RNAs perform their function by forming specific structures, which can change across cellular conditions. Structure probing experiments combined with next generation sequencing technology have enabled transcriptome-wide analysis of RNA secondary structure in various cellular conditions. Differential analysis of structure probing data in different conditions can reveal the RNA structurally variable regions (SVRs), which is important for understanding RNA functions. Here, we propose DiffScan, a computational framework for normalization and differential analysis of structure probing data in high resolution. DiffScan preprocesses structure probing datasets to remove systematic bias, and then scans the transcripts to identify SVRs and adaptively determines their lengths and locations. The proposed approach is compatible with most structure probing platforms . When evaluated with simulated and benchmark datasets, DiffScan identifies structurally variable regions at nucleotide resolution, with substantial improvement in accuracy compared with existing SVR detection methods. Moreover, the improvement is robust when tested in multiple structure probing platforms. Application of DiffScan in a dataset of multi-subcellular RNA structurome and a subsequent motif enrichment analysis suggest potential links of RNA structural variation and mRNA abundance, possibly mediated by RNA binding proteins such as the serine/arginine rich splicing factors. This work provides an effective tool for differential analysis of RNA secondary structure, reinforcing the power of structure probing experiments in deciphering the dynamic RNA structurome. The authors present DiffScan, an advanced tool for normalization and differential analysis of RNA structure probing experiments, combining their power in deciphering the dynamic RNA structurome and facilitating the discovery of RNA regulatory functions. Deciphering RNA structure is informative for understanding RNA functions. In recent years, diverse structure probing (SP) methods have been developed to study RNA secondary structure in various cellular contexts, which utilize chemicals that react differentially to nucleotides according to their local stereochemistry, pairing status, solution environment, etc5. Coupled with next generation sequencing technologies, SP experiments can be performed at high throughput, providing a transcriptome level view of RNA secondary structure6, i.e., RNA structurome. There are various mature high-throughput SP platforms, such as SHAPE-Seq8, DMS-Seq9, and icSHAPE10. These platforms offer flexible options for tackling different biological problems, and have achieved success in uncovering pervasive links between RNA structure and RNA function11.RNA molecules play important roles in myriad cellular processes by forming specific structures12. For example, the ubiquitous yybP-ykoY motif has been shown to adopt distinct structures in response to manganese ions, thus exerting regulatory consequences on protein translation in both Escherichia coli and Bacillus subtilis13. RNA structural variations have also been reported to regulate the binding of trans-acting factors and RNA stability in multiple organisms, including human, yeast, and zebrafish18.Studies have examined how the structures of particular RNA molecules change across multiple cellular conditions, revealing explicit connections between RNA structure and RNA function19 and RASA20 directly model and compare raw read counts at each nucleotide position, and then identify regions enriched for position-level signals. However, their models are tailored for specific experimental protocols, and it is not straightforward to extend them to accommodate more emerging SP techniques. ClassSNitch21 uses machine learning methods to predict SVRs from pre-defined regions, such as segments of RNA transcripts harboring single-nucleotide mutations. It requires manually labeled SHAPE data for model training, which is based on visual judgements from experienced RNA scientists. While this method can obtain high prediction accuracy, the requirement of manual curation makes it prohibitive to generalize the strategy to more SP platforms. StrucDiff22, deltaSHAPE23, and dStruct24 take reactivities as input, which are estimated from raw read counts to summarize the pairing status at each nucleotide position. They search for SVRs with pre-specified search lengths to aggregate differential signals24. However, the choice of search length is based on prior domain knowledge, which can be subjective. Unlike the above methods which directly detect SVRs, diffBUM-HMM25 infers structural variation at position level by calculating a posterior probability of differential modification for each nucleotide position.To explore the SP experiments to uncover dynamic RNA structures, quantitative tools that contrast SP experiments to identify structurally variable regions (SVRs) are in great demand. Several methods have been proposed to identify SVRs. PARCEL26. As a result, searching with fixed search length can lead to insufficient detection power and inaccurate boundary mapping, when the prespecified search length deviates greatly from the true length. Second, SP platforms differ in utilization of additives, specific experimental protocols, and preprocessing pipelines, yielding distinct data types and distributions in output27. As a result, it is desirable but usually not easy to extend methods developed for one platform to another. Third, SP data can be confounded by systematic bias5, which should be removed from reactivities of the two compared conditions prior to differential analysis28. However, the performance of existing normalization techniques with multiple SP platforms has not been evaluated in benchmarking comparisons. Finally, rigorous error control is also highly impactful on the accuracy and biological relevance of output from SVR detection24, especially for transcriptome-scale analyses.Previous methods have addressed the many challenges of differential analysis of SP data to varying degrees. Continued development of methods to tackle the following challenges will advance insights from SP data. First, SVRs manifest great variation in length, ranging from a few to several dozens of nucleotide positionsWe advance the state of the field by developing DiffScan, a computational framework for differential analysis of SP data at nucleotide resolution. DiffScan normalizes SP reactivities via a built-in Normalization module, which is compatible with various platforms, and then looks for SVRs via a Scan module. The Scan module locates SVRs at nucleotide resolution, and rigorously controls family-wise error rate. We demonstrate with large-scale simulated datasets and benchmark datasets that DiffScan can achieves superior or comparable power and accuracy in SVR detection across various platforms, compared to state-of-art methods. We apply DiffScan to a recent icSHAPE dataset of RNA structurome in multiple subcellular compartments , and reveal the potential roles of SVRs in regulating mRNA abundance, possibly mediated by RNA binding proteins such as the serine/arginine rich splicing factors.p values.DiffScan comprises a Normalization module and a Scan module Fig. . SP reac28, including transcript abundance, sequencing depth, and signal-to-noise ratio (see Methods). Owing to discrepancies in these factors, the SP reactivities obtained from two conditions lacking substantial biological differences may show substantial differences29 . Next, the reactivities of within-condition replicates, if available, are processed by quantile normalization36. These methods can accurately map regional signals and control family-wise error rates during multiple testing of interrelated hypotheses. Scan statistics have been successfully applied to many areas including molecular biology37 and human genetics38. The Scan module of DiffScan was developed with the goal to identify SVRs at nucleotide resolution in a data-adaptive fashion. In brief, the Scan module slides through the transcripts to enumerate overlapping regions of different lengths, identifies regions with maximal differential signals, and evaluates their statistical significance. In detail, we first quantify positional differential signals by calculating a p value for each nucleotide position by Wilcoxon test, which contrasts reactivities between conditions in a small window surrounding the nucleotide position. Note that the Normalization module does not enforce any specific distributions of the normalized reactivities  scores.The sum in the numerator aggregates positional differential signals, while the denominator penalizes the extension of a candidate region. A discussion of the We search in the transcripts with sliding windows of different lengths, and calculate the scan statistic for each candidate region Fig. . A Monte40 and one type of icSHAPE reactivity17 (see Supplementary Methods). Biologically, it is often the case that a particular RNA molecule will occur as a mixture of structural conformations in a given cellular condition42, so SVRs detected between conditions represent altered proportions comprising these mixtures of structural conformations. Thus, the between-condition differences in RNA secondary structures can be very subtle in reality. To mimic the complexity of the landscape of RNA secondary structures, we sampled multiple structural conformations from an ensemble of energy-function-based predictions, and mixed them in varying proportions in the simulated datasets (see Methods). The reactivities were thereafter simulated based on the pairing status of each nucleotide.We simulated RNA secondary structures in two conditions, and generated SP reactivities based on the simulated secondary structures in each condition using three types of empirical models representing different SP platforms, including two types of SHAPE reactivity17. The lengths of the RNA transcripts ranged between 60 nt and 1,972 nt, and the simulated SVRs covered 1.6% to 67.3% of the nucleotide positions of the RNA transcripts  cells23) and to three different values  for dStruct. The following discussions are based on dStruct with minimum search length of 5 nt. The results corresponding to minimum search length of 1 nt and 11 nt are consistent and provided in Supplementary Figs. We compared the performance of DiffScan with two other SVR detection methods, deltaSHAPE and dStruct. Note that DiffScan adaptively determines the lengths of SVRs from data, whereas deltaSHAPE uses a fixed search length and dStruct uses a prespecified minimum search length. We set the search length to 5 nt for deltaSHAPE  and the simulated SVRs. DiffScan consistently has the largest Jaccard index in all 9 settings Fig.  and 8a. Next we evaluated the accuracy of SVR boundary mapping by the average distance from the predicted SVRs to the true SVRs. In the ideal case, if a predicted SVR sits within a true SVR, the distance for each nucleotide in the predicted SVR is zero. Oppositely, if a predicted SVR is off-target, i.e., containing many nucleotides that are neither in or close to the true SVRs, the distances will be large. The average distance provides complementary information of Jaccard index, as predicted SVRs that are far away from any true SVRs will have a greater distance than those close to a true SVR. An illustrative example for the average distance is provided in Supplementary Note Finally, we evaluated the precision and recall rate, and the specificity (see Methods) of the SVR detection methods. Among all three methods, DiffScan achieves the best precision at the same recall rate Fig.  and 8c. 24 to compare the above methods. Briefly, DiffScan and dStruct outperform deltaSHAPE, RASA, and PARCEL regarding to Jaccard index and precision at fixed recall rate, demonstrating that the advantage of DiffScan is not specific to the simulation framework  from multiple SP platforms, including SHAPE-Seq and icSHAPE. In these datasets, no SVRs are present between the compared conditions. Thus, any significant SVRs identified by the detection methods would represent false positives. Other SVR detection methods were included based on their applicability to the negative control datasets  has five annotated SVRs, ranging in length between 1 nt and 8 nt. The RRE dataset43 is from a SHAPE-Seq experiment that measured the secondary structure of the HIV Rev-response element in the presence or absence of the Rev protein. There are seven annotated SVRs for Rev-RRE interactions in the transcript (369 nt in length), with lengths ranging between 7 nt and 39 nt.We used two benchmark datasets with explicitly annotated SVRs between the compared conditions, curated by Choudhary et al.We applied DiffScan, deltaSHAPE, diffBUM-HMM, dStruct, PARCEL, and RASA to identify SVRs in the two datasets. Visualization of the predicted SVRs and the annotated SVRs are provided in Supplementary Figs. In addition, their performance is compared by the Jaccard Index and average distance to true SVRs. DiffScan had the second largest Jaccard index in the Flu dataset and the largest Jaccard index in the RRE dataset Fig. . The Jac17. The DiffScan-predicted SVRs for the Ch versus Np , nucleoplasm (Np), and cytoplasm (Cy) Np Fig.  and for  Np Fig.  comparis Np Fig. . In addi44 to search for RBPs with binding motifs enriched in the predicted SVRs from the Np versus Cy comparison (see Methods). 27 RBPs were identified . Significant enrichment was found for the predicted SVRs from the Ch versus Np comparison and the Np versus Cy comparison  curated in the GWAS Catalog databaseson Fig. . The res60, while the icSHAPE platform utilizing slow-acting reagent allows in vivo transcriptome-wide structure probing17. As demonstrated with simulated and benchmark datasets, the excellent performance of DiffScan in terms of statistical power and accuracy for identifying SVRs is robust across different SP platforms. Third, DiffScan rigorously controls for the family-wise error rate in SVR detection, which is particularly influential for transcriptome level analyses.We have developed DiffScan, a computational framework for differential analysis of RNA secondary structure measured in multiple SP platforms. Our method provides several advantages. First, it adaptively estimates the lengths and locations of SVRs with single-nucleotide resolution. Compared to existing SVR detection methods, predicted SVRs by DiffScan are closer to true SVRs, both from simulated and benchmark datasets. Second, DiffScan is compatible with multiple SP platforms with robust performance. Existing SVR detection methods are usually tailored for a specific SP platform, and their generalization to other SP platforms can be prohibitive. Our method flexibly accommodates multiple SP platforms through its Normalization module and using the nonparametric test we implemented. This enables flexible analysis that is adaptable to suitable data types reflecting particular biological problems of interest. For example, the SHAPE-Seq platform with fast-acting reagent is suitable for in vitro studies of RNA folding dynamics61. In addition, when the structurally invariant set can be specified by prior knowledge, the normalization step can be easily adapted. Second, we recognize the statistical power of DiffScan is not optimized for a particular SP platform, so when the distribution of reactivities is known a priori, use of an appropriate parametric test would yield increased power. Third, proper modeling of inter-replicate variability when calculating reactivities can potentially improve the overall performance of DiffScan. Fourth, we note that the ultimate goal of studying SVRs is to understand the functional roles of RNA structure, which often involves cross-examination of other data sources, including motif analysis of RBPs, multi-omics datasets, etc. Incorporation of these multi-source data may help to accurately annotate SVRs11.DiffScan has several limitations. First, the Normalization module in DiffScan relies on a structurally invariant set of nucleotide positions, which is identified via a built-in data-driven strategy. The strategy would fail in extreme cases when almost all nucleotide positions in the studied transcripts exhibit structural variations, although we expect it is rarely the case in practice24. The Flu dataset probed the Bacillus cereus crcB fluoride riboswitch in vitro with and without fluoride ions, with four replicates for each condition. The transcript is 100 nt in length, and nucleotide positions 12–17, 22–27, 38–40, 48, and 67–74 were identified as SVRs between conditions. The RRE dataset studied the HIV Rev-response element, with three replicates measured in the presence and absence of Rev. The Rev-RRE interaction sites are considered SVRs. We obtained raw read counts and reactivities for the Flu dataset and reactivities for the RRE dataset, based on data availability.We evaluated the power of DiffScan and other SVR detection methods in two SHAPE-seq datasets, of which the SVRs were curated in Choudhary et al.10, by contrasting the two in vitro samples (Control 5) and the two in vivo samples (Control 6).Synthetic negative control datasets were constructed from real SP data to assess the specificity of DiffScan and existing methods. For construction, we randomly split replicates in a single condition into two groups, and identified SVRs by contrasting the two groups. Any reported SVRs were considered false positive results. In particular, control datasets 1–4 were obtained by randomly splitting samples in the Flu dataset in the absence (Control 1) and presence (Control 2) of fluoride, and the RRE dataset with (Control 3) and without Rev (Control 4). To include more SP platforms in evaluation, we constructed another two control datasets from an icSHAPE dataset of mouse SRP RNA (272 nt)17 in Sun et al., which mapped RNA secondary structure across HEK293 cellular compartments including chromatin, nucleoplasm, and cytoplasm. Transcripts mapped to mitochondrial genome were excluded to eliminate possible contamination by lysed mitochondria. Transcripts with RPKM less than 5 or RT stop less than 2 for all nucleotide positions were excluded62. Then nucleotide positions with coverage less than 10 were removed. As a result, 1,277 transcripts  were compared between chromatin and nucleoplasm, and 1,815 transcripts  were compared between nucleoplasm and cytoplasm.To apply DiffScan in transcriptome level analysis, we downloaded the icSHAPE datasetnA replicates from condition A, and the reactivity of nucleotide position j in replicate i is n is the length of the transcript. Similarly, for nB replicates from condition B, the reactivity of nucleotide position j in replicate k is Suppose that there are Step 1: 90% winsorization is applied to the reactivities in each replicate separately to remove outliers, i.e., the bottom 5% of reactivities are set to the 5th percentile while the upper 5% of reactivities are set to the 95th percentile. After that, reactivities are scaled into range  by subtracting the minimum and then dividing the result by the new maximum.63 and has been found to be advantageous in secondary structure prediction compared to the 2–8% normalization method34.Step 2: Quantile normalization is applied to within-condition replicates to match the quantiles, which is frequently used in normalization analysis of genome-wide assaysS, i.e., nucleotide positions that do not exhibit structural variation across conditions. The reactivities are normalized so that the adjusted reactivities are similar between conditions in the structurally invariant set. The determination of S is described in Supplementary Methods. Then we learn a linear transformation rule from S, which converts reactivities of one condition to the same level of those of the other condition in S, and extrapolate the transformation to all nucleotide positions to obtain normalized reactivities.Step 3: To make between-condition reactivities comparable, we first determine a structurally invariant set S by fitting the following robust regression utilizing iterated re-weighted least squares with Huber’s M estimate64. . We implemented a Monte Carlo approach to control the family-wise error rate for DiffScan (see Supplementary Methods).In view of the dynamic locations and lengths of SVRs, we scan the transcripts by enumerating all candidate regions with suitable lengths, i.e., every region in We simulated reactivities for two conditions through the following three steps.17 were randomly selected . For each RNA sequence, we sampled 10 conformations from the Boltzmann distribution of secondary structure65 utilizing the RNAsubopt program in ViennaRNA version 2.4.1566.Step 1. 1000 human RNA sequences in an icSHAPE dataset39 and Sükösd et al.40 as fitted from SHAPE data, and also reactivity distributions we fitted from icSHAPE data (Supplementary Methods).Step 2. Conditional on the pairing status of each nucleotide in a transcript, the reactivities can be sampled from pre-trained reactivity distributions. Three distributions were used: Cordero et al.cth conformation in condition A, with cth conformation was 17) of all nucleotide positions of the 100 transcripts, we allocated 90% of the weight to the first two conformations and randomly distributed the remaining 10% to the other eight conformations. We then generated reactivities in condition A and B separately by changing the weights of the first two conformations. Thus, nucleotide positions with different pairing status between the first two conformations form SVRs. Furthermore, replicates were simulated by adding random noise , to access whether the false positive discoveries of a method can be controlled at a nominal level, we calculated the position-level false positive rate as the number of predicted nucleotide positions at significance level 0.05 divided by the length of the transcript.In the benchmark datasets with annotated SVRs (Flu and RRE)—similar to our processing of the simulated datasets—we calculated the Jaccard index and average distance between top predicted nucleotide positions and annotated SVRs.23 was utilized to implement deltaSHAPE. As far as we know, there is no official software release for PARCEL or RASA; and we implemented them utilizing custom scripts from existing literature24. The R package dStruct version 1.0.0 was utilized to implement dStruct. The scripts in Paolo Marangio’s GitHub page (https://github.com/marangiop/diff_BUM_HMM) were used to implement diffBUM-HMM.The software deltaSHAPE version 1.0 available at the Weeks lab websitej in the case experiment, Nj is the number of times that position j is exposed to the probing molecules, and pj is the probability that position j is modified. The maximum likelihood estimator for pj is j in the control experiment  from the MEME suite version 5.2.044, and the number of significant hits in each SVR were counted.We collected 1193 motifs of 159 RBPs from the ATtRACT databasep value (Benjamini-Hochberg method) less than 0.05 were considered significantly enriched.To evaluate the significance of motif enrichment, we randomly sampled null regions in non-SVR regions in the same transcripts, with region length matched to the predicted SVRs. The number of significant hits in each null region is recorded. After that, we performed a one-sided Wilcoxon signed-rank test for the two count vectors of significant hits to evaluate the significance of enrichment. Motifs with corrected 59. Proportions of trait-associated SNPs  in SVR and non-SVR positions were compared, and the statistical significance was evaluated with Fisher’s exact test.We downloaded the summary statistics of the published genome-wide association studies (GWAS) curated in the GWAS catalog database (v1.0)Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


To better understand how positive-strand (+) RNA viruses assemble membrane-associated replication complexes (RCs) to synthesize, process, and transport viral RNA in virus-infected cells, we determined both the high-resolution structure of the core RNA replicase of chikungunya virus and the native RC architecture in its cellular context at subnanometer resolution, using in vitro reconstitution and in situ electron cryotomography, respectively. Within the core RNA replicase, the viral polymerase nsP4, which is in complex with nsP2 helicase-protease, sits in the central pore of the membrane-anchored nsP1 RNA-capping ring. The addition of a large cytoplasmic ring next to the C terminus of nsP1 forms the holo-RNA-RC as observed at the neck of spherules formed in virus-infected cells. These results represent a major conceptual advance in elucidating the molecular mechanisms of RNA virus replication and the principles underlying the molecular architecture of RCs, likely to be shared with many pathogenic (+) RNA viruses. CryoEM structure of CHIKV replication complex reveals multicomponent RNA synthesis nanomachine stuck in host cell plasma membrane. Alphavirus, family Togaviridae) is a mosquito-borne pathogen that has spread globally and caused a substantial health burden of acute febrile illness progressing to debilitating, often chronic polyarthritis in millions of people. In addition, CHIKV can also induce rare but lethal encephalitis  and cryo–electron tomography (cryo-ET) methods to determine the molecular architecture of the alphavirus RC. The molecular structure of the reconstituted RC core (nsP1 + 2 + 4) fits nicely into part of the active RC density obtained from subtomogram averaging of the necks of the alphaviral spherules. Together, these data provide unprecedented molecular basis of the alphavirus genome replication process, likely applicable to other (+) RNA viruses, such as coronaviruses and flaviviruses.To reconstitute a functional alphavirus RC , we prep2, indicating that the nsP1:nsP4 interface is highly stable (fig. S3A). Intriguingly, the nsP1 ring acts as a chaperone to interact with and stabilize the otherwise highly dynamic nsP4  and the palm subdomain of nsP4 with a buried area of 906 Å2 . A flexiThe nsP4 RdRp resides at the center of the RC core, well stabilized by the surrounding nsP1 and nsP2. Almost all 611 residues of the nsP4 are well resolved in the cryo-EM density map, which includes the intrinsically disordered NTD (amino acids 1 to 105) and those disordered regions from the fingers subdomain  not captured in the crystal structures of the alphavirus nsP4 RdRps from Ross River virus (PDB 7F0S) and Sindbis virus (PDB 7VB4)  and reveN = 101 spherules), respectively. Proximal to the spherules, dense condensates in the cytosol are often observed proximal to RCs at the neck . At this time point, all RCs should be matured with fully processed nsPs and dsRNA replication intermediate . The swithe neck . Such deSubtomogram averaging of the spherule neck regions revealed a 12-fold symmetric complex at 7.3 Å resolution, consisting of a PM-associated crown ring connected to a larger, smooth ring on the cytoplasmic side . TogetheWe then determined an asymmetric reconstruction of the RC, revealing that the plug density in the center of the crown is positioned asymmetrically with a small gap on one side  and 2. AIn addition to the RCs positioned at the neck of spherules, we observed many ring-like complexes smaller than RCs docked to the inner leaflet of the PM in the absence of RNA or membrane invaginations. These macromolecular complexes were especially numerous on thin cell extensions with underlying bundled cytoskeleton filaments  10, 11,. NoticeaIn this study, we performed a multiscale study for the structural and functional characterization of the CHIKV replication machinery in vitro and in the infected cell using combinatory cryo-EM methods. Our work provides an integrated molecular view of the structure of a central core portion of the alphavirus nsP assembly within the RCs. The nsP1 dodecameric ring of nsP1 + 2 + 4 RC core architecture did not deviate much from the previously published nsP1 ring structure (PDB 7DOP)  RNA replication . The sphg after 5 days. The protein expression was boosted by 10 mM sodium butyrate 16 to 20 hours after transfection. The cell pellet was subsequently lysed in lysis buffer WI  with 1 mM phenylmethylsulfonyl fluoride (PMSF) protease inhibitor (Sigma-Aldrich), 1% n-dodecyl-β-d-maltoside (Exceedbio), and BioLock solution  on a rotator at 4°C for 1.5 hours and pulse-sonicated for 5 min. The lysate was clarified with 100,000g centrifugation at 4°C in XPN-100 ultracentrifuge (Beckman Coulter). The Strep-Tactin Sepharose slurry beads  were incubated in Econo-Pac column (Bio-Rad) with the clarified supernatant. The nsP1-bound strep-bead was equilibrated to assembly buffer WII  in 5 column volume (CV) wash for downstream RC assemblies and RNA polymerase assay (refer to below).The preparation of nsP1 was adapted from (6-SUMO) tag and recombinantly purified as described in  and nsP2 helicase domain  were both subcloned into pSUMO-LIC expression vector carrying N-terminal hexa-histidine tag fused with cleavable SUMO  supplemented with 2.5% glycerol and then induced at 0.5 mM isopropyl-β-d-thiogalactoside (Gold Biotechnology) after a measured OD600  of ~0.8 for 18 hours at 18°C. The following purification process for nsP3MZ was maintained throughout at 4°C. The purification started with harvesting the bacterial pellet at 4000 rpm followed by lysis with buffer A  supplemented with 1 mM PMSF in a PANDAPLUS2000 (GEA) homogenizer. The supernatant obtained from 100,000g ultracentrifugation was incubated with Ni-NTA slurry beads (Bio Basic) for 1 hour. The nsP3MZ-bound Ni-nitrilotriacetic acid (NTA) was washed for 20 CV of buffer A and eluted in buffer B (buffer A with 300 mM imidazole). The eluant was cleaved with SUMO protease (at 10:1 w/w ratio) for 16 hours in 0.5× buffer B. Then, the tag-removed nsP3MZ was purified subsequently in HiLoad 16/600 Superdex 200 pg column (Cytiva) in buffer C  as a monomer. The monomeric peak fractions were concentrated to desired concentration in buffer C to flash freeze for long-term storage at −80°C till required.The nsP3MZ gene  was cloned from the same CHIKV cDNA described in  was sourced from ONNV  and cloned into pSUMO-LIC expression vector with N-His3COO)2, 2.5 mM MnCl2, 5 mM TCEP, 0.01% GDN, NEB murine RNase inhibitor (0.5 U/μl), and 1.25% sucrose (~pH 8)] prepared in nuclease-free water (HyClone). The reactions were initiated with 2 mM adenosine triphosphate (ATP) and quenched in a discontinuous time point manner for 0.2 to 18 hours after incubating at ~25°C in dark. The reactions were quenched with equal volume of denaturing RNA loading dye  to run on 17.5% denaturing urea-PAGE at 220 V for 1.5 to 2 hours. The urea-PAGE was imaged via Bio-Rad Gel-Doc XR+ Imager using Fluorescence Blot ALEX488 for analysis.The RNA polymerase assay is optimized and uses the same T1 RNA template from , a DNA template with T7 promoter (5′TAATACGACTCACTATA 3′) was ordered from Integrated DNA Technologies to perform IVT according to manufacturer recommendation from Hiscribe T7 High Yield RNA Synthesis Kit (NEB). The IVT product was purified via the NEB-recommended phenol-chloroform extraction method and Monarch RNA Cleanup Kits (NEB).T2 RNA sequences:Sense strand: 5′GGGAUAAUGCAAGCAUACCGAUCUUCCAACGUUUCUCCGAACCCACAGGGACGUAGGAGAUGUUAUUUUGUUUUUAAUAUUUC3′Antisense strand: 5′GGGAUAAUGCAAGCAUACCGAUCUUCCAACGUUGAAAUAUUAAAAACAAAAUAACAUCUCCUACGUCCCUGUGGGUUCGGAGA3′g in Amicon Ultra (Merck) and/or Pierce (Thermo Fisher Scientific) protein concentrators of 100 kDa molecular weight cutoff (MWCO) for the abovementioned RNA polymerase assay and structural studies. The RCs assembled at affinity pulldown and anion-exchange chromatography were fractionated and sampled for negative-staining transmission electron microscopy (TEM) on carbon-coated copper 300-mesh grids  for screening and optimization of RC reconstitutions.All in vitro reconstitutions and sample preparations were done at 4°C unless otherwise mentioned. The CHIKV-nsP1–immobilized strep-bead was incubated with the desired combinations of nsPs  to produce RC assemblies: nsP1 + 2 + 4 and nsP1 + 4. The strep-beads consisting of RC assemblies were eluted in the abovementioned buffer WII supplemented with 10 mM desthiobiotin at ~pH 8 and loaded directly to a HiTrap Q-FF column (Cytiva) for linear elution from 0.15 M NaCl to 1 M NaCl. The eluants were concentrated at 3000 to 7000The nsP1 + 2 + 4 RC assembly was mixed with T2 RNA and NTP in 1× buffer Z at 1:10:100 molar ratio for 1 hour at ~25°C before concentrating down to ~30 μl. The cryo-EM grid preparation (including single-layer graphene grid fabrication) was adapted from  supplemented with penicillin and streptomycin, 10 mM Hepes, nonessential amino acids, and 10% fetal bovine serum (HyClone). CHIKV vaccine strain 181/clone 25 (CHIKV-181) was amplified in BHK21 cells. CHIKV vaccine strain 181/clone 25 (CHIKV-181)  was obtained from BEI Resources, National Institute of Allergy and Infectious Diseases, National Institutes of Health, and amplified in BHK21 cells.The human bone epithelial cell line U2OS  is a female cell line purchased from the American Type Culture Collection (ATCC). Hamster fibroblast cell line BHK21 cells  were purchased from ATCC. Cells were maintained at 37°C with 5% humidified COU2OS cells grown on fibronectin-coated gold 200 mesh R2/2 grids (Quantifoil) were infected with CHIKV-181 at a multiplicity of infection of 50 for an incubation period of 8 hours. The grids were then washed with PBS and a solution of 10 nm of bovine serum albumin gold tracer  was added directly before vitrification. Grids were blotted and plunged into liquid ethane using the LEICA EMGP plunge freezer device. Grids were stored under liquid nitrogen conditions until TEM data collection.−/A2, distributed over 51 tilt images, covering an angular range of −50° to +50° with an angular increment of 2°. Motion between frames of each tilt image in the tilt series was corrected using patch-tracking in MotionCor2 software  operated at 200 kV with a post-column energy filter (20 eV) and a K2 Summit detector with a calibrated pixel size of 3.54 Å. Single-axis, bidirectional tilt series were collected using SerialEM software  particles in different orientations were picked from tomograms at ×4 binning. These RC particles were input to the EMAN2 initial model generation program, performed in two steps. First, particles were iteratively aligned with C1 symmetry for three iterations and aligned to the symmetry axis. Then, an additional five iterations were performed, applying C12 symmetry to produce an initial model with low-resolution features of the spherule base, PM, and RC. This initial model was filtered to 40 Å resolution and used for subsequent 3D subtomogram refinement of the full dataset. Subtomogram averaging was then performed with 724 RC particles at ×1 binning (3.54 Å per pixel) from 49 tomograms while applying C12 symmetry with a loose mask around RC protein density to exclude the surrounding membrane from alignment. Afterward, subtomogram refinement and additional sub-tilt refinement of particle translation and rotation produced a final RC map at 7.3 Å resolution  with C12 symmetry applied. A soft mask was applied to focus alignment of particles on the protein ring density rather than the membrane. Subtomogram refinement and sub-tilt refinement of translation and rotation, again with C12 symmetry applied, produced a final map at 9.9 Å resolution (


The novel coronavirus disease 2019 (COVID-19) pandemic poses a serious public health risk. In this report, we present a modified sequencing workflow using short tiling (280bp) amplicons library preparation method paired with Illumina’s iSeq100 desktop sequencer. We demonstrated the utility of our workflow in identifying gapped reads that capture characteristics of subgenomic RNA junctions within our patient cohort. These analytical and library preparation approaches allow a versatile, small footprint and decentralized deployment that can facilitate comprehensive genetics characterizations during outbreaks. Based on the sequencing data, Taqman assays were designed to accurately capture the quantity of subgenomic ORF5 and ORF7a RNA from patient samples and demonstrated utility in tracking subgenomic titres in patient samples when combined with a standard COVID-19 qRT-PCR assay. SARS-CoV-2 patients present with a range of severity and it is unclear how infectious asymptomatic cases are although they are believed to contribute to the spread of the virus. The proportion of asymptomatic patients have been estimated to range from 40%–45% and tend to be younger and do not have preexisting conditions . ConversCoronaviruses express 3’ proximal ORFs through subgenomic RNAs (sgRNAs) that are generated by a discontinuous transcription mechanism that is unique to RNA viruses . A study53 patients admitted to the National University Health System, from Feb to May 2020, with SARS-CoV-2 confirmed by RT-PCR  from nasopharyngeal swabs were included in this study. Ethics approval was obtained from the National Healthcare Group Institutional Review Board.400 µl of nasopharyngeal swab solubilized in UTM was extracted using EZ1 virus mini kit (Qiagen) into 90 µl elution buffer. An aliquot was heat-inactivated at 60°C for 30 min as required by Ministry of Health (Singapore) to prevent SARS-COV-2 infection and 6.5 µl of the heat-inactivated nucleic acid was reverse transcribed using SuperScript III (Life Technologies)  using 0.25 µl of Random Primer mix (NEB) using the following protocol: 65°C 20s, 4°C 60s, 55°C 50 min, 95°C 5 min.1 µl of the cDNA was then used for each pooled PCR in the following 10 µl Q5 polymerase reaction mix (NEB) , 0.1 µl Q5 polymerase) using cycling protocols of 98°C 15s, 63°C 3 min s, 38 cycles). The two pools were combined after PCR and purified using 18 µl AmpureXP beads (Beckman-Coulter) as per manufacturer’s protocol and eluted in 20 µl water. 10ng of the amplified product was prepared for library with ligation and barcoded using the NEXTFLEX Rapid DNA Seq 2.0 kit (Perkins-Elmer) as per manufacturer’s instructions and eluted in 25 µl of water. Samples were pooled at 4-6 samples per run and diluted to 50pM before loading into the iSeq100 cartridge  as per manufacturer’s instructions.The critical step in the analysis is the alignment of partial transcript reads spanning sub genomic junctions to the reference genome. To achieve this, we developed a gapped alignment workflow that combines GSNAP  and V-ASThe filtered gapped read SAM entries from GSNAP alignment, were further narrowed down to those spanning the known canonical subgenomic regions using RSAMTOOLS in R . The quaReal-time PCR with SyBr Green was performed with 10 µl reactions using the FirePol MasterMix (Solis Biodyne) with 1 µl of 1:5 dilution of cDNA prepared previously for sequencing and 0.5 µl of 10 µM primers. For subgenomic RNA, we used covid_1 primer_left and covid_113/133/137/139/143/145/146/148 primer_right from the pool PCR primer set  for qPCRAs there were subgenomic amplicons detected at low viral titres at Day 13 and 15 that reflected non-specific amplification with SyBr Green qPCR using ARTIC primers, we wanted to compare how one-step Taqman qRT-PCR assay fared compared to RT followed by SyBr Green qPCR. Taqman PCR were designed with one primer in the TRS-L and the other primer and the Taqman probe located within the subgenomic fragment about 20,000 nuclceotides away. Taqman probes resulted in better concordance between subgenomic RNA and genomic RNA and can be is empirically more sensitive compared to the two-step qRT-PCR as seen from the ability to detect the subgenomic RNA on P07 Day 12 .Real time probe qPCR measurements were performed Applied Biosystems QuantStudio system. Primers were designed to span 7b-mrna (seq_143) and E-mRNA (seq_133) respectively  and the Quantification of the relative abundance of sub-genomic transcripts to genomic transcripts were performed by obtaining delta Ct change defined by the difference between the geometric mean of the Taqman assay targeting sub-genomic transcripts described above and the fortitude assay. Wilcoxon ranked sum test with continuity correction is then applied to calculate the significance of the delta CT changes across time.https://artic.network) to generate genome tiling amplicons approximately 300 nt in length to be compatible with 2 × 150bp sequencing chemistry. This protocol allows generation of consensus COVID-19 genome and dentification of single nucleotide variants  daily from day 6 of illness to day 15 . At eachIn this article, we present a method for workflow and Taqman probes for the detection of subgenomic RNA which may have diagnostic value in the COVID-19 pandemic, potentially for determining the infectivity of patients. However, further studies will need to be done to validate this hypothesis. Other than ORF1ab, which spans roughly two-thirds of the coronavirus genome and codes for replicase-transcriptase proteins, the other polypeptides including the spike and envelope proteins are dependent on the production of negative strand subgenomic RNA that has the transcription-regulating sequence (TRS) “spliced” next to the translational start site of these polypeptide . This alvice versa (Absence of subgenomic RNA in infected cells will likely mean the infected cells are not capable of producing active virions. If active infectious virions are produced and actively transmitting between cells within the host, we hypothesize there will be subgenomic RNA produced. As production of subgenomic RNA is dependent on the intact replicase/transcriptase complex skipping a large portion of the genome and not produced spontaneously from just reverse transcription of the virus, it is distinct from viral fragment residues present in the virus at late stages of infection  and may ce versa . Howeverce versa . They noin vitro activity against SARS-CoV-2 replication (Lopinavir has shown lication , and thelication . Moving In summary, we demonstrated the ability to detect subgenomic RNA using the ARTIC protocol with low sequence depth and showed that we can track subgenomic distribution and contribution against genomic signal in multiple patients. We showed that there is correlation between subgenomic percentage, subgenomic Ct, genome coverage and genomic Ct with time after symptom first appears. Importantly, we developed a Taqman assay against ORF5 and ORF7a subgenomic RNA that we believe can be used to more closely monitor if subgenomic RNA is reflective of infectivity and can readily be incorporated into other available SARS-COV-2 diagnostics. We believe that this will make a significant contribution to public health efforts to control the virus as well as therapeutic studies evaluating the effects of antiviral therapies.


Coral reefs offer natural coastal protection by attenuating incoming waves. Here we combine unique coral disturbance-recovery observations with hydrodynamic models to quantify how structural complexity dissipates incoming wave energy. We find that if the structural complexity of healthy coral reefs conditions is halved, extreme wave run-up heights that occur once in a 100-years will become 50 times more frequent, threatening reef-backed coastal communities with increased waves, erosion, and flooding. As a result, the number of people exposed to intense coastal storms and flooding also markedly increased3. Climate change is expected to further exacerbate the risks of flooding due to rising seas5 and changes in weather patterns8. In response, societies are called to design effective coastal protection schemes9, which preserve or strengthen the current natural protection offered in the form of ecosystem services10.By March 2020, the world’s human population grew to more than 7.8 billion people11. Shallow water coral reefs act as natural breakwaters12, and can attenuate up to 98% of the incoming wave energy13. However, coral reefs are being rapidly degraded by intensifying anthropogenic stressors, including ocean acidification, rising seas, pollution, and sedimentation14. Such perturbations usually reduce living coral cover and subsequently habitat structural complexity, thereby leading to reef flattening, a process observed worldwide15. Such trends reduce the natural protective capacity of reefs, further intensifying coastal flood and erosion risks16. Although the link between wave attenuation and reef structural complexity is widely accepted among scientists in theory, quantitative examinations of the protective capacity of reefs under real world conditions as anticipated for the near future are yet limited18. Indeed, most of the studies investigated the wave energy dissipation in conditions where coral reefs would be entirely wiped out or not19, while others chose a wider geomorphology-based approach exploring flooding risk as a function of reef proximity or geologic structure22.This is particularly true in tropical areas, where more than 500 million people are protected by coral reefs2%).Here we report field observations and numerical models to quantify the natural protection offered by coral reefs at a high-energy site in Mo’orea, French Polynesia. First, we monitor changes in coral community abundance and size-distribution over a 10-year full cycle of disturbance and recovery (2005–2016), and quantify the associated reef structural complexity  within a 10 m2 transect on the outer slope23. To quantify reef structural complexity, we built a 3D model of the coral assemblages distributed along a cross-section of the reef substrate separating the 20 m water depth from the reef crest, representing a 160 m stretch along the reef slope  for the three most conspicuous taxa (i.e.28. We extract 30-year offshore wave data (1980–2010) from a wave hindcast29  is encircled by coral reefs, 500–700 m wide with a dominant swell direction coming from the southwest. In this study, we focus on Ha’apiti, a site with a southwest orientation that is considered as a high-energy sitehttps://diffusion.shom.fr.The beach profile and the reef morphology are measured using airborne bathymetric and topo-bathymetric lidar surveys conducted in June 2015 by the Service Hydrographique & Océanographique de la Marine (SHOM). The bathymetric data are defined by the combination of bathymetric laser (for the submerged part of the beach) and topo-bathymetric laser . The data come at 1 m resolution and are available at 33 is used to estimate the Nikuradse  roughness34 of the coral reef surface along the beach profile sections covered by the pressure transducers. The method is described in detail in the references provided above and uses the conservation of energy equations to obtain estimates of wave energy dissipation from friction. We obtain more than 300 kn estimates for each pair of sensors, each representing a different geomorphologic section. Since the field measurements took place in 2015, the kn outputs obtained from the fore reef section concur with the reef structural complexity estimates of that year  and apply the following procedure. First, for the reef sections, we apply the relationship between the reef structural complexity and kn . Second, for the sandy section, we define kn on the grounds of the mean grain size (d50 = 63 μm). Applying this workflow .Spectral attenuation analysis of the water level measurements35. The rationale of using a Boussinesq type model instead of other types of models (e.g. SWAN) is that the former is able to describe in detail (i.e. 1 m grid resolution) several hydrodynamic parameters  in the swash zone. The model is defined as follows:U is the mean over the depth horizontal velocity, ζ is the surface elevation, d is the water depth, uo is the near bottom velocity, h = d + ζ, 36, E is an eddy viscosity, τb is the bed friction term and B = 1/1535.Nearshore wave propagation is simulated using a nonlinear wave model based on the Boussinesq Equations36. However, in the swash zone, surface roller is not present and the eddy viscosity concept is used to describe the breaking process. The term E in Eq.  up to the run-up point (landward boundary). We start from a first estimate of the run-up R using the Stockdon formulasing Eq. . The fin35. The numerical solution is based on the fourth-order time predictor–corrector scheme39. Therefore, the bed friction term τb is calculated such as:fw is the bottom friction coefficient40, which is an explicit approximation to the implicit, semi-empirical formula given by Jonsson, 196741.where αo is the amplitude of the near-bed wave orbital motion and kn is the Nikuradse roughness height.The ‘dry bed’ boundary condition is used to simulate run-upvs. not unhealthy); and (ii) 1000 percentiles of wave conditions. The wave conditions are produced as follows: (i) from the weekly values, we estimate all significant wave height (Hs) percentiles from 0.1 to 100, with a step of 0.1; (ii) the resulting 1000 Hs values are linked to the corresponding peak wave period Tp using a copula expressing the dependence of the two variables42. The output of the simulations is the nearshore Hs and 2% exceedance run-up (R2%) height for each of the 1000 conditions and 10 coral reef states. To quantify how the coral reef states are altering wave propagation during extreme events, we apply extreme value analysis to estimate the R2% for different return periods43. We then compare how the return period curves changed from the two coral reef states and we define the change in frequency of extreme R2% under unhealthy coral reefs. It is important to highlight that the tidal range is < 20 cm at our study site, so will have negligible effect compared to other parameters. In systems with higher tidal range, tidal water level fluctuations should be also considered in the probabilistic framework.We use our wave propagation model to assess how different coral reef states affect the impact waves have on the coast. We run an ensemble of 10,000 simulations that covers all the possible combinations of (i) 10 bottom roughness profiles expressing the different observed coral reef states , and the reef structural complexity (SC). Our model is built in the R package brms45 and follows the following structure:s,offshore is the significant offshore wave height (m). The prior sampling is specified to follow a Gaussian  is lower than 1.0546. Drawing on our model, we predict the run-up according to six offshore wave height conditions (i.e. from 1 to 6 m with a step of 1 m) and the structural complexity gradient at Mo’orea from 2005 to 2016. In order to define the residual run-up height according to Hs, offshore and the reef structural complexity, we subtract from our run-up estimates the minimum value obtained for each offshore wave height condition.Using the outputs of our simulations, we develop a Bayesian model which includes all the interdependencies between the run-up, the significant offshore wave height  roughness34 of healthy reefs at Mo’orea is 0.42, with a very likely range of 0.34–0.52. We combine the simultaneous kn and structural complexity measurements in Monte Carlo simulations, in order to produce continuous kn profiles for all observed coral reef states, which we use in our wave propagation simulations. In line with previous studies13, our modeling results show that coral reefs absorb 77% to 91% of the incoming wave energy, with the range expressing the variability of wave and structural complexity conditions. We also confirm the previously reported strong correlation between structural complexity and wave dissipation28, and hence the reduction of wave run-up height on the coast  compared to a wave with Hs = 1 m  has been projected


The relationship between conserved structural motifs and their biological function in the virus replication cycle is the interest of many researchers around the world. RNA structure is closely related to RNA function. Therefore, technological progress in high-throughput approaches for RNA structure analysis and the development of new ones are very important. In this mini review, we discuss a few perspectives on the structural elements of viral genomes and some methods used for RNA structure prediction and characterization. Based on the recent literature, we describe several examples of studies concerning the viral genomes, especially severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza A virus (IAV). Herein, we emphasize that a better understanding of viral genome architecture allows for the discovery of the structure-function relationship, and as a result, the discovery of new potential antiviral therapeutics. It is known that RNA function is closely related with its secondary and higher-order structure. RNA molecules play important roles in diverse catalytic and regulatory biological processes and can serve as disease biomarkers and therapeutic targets. Therefore, it is of great importance to understand the RNA folding properties and broaden our structure-function characterization capabilities. In RNA viruses, its genetic material is used throughout the viral replication cycle, highlighting its crucial role in the virus biology. Questions have arisen on how to best understand the RNA structure features from a biological function perspective, however, the determination of the RNA architecture is still challenging.Many efforts have been made to study the structure, composition and organization of viral genomes. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) contains enveloped, non-segmented, positive-strand genomic RNA with a 5′ cap and 3′ poly-A tail that allow it to serve as an mRNA during viral protein translation. At the 5′ end, long ORF1ab poly-protein is located followed by four structural  and several nonstructural proteins. In addition, coronaviruses with a positive-strand ssRNA have the largest RNA genome . Based oGaining the information on RNA architecture has a great contribution to reveal its biological function within the virus and can be useful for designing various antiviral strategies. However, vRNA dynamics and their propensity to continuous mutations are the main obstacles in designing efficient tools. Therefore, discovering the highly conserved structural elements within the genome and the development of novel methodologies are crucial to RNA structure and function characterization. Based on the available literature, we observed technological progress in the field of structural biology, especially in approaches for high-throughput structure determination. Next generation sequencing (NGS), molecular dynamics (MD) simulation and other emerging methods  offer scientists the possibility to know the viral genome structural organization at increasingly high resolutions and native conditions.This mini review summarizes a few perspectives on the viral genome structure and some approaches utilized for structural characterization. Overall, this mini review can be divided into two main parts: (i) the first concerning some viral RNA structural motifs and their potential function, and (ii) the second describing several methods to vRNA folding determination. We will discuss some recent structural studies addressing different human viruses, especially SARS-CoV-2 and IAV. Herein, we will emphasize that a better understanding of viral genome architecture allows for the discovery of structure-function relationships.The polymorphic nature and conformational flexibility of RNA facilitates their formation of a variety of structures. Many cellular processes are often regulated by RNA secondary and tertiary structures. The ability of the RNAs to fold into stable three-dimensional (3D) structures is important to RNA transcription, translation and splicing . In viruIn general, RNA secondary structures consist of single-stranded and double-stranded regions, while the 3D arrangement of RNA includes helical duplexes a, tripleFrom the literature we know that within the viral RNA genome so-called functional RNA domains can be distinguished. Among them are the internal ribosome entry sites (IRES) playing an important role in translation initiation. They can be divided into packed IRESs that are highly conserved, extended IRESs with compact regions, flexible IRESs and inducible IRESs . VariousIn addition to the presence of local structural motifs within the viral RNA genomes, there is an extensive RNA structure across the entire genome called genome-scale ordered RNA structure (GORS). It has been suggested that GORS is associated with viral persistence or regulates the host innate defense, potentially by shielding viral RNA from recognition in the cell ,22. MoreTo this day, a lot of research has been carried out to study the secondary and tertiary RNA structures of different viruses ,26,27,28For instance, Woodside and co-workers described structural dynamics of pseudoknot from SARS-CoV-2 genome showing conformers which have distinct topologies with different stability and stem-loop structures . The autDespite the flexibility and dynamics of RNA molecule, there are structural motifs that remain conserved across the strains and can serve as a potential target for antiviral therapies. One of the widely described structural motifs is a panhandle structure present in the IAV genome b 4]. Al. Al4]. AIt is worthy of note that the interacting RNA sequences can fold into stem-loops by tertiary interactions known as a ‘kissing-loop’ mechanism d. Many RAmong the number of structural motifs present in the viral RNA genomes, it is worth mentioning the G-quadruplex structure formation e. In recViral RNA secondary and tertiary structures can be determined using the combination of computational and experimental studies. However, the flexible nature of vRNA structure makes its characterization more complex. In addition to the structural dynamics of vRNA, the length of this molecule can also be limitation, because long vRNAs can form both local or long-range interactions. Therefore, the structural analysis of such RNAs requires highly robust and reproducible methods.To this day, many bioinformatics tools and experimental techniques have been developed for studying the RNA folding. RNA structure prediction is a mainstream approach for the structural analysis of RNA. Computational methods are based on thermodynamic calculation of minimum folding energies, phylogenetic analysis based on stochastic context-free grammars (SCFGs) and methods that combine thermodynamic and covariance detection approach. Overall, popular approaches for predicting RNA folding can be divided mainly into two categories: single-sequence and homologous-sequence methods . The firThe second type of software to predict the RNA structure is based on the determination of base pairs conserved among large number of homologous sequences. An example of such a program is TurboFold for predicting secondary structures for multiple RNA sequence alignments . RecentlIn the literature there are several papers describing prediction and analysis of viral RNA structural motifs using computational approaches ,65,66. OAdvances in the field of bioinformatics and computational techniques enable the exploration of large and complex RNA structures. It is feasible to detect a long-range base pairing or intermolecular interactions, that are common in vRNAs and during vRNAs-host RNA interactions . RNA 3D A range of RNA structure prediction tools and available resources of data have provided insight into the folding pathway of RNA molecules, including viral RNAs. Furthermore, advances in the next generation sequencing (NGS) and in the high-throughput sequencing have greatly enhanced the RNA structure determination and facilitated the large-scale structural analysis. The NGS technology is a powerful tool for massive scale RNA analysis through cDNA sequencing, also providing information on strand orientation (RNA-seq). Additionally, NGS approaches can often provide information on structure with nucleotide or near-nucleotide resolution. A typical workflow of NGS method consists of RNA fragmentation, reverse transcription into cDNA, and then library preparation according to the NGS methodology of choice. For instance, by the addition of adapters containing specific sequences interacting with the NGS platform, i.e., the surface of the flow cell—Illumina or the beads—Ion Torrent. After sequencing, bioinformatics analysis is conducted to determine the reactivity of individual nucleotides . OverallIn order to analyze the IAV RNA structure at the single-nucleotide resolution, Dadonaite et al. used selective 2′-hydroxyl acylation analyzed by primer extension and mutational profiling method (SHAPE-MaP) . This teIn addition to the computational structure prediction tools described above, as well as HT- sequencing approaches, molecular dynamics (MD) is currently intensively expanding field. Molecular docking is a crucial technique for generating the hypotheses about structure-function relationships in RNA molecules. By computational simulation, it is possible to investigate various biomolecular processes, including conformational dynamics, ligand binding, molecules folding, and revealing the atom positions at very fine temporal resolutions. Hence, MD simulation in molecular biology or virology is an extremely helpful way to understand the molecular architecture of viral RNAs. In 2020, Selvaraj et. Al. performed structure-based virtual screening and MD simulation of SARS-CoV-2 guanine-N7 methyltransferase (nsp14) as a drug target . The autOverall, the improvement of existing methodology and development of effective protocols for HT techniques will enable us to investigate RNA secondary structure, as well as genome- or transcriptome-wide RNA-RNA interactions. Additionally, integrating the sequencing data obtained from HT methods with MD simulation results can provide important insights into the viral assembly process and be very helpful for understanding the infection process and pathogenesis, as well as the virus maturation in host cells.The characterization of vRNA structural elements is a first step for rational design of antiviral agents. Nowadays, there is an urgency to identify and develop drugs against the highly pathogenic viruses. An effective and quick approach is a high-throughput screening (HTS) allowing to test existing therapeutics or identify new drug candidates. HTS identifies compounds  starting from calculating the energy-minimized structures, followed by molecular docking of these structures against specific targets, for example, viral proteins. Crucial in the HTS method is performing biochemical and biological tests using a validated target representing a disease state. A complementary method is a computational screening named virtual HTS (vHTS), which computes the binding affinity of the target to the library compounds. This approach can be divided into two groups: (i) ligand-based methods that are based on the similarity between the targets and compounds, and (ii) receptor-based methods that rely on the complementarity between the compounds of interest and the target binding sites ,80,81.Several examples of high-throughput activity assays against different viral proteins have been described and validated ,85,86,87As mentioned above, RNA structure prediction should be complemented by experimental validation. To improve the accuracy of computational modeling and MD simulations, several approaches for experimental RNA structure determination can be proposed ,27,89,90For instance, Heise and co-workers applied the SHAPE-MaP approach to investigate RNA secondary structure of the Chikungunya Virus (CHIKV) genome and 3′UTR variants of CHIKV . Based oWhen looking for functional RNA elements within the viral genomes, different high-throughput and interactome strategies can be utilized. SHAPE mapping can be combined with crosslinking and ligation-based RNA-probing strategies. The usage of icSHAPE (in vivo click SHAPE) and PARIS  or SHAPE-MaP and SPLASH allowed for the discovery of secondary structure integration and function in Zika virus and dengue virus . To asseAn interesting approach, i.e., dual crosslinking, immunoprecipitation and proximity ligation (abbreviated as 2CIMPL) to identify RNA-RNA interactions of influenza virus was proposed . Using tAs it was emphasized before, numerous methodologies have been developed to characterize the architecture of viral RNA genomes. To provide complementary information to these methods, cryo-electron microscopy (cryo-EM) can be used to solve the high-resolution structures of viral assemblies. Technological progress in cryo-EM field gives scientists the opportunity of a better understanding the virus behaviors including structure organization of its genome. The EM approach includes many imaging techniques, such as cryo-electron tomography, single-particle analysis or microcrystal electron diffraction. In cryo-EM strategies, preparing samples is usually based on their immobilization in vitreous ice via rapid freezing of liquid solution that allows to keep near native cellular environment and protect from radiation damage ,97. The Very recently, Zhang et al. presented the structural and functional results obtained from chemical mapping experiments, cryo-EM and biological assays (in vitro and in cellulo) . They soRNA function is closely related to its structure, hence, there is a continual necessity to investigate the genome structure and organization, both in vitro and in a physiological environment. Nowadays, there are numerous methods to study secondary and tertiary structures of different genomes including viruses conserved structural RNA motifs can be involved in various processes crucial for the viral replication cycle, including genome assembly and viral packaging. Therefore, accurate structural analysis of these elements should be based on an appropriate choice of modeling and experimental methods, and proper optimization and data interpretation. Although advances in RNA structure analysis provide information regarding RNA secondary and tertiary structure, much remains to be discovered and additional work is needed to evaluate the full spectrum of vRNA structure and functions in the cell.Overall, vRNA dynamics and its propensity to continuous mutations are the main obstacles to development of efficient antiviral strategies. Therefore, finding well-described conserved structural motifs is crucial for the alternative treatment of different viral diseases in humans. vRNA can be considered a promising target for novel therapeutics. Michalak et al. suggested that structured regions of the IAV genome that are important for virus biology, can serve as a target for antisense oligonucleotides to inhibit IAV proliferation in cells .Additionally, the identification of conserved structural motifs within the IAV and SARS-CoV-2 genomes can result in the development of so-called ‘programmable antiviral agents’. Various pandemic IAV strains were analyzed by SHAPE-chemical mapping and conserved motifs were discovered. Targeting critical conserved vRNA structures, by for instance LNA-modified oligonucleotides results in potent antiviral activity both in vitro and in vivo . This tyTo sum up, the combination of extensive previous work and future efforts aimed at RNA structure determination and biological function identification will not only improve our basic scientific knowledge, but can also clarify the nature of the relationship between RNA structure and its functions, and further, could lead to discovering novel targets within viral genomes for efficient antiviral strategies.


Schizosaccharomyces pombe, in order to quantify the contribution of each pathway to heterochromatic silencing. We found that transcriptional silencing consists of two components, reduced RNA Pol II accessibility and, unexpectedly, reduced transcriptional efficiency. Heterochromatic loci showed lower transcriptional output compared to euchromatic loci, even when comparable amounts of RNA Pol II were present in both types of regions. We determined that the Ccr4–Not complex and H3K9 methylation are required for reduced transcriptional efficiency in heterochromatin and that a subset of heterochromatic RNA is degraded more rapidly than euchromatic RNA. Finally, we quantified the contribution of different chromatin modifiers, RNAi and RNA degradation to each silencing pathway. Our data show that several pathways contribute to heterochromatic silencing in a locus-specific manner and reveal transcriptional efficiency as a new mechanism of silencing.Heterochromatic silencing is thought to occur through a combination of transcriptional silencing and RNA degradation, but the relative contribution of each pathway is not known. In this study, we analyzed RNA Polymerase II (RNA Pol II) occupancy and levels of nascent and steady-state RNA in different mutants of  At centt) locus . This let) locus ,3,5.Current data suggest that heterochromatic silencing occurs through a combination of transcriptional silencing and RNA degradation. At the level of transcriptional silencing, HP1 proteins bind H3K9me nucleosomes and recruit downstream-acting complexes . HP1 proS. pombe) and 3′-5′ degradation by the exosome (Rrp6)  .How transcriptional silencing and RNA degradation pathways collaborate and their relative contributions to silencing are not known. In this study, we quantified the contribution of these pathways to heterochromatic silencing by analyzing RNA Pol II occupancy, nascent RNA and steady-state RNA in different fission yeast mutants. We also defined the heterochromatic factors that contribute to transcriptional silencing and/or RNA degradation. We found that transcriptional silencing occurs through reduced RNA Pol II accessibility, as previously proposed, but, unexpectedly, also through reduced transcriptional efficiency, a mechanism not previously implicated in silencing. Our data revealed that RNA Pol II transcriptional output is lower at heterochromatic loci compared to euchromatic loci relative to levels of RNA Pol II occupancy. We determined that the Ccr4–Not complex and H3K9 methylation are essential for the reduced transcriptional efficiency at heterochromatin and quantified the contributions of heterochromatin factors to the reductions in RNA Pol II occupancy, transcriptional efficiency and RNA stability.S. pombe strains used in this study are listed in et al.  and 500 μl phenol-chloroform-isoamylalcohol  and incubated at 65°C for 10 min with constant mixing. The organic and aqueous fractions were separated by centrifugation at 20 000 × g for 10 min. Nucleic acids in the aqueous fraction were precipitated with ethanol and then treated with DNAse I (Thermo Scientific) for 1 h at 37°C. DNAse was removed by a second phenol-chloroform-isoamylalcohol extraction and ethanol precipitation.Total RNA was isolated of a 2 ml yeast culture with ODThe poly(A) RNA library was obtained using the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (NEB) including the NEBNext Poly(A) mRNA Magnetic Isolation Module. Libraries were sequenced on Illumina HiSeq platform.600 of 1.2 were cross-linked with 1% formaldehyde (Roth) for 15 min at room temperature. The reaction was quenched with 125 mM glycine for 5 min. The frozen pellet was resuspended in 500 μl lysis buffer  with 1 mM PMSF and Complete EDTA free Protease Inhibitor Cocktail (Roche). Lysis was performed with 0.25–0.5 mm glass beads (Roth) and the BioSpec FastPrep-24 bead beater , 8 cycles at 6.5 m/s for 30s and 3 min on ice. DNA was sheared by sonication  35 times for 30 s with a 30 s break. Cell debris was removed by centrifugation at 13 000 × g for 15 min. The crude lysate was normalized based on the RNA and Protein concentration  and incubated with 1.2 μg immobilized  antibody against Anti-RNA polymerase II CTD repeat YSPTSPS (phospho S2) antibody - ChIP Grade  for at least 2 h at 4°C. The resin with immunoprecipitates was washed five times with each 1 ml of lysis buffer and eluted with 150 μl of elution buffer  at 65°C for 15 min. Cross-linking was reversed at 95°C for 15 min and subsequent RNase A (Thermo Scientific) digest for 30 min followed by Proteinase K (Roche) digest for at least 2 h at 37°C. DNA was recovered by phenol–chloroform–isoamylalcohol  extraction with subsequent ethanol precipitation. For sequencing, a ChIP-seq library was made using the NEBNext Ultra II DNA Library Prep Kit for Illumina kit (NEB). Libraries were sequenced on Illumina HiSeq platform.50 ml yeast cultures with an ODRNA IP was performed like ChIP but without RNase A digest, using Anti-RNA polymerase II CTD repeat YSPTSPS (phospho S2) antibody—ChIP Grade . After phenol-chloroform-isoamylalcohol extraction, DNA was digested with DNAse I (Thermo Scientific) for 2 h at 37°C. RNA was recovered with a second phenol-chloroform-isoamylalcohol purification and ethanol precipitation. Sequencing libraries were produced using the NEBNext Ultra II Directional RNA Library Prep Kit for Illumina (NEB). Libraries were sequenced on Illumina HiSeq platform.S. pombe reference genome  using splice-aware alignment tool STAR version 2.7.3a (Sequencing reads obtained in the poly(A) RNA sequencing (pA RNA), Pol II ChIP, nascent RNA sequencing (Pol II RIP) and total RNA sequencing experiments were mapped to the n 2.7.3a . AlignmeGenomic read counts were obtained using a custom script that extended basic htseq-count  functionFor the analysis in ix, by counting the number of read alignments x overlaping each genomic location i. Multi-mapping reads in this case fractionally contribute 1/NH to corresponding locations in the coverage profile, where NH is the number locations the read maps to. For visualization we used a custom R script that normalizes each profile by sequencing depth, making them comparable across datasets. Here, sequencing depth is computed as milions of reads that map to protein-coding genes or heterochromatic regions.Read coverages  relative to the level of Pol II Occupancy (Pol II ChIP):For each mutant, let θRNA levels being the result of RNA synthesis and degradation, we define RNA stability as the ratio of steady state RNA levels (pA RNA-seq) over the amount of newly synthesised RNA (Pol II RIP):mu) relative to wild-type (wt) cells:Changes in Pol II occupancy were quantified by the log fold change of Pol II occupancy levels (Pol II ChIP) in each mutant , RNA Pol II occupancy was ∼4-fold higher in clr4Δ cells compared to wild-type cells  compared to protein-coding loci Figure , and thidg/dh and subtelomeric tlh regions, which are known to be degraded by RNAi and by the Ccr4–Not complex, respectively  reach transcriptional efficiency that is comparable to euchromatic regions    being the major silencing pathway  Figure  wherein ) Figure , which i pathway .ago1Δ), lacking chromatin modifiers  or RNA degradation components   ; error bd mot2Δ) ,13. Rrp6tlh repeats, only chromatin modifiers are required to limit RNA Pol II occupancy , but not on RNA degradation factors Rrp6 and Exo2 Figure . Those cx Figure . The Ccrpe cells . This sutlh repeats is comparable in caf1Δ and wild-type cells, caf1Δ cells produce ∼10 fold more nascent RNA from that locus than wild-type cells  which would have accessible 3′ ends, and this could potentially stall RNA Pol II.To test if the deadenylase activity of the Ccr4–Not complex is required for reduction of transcriptional efficiency in heterochromatin, we introduced point mutations into the active site of the two deadenylases in the complex, Caf1 and Ccr4 . The muttlh locus  and H3K9me (Clr4), but not other heterochromatic factors or individual RNA degradation factors examined  at different heterochromatic loci Figure . It shoucaf1Δ, ccr4Δ and mot2Δ cells, the overall loss of silencing at centromeric repeats is small and increased transcriptional efficiency is compensated by reduced RNA Pol II occupancy and increased RNA degradation , since transcriptional efficiency is the dominant silencing pathway in those regions, and increased degradation by RNAi . In agreement with our previous calculations Figures  and 4, tg Figure .Our data show that heterochromatic silencing occurs through different mechanisms. First, RNA Pol II accessibility is reduced at heterochromatic regions, which also reduces overall transcription at those loci Figure . Second,clr4Δ or ago1Δ  is proportionally lower than in euchromatic regions. This silencing mode occurs at all heterochromatic regions in fission yeast cells and might be conserved in other organisms as well. In fact, this mode of silencing might be analogous to SIR-mediated silencing in ormation .caf1Δ cells. Notably, H3K9me levels at subtelomeric tlh and centromeric dg/dh repeats were only modestly affected in caf1Δ cells  under the accession number GSE186273.The sequencing data reported in this paper have been submitted to the NCBI Gene Expression Omnibus (GEO; https://github.com/canzarlab/heterochr_silencing.The whole data processing pipeline containing Python and R code is available in the form of Jupyter notebooks at gkac403_Supplemental_FileClick here for additional data file.


Given the emergence of the severe acute respiratory syndrome‐coronavirus‐2 (SARS‐CoV‐2), which particularly threatens older people with comorbidities such as diabetes mellitus and dementia, understanding the relationship between Covid‐19 and other diseases is an important factor for treatment. Possible targets for medical intervention include G‐quadruplexes (G4Qs) and their protein interaction partners. We investigated the stability and conformational space of the RG‐1 RNA‐G‐quadruplex of the SARS‐CoV‐2 N‐gene in the presence of salts, cosolutes, crowders and intrinsically disordered peptides, focusing on α‐Synuclein and the human islet amyloid polypeptide, which are involved in Parkinson's disease (PD) and type‐II diabetes mellitus (T2DM), respectively. We found that the conformational dynamics of the RG‐1 G4Q is strongly affected by the various solution conditions. Further, the amyloidogenic peptides were found to strongly modulate the conformational equilibrium of the RG‐1. Considerable changes are observed with respect to their interaction with human telomeric G4Qs, which adopt different topologies. These results may therefore shed more light on the relationship between PD as well as T2DM and the SARS‐CoV‐2 disease and their molecular underpinnings. Since dysregulation of G4Q formation by rationally designed targeting compounds affects the control of cellular processes, this study should contribute to the development of specific ligands for intervention. Pre‐existing conditions play a critical role in disease progression after infection with severe acute respiratory syndrome coronavirus 2. In this study, we sought to unravel the underlying mechanisms related to the interactions between peptides relevant to type II diabetes mellitus and Parkinson's disease and the G‐quadruplex‐forming sequence RG‐1 of the viral RNA. Next to the respiratory tract, the COVID‐19 virus affects several other organs.2, 3 CIn addition to eukaryotes and prokaryotes, G4Q motifs are also considered key elements in regulating the life cycle of viruses.8, 9 TIt is well known that the structure and thus the function of RNAs such as RNA‐G4Qs are very susceptible to changes in their environment, that is, they respond sensitively to the presence of salts, osmolytes, crowding agents, and to the binding of proteins, which are also abundant in the biological cell.22, 23In this study, we explored the effects of central environmental factors, including salts, osmolytes, crowding agents and intrinsically disordered peptides (IDPs), on the conformational landscape of the RG‐1 G4Q of SARS‐CoV‐2. As IDPs we selected α‐Synuclein (α‐Syn) and the human islet amyloid polypeptide (hIAPP). α‐Syn regulates neurotransmitter vesicle cycling, but is, under pathological conditions, closely associated with Parkinson's disease (PD). PD results from abnormal aggregation of α‐Syn, and these aggregates are found primarily in Lewy bodies, the hallmark of PD.25, 26R, of the two attached dyes and thus different FRET efficiencies, E, because E=R06 ⋅ (R06+R6)−1.[R0, is the distance at which 50 % of the excited donor molecules will be deactivated . As it is well known that monovalent cations play an important role in the stability of the G4Qs, smFRET measurements were carried out in the presence of monovalent  salts to quantify their effect on the conformational transitions of the RG‐1 SARS‐CoV‐2 RNA. Of note, in diseased cells such as those of cancer and neurodegenerative diseases, the concentration of intracellular ions is typically changed by overexpression (or inactivation) of disease specific ion channel proteins. We observed that the stability of the RG‐1 SARS‐CoV‐2 RNA‐G4Q is highly susceptible to the concentration of monovalent K+. In the absence of K+ cations (pure buffer solution), two peaks were observed in the FRET histograms, located at E≈0.3, representing the unfolded conformation) and at E≈0.5–0.8, representing a metastable or partially folded conformation, respectively , only the fully folded conformation is retained  experiments. This method avoids ensemble averaging and enabled us to elucidate the structure and conformational dynamics of the quadruplex and how the equilibrium between conformational substates is affected by the different solution conditions.48, 49+ cation was found to be unable to stabilize the folded conformation of the RG‐1 RNA‐G4Q , which can be attributed to a parallel topology of the G4Q  compared to Na+ (0.95 Å), we may assume that Na+ should be small enough to be coordinated within the plane of a G‐quartet. Nevertheless, the free energy of binding to the RG‐1 RNA quadruplex seems to be much lower for K+, promoting the formation of a stable parallel‐folded G‐quadruplex beyond 15 mM KCl. For comparison, the htel telomeric DNA quadruplex adopts a hybrid‐stranded conformation in the presence of K+ and an antiparallel basket structure in the presence of Na+.[+ by an additional stacking interaction across the third tetrade provides sufficient stability, which is not possible in the case of the RG‐1 G4Q.Various factors contribute to the stability of G‐quadruplexes and determine their folding topology, including π‐stacking interactions, H‐bonding, cation binding, and hydration changes.50, 51The intracellular environment is crowded with biomolecules that occupy a significant portion (up to 30 %–40 %) of the cellular volume, which is expected to affect the free energy and conformational landscape of biomacromolecules.57, 58E≈0.9 in the E‐histograms to a parallel folded conformation, the peak at E≈0.5–0.8 to a partially folded conformation, and that at E≈0.3 to the unfolded state structure. At high urea concentrations (6–8 M), a single conformation is mainly seen in the FRET histogram only, which can be attributed to a partially folded state. The partially folded conformer is steadily stabilized upon increasing the urea concentrations, reaching population values around 80 %. No drastic changes are seen in the CD spectrum of the partially folded state compared to the folded structure. For example, the CD spectrum of RG‐1 G4Q in 15 mM K+ and 8 M urea at room temperature showed a positive CD band around 270 nm, which is little red‐shifted compared to the pure buffer solution , urea is a good choice. Urea is a natural organic cosolute with implications in maintaining osmotic pressure in cells and is present in rather large concentrations in various living organisms.E≈0.8 to E≈0.7  is populated at higher hIAPP concentrations (5–10 μM). For comparison, the conformation of htel DNA‐G4Q was found to be less affected by the hIAPP  to the hybrid topology (E≈0.6) was observed. In the presence of 10 μM IAPP, also a small amount of unfolded DNA (E≈0.3) was detected.Owing to the correlation found between type II diabetes and the SARS‐CoV‐2 disease, we may expect an effect of hIAPP on the replication and translation of the virus RNA.46, 47Protein‐RNA interactions are a vital mechanism inside the cell to perform RNA synthesis, translation and degradation.α‐Syn is a natively disordered protein that is positively charged (+4) at the N‐terminus and contains mainly lysine residues that can interact with the negatively charged phosphate groups of the nucleic acid, along with other amino acid residues that nonspecifically interact with the nucleobases of the RNA‐G4Q. α‐Syn has 17 glutamate residues along with 3 asparagines and 6 glutamines. Earlier reports suggested that lysine and arginine strongly favor interaction with guanine bases and largely account for the abundance of hydrogen bonding interactions with the bases.In recent years, G‐quadruplexes have received considerable attention as therapeutic targets for chemical intervention in biological functions. Since modulation of the stability and conformation of G4Qs has been found to play a significant role in many human pathological and neurodegenerative diseases, the underlying mechanisms need to be explored to be able to target G4Qs. We have shown that the stability and conformational landscape of the RNA G4Q‐forming sequence RG‐1, which is located in the coding sequence region of the SARS‐CoV‐2 nucleocapsid phosphoprotein (N), is broadly affected by salt, osmolyte, crowding and intrinsically disordered peptides.+ cations stimulate formation of the fully folded structure, while hIAPP as well as high concentrations of urea induce a partially folded conformation of the RG‐1 RNA‐G4Q. Only high temperature leads to full unfolding of RG‐1. The interaction with salts, cosolutes, crowders and peptides are factors that affect the conformational dynamics and stability of the G4Qs and helped us explore the overall conformational landscape of the different quadruplexes accessible. Since dysregulation of G4Q formation by rationally designed targeting compounds is detrimental in regulating cellular processes, this knowledge should be helpful in designing specific ligands for intervention. Next to the number of tetrades that determines the stability of the G4Qs, the loop sequences are unique and not only provide a structural distinction responsible for the diverse topologies observed, but may also have the ability to specifically interact with designer ligands.Figure To date, the link between SARS‐CoV‐2 and the metabolic and pathological diseases observed is not well understood. The formation of RG‐1 G4Q has been shown to reduce levels of N‐protein by inhibiting its translation, hence might be a promising therapeutic target to fight SARS‐CoV‐2. We have seen that both amyloidogenic peptides, α‐Syn and hIAPP, affect the conformational equilibrium between folded and unfolded states, to a different extent, however, in a sequence‐specific way. Considerable changes are observed with respect to their interaction with htel G4Qs. hIAPP stabilized a partially folded state of the RG‐1 RNA‐G4Q, whereas α‐Syn strongly stabilized the folded state of the quadruplex. Since G‐quadruplexes and their interaction with regulatory proteins play an important role as on/off‐switches that modulate polymerase activity, the interaction with these amyloidogenic peptides may alter expression profiles of disease‐modifying genes. Therefore, these results may shed more light on the relationship between PD and T2DM and the SARS‐CoV‐2 disease and its molecular basis.Materials: Ficoll 70 was purchased from Carl Roth GmbH+Co. KG  and was used as received without further purification. Human IAPP (islet amyloid polypeptide or amylin) was purchased from GenScript . Before use, the IAPP peptide was dissolved in 1,1,1,3,3,3‐hexafluoro‐2‐propanol (HFIP) and incubated for at least 30 min to ensure an aggregate‐ and seed‐free sample. The buffer solution used in the measurements contains 20 mM Tris‐HCl (pH 7.4) and was filtered by a 0.45 μm sterile Whatman Puradisc 30 syringe filter. The RNA and DNA sequences with and without fluorophore labels were purchased from biomers . For the exact sequences, please refer to the Supporting Information. The expression and purification of α‐synuclein was carried out as described before[Single‐molecule FRET (smFRET) measurements and data anaylsis: SmFRET measurements were carried out using a confocal fluorescence microscope  under freely diffusing conditions. The pulsed interleaved excitation (PIE) FRET technique was used to separate dually labeled from singly labeled species.S, which is the ratio of photons emitted after donor excitation and the sum of total photons emitted after donor and direct acceptor excitation. For a donor‐only species, S=1, and for an acceptor‐species only, S=0. A green laser pulse at 560 nm  and a red laser pulse at 635 nm  were used to excite the donor Atto 550 and acceptor Atto 647N, respectively. A quad band dichroic mirror  was used to reflect both the green and red laser light to the entrance port of the fluorescence microscope. Donor and acceptor fluorescence signals were separated to two different detection channels, first by using a dichroic mirror , followed by band pass filters FF 01‐593/40 (Semrock) and FF 01‐676/29 (Semrock). Two SPCM‐AQR series single photon avalanche diodes (SPAD) were used as detection channels for the donor and acceptor fluorescence. Time‐correlated single photon counting (TCSPC) data were first analyzed with Multiparameter Fluorescence Detection (MFD)CD measurements: The topology of G‐quadruplexes can be determined by monitoring the positive and/or negative circular dichroism (CD) signals at specific wavelengths. The CD measurements were performed using a JASCO J‐715 spectropolarimeter in 20 mM NaxHxPO4 buffer at pH 7.5 in the presence of 15 mM or 140 mM KCl solution. The concentration of the RNA construct was 20 μM. The molar ellipticity was calculated using the strand concentration. The pathlength of the quartz‐glass cell was 1 mm.The authors declare no conflict of interest.1As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.Supporting InformationClick here for additional data file.