on the underlying thermodynamic parameters of the energy model. Given a nearest-neighbor energy model, it is now computationally efficient to rapidly sample the Boltzmann suboptimal ensemble  folding in vitro versus in vivo. Quantitative comparison of these two conditions effectively enables us to deconvolute the effect of the cellular environment on mRNA folding. The structural ensembles of these long and flexible RNAs tend to be far more complex than the structural ensembles of riboswitches. As such, we require tools that enable “sorting the forest from the trees” to understand these large and complex molecules. We present here an experimental high-resolution comparison of SHAPE data for the human A), we compute its partition function (probability of basepairing (B). These point mutations are experimentally determined to be maximally disruptive of structure , as defined by C) (P(xi) vectors based on their Euclidean distance (D). We then perform Boltzmann stochastic sampling on the two most divergent partition functions (E), and create nestedness feature vectors from the sampled structures . We iteratively add additional Boltzmann ensemble samples of divergent single point mutant sequences until the map of conformational space converges (G), as follows:Our strategy for establishing a conformational map of an RNA ensemble is illustrated in epairing , 34, 35)epairing B. Thesetructure . The puride, xij . Our goaed by C) , 37, 38.distance  to identdistance D. We thunctions E, and ctures F; , to genetures F;  Fig. 2A, we coF). Metric multidimensional scaling (MDS) projects the structure clusters into 2D space by finding the positioning of points in 2D space that best recapitulates the original Euclidean distances calculated from the structure cluster representations (H)  . MDS calA). SHAPE data can be included to direct the ensemble prediction (B), and these frequencies are then scaled as relative diameters for each bubble in the resulting plot (C). Bubbles are colored according to their 2D distances, where groups of similar clusters are closer on the viridis color scale in matplotlib , EnsembleRNA, was created for the visualization of RNA structural ensembles. The package produces bubble charts for the map of conformational space and the wild-type RNA, and allows for comparison between structural ensembles. The package is available at http://ribosnitch.bio.unc.edu/software. A Python package  and directly PCR-amplified the coding sequence with a 5′ primer containing the T7 promoter for in vitro transcription . To remove DNA after transcription, we treated the reaction with TURBO DNase for 15 min at 37°C . Standard bead cleanup was performed between each step . The transcribed RNA was folded at 37°C in buffer containing 100 mM Na-HEPES, pH 8.0, 100 mM NaCl, and 10 mM MgCl2. One μg of RNA was treated for 5 min with either 10% dimethyl sulfoxide (DMSO) or DMSO containing the RNA modifying agent 1-methyl-7-nitroisatoic anhydride (1M7) at a final concentration of 10 mM.SHAPE-MaP experiments were performed in vitro . We obtaβ-actin in the 1000 Genome Cell Lines GM07037 and GM12003 (https://www.coriell.org/). Approximately 50,000,000 cells were collected by centrifugation, resuspended in 1 mL of folding buffer (as in in vitro SHAPE protocol) supplemented with 400 U murine RNase inhibitor, and sonicated three times at 10% power for 10 s . These samples were incubated at 37°C for 10 min, after which either DMSO  or 500 mM 1M7 in DMSO  was added for 5 min with three separate additions. RNA was isolated with Trizol reagent (ThermoFisher Scientific), followed by treatment with TURBO DNase and removal of the majority of ribosomal RNA .We performed in vivo SHAPE-MaP experiments for β-actin mRNA (NM_001101) to align sequences derived from the in vitro samples or the entire genome (hg38) to align sequences derived from the in vivo alignment. The β-actin data are in the file SNRNASM . Free energy bonuses were incorporated between nucleotides to model the in vivo basepairing interactions. To select the final RNA model, we used a hierarchical clustering analysis based on the pairwise root mean square deviation of the phosphates and the potential energy as determined by the DMD force field. The coarse-grained RNA model was reconstructed to an all-atom model to combine with the KH34 protein system. We then equilibrated the entire RNA/protein complex using all-atom DMD simulations at a temperature of 0.4 kcal/(mol∗kB) and included static constraints on the protein and harmonic constraints on the zipcode binding regions of the RNA strand.An RNA/protein complex was generated from a starting model of two DNA strands bound to the KH34 protein . A custo∗kB).We incorporated the in vitro secondary structure as constraints in coarse-grained replica exchange DMD simulations, using the same settings as those in the in vivo RNA system. We then performed an root mean square deviation-based clustering analysis to determine the centroid and reconstructed an all-atom model at a temperature of 0.4 kcal/(molhttp://wordom.sourceforge.net/) (∗kB) for both RNA systems. The in vivo system included static constraints on the protein and harmonic constraints on the zipcode binding protein-interacting regions of the RNA. We calculated the mean using 3-nucleotide windows and SD of the RMSF based on the three DMD simulations for each system.The dynamics of the 2′ hydroxyl groups of the in vitro and in vivo RNA strands were calculated using the root mean square fluctuation (RMSF) with the Wordom software package (ge.net/) . RMSF caadd riboswitch in B).Our first goal in creating a visualization of a structural ensemble was to establish a robust and consistent 2D representation of the conformational space of RNA. Traditionally, principal component analysis is used to identify two Eigenvectors for projection , 25. OneB). Data abstraction identifies clusters of similar structures that likely have similar function. This cluster representation reduces the map size, thereby creating a more accurate and interpretable visualization of secondary structure. Projecting the structure clusters into two dimensions using metric MDS optimizes their distances (B). Experimental structure probing data can be included to guide the ensemble prediction . This visualization produces a separation in 2D space between conformations that can bind adenine and activate translation and conformations that cannot bind adenine.The eaminase , 54. Thieaminase , 54. The and NMR , 46. Ind and NMR B. This add riboswitch is particularly well suited for the application of SHAPE data. Without experimental data to guide structure prediction algorithms, the accepted bound conformation dominates (A), and differences in structure that result from changes in environment cannot be discerned. However, including SHAPE data in the ensemble prediction algorithms reveals differences in the add riboswitch structure with and without ligand (B and C). In each ensemble, the respective structure observed in crystallography and NMR dominates. Thus, our visualization approach combined with SHAPE-directed structural modeling captures key structural features of the ensemble . Overall, we observe high correlation between the two data sets for a majority of the mRNA’s span, with a mean correlation coefficient of 0.88. This result can be seen clearly in B and C) observed for this region; high median SHAPE is correlated with higher ensemble entropy, i.e., multiple alternative conformations , we compute the windowed SHAPE correlation coefficient between the two data sets and plot this correlation in rmations .-probed mRNA was transcribed in the absence of a polyA polymerase, therefore it was not polyadenylated, which likely explains the differences near the 3′ end because the in vivo mRNA is most likely polyadenylated (and 5′-capped). The region of difference we chose to further characterize structurally occurs 3′ of the stop codon. This region in the mRNA contains functional elements known as the Zipcode Protein Binding Protein Sites (ZPBS1 and ZPBS2). Binding of the zipcode binding protein (ZBP1) mediates mRNA localization and translation, hence the name of the protein . Nonetheless, the dominant conformation in vitro (A) is still significantly populated in vivo (B). Thus, our visualization suggests a more complex ensemble of conformations in vivo. To further understand the structural context of the shift in ensemble, we visualized the secondary structure medoid for each of the largest structure clusters in vivo and in vitro. Although in both conformations the Zipcode Binding Protein Sites (ZBPS) are unpaired, in vivo the dominant confirmation shows ZBPS1 and ZBPS2 in a contiguous unpaired region, consistent with the larger in vivo SHAPE values. Importantly, the SHAPE reagent is not a footprinting reagent and is only minimally affected by nucleotide accessibility . By using the secondary structure as initial constraints, we aimed to estimate the root mean square fluctuations (RMSFs) of the RNA backbone. We show these data for the apo and bound simulations in E and F, overlaid with the in vitro and in vivo SHAPE data, respectively. We observed qualitative agreements between the experimental SHAPE data and the simulations, suggesting these molecular models captured overall aspects of the conformational ensemble. One important aspect of these comparisons, especially in the case of the in vivo data, is that the SHAPE data are an ensemble average over all of the β-actin mRNA molecules in the cell. Because ZBP1 binding represses translation, some message molecules are likely not bound by ZBP1, a situation that may explain why a shift to multiple conformations is observed in vivo as opposed to observing only the bound conformation. Nonetheless, these data demonstrate the value of visualizing the structural ensemble to explain structure/function relationships in an mRNA.To further understand the in vivo structural rearrangement, we performed molecular simulations of apo and bound mRNA conformations , C and DRNA structure is the key component of cellular function in highly specific instances; the ribosome’s unique catalytic core is a prime example of the role of a specific RNA structure in performing protein synthesis , 68, 69.Our approach to visualizing the suboptimal ensemble is designed to resolve some of the longstanding problems with obtaining a stable projection that allows comparisons of ensembles. A priori, this visualization approach requires sampling the entire suboptimal space to identify good principal components. For any biologically relevant RNA, such sampling rapidly becomes computationally intractable because the number of suboptimal conformations increases exponentially with length , 77. ThuV. vulnificus add riboswitch leverage the empirical relationship between SHAPE reactivity and the free energy of folding to recapitulate the apo and bound RNA ensembles  and low (structured) median SHAPE , resultsapproach .A significant result of this analysis is the median windowed SHAPE, which overall appeared higher in vivo relative to in vitro for the ZPB1-binding region. This result may seem counterintuitive, as the ZBP1 would be expected to protect the RNA from the 1M7 reagent. Although protein binding is detectable by SHAPE comparisons in vitro to in vivo , 80, SHAA and B) successfully reports a shift in the ensemble, but the model does not suggest a totally dominant alternative in vivo conformation. This restriction is in contrast to the add riboswitch, in which ligand excess shifts the ensemble to almost completely the on-conformation (C). It is important not to overinterpret the relative ratios of the two dominant conformations proposed for the ZBP1-binding region modeled in B. However, the model is consistent with our expectation of a mixed population of ZBP1-bound and unbound β-actin mRNA. Also, the fact that the ZBP1 has two binding sites and these sites are not always simultaneously occupied (β-actin mRNAs in the cell, but still requires biological knowledge to be fully interpretable.Our model , A and Bormation C. It isoccupied , 84 is aβ-actin mRNA to determine if the models agreed qualitatively with the SHAPE data. Because SHAPE chemistry measures backbone flexibility (E and F. For the ZBP1-binding region between ZBPS1 and ZBPS1, the agreement between the simulation and SHAPE data is better for the in vitro model compared to the in vivo simulation. For the in vivo model, we constrained both ZBPS1 and ZBPS2 to the binding pockets, which explains the low flexibility of ZBPS1 and ZBPS2. The higher SHAPE data for these two binding sites in vivo are consistent with a significant subset of mRNAs being unbound, which agrees with our ensemble model that suggested a further opening of the structure.We performed constrained molecular dynamics simulations of the two proposed structural models of xibility , 85, we β-actin mRNA and the V. vulnificus add riboswitch are two well-characterized systems in which ensemble visualization improves the interpretation of environmentally imposed structural differences. By releasing a software package to create these visualizations easily, we encourage the RNA folding community to simulate more than just minimum free energy structures and to explore the suboptimal ensemble for all mRNAs existing in a cell. It is not clear whether suboptimal alternative conformations are a necessary component of RNA function in the cell or a by-product of the rules that govern RNA folding (In summary, we have developed a computationally based visualization approach that faithfully represents ensemble mRNA populations and the effects of environment on the ensembles. The  folding , 88, 89.A.L., C.T.W., L.L., D.G., B.W., and N.V.D. designed the experiments. C.T.W. designed the visualization algorithm. L.L. performed SHAPE-MaP experiments. B.W. performed molecular dynamic simulations. A.L., C.T.W., L.L., and B.W. analyzed data. A.L, C.T.W., L.L., and B.W. wrote the manuscript.


In this review, Zinder and Lima highlight recent advances that have illuminated roles for the RNA exosome and its cofactors in specific biological pathways, alongside studies that attempted to dissect these activities through structural and biochemical characterization of nuclear and cytoplasmic RNA exosome complexes. The eukaryotic RNA exosome is an essential and conserved protein complex that can degrade or process RNA substrates in the 3′-to-5′ direction. Since its discovery nearly two decades ago, studies have focused on determining how the exosome, along with associated cofactors, achieves the demanding task of targeting particular RNAs for degradation and/or processing in both the nucleus and cytoplasm. In this review, we highlight recent advances that have illuminated roles for the RNA exosome and its cofactors in specific biological pathways, alongside studies that attempted to dissect these activities through structural and biochemical characterization of nuclear and cytoplasmic RNA exosome complexes. The eukaryotic RNA exosome is a conserved multisubunit protein complex that catalyzes 3′-to-5′ processing or degradation of a vast array of different RNA substrates . Since iSaccharomyces cerevisiae, the exosome includes a nine-subunit core (Exo9) that interacts with Dis3 to form a 10-subunit complex (Exo10Dis3). The Exo9 core lacks catalytic activity, while Dis3 catalyzes endoribonuclease (endo) and processive 3′-to-5′ exoribonuclease (exo) activities (Dis3 contributes to translation-dependent mRNA surveillance pathways such as nonstop decay (NSD), nonsense-mediated decay (NMD), and no-go decay (NGD)   . While R defects ,b.DIS3/RRP6/C1D) and that a nucleolar exosome may include Exo9 and RRP6 as the only nuclease, presumably associated with C1D (Exo11RRP6/C1D). Mammalian cells lacking DIS3 cannot grow, and mutations that disrupt both DIS3 exo and endo activities are synthetic-lethal in HeLa cells, indicating that DIS3 activities are not redundant with other RNA decay pathways  to prevent their maturation  . Most mue models . While me models . Furtherturation . Because systems  and MM  . A screeDrosophila cells with the disparate RNA viruses vesicular stomatitis virus, Sindbis virus, or Rift Valley fever virus (RVFV) was potentiated by knockdown of exosome and TRAMP components. While normally restricted to the nucleus (The Trf–Air–Mtr4 polyadenylation (TRAMP) complex is a set of cofactors that prepares RNA substrates for degradation by the nuclear RNA exosome  structures have revealed architectures for intact yeast cytoplasmic and nuclear RNA exosomes in complex with RNA substrates and cofactors. Combined with biochemical and genetic studies, these structures illuminate roles for the noncatalytic core in modulating the activities of the associated ribonucleases and the impact of RNA path selection with respect to the fate of RNA substrates.The architecture of the exosome core and its similarity to bacterial and archaeal RNases have been reviewed recently . Briefly2+-dependent hydrolytic 3′-to-5′ exoribonuclease activity  include an active site that catalyzes processive Mgactivity . Dis3 fractivity . Dis3 enactivity . The nucactivity . The catactivity .An early hypothesis proposed that RNA degradation by the exosome was conceptually similar to protein degradation by the proteasome . In thisS. cerevisiae RNA exosome complexes with Dis3 revealed two prominent conformations for the enzyme  that wraps around the S1/KH cap and PH-like ring . In seveA model in which RNA accesses the Rrp6 catalytic site via interactions with the S1/KH ring proteins is supported by UV cross-linking, biochemical analysis of complexes with mutant S1/KH or Rrp6 subunits , 2017, a+ transcripts that accumulate in yeast strains lacking Rrp6 can be partially rescued by expressing catalytically inert Rrp6  polymerase  is a small protein with functions in RNA metabolism  reconstructions revealed that the Ski7 NTD interacts with the Exo9 core via surfaces that overlap with those used by the Rrp6 CTD  is present in at least two other complexes, the nuclear exosome targeting (NEXT) and poly(A) tail exosome targeting (PAXT) complexes . These cS. pombe, a nuclear Mtr4-like helicase, Mtl1, associates with a zinc finger protein, Red1, to form the MTREC core complex, which is involved in heterochromatic silencing and degradation of meiotic RNAs, CUTs, and unspliced transcripts  could not be modeled (Another recent study addressing rRNA processing uncovered a conserved motif (arch-interacting motif [AIM]) in processome proteins Utp18 and Nop53 that interacts with the arch of Mtr4 to recruit the exosome . They fo modeled . Mutatio modeled .S. cerevisiae lacks activity on synthetic RNA containing a 3′ phosphate, while Dis3 family enzymes readily degrade these RNAs  generates a 2′,3′-cyclic phosphate at the 3′ end as a product of its exonuclease activity, and U6 is polyadenylated and rapidly degraded in a Δe strain , 2015. Te strain .2 site of internally transcribed spacer 2 (ITS2) (To generate the 7S rRNA (5.8S rRNA with a 3′ extension), the Las1 endonuclease component of the Las1 complex cleaves the rRNA precursor molecule at the C2 (ITS2) , resulti2 (ITS2) D, the 2′2 (ITS2) D. This p2 (ITS2) . While tThe RNA exosome and its cofactors provide a versatile platform for targeting a wide variety of RNA substrates for processing and/or degradation in the nucleus and cytoplasm. Although the exosome was discovered two decades ago, it seems that we are just beginning to piece together its complexity. Armed with various cofactors that presumably recruit and/or localize the exosome to different subcellular compartments or complexes, it remains unclear how particular complexes compete with one another to promote these functions. With respect to structural studies, it remains a significant challenge to capture intermediates during recruitment and/or degradation, as most factors require a 3′ end for activity. For instance, TRAMP-mediated decay includes at least four enzymes that bind an RNA 3′ end: a 3′ nontemplated poly(A) polymerase, a 3′-to-5′ RNA helicase, and two 3′-to-5′-directed exoribonucleases . Perhaps


RNA-binding proteins (RBPs) play important and essential roles in eukaryotic gene expression regulating splicing, localization, translation, and stability of mRNAs. We describe ultraviolet crosslinking and affinity purification (uvCLAP), an easy-to-use, robust, reproducible, and high-throughput method to determine in vivo targets of RBPs. uvCLAP is fast and does not rely on radioactive labeling of RNA. We investigate binding of 15 RBPs from fly, mouse, and human cells to test the method’s performance and applicability. Multiplexing of signal and control libraries enables straightforward comparison of samples. Experiments for most proteins achieve high enrichment of signal over background. A point mutation and a natural splice isoform that change the RBP subcellular localization dramatically alter target selection without changing the targeted RNA motif, showing that compartmentalization of RBPs can be used as an elegant means to generate RNA target specificity. RNA-binding proteins have important roles in gene expression and regulation. Here the authors develop uvCLAP to purify proteins and determine their binding sites without the use of radioactivity. The message of an RNA can be changed, muted, enhanced, localized, or delayed post-transcriptionally through the collective action of many RNA-binding3 and RNA-modifying proteins4 that act on these RNA transcripts via binding to specific RNA elements5.Transcriptional control of gene expression is a highly regulated and intensely studied phenomenon that requires a plethora of DNA-interacting proteins and other upstream factors that integrate intracellular and extracellular information to effect an appropriate transcriptional response6, iCLIP7, PAR-CLIP8, and CRAC9, have become the prevalent experimental method for determining binding sites of RNA-binding proteins (RBPs) in living cells. To date, CLIP-Seq has been used to determine binding sites of more than 100 distinct RBPs11. These experiments lead to the recent elucidation of the complex nature of RBP-mediated post-transcriptional regulation. To further uncover the intricate regulatory relationships in the cell, where multiple RBPs can compete or cooperate with each other for target site selection12, researchers will need to probe multiple RBP isoforms, to investigate RBPs with modified or disabled functionalities, to examine multiple candidates involved in a regulatory process, and also to compare results from multiple cell types and knockdown/overexpression scenarios. Widespread application of current CLIP-Seq methods in this manner is generally thwarted by the common use of radioactive labeling of crosslinked RNA, lengthy protocols, and dependence on the availability of high-quality antibodies.CLIP-Seq approaches, such as HITS-CLIPHomo sapiens, Mus musculus, and Drosophila melanogaster and perform experiments for a wide variety of RBPs, namely the KH domain-containing RBPs QKI-5, QKI-6, KHDRBS1-3, and hnRNPK, the DEAD-box helicases eIF4A1 and EIF4A3, the DExH-Box helicases MLE13 and DHX914, the member of the exon junction complex (EJC) MAGOH, and the mouse MSL complex members MSL1 and MSL2. In addition, we probe mutant constructs of KHDRBS1, KHDRBS2, and MLE. In total, we analyze 23 uvCLAP experiments (investigating 15 RBPs) in human, mouse, and fly.Here, we present ultraviolet crosslinking and affinity purification (uvCLAP), a method for identifying in vivo binding sites of RBPs that is fast, robust, does not rely on radioactivity, and that allows to quantify the amount of nonspecific background to obtain transcriptome-wide high-resolution RNA–protein interaction maps with high specificity. We evaluate uvCLAP on the three major model organisms 15.The hallmark of most CLIP-seq approaches is radioactive labeling of RNA that is covalently bound to a protein. Since proteins tend to run at discrete positions on a sodium dodecyl sulfate (SDS)/lithium dodecyl sulfate-polyacrylamide gel electrophoresis setup, cutting out the labeled region removes contaminating RNA and RNA–protein complexes as they migrate to different regions of the gel. In addition, the crosslinked protein–RNA complex is often electrophoretically transferred to a nitrocellulose membrane, which typically binds to proteins (and consequently protein–RNA adducts) but not to free RNA. This transfer is meant as an additional step for removing non-crosslinked RNA but can also introduce additional contaminating RNA6-biotinylation sequence-His6 tandem (HBH) tag16 that allows rapid and ultra-clean purifications without the use of antibodies. We also added a 3xFLAG tag right before the HBH tag to increase the versatility of the construct, which we will refer to as the 3FHBH tag. In contrast to antibodies, tagged constructs can be expected to have similar pulldown efficiency, leading to improved comparability across multiple conditions. To reveal the genomic origin of nonspecific background, we decided to include background controls employing mock pulldowns, using the expression vector carrying the 3FHBH tag without a gene insert. As a straightforward method for capturing the quantitative relationship between pulled down RNA and nonspecific background, we combined signal and control libraries prior to amplification and sequencing.We reasoned that a stringent tandem affinity purification protocol would circumvent these time-consuming and critical steps while nonetheless removing contaminating free RNA and proteins that might nonspecifically co-purify with the tagged proteins. To this end, we decided to use the HisThe complete uvCLAP protocol from cells-on-plates to sequencing libraries takes 4 days with the use of a single gel purification step Fig. . Briefly17. Taken together, this setup should preserve the quantitative relationship between multiplexed uvCLAP samples  for detecting individual crosslinked RNAs. Since the quantitative relationship between signal and control becomes unclear when samples are independently amplified and sequenced, the multiplexed libraries were subjected to joint amplification and sequencing. This also allowed us to circumvent an additional challenge typically arising with the empirical measurement of CLIP-Seq background controls: the combination of ineffectual pulldown and stringent washing typically results in small library sizes requiring high numbers of PCR cycles in order to be suitable for a dedicated sequencing runles Fig. .Fig. 2uvHomo sapiens, Drosophila melanogaster, and Mus musculus , indicating that uvCLAP events capture the amount of pulled down RNA exceptionally well.The quantitative relationship between samples is preserved, if the proportions of observed events match the proportions of RNA in the underlying samples. To determine to what extent the multiplexed treatment of specific and nonspecific pulldown conditions would preserve the quantity of detected RNA, we compared the total number of crosslinking events between pairs of biological replicates, each sharing the respective pulldown condition and construct expression. In total, we compared 23 specific and 7 nonspecific conditions in 6 multiplexed uvCLAP runs in lus Fig. . This re18.We next asked, to what extent the quantitative relationship is preserved at the binding site level. Therefore, we compared peaks, called by slicing the genome into bins, between each pair of biological replicates. For all 100 nucleotide bins covered by at least two events in both replicates, we calculated fold changes between the replicates Fig. . The fol19 or CLIP-Seq20 samples. Having matched amplification and sequencing conditions of multiplexed samples, we should be able to dispose with the need for library normalization. To investigate to what extent library normalization would be required, we determined library normalization factors for uvCLAP biological replicates using the median ratios of counts method21 .Drosophila EIF4A3  and no enrichment for Drosophila MAGOH , which are two small libraries with 517,000 and 392,000 crosslinking events. Experiments for most proteins achieved more than 14-fold enrichment over the controls .To get an overview of the global relationship between events in signal and control libraries, we calculated the total enrichment of specific versus nonspecific conditions based on the total number of crosslinking events per library (replicates combined) Fig. . This anIn case of the fly experiments, we observed an increase in the absolute number of control events Fig. , which wDrosophila library K1, which combined the RNA helicase MLE and several of its mutants. MLE has a unique mode of binding and predominantly targets only two noncoding RNAs22, one of which was expressed in the cell line used for the uvCLAP experiments. Binding to a single gene necessarily leads to small libraries, elevating the absolute counts of control events. Nonetheless, uvCLAP performed well in this unique and challenging setting, which allowed us to thoroughly investigate the functional unit necessary for dosage compensation in Drosophila13.We also detected an above-average absolute number of control events for 23. While low absolute amounts of unspecific background are a necessary condition for achieving high specificity with CLIP-Seq, the pulled down RNA must also be sufficiently enriched over the background to obtain meaningful results. Our results show that low background levels relative to the signal can only be presumed under ideal conditions, that is, for proteins that crosslink well and bind to a large number of sites.Our observation that uvCLAP nonspecific background appears more pronounced for RBPs with small library sizes, matches, and extends the results from the evaluation of nonspecific background for PAR-CLIP by Friedersdorf and KeeneWe conclude that the quantification of nonspecific background is required to reliably determine the binding of putative RBPs using CLIP-Seq. Additionally, global enrichment over background controls can serve as a straightforward means to identify problematic experiments; the extent of background relative to signal must be checked on a case-by-case basis.24. This effect was very pronounced for the high-size fractions to the extent that only few crosslinking events could be identified despite a large number of reads. In consequence, we decided not to use the high-size fractions and omitted their sequencing for Drosophila EIF4A3 and MAGOH uvCLAP.Having separately tagged size fractions available, we observed that events from high-size fractions  and mid-size fractions  were represented by more duplicate reads than the short-size fractions , which may indicate a length bias introduced by PCR amplification25.To further investigate the influence of cDNA length on the amount of duplicate sequences, we evaluated the number of uniquely aligned reads and resulting events of the low-size and mid-size fractions in relation to the length of the corresponding alignments Fig. . The res26 peaks  and calculated Spearman's correlations based on the counts of crosslinking events located on the resulting 764,727 non-overlapping regions  was much higher than the average correlation of 0.08 between unrelated replicates (excluding comparisons between KHDRBS1-3 and between QKI-5 and QKI-6). We also observed high correlations between replicates of KHDRBS2 and its pseudo-mutant construct KHDRBS2R489K, but not between replicates of KHDRBS1 and its mutant KHDRBS1Y440F that differ in cellular localization  near exon–exon junctions, we realigned the libraries using the splice-aware mapper HISAT2ons Fig. , a patteDrosophila EIF4A3. Sixty-eight percent of the peaks located on protein-coding genes were annotated as coding exons  that makes them predominantly cytoplasmic35.Next, we evaluated QKI, a protein with a previously identified sequence specificity, that is known to regulate target-mRNA stability34. We could confirm this behavior for our triple-tagged constructs via anti-FLAG staining . JAMM produces two sets of peaks: a full set of peaks containing also small peaks and peaks with few reads, and a smaller, filtered set where these peaks are removed . To facilitate the comparison of the peaks from the two methods, all peaks were extended to a minimal length of 41 nucleotides, where adjacent or overlapping peaks were merged.We next compared uvCLAP QKI binding sites with sites derived from QKI PAR-CLIP40. Those include differences in RNase digestion, the wavelength used for crosslinking, and preferential crosslinking to 4-thiouridine41, as well as the use of different isoforms and differently tagged expression vectors.A comparison of the overlaps between the full uvCLAP peak sets identified 7761 sites shared between QKI-5 and QKI-6 and 2685 sites shared between uvCLAP and PAR-CLIP, leaving several thousand sites exclusive to each of the three experiments Fig. . Using tTo further investigate the quality of uvCLAP peaks, we determined the occurrence of the consensus motif AYUAAY Fig. . For the42, KHDRBS244, and KHDRBS344 AA repeats38. A preference for UAA was also shown for all three KHDRBS proteins by RNAcompete5. For KHDRBS1, 31.5%  from the full set of peaks and 40.75% (7902 sites) from the filtered set of peaks were also found in KHDRBS1 eCLIP for K563 cells17 , but can also recognize C-rich sequences (hnRNPK). Overall, the uvCLAP-derived binding sites for QKI-5, QKI-6, KHDRBS1-3, and hnRNPK matched prior knowledge regarding binding motifs and binding site localization.We found considerable numbers of common peaks when comparing to PAR-CLIP and eCLIP data, however, all comparisons also showed peaks unique to uvCLAP, PAR-CLIP, and eCLIP. This is a common observation when comparing CLIP-Seq results on the peak leveluvCLAP allows to identify and characterize targets of RBPs in vivo without having to resort to labor-intensive techniques that use radioactive substances. Using highly stringent purification conditions, we are able to remove nonspecifically interacting RNA Fig.  and othe13).A major benefit of joint amplification and sequencing of multiplexed uvCLAP experiments is the preservation of RNA quantities, which allowed us to directly determine the observed proportion of nonspecific background within uvCLAP libraries. For most experiments, we detected very low amounts of background. The vast majority of called peaks were not located in the vicinity of control events, emphasizing the capability of uvCLAP tandem purification to effectively remove nonspecific background. Experiments with low signal-to-control ratio and increased occurrence of control events near binding sites are known to either inefficiently crosslink (EIF4A3), bind to very few sites (MLE), or were deliberate mutations with impaired binding , MLEuvCLAP experiments revealed that STAR proteins bind to similar RNA motifs despite their different subcellular localizations. This ability to identify distinct RNA motifs for highly related proteins further highlights the versatility and robustness of uvCLAP. The determination of in vivo RNA–protein interactions by uvCLAP reveals the multi-faceted nature of RBPs and uncovers compartmentalization of RBPs as an additional mechanism determining RNA target specificity.14. Drosophila S2 cells were a gift of Prof. Matthias Hentze are maintained in Schneider’s Drosophila Medium  and stable cell lines are generated by transfecting the cells with expression vectors that carry a Neomycin-resistance casette and subsquent selection with 1 mg/mL geneticin  for 2 weeks13.Flp-In™ T-REx™ 293 cell line was purchased from Thermo Fisher Scientific  and is maintained with DMEM-Glutamax (Gibco 31966) and 10% fetal bovine serum (FBS). The original cell line was maintained in zeocin-containing and blasticidin-containing medium according to the manufacturer’s protocol  and the zeocin selection is exchanged with hygromycin upon transgene transfection for stable cell line generation. All the transgenes were cloned into pCDNA5-FRT/To  with a C-term 3xFLAG-HBH tag and were co-transfected with pOG44 plasmid with a 1:9 DNA concentration ratio as suggested by the manufacturer’s protocol. Cells were re-plated in different dilutions  24 h after the transfection and selection with 150 µg/mL hygromycin was initiated 48 h after transfection. Cell lines were maintained with blasticidin and hygromycin at all times and the transgenes were induced with with 0.1 µg/mL doxycycline for 16 h both for the uvCLAP and immunofluorescence experiments. Mouse embryonic stem cells were maintained with 15% FBS, 2000 U/mL leukemia inhibitory factor, sodium pyruvate, nonessential amino acid, and 0.1 nM β-mercaptoethanol supplemented into DMEM-Glutamax. CRISPR/Cas9 facilitated endogenous tagging of the mouse Msl1, Msl2, and Dhx9 was performed in a mouse ES cell line  using CRISPR-Cas9 and single-stranded oligo donors targeting the endogenous lociDoxycycline-induced cells were crosslinked with 4% methanol-free formaldehyde in phosphate-buffered saline (PBS) at room temperature for 10 min and permeabilized with 0.1% Triton-X and 1% bovine serum albumin (BSA) in PBS for 30 min at room temperature. Primary FLAG-M2  antibody was diluted (1:500) in PBS with 0.1% Triton-X and 1% BSA and incubated with fixed cells at 4 °C for ~16 h. Fluorescently labeled secondary antibodies with the appropriate serotype were used reveal target proteins. Hoechst 33342 to stain DNA. Imaging was performed with a Leica SP5 confocal microscope.2 UV-C light at ~254 nm). Following crosslinking, cells are pelleted by centrifugation, snap-frozen in liquid nitrogen, and kept at −80 °C until use. Cells are then defrozen on ice and lysed with 0.5 mL of lysis buffer , mildly sonicated, and immunoprecipitated with anti-FLAG beads for 1 h at 4 °C or incubated with His-Tag Pulldown Dynabeads  for 10 min. The beads are washed with lysis buffer and bound material is eluted with 3xFLAG peptide (250 µg/mL) or 250 mM imidazole in respective lysis buffer. The eluate is then incubated with MyONEC1 beads to collect biotinylated target protein, after which the beads are washed with high-stringency buffers  to aggressively remove nonspecific interactors. To trim the crosslinked RNA, the beads were resuspended with 1 mL of NDB , to which 2 µL of TURBO DNase  and 10 µL of diluted RNaseI ) were added. The solution is incubated at 37 °C for 3 min and immediately transferred to a metal block cooled on ice. Dephosphorylation of cyclic phosphate groups was carried out with T4 PNK  in a low pH buffer (25 mM MES (2-(N-morpholino)ethanesulfonic acid), pH 6.0; 50 mM NaCl; 10 mM MgCl2; 0.1% Tween-20; 20 U RNasin ; 10 U PNK; 20 min at 37 °C). 3′-Linkers are then ligated with T4 RNA ligase 1, excess adapters are washed away, and 3′-tagged, crosslinked RNA is released with proteinase K digestion and column purification (Zymo DNA Clean and Concentrator). Reverse transcription is carried out with SuperScript III and barcoded reverse transcription primers. After reverse transcription, relevant samples are mixed and the cDNA is separated on a 6% 6 M urea PAA gel. Size-fractionated cDNA is then circularized with CircLigase, linearized by restriction digestion, and amplified by PCR to generate sequencing libraries.Doxycycline-induced FLPin Trex HEK293 cells are rinsed with PBS and the protein of interest is crosslinked to its cognate RNA by UV irradiation  or when affinity-tagging changes target protein localization and/or activity.Silver gel staining Fig.  was perfIn order to evaluate purified RNA in a uvCLAP experiment Fig. , followiThe uvCLAP tri-barcode approach—based on ScriptSeq PCR primers and two custom 5 nucleotide barcodes adjoining the 5′-adapter and 3′-adapter—allows to flexibly tag source libraries according to multiple experimental conditions. This tagging strategy enabled us to tag pulldown conditions, size fractions, and biological replicates prior to PCR amplification and sequencing. Custom 5′-tags and 3′-tags were used to distinguish pulldown condition and biological replicates. Size fractions were differentiated using commercially available ScriptSeq PCR primers.ScriptSeq primer sequences were: AATGATACGGCGACCACCGAGATCTACACTC TTTCCCTACACGACGCTCTTCCGATCT (5′ adapter) and CAAGCAGAAGACGGCATACGAGATNNNNN NGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT .50 to design tags robust to indel (insertion/deletion) and substitution errors. For this purpose, we created a set of five nucleotide tags with minimal pairwise edit distance of 3, ensuring that 1 indel or substitution error can be corrected. The initial set of 31 candidate tags, created so as not to contain polybases or self-complements, was further filtered for tags containing nucleotide repetitions at either end (11 tags) and tags reverse complementary to the adapter (5 tags), leaving 15 tags for use by uvCLAP . Any pair of tags created according to these patterns has a guaranteed minimum edit distance of 2; correspondingly, these tags are not error correcting in the context of indel and substitution errors. These tags are robust to substitution errors as are predominantly produced by Illumina-type sequencing, requiring four substitutions to erroneously assign any given tag to the wrong pattern.1T2T3T4T5NN , yielding 1024 different combinations. The semi-random barcodes positioned adjacent to the 3′-adapter provide additional 48 different combinations. In combination, these serve to extend the detectable number of crosslinking events per nucleotide position to at least 49,152 events. To reliably detect the barcodes positioned at both ends of the genomic inserts, uvCLAP by default uses paired-end sequencing. This also allows the use of the genomic positions of both insert ends during PCR duplicate removal to further increase the number of detectable crosslinking events, depending on the number of different insert lengths in the sequenced library.uvCLAP uses random barcodes that serve as UMIs to identify individual crosslinking events from sets of potentially large PCR duplicates. Here, edittag-designed 5′-tags were interleaved with five random nucleotides according to the pattern NNNTEach multiplexed uvCLAP contains at least two biological replicates of signal and control libraries. Joint amplification and sequencing of these experiments preserves RNA quantities between the multiplexed libraries both at the global and at the binding site level, allowing their direct comparison. While methods that necessitate library normalization only allow the comparison of conditions that share a large number of binding sites of similar strengths, uvCLAP preserves the quantitative relationship of samples with identical pulldown conditions; the proportions of observed events correspond to the proportions of RNA in the samples. Hence, a direct comparison between conditions can be performed whenever the comparison of the RNA amounts would be meaningful, that is, when the target RBPs are normally expressed and when the pulldown efficiency of the targeted RBPs is similar. For this purpose, we suggest using endogenously tagged proteins for the comparison of specific pulldown conditions. Preservation of relative RNA quantities makes uvCLAP ideally suited for the investigation of differential binding of multiple protein isoforms or knockdown conditions. The straightforward comparability of arbitrary pulldown conditions also allows for a detailed investigation of altered binding exhibited by RBP deficiency mutants. Importantly, these controls are also effective when there is no prior knowledge of the expected binding behavior.51. Barcodes and UMIs were extracted using custom scripts. To reliably remove readthroughs into barcode regions containing random and semi-random nucleotides, five nucleotides (corresponding to the length of semi-random 3′ tags) were clipped from the 3′ ends of first mate reads, 10 nucleotides (corresponding to the length of 5′ tags and UMIs) were clipped from 3′ ends of second mate reads. Since any genomic sequence removed by this step is guaranteed to be contained in the other mate, no information is lost. Bowtie2 (version 2.2.2)52 was used to map demultiplexed and processed reads to reference genomes hg19, dm3, and mm10. Uniquely mapped reads were extracted by removing all reads for which multiple alignments could be identified by bowtie2 as indicated by the “XS:i” SAM flag. Alignments sharing UMIs and start coordinates of first and second mate reads were combined into individual crosslinking events. We removed all crosslinking events supported by less than 10% of the reads compared to the event with the highest number of reads at the same genomic position as they were most likely spurious events arising from errors introduced into UMIs during amplification or sequencing22.uvCLAP libraries were demultiplexed and adapters removed using Flexbar (version 2.32)32 alignments were performed using version 2.0.5 and “parameters --fr --no-mixed --no-discordant”.HISAT253, enrichment over shuffled input signal56, or modeling of read-count distributions58 not suitable because of the specifics of our data.Peak calling refers to the identification of discrete binding sites from reads or binding events and usually  includes an additional filtering to reduce the number of false-positive binding sites. However, we found most strategies such as the presence of specific mutations caused by crosslinked nucleotides23. The second approach uses global read-count distributions and assumes that most reads are nonspecific. This assumption is again not indicated by our data. A third possibility would be to omit any filtering after the initial peak identification presuming that our libraries are mostly free of background7. This was also rejected as there are varying numbers of nonspecific events seen in our data.The majority of peak calling methods compare the number of crosslinking events of a region to a baseline distribution in order to determine regions with a significantly enriched number of events. There are in general two principal approaches for determining this baseline. The first approach, namely shuffling of the gene-wise input signal, assumes a uniform distribution for the baseline. However, this uniformity is neither observed with uvCLAP nor the PAR-CLIP nonspecific background26, a universal peakfinder that is able to integrate information from uvCLAP biological replicates and background controls, and PEAKachu27 a stringent peak caller that utilizes the rigorous statistical evaluation of DESeq259. JAMM typically reports a large number of peaks, allowing for a flexible posterior filtering.Thus, we looked for a peak calling method that is able to incorporate quantitative controls into the peak calling procedure. This requirement is satisfied by JAMM26 26 based on crosslinked nucleotides of biological replicates of signal and control libraries. An additional set of peaks was called using PEAKachu27 .Peaks were called using JAMM60 (version 2.3.5)60 based on the number of events on the merged peak regions of the full sets of JAMM peaks. Spearman's correlations of biological replicates based on PEAKachu peaks were calculated using bedtools61, parallel62 and R (version 3.4.2).Pairwise Spearman's correlations for Fig. 36 sequence models were trained on 10,000 randomly selected peaks and roughly equal numbers of unbound sequences, using default parameters (GraphProt version 1.1.3)36. Unbound sequences were selected by randomly placing peaks within genes with at least one binding site and at least 100 nucleotides apart from any bound site. For training and motif generation, the 60 nucleotides surrounding peak centers were used. Motifs were generated based on the 5% highest-scoring peaks among the 10,000 bound training instances.GraphProt63 (version 0.7.9)63.Venn diagrams comparing uvCLAP, PAR-CLIP, and eCLIP peaks were created using pybedtoolsFLAG-HRP  used at 1:2000 dilution in Fig. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE87792) (MLE) and GSE85155 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE85155) . PEAKachu peaks are available at Zenodo (https://zenodo.org/record/1063948)64.The uvCLAP data in this study has been deposited to the Gene Expression Omnibus database under the accession numbers GSE87792 (Supplementary Information(PDF 6505 kb)Peer Review File(PDF 321 kb)


Drosophila species and the primate lineage using DeepRed. Our work illustrates DeepRed’s state-of-the-art performance; it may decipher the hidden principles behind RNA editing, making editing detection convenient and effective.RNA editing is a post-transcriptional RNA sequence alteration. Current methods have identified editing sites and facilitated research but require sufficient genomic annotations and prior-knowledge-based filtering steps, resulting in a cumbersome, time-consuming identification process. Moreover, these methods have limited generalizability and applicability in species with insufficient genomic annotations or in conditions of limited prior knowledge. We developed DeepRed, a deep learning-based method that identifies RNA editing from primitive RNA sequences without prior-knowledge-based filtering steps or genomic annotations. DeepRed achieved 98.1% and 97.9% area under the curve (AUC) in training and test sets, respectively. We further validated DeepRed using experimentally verified U87 cell RNA-seq data, achieving 97.9% positive predictive value (PPV). We demonstrated that DeepRed offers better prediction accuracy and computational efficiency than current methods with large-scale, mass RNA-seq data. We used DeepRed to assess the impact of multiple factors on editing identification with RNA-seq data from the Association of Biomolecular Resource Facilities and Sequencing Quality Control projects. We explored developmental RNA editing pattern changes during human early embryogenesis and evolutionary patterns in  RNA editing plays a critical role in many biological processes, including neuronal function3, cancer development6, embryogenesis8, and immune response9. A-to-I editing, the most common type of RNA editing in metazoans10, refers to the process of adenosine (A) deamination to inosine (I); I is then decoded as guanosine (G) in translation, which is mediated by members of the double-strand RNA-specific adenosine deaminase act on RNA (ADAR) family11. A-to-I editing can expand the transcriptomic and proteomic diversity by generating RNA mutations and altering gene regulation. It is evolutionarily conserved and is hypothesized to facilitate adaption in metazoans13. Recoding A-to-I editing sites have been shown to be essential to cellular function and development by inducing amino acid changes in coding regions15. Previous studies showed that the abnormality of A-to-I editing is closely related to human diseases, especially in central nervous system (CNS) diseases and cancers20. Abundant A-to-I editing sites in CNS affect receptors and ion channels expression21. The editing level of A-to-I editing is significantly elevated in various cancer tissues22. The prevalence and biological function of A-to-I editing have been illuminated widely. Besides, there are still some non A-to-I editing sites that being verified to be functionally meaningful25. Therefore, it is vital to identify RNA editing sites accurately.RNA editing is a post-transcriptional modification26. The development of powerful computational pipelines has made the study of RNA editing using RNA-seq data pervasive. In 2013, Li developed the first methods, including the separate samples and pooled samples methods, to identify RNA editing sites using RNA sequencing data alone without the need for matched genome sequencing27. After that, a prediction method was established to accurately predict constitutive RNA editing sites by using new parameters namely Hits Per Billion-mapped-bases (HPB) and Potential SNP Score (PPS)28. GIREMI is a software package that utilizes allelic linkage and generalized linear models to distinguish between RNA editing sites and genetic variants in a single RNA-seq sample29. RNAEditor provides an easy-to-use tool to identify RNA editing events and developed a clustering algorithm to find editing islands30. These methods, which use RNA-seq data alone with prior-knowledge-based filtering, have greatly facilitated RNA editing detection and effectively use public transcriptomic sequencing datasets without available DNA sequencing data. However, the filtering steps based on prior knowledge and public genome annotations, such as Alu repeats, genomic duplications, and pseudogenes, are cumbersome and time consuming. Moreover, arbitrary and artificial combinations of filtering steps may result in different candidate RNA editing sites. Furthermore, these methods suffer from limited generalization and applicability to other species due to insufficient genomic annotations and prior knowledge. For example, the absence of Alu repeat annotations in the Drosophila genome makes the identification of RNA editing inefficient, whereas the deficiency of single nucleotide polymorphism (SNP) information in some species makes the GIREMI and RNAEditor non-applicable. In 2016, two user-friendly web servers, called PAI and iRNA-AI, were established to identify A-to-I editing from RNA sequence information alone32. These two methods both employed support vector machine as classifier. PAI incorporated six RNA physiochemical properties and global sequence order information to identify A-to-I editing sites in D. melanogaster31. iRNA-AI incorporated the chemical properties of nucleotides and their sliding occurrence density distribution to identify human A-to-I editing sites32. These two methods provide insights into the identification of RNA editing sites based on machine learning method.The emergence of next-generation sequencing technologies has greatly facilitated the identification of RNA editing sites by using high-throughput sequencing based methods33 and achieved 97.9% positive predictive value (PPV), demonstrating the high prediction accuracy of our DeepRed method. Additionally, using experimentally verified RNA-seq data in K562 and HepG2 cell lines34, DeepRed achieved consistently superior performance in comparison with current state-of-the-art methods, including separate samples, GIREMI, RNAEditor and Prediction methods. Further performance assessment of DeepRed in mass RNA-seq data from large-scale RNA-seq studies demonstrated that DeepRed exhibited superior performance with the highest A-to-I ratio, lowest false discovery rate, and highest computational efficiency relative to these state-of-the-art methods.In this study, we present a deep learning-based method called DeepRed that accurately identifies RNA editing by learning and summarizing essential features from the surrounding primitive sequence of candidate SNVs directly without requiring prior-knowledge-based filtering steps. DeepRed achieved 98.1% and 97.9% area under the curve (AUC) in a training set and test set, respectively, indicating that DeepRed manifests good generalization ability. Then, we validated our DeepRed method using independently experimentally verified RNA-seq data from a U87 cell line35 and Sequencing Quality Control projects (SEQC)36 projects to assess the impact of a series of factors, including flanking bases of RNA editing sites, library preparation, RNA degradation, sequence depth, laboratory, read mapping, and variant calling, on the identification of RNA editing. We found that the identified number of RNA editing sites between intact and degraded RNA and different library methods, laboratories, sequence depths, or combinations of read mapping and variant calling methods varied largely. However, the prediction accuracy of RNA editing identified in these conditions, except for sequence depth, are roughly consistent. The prediction accuracy of RNA editing sites may deteriorate at depths less than 15 million mapped reads, and 15 million mapped reads can ensure adequate accuracy of RNA editing identification. The reproducibility of RNA editing sites in these experimental conditions are relatively low, whereas the reproducibility of RNA editing sites identified between different read mapping and variant calling methods is much higher.Moreover, we used RNA-seq data from the Association of Biomolecular Resource Facilities (ABRF)Drosophila species. We discovered the stage-specific change pattern of RNA editing at 8-cell, morula and late blastocyst stages during human early embryogenesis and the evolutionary conservation of RNA editing between close lineages. Together, our work illustrates the superior performance of DeepRed at identifying RNA editing from RNA sequences without genomic annotations or prior-knowledge-based filtering steps. DeepRed will make the detection of RNA editing convenient and effective and will facilitate future studies on RNA editing.Finally, we applied DeepRed to explore the developmental pattern of RNA editing changes during human early embryogenesis and the evolutionary pattern of RNA editing in the primate lineage and We developed DeepRed, a deep learning-based hybrid framework integrated with ensemble learning, to precisely and conveniently predict RNA editing sites using RNA-seq data alone. DeepRed learns and summarizes essential features from the surrounding primitive sequence of candidate SNVs directly without requiring complicatedly prior-knowledge-based filtering steps. It could identify all 12 possible types of RNA editing sites, of which A-to-I is the most prevalent mismatch type. Based on the positive and negative sets that we constructed Fig. . Our Dee33 using modified 5-fold cross-validation  PPV.We validated whether the editing sites identified with DeepRed were bona fide editing events by identifying RNA editing sites using an independent U87 test sample from ENCODE and comparing the sites to those identified from experimental samples of wild-type and knock-down U87 .Next, we applied DeepRed to RNA-seq data from the ABRF41 based on site importance score namely the mean decrease in Gini index to assess the site importance for prediction. We found that the site importance score decreased with the increasement of distance to the candidate site . The number of identified RNA editing sites varied largely with different read mapping methods and variant calling methods. However, the A-to-I ratios were all approximately 70% for different read mapping methods and variant calling methods, indicating that the different read mapping methods and variant calling methods had little effect on the identification accuracy of RNA editing sites . Further reproducibility analysis demonstrated that when different combinations of read mapping and variant calling methods were used, the reproducibility of the identified RNA editing sites between any two combination methods was relatively high, ranging from 52.29% to 95.36%, except for the combination of STAR and Tophat with SAMtools as the variant calling method (43.79%)  Fig. . TogetheDrosophila species and the primate lineage. First, we applied DeepRed to identified RNA editing sites using single-cell RNA-seq data from human embryos spanning from the oocyte to late blastocyst stage47  and continually decreased at the morula  and late blastocyst  stages . We applied DeepRed to RNA-seq data from adult whole bodies of D. melanogaster, D. simulans, D. ananassae, D. pseudoobscura, D. mojavensis, and D. virilis48 51, and embryonic left-right separation, occur in a stage-specific fashion52. We demonstrated that RNA editing could describe the genetic relationship between species through the evolution analysis of RNA editing in Drosophila species and the primate lineage. The consistent evolutionary pattern of RNA editing found in Drosophila species and the primate lineage agrees well with findings from previous studies53. These results demonstrated the broad applicability of our DeepRed method and confirmed the reliability of DeepRed from an application point of view.The exploration of RNA editing in human early embryogenesis showed that the number of A-to-I RNA editing sites changes in a stage-specific manner during human early embryogenesis. We noticed that genome-wide A-to-I RNA editing sites dramatically decreased at the 8-cell, morula and late blastocyst stages, suggesting that the sudden drop of A-to-I RNA editing sites may be associated with a specific regulatory mechanism and may have important biological significance during human early embryogenesis. The dramatically decreased number of RNA editing sites at the 8-cell and morula stages aligned well with that results of a previous studyTaken together, our DeepRed method could decipher the hidden principles behind RNA editing by extracting and learning features from the raw sequences directly and predicted RNA editing sites accurately without prior-knowledge-based filtering steps or genome annotations. Our DeepRed method will make the detection of RNA editing convenient and effective and will facilitate the study of RNA editing.38. For each cell type, we used STAR46 (Version: 2.5.2b) and GATK43 (version 3.5.0) to call SNVs from RNA-seq data, then used the separate samples or pooled samples method proposed by Li’s group to identify the RNA editing sites27 . Thus, the candidate SNVs were classified into RNA editing sites, SNPs and other. Then, we constructed separate and pooled gold standard sets for each cell using the RNA editing sites identified by the separate samples and pooled samples methods as the positive set and using SNPs and other SNVs as the negative set  and negative set (SNPs and other SNVs) using 64 RNA-seq samples of 32 cells/tissues (two replicates per cell line) from the ENCODE projectet Table . The ratet Table . Of the et Table .We designed the single-cell module to be a two-level ensemble classifier that used a simple averaging method to combine multiple ensemble Deep Neural Networks (DNNs) together Fig. . We adopFirst, we optimized the structure of the individual DNN in ensemble DNN. Each individual DNN took the 201-bp primitive sequence centred at candidate SNVs as an input. One-hot-encoding was applied to encode the bases “A”, “T”, “C”, and “G” of the 201-bp primitive sequence as , ,  and , respectively. We fixed the number of hidden units in each hidden layer to 10, 50, 100, 500 and 1,000, and we tuned the structure of hidden layers using modified 5-fold cross-validation  by grid search. In total, up to four hidden layers were searched. The optimized structure of individual DNNs consisted of two hidden layers with 1,000 units in the first hidden layer, 100 units in the second hidden layer, and a Softmax output layer with 3 output units.Then, we used modified 5-fold cross-validation to assess the performance of ensemble DNN with different numbers of individual DNNs with the optimized structure Fig. . The per41 + C42 + C43 + C44 = 15). We found that the 101-bp and 41-bp scale combination achieved the best performance. Thus, the single-cell module combined two ensemble DNNs consisting of 20 individual DNNs each with input scales of 101 bp and 41 bp . The single-cell module of DeepRed achieved 0.933 AUC and 86.6% geometric mean (GM), suggesting that the single-cell module illustrates superior performance. Then, we used the optimized structure of the single-cell module as the basic unit of the separate and pooled components of DeepRed.Finally, we evaluated the performance of the single-cell module on U87 dataOur DeepRed method is a hybrid framework integrating deep learning and ensemble learning in which the input data flow from the lowest level individual DNN of the single-cell module to the highest level DeepRed, i.e., from the individual DNN, ensemble DNN, single-cell module, and separate/pooled component to DeepRed. We assessed the performance of the hybrid structure of DeepRed at each level using the training set and independent test set . We found that the average AUC increased in a level-wise manner and that the deviation of AUC decreased in a level-wise manner. Our result demonstrated that the hybrid structure of DeepRed, which integrated deep learning with ensemble learning, promoted the identification accuracy of RNA editing, further confirming the advantages of the hybrid framework of DeepRed.27 40 projects 48 (Table 49 (Table 43 (Version: 3.5.0), whereas for Drosophila analyses, SNVs were called using SAMtools54 (Version: 1.3.1) . Information on the reference genome, dbSNPs and gene model used in this study are listed in Table We applied our DeepRed method to identify RNA editing sites in the oocyte, zygote, 2-cell, 4-cell, 8-cell, morula, and late blastocyst stagesis Table . We down48 Table  and the 49 Table . For humhttps://github.com/wenjiegroup/DeepRed.The source code for our DeepRed method can be freely accessed at Supplementary infoTable S1Table S2Table S3Table S4Table S5Table S6Table S7Table S8Table S9Table S10Table S11Table S12Table S13Table S14Table S15Table S16Table S17Table S18