Three-dimensional (3D) structures dictate the functions of RNA molecules in a wide variety of biological processes. However, direct determination of RNA 3D structures in vivo is difficult due to their large sizes, conformational heterogeneity, and dynamics. Here we present a method, Spatial 2′-Hydroxyl Acylation Reversible Crosslinking (SHARC), which uses chemical crosslinkers of defined lengths to measure distances between nucleotides in cellular RNA. Integrating crosslinking, exonuclease (exo) trimming, proximity ligation, and high throughput sequencing, SHARC enables transcriptome-wide tertiary structure contact maps at high accuracy and precision, revealing heterogeneous RNA structures and interactions. SHARC data provide constraints that improves Rosetta-based RNA 3D structure modeling at near-nanometer resolution. Integrating SHARC-exo with other crosslinking-based methods, we discover compact folding of the 7SK RNA, a critical regulator of transcriptional elongation. These results establish a strategy for measuring RNA 3D distances and alternative conformations in their native cellular context. Determination of RNA 3D structures in vivo is a challenging problem. Here, the authors describe a chemical crosslinking method (SHARC) that they use in combination with RNA sequencing to measure distances between nucleotides in RNA 3D structures. To perform these tasks, RNA must fold into complex three-dimensional (3D) structures that undergo intricate conformational transitions7. Physical methods can be applied to elucidate RNA structure, such as nuclear magnetic resonance (NMR), cryo-EM, and crystallography. These approaches have helped characterize RNA structures, often at atomic resolution, but require well-behaved and purified samples, whereas cellular RNA structures can be highly dynamic and heterogenous2. Alternatively, numerous low-resolution approaches, such as chemical mapping and crosslinking, are high-throughput and can be applied in vivo. These low-resolution methods can be coupled with ever-improving computational tools to build 3D models8.RNA plays critical roles throughout the cell, ranging from carrying genetic information to regulation and catalysis13. Correlated chemical probing methods such as multiplexed •OH cleavage analysis (MOHCA), mutate-and-map (M2), and RNA interacting group mutational profiling (RING-MaP) infer spatial proximity of nucleotides but provides fuzzy distances to constrain 3D modeling18. While these methods are improvements over 1D DMS chemical mapping, they are often limited to smaller RNAs as they require the two correlated nucleotides on the same sequencing read, and the sequencing coverage scaled exponentially with RNA length. Furthermore, MOHCA and M2 are only applicable to in vitro synthetic RNAs, while RING-MaP is limited by the noisy background and low correlation levels14.Chemical probing, such as selective 2′-hydroxyl acylation (SHAPE) and dimethyl sulfate (DMS) alkylation, report various aspects of nucleotide flexibility and have been used to constrain local secondary structure predictions19. Recently developed psoralen-crosslinking-based methods, such as PARIS, LIGR-seq, SPLASH, and COMRADES directly capture base pairs either within or between different RNA molecules in high throughput24. Psoralen crosslinks staggering pyrimidines in opposite strands through [2 + 2] photocycloadditions. At the cost of low efficiency, this reaction offers high specificity, is challenging to reverse, and is limited to uridines in helical regions. Even though the gapped reads from such methods can go down to 15 nucleotides on each arm, unambiguous identification of base pairs remains challenging. Recently reported bifunctional acylating crosslinkers, BINARI, react with the 2′-OH on all four nucleotides and offer an approach to capturing nucleotide pairs in spatial proximity crosslinking capacity to 3D space25. However, the nine-step synthesis, large molecular size, and complex reversal mechanism rendered the BINARI compounds unsuitable for cellular application to measure RNA tertiary contacts on a transcriptome-wide level25.Crosslinking and proximity ligation represent an alternative strategy to capture spatial distances among nucleotides, overcoming the limitations of correlated chemical probingThis study develops highly efficient and accessible 2′-hydroxyl acylation chemistry for crosslink-formation and -reversal in living cells (SHARC), overcoming the technical challenges in the preparation and application of BINARI reagents. We develop an exonuclease (exo) trimming approach to pinpoint crosslinked nucleotides, improving the precision of distance measurements to the crosslinked atoms (2′-O in ribose). The integration of SHARC crosslinking, exo trimming, proximity ligation, and high throughput sequencing (SHARC-exo) enables transcriptome-wide analysis of spatial distances between nucleotides at nanometer resolution in cells, without sequence length limitations. We rigorously benchmarked the distance measurement and structure capture using complex, yet well-studied models in cells, such as the ribosome, spliceosome, 7SL, and RNase P, revealing both static structures, interactions, and alternative conformations. The incorporation of distance measurements into Rosetta-based 3D modeling dramatically improved structure resolution. We combined SHARC-exo with established methods, such as PARIS and CLIP, to discover compact folding of the 7SK RNA, a critical regulator of transcriptional elongation in higher eukaryotes. These experiments demonstrate the power of integrating multiple orthogonal approaches to capture proximity constraints in complex RNAs to study their structures. Together, we developed cheap and easily synthesized compounds that dramatically outperform known crosslinking tools, providing the community with a strategy for understanding RNA 3D structures and alternative conformations in cells.28. Knowledge of these tertiary contacts significantly improves the modeling of complex RNAs29. To determine these constraints for RNAs in cells, we sought to develop a set of bifunctional and reversible 2′-hydroxyl acylation reagents with flexible linkers  as a candidate to test further based on these results. To characterize the reaction kinetics of DPI at physiological pH 7.4 at room temperature, we measured its hydrolysis with NMR and found that 50% was hydrolyzed after 5 min   Fig. . From th1 was crosslinked with DPI, purified, and 10 µM of crosslinked RNA was incubated in 100 mM Borate buffer pH 10.0 for 2 h at 37 °C. The crosslinked RNA was nearly fully reversed without apparent degradation  gel24, where the second dimension is denser than the first . The differential gel densities enable the separation of crosslinked from non-crosslinked fragments. The crosslinked fragments migrate as a smear above the diagonal . Each group corresponds to one specific pair of nucleotides that are close to each other. The gapped reads should reveal trimmed 3′ ends at a fixed distance from the actual crosslinking sites . The spatial distances between the crosslinked nucleotides  are determined by the length of the linkers and the flexibility of the RNA structure.Having demonstrated efficient SHARC crosslinking and reversal, next we developed a strategy, exonuclease (exo) trimming, to measure inter-nucleotide distances, based on our previously established PARIS method32 Fig. . Crosslinal Fig. , therefoTo validate the exo trimming approach, we first applied it to PARIS experiments, where the well-established crosslinking preference of psoralen enables rigorous testing of the trimming efficiency. After RNase R treatment, the reads are significantly shorter  and the TNA method, fragmented RNA with RNase III, and isolated the crosslinked fragments using the DD2D gel method. We recovered 1.01%, 1.31%, and 1.89% RNA fragments as crosslinked using 5, 12.5, or 25 mM DPI, respectively  . To determine whether trimming precisely reveals the crosslinked nucleotides, we searched for nucleotides along each arm closest between the two arms  Fig. . In contrms Fig. . Not sur39. We further split tertiary contacts to the stable core and the more flexible Expansion Segments (ESs), many of which are not resolved with cryo-EM , and U4/U6 snRNAs in the spliceosome , featuring eight helical regions, among which a terminal helix (M1) circularizes 7SK55. More recent work using the evolutionary coupling method that detects spatial interactions failed to identify the M1 terminal helix  reads that represent RNA duplexes, PARIS also captures more complex structures in the form of multi-segment reads, where two structures that form together in one molecule are crosslinked ligated and sequenced , therefore generally applicable to various types of crosslinkers. The high throughput SHARC-exo method measures spatial distances between nucleotides either within an RNA or between different RNA molecules in living cells with high efficiency. We show that SHARC-exo distance information can be used to constrain Rosetta-based 3D RNA modeling, therefore opening the possibility of understanding the 3D structures of the entire transcriptome in vivo. Using the ribosome as an example, we demonstrate that SHARC-exo also reveals highly heterogeneous conformations of ESs in cells, challenging to characterize using conventional physical methods. Finally, we integrated SHARC-exo with two other methods, PARIS and CLIP, to conclusively determine a secondary structure model for the 7SK RNA and reveal a compact folding of the multiple helices. These results highlight significant advancements compared to previous methods for RNA 3D structure analysis.24. We anticipate that direct high throughput analysis of RNA 3D structures in vivo will reveal new principles of RNA structure formation and function. Given the critical roles of RNA in human genetic and infectious diseases, in vivo, 3D structural information is invaluable for developing RNA-based and RNA-targeted therapeutics.Future improvements and extensions of the SHARC-exo principle will further enhance its versatility and reliability and broaden its applications. First, despite our careful in vitro analysis of the SHARC reagents, the kinetics of the two-step acylation and hydrolysis reactions are difficult to characterize experimentally and theoretically because they are not necessarily decoupled or orthogonal. These reactions are likely different in vitro and in vivo, making it challenging to develop a simplified model to study them in detail. Nevertheless, a better understanding of these reactions is important for further improvement of crosslinking chemistry. The current RNase R trimming is not 100% efficient, likely due to the presence of SHARC mono adducts on RNA. Even though our computational identification of trimming stop sites uses the 3′ end medians, which is robust against outliers, further improvement of this experimental step will increase the resolution and accuracy. Most cellular RNAs are associated with proteins. Incorporating RNA–protein interactions and protein structure information will enable 3D modeling of RNP complexes in cells. Current acylation-based crosslinkers apply to all four nucleotides yet are limited to flexible ones. In some highly structured RNAs, the number of flexible and, therefore, cross-linkable nucleotides might be moderate  was obtained. Note that imidazole is formed as a byproduct in the reaction and is present in all spectra, which can be found in the 1,1′-Oxalyldiimidazole was purchased from Tokyo Chemical Industries. All other activated dicarboxylic acids were synthesized  and 30 volumes of absolute ethanol. RNA was precipitated for 1 h at −20 °C and then centrifuged  for 40 min at 4 °C. The pellet was washed with 70% ethanol, air-dried, and resuspended in 10 μL RNase-free water. Precipitated RNA was analyzed using 20% PAGE and imaged using Sybr Gold and a Bio-Rad Gel Documentation System  and safeVIEW-MINI2 Imaging System. The distribution between unreacted RNA 1 and crosslinked RNA was determined by quantifying the band intensity with ImageJ (V1.52t). All experiments were performed in triplicate.The model RNA g) for 40 min at 4 °C. The pellet was washed with 70% ethanol, air-dried, and resuspended in 10 μL RNase-free water. RNA was analyzed using 20% PAGE and imaged using Sybr Gold and a Bio-Rad Gel Documentation System and safeVIEW-MINI2 Imaging System. The distribution between unreacted RNA 1and crosslinked RNA was determined by quantifying the band intensity with ImageJ (V1.52t). All experiments were performed in triplicate.Five microlitres of 10 μM crosslinked RNA in water were diluted with 45 μL 100 mM borate buffer pH 10.0 and incubated for 2 h at 37 °C. Reactions were quenched by addition of 50 μL of precipitation solution  and 300 μL of absolute ethanol. RNA was precipitated for 1 h at −20 °C and then centrifuged  and HEK293T (CRL-3216) cells were purchased from ATCC and maintained in Dulbecco’s modified Eagle’s medium  + 10% fetal bovine serum  + Pen/Strep antibiotic, in 37 °C incubators with 5% CO2 product to escape. Mixed solutions were left at room temperature to react for 30–60 min before crosslinking.SHARC reagents were made by dissolving 1-part SHARC reactant in 200 μl anhydrous DMSO  and 2 parts CDI Sigma, 115,533) in 250 μl DMSO. Dissolved SHARC reactant was pipetted into the tube containing CDI. After briefly vortex and spinning down, a needle was inserted into the top of the 1.5 mL centrifuge tube to allow the COHela and HEK293T cells with 80% confluency in a 10 cm dish were washed twice with 1× phosphate-buffered saline (PBS). Then cells were collected, resuspended in 1× PBS, and transferred into a 1.5 ml tube with a final volume of 900 μl. For each tube of cells, added 100 μl of SHARC crosslinker to make the final concentration of 0, 5, 12.5, and 25 mM. Cells were incubated in a rotator at room temperature for 30 min. After crosslinking, crosslinking solution was removed and cells were washed twice with 1× PBS.™, AM9625), and water to a final volume of 600 μl. Each sample was passed through a 25 or 26 G needle about 20 times to further break the insoluble material. Proteinase K (PK)  was added to a final concentration of 1 mg/ml, and PK treatment was performed at 37 °C for 1 h on a shaker at 1000–1200 × g. After PK digestion, 60 μl of 3 M sodium acetate (pH 5.3) , 600 μl of water-saturated phenol (pH 6.6) , and 1 volume pure isopropanol were added to precipitate total nucleic acids by spinning at 17,000 × g for 20 min at 4 °C. After twice washing using 70% ethanol, total nucleic acids were resuspended in 300 μl of nuclease-free water. For 100 μg of TNA samples, 50 units of TURBO™ DNase  were added to remove DNA at 37 °C for 20 min. Then added 20 μl of 3 M sodium acetate, an equal volume of water-saturated phenol, two-volume of pure isopropanol to precipitate RNA sample by spinning 20 min at 12,000 × g at 4 °C.For each 10 cm dish cell, added 100 μl of 6 M GuSCN  and lysed cells with vigorous manual shaking for 1 min. Then, cell lysate was added 12 μl of 500 mM EDTA , 60 μl of 10× PBS , 3 μl of GlycoBlue , three-volume of pure ethanol were added to precipitate RNA. Fragmented RNA was resuspended in RNase-free water.A 10 μg of cross-linked RNA was fragmented using 10 μl of RNase III  with 5mM MnClFirst dimension gel. Prepare 8% 1.5 mm thick denatured first dimension gel using the UreaGel system  with MOPS buffer . Briefly, 3.2 ml UreaGel concentrate, 5.8 ml UreaGel diluent, 1 ml 10× MOPS buffer, 80 μl 10% of APS, and 4 μl TEMED  were mixed to make 8% first dimension gel. Loading dsRNA ladder  as molecular weight marker. Run the first dimension gel at 30 W for 7–8 min in 1× MOPS buffer. After electrophoresis was finished, staining the gel with SYBR Gold  in 1× MOPS buffer and excising each lane between 50 nt to topside from the first dimension gel. The second dimension gel can usually accommodate three gel splices.Second dimension gel. Prepare the 16% 1.5 mm thick urea denatured second dimension gel using the UreaGel system with MOPS buffer. Briefly, 6.4 ml UreaGel concentrate, 2.6 ml UreaGel diluent, 1 ml 10× MOPS buffer, 80 μl 10% of APS, and 4 μl TEMED were mixed to make 16% first dimension gel. Using prewarmed 1× MOPS buffer to fill the electrophoresis chamber to facilitate denaturation of the cross-linked RNA. Run the second dimension at 30 W for 50 min to maintain high temperature and promote denaturation. Gels were imaged using the iBright FL1500 Image System . A gel containing the cross-linked RNA above the diagonal from the 2D gel was excised and crushed for RNA extraction.RNase R is a 3′ → 5′ exonuclease that is capable of unwinding and digesting double-stranded RNA with a 3′ overhang. Purified crosslinked RNAs from DD2D gel were treated with 20 units of RNase R  in 1× RNase R digestion buffer with 5 mM ATP at 45 °C for 2, 12, and 24 h, respectively. Control RNA was without RNase R treatment. After RNase R treatment, the one-tenth volume of 3 M sodium acetate (pH 5.3), 3 μl of GlycoBlue, three-volume of pure ethanol were added to precipitate RNA.™, AM2696) and 1 μl of 0.1 mM ATP were added to 10 μl of purified dsRNA fragments2. Ligation mixture was incubated at room temperature overnight. After ligation, the samples were boiled for 2 min to stop the reaction. After heat denaturation, samples were centrifuged to remove the precipitate and then precipitated by ethanol.Purified RNA fragments were proximity ligated by T4 RNA Ligase1 . Briefly, 2 μl of 10× ligation buffer, 5 μl of T4 RNA Ligase, 1 μl of SuperaseIn  was added, and nuclease-free water was added to bring decrosslinking buffer to 1×. Samples were incubated for 2 h at 45 °C to guarantee reversal (this is higher than the temperatures used in the in vitro experiments). After reverse crosslinking, RNA was purified with three-volume of ethanol and 1 μl of GlycoBlue.Reverse crosslinked RNAs were heated at 80 °C for 90 s, then snapped cooling on ice. To each sample, 3 μl of 10 μM ddc adapter /5rApp/AGATCGGAAGAGCGGTTCAG/3ddC/, 1 μl of T4 RNA ligase 1, 2 μl of DMSO, 5 μl of PEG8000, 1 μl of 0.1 M DTT, 1 μl of SuperaseIn and 2 μl of 10x T4 RNA ligase buffer were added to perform adapter libation at room temperature for 3 h. After adapter ligation, the following reagents were added to remove free adapters: 3 μl of 10x RecJf buffer , 2 μl of RecJf , 1 μl of 5′Deadenylase , 1 μl of SuperaseIn, Reaction was incubated at 37 °C for 1 h. Then 20 μl of water was added to each sample to make a total volume of 50 μl and Zymo RNA clean and Concentrator-5  was used to purify RNA.™, 18090010) was used to perform reverse transcription. The reaction buffer was optimized Mn2+ buffer (1×): 50 mM Tris-HCl (PH 8.3), 75 mM CH3COOK, and 1.5 mM MnCl2. Briefly, 1 pmol of barcoded RT primer and 1 μl of 10 mM dNTP were added to RNA samples and heated at 65 °C for 5 min in a PCR block, chilling the samples on ice rapidly. Then 4 μl of 5× Mn2+ buffer, 2 μl of 0.1 M DTT, 1 μl of SuperaseIn and 1 μl of SSIV were added to each sample. The mixed sample was incubated at 25 °C for 15 min, 42 °C for 10 h, 80 °C for 10 min; hold at 10 °C. After reverse transcription, 1 μl RNase H and RNase A/T1 mix were added and incubated at 37 °C for 30 min in a thermomixer to remove RNA. Synthesized cDNA was purified using Zymo DNA clean and Concentrator-5.SuperScript IV (SSIV) , 1 μl of 50 mM MnCl2 and 10× CircLigaseII™ buffer were added to cDNA sample and performed circularization at 60 °C for 100 min. An 80 °C treatment for 10 min was followed to stop the reaction. The circularized cDNA products were directly used to library PCR. Library PCR preparation was performed3. PCR products were run on 6% native TBE gel. A gel containing DNA products from 175 bp and topside (corresponding to >40 bp insert) was excised and crushed for DNA extraction.1 μl of CircLigase1HR2) DNA with T7 promoter (TAATACGACTCACTATAG) was purchased from twist bioscience. After PCR amplification, the DNA was cleaned up using the Qiagen PCR Purification Kit and purified using an 8% native polyacrylamide gel. The P4-P6 (1HR2) RNA was transcribed using the MEGAscript T7 Transcription Kit from Thermo Fisher (AM1334) from 136 ng of DNA template and purified on denatured polyacrylamide gels. 10 μg of P4–P6 RNA, 10 μL of refolding buffer, and water was added to a final volume of 44 μL per sample. The RNA was then denatured by incubating at 90 °C for 5 min followed by snap cooling on ice. 1 μL of 500 mM MgCl2 was then added to each sample while cold and then mixed. Samples were then allowed to come to room temperature over several minutes to refold. After refolding, either 5 μL of DMSO for controls or 5 μL 50 mM DPI was added to each sample. Samples were then incubated at room temperature for 30 min. After incubation, samples were purified using ethanol precipitation. The crosslinked RNA was then converted into cDNA libraries as described above. In particular, we divided the crosslinked RNA fragments from the DD2D gels into 2 fractions, where one was treated with RNase R at 37 °C for 2 h, while the other was not treated. The cDNA library was sequenced on a MiSeq machine.The P4–P6 . The 3′end adapters of sequencing data were removed using Trimmomatic (v0.36). PCR duplicates were removed using readCollapse script from the icSHAPE pipeline. After removing 5′ header, reads were mapped to manually curated hg38 genome using STAR (v2.7.0 f) programhttps://github.com/zhipenglu/CRSSANT)5. cont.sam, continuous alignments; gap1.sam, non-continuous alignments with one gap; gapm.sam, non-continuous alignments with more than one gaps; trans.sam, non-continuous alignments with the two arms on different strands or chromosomes; homo.sam, non-continuous alignments with the two arms overlapping each other; bad.sam, non-continuous alignments with complex combinations of indels and gaps. Gap1. and gapm alignments containing splicing junctions and short 1–2 nt gaps were filtered out using gapfilter.py (https://github.com/zhipenglu/CRSSANT). Then filtered gap1.sam, filtered gapm.sam and trans.sam were used to analyze RNA structures and interactions.The primary mapping alignments were extracted from SampleAligned.sortedByCoord.out.bam using SAMtools (v1.8), and classified into six different types using gaptypes.py (https://github.com/zhipenglu/CRSSANT). After DG clustering, crssant.py verifies that the DGs do not contain any non-overlapping reads, i.e., any reads where the start position of its left arm is greater than or equal to the stop position of the right arm of any other read in the DG. If the DGs do not contain any non-overlapping reads, then the following output files ending in the following are written: Sample.sam: SAM file containing alignments that were successfully assigned to DGs, plus DG and NG annotations; dg.bedpe: bedpe file listing all duplex groups.Filtering alignments were assembled to DGs and NGs using the crssant.py script . The bed output file (from crssant.py script) can be visualized in IGV, where the two arms of each DG can be visualized as two “exons”, or as an arc that connects far ends of the DG.Assembled alignments with DGs tag were displayed using integrative Genomic Viewer (IGV)To analyze the RNase R trimming efficiency . Watson-Crick and non-Watson-Crick base pairs were analyzed using the DSSR software (v1.7.7)7. The 3D structures of the ribosome were visualized by the PyMOL system . Spatial distances in the cryo-EM model were extracted directly for use. The resolution of the human ribosome cryo-EM model is highly variable across the entire complex . Although the average resolution is 5.4 Å, the lowest goes to 21 Å. The ribosome structure analysis and conclusions are based on longer distance intervals, e.g., 0–20 and 20–40 Å. The modeling runs also used 0–20 and >20 Å as the intervals for penalty calculations. In addition, the low-resolution regions are confined to the expansion segments and do not affect the analysis of the stable core regions  were collected and used to construct the interaction matrix. To build the physical interaction map of 28S rRNA and 18S rRNA, the cryo-EM model of the 28S rRNA and 18S rRNA was downloaded from RCSB Protein Data Bank (PDB)  were concatenated into one reference, separated by 100 nt “N”s, was added back. The curated hg38 genome contained 25 reference sequences, or “chromosomes”, masked the multicopy genes, and added back single copies. This reference is best suited for the PARIS analysis. SHARC-seq reads were mapped to representative RNAs were collected and used for IGV visualization.The ribose 2′OH in every flexible nucleotide (single-stranded or icSHAPE activated) was used to calculate the cross-linking distance. The minimum distance between two arms’ flexible nucleotide was used to analyze the minimum distance distribution. The distance between No.3 to No.7 flexible nucleotides from the 3′end of each arm was used for 3–7 distance distribution analysis.9 and Wakeman and Maden (1989)10. The SHARC-seq reads with ≥40 Å between two arms were collected and separated to core and expansion alignments. The dynamic reads were selected based on the rules that one arm mapped to the same region of rRNA, other arm mapped to different regions. The selected dynamic alignments were loaded to IGV for visualization.The core and expansion segment boundaries of rRNA were derived from Chandramouli et al. (2008)12. Helices of secondary structure regions were pre-built with the example command below to save computational expense: rna_helix.py -seq cag cug -resum 5–7 27–29 -o example_helix_1.pdb. The 920–1080 nucleotide region of the human 18S RNA was modeled with and without SHARC determined constraints. For the model set without SHARC constraints, no cst file or flag was used. For the model set with SHARC constraints, the following linear energy function example command was used to assign constraints for 2′OH atoms that participated in the crosslinking reaction so that between 0A–20A there is no energy penalty and to apply a linearly scaling energy penalty if the atoms are >20 A apart.Rosetta software (version 2020.08.61146) was used to model RNAs for this studyAtomPair O2′ 63 O2′ 117 LINEAR_PENALTY 10.0 0 10 1.0. Here 10.0 is the ideal distance between the atoms in Å, 0 is the energy penalty assigned to the range, 10 is the tolerance for the energy trough and 1.0 is the slope constraint.Models were built with the command shown below using fasta, secondary structure and constraint files . For the native file used to get a rms for these files, the 920–1080 region of the 18S RNA was cut out using pymol and renumbered using renumber_pdb_in_place.py.rna_denovo.static.linuxgccrelease -nstruct 1000 -fasta../18s_920_1080.fasta -s../18s_920_1080_helix_1.pdb../18s_920_1080_helix_2.pdb-secstruct_file../18s_920_1080.secstruct -cst_file../18s_920_1080.cst -native../18s_920_1080_renumbered.pdb -minimize_rna true -out:file:silent 18s_920_1080_tert.outAfter modeling runs were finished, models were extracted using easy_cat.py. Example: easy_cat.py directory. To extract the top 1% scoring models from the bulk of the models for each run condition the following command was used (in this example 200 models are extracted): silent_file_sort_and_select.py [example_file.out] -select 1–200 -o [example_file_cluster.out]. The lowest 1% energy models were then clustered from each run condition to inspect the different pose topologies that existed within the lowest energy scoring models. Clustering was done with the command shown below (in this example 10 clusters are made using a cluster radius of 5 Å: rna_cluster.static.linuxgccrelease -in:file:silent [example_file.out] -out:nstruct 10 -cluster:radius 5 -out:file:silent [example_file_cluster.out]. Clusters were extracted with the following command. The -no_replace_names flag here is used to prevent clusters from being renamed: python extract_lowscore_decoys.py [example_cluster_file.out] -no_replace_names.1HR2 and is as follows:….((((.(….((((((….(((..(((((((..(((((((((….)))))))))……………..)))….).))).)))…))))))…).))))((…((((…((((((((…..))))))))..))))…))..Models were generated using the following sample command line: rna_denovo.static.linuxgccrelease -nstruct 1000 -fasta../1hr2.fasta -secstruct_file../1hr2.secstruct -s../1hr2_helix* -cst_file../1hr2_trim_tert.cst -native../1hr2_chain_a_native.pdb -out:file:silent 1hr2_20A.out -minimize_rna true. Models generated without constraints had the –cst_file flag and cst file omitted from the command. The top 5 DGs by number of reads in SHARC-exo data, each with >3% of total reads, were used to constrain modeling with the equivalent DG being used for SHARC-constrained models. Linear atom pair constraint was set so that distances within 20 Å carried no penalty and distances greater then 20 Å were penalized with a slope of 1. RMSD values for models against the 1HR2 crystal structure was calculated. The top 100 scoring models from each group were clustered into 5 groups with a cluster radius of 5 Å. Wilcox Ranked Sum Tests were performed between each two groups of top 100 models with the following R command wilcox.test .The P4-P6 secondary structure was determined from PDB:7: x3dna-dssr -i=input.pdb -o=dssr.out. As the structure did not contain bases for all models so we went by hand to assign addition bases pairs base on geometry. Secondary structure is as follows: ((..(((((((.((((.((((……….(((((…((((………(.((………………….)).)……………..))))………….))))).)))).)).)).)))))))…(((((…..((((..((.((((((….))))))))……………((((((((((…..))))))))))…))))..)))))…..)).First, the minimal 28 S segment that contained both regions are limited to residues 3935 – 4175. Secondary structure was determined by running x3dna on the extracted segmentWe generated 15317 models using FARFAR by command: rna_denovo.default.macosclangrelease -s stem_1.pdb stem_2.pdb helix_1.pdb helix_2.pdb -nstruct 1000 -fasta test.fasta -secstruct_file test.secstruct -minimize_rna false -cst_file test.cst, where the pdbs contained the original static structures of helices and stems not included in the contact. Linear atom pair constraint was set such that distances within 20 Å carry no penalty, while distance above 20 Å is penalized at a slope of 1. For each of the models, we checked for steric clashes of the rest of the RNA and local proteins by comparing the distance between each phosphate of each modeled RNA nucleotide to the phosphate of each remaining RNA and the c-alpha of an atom of each amino acid. A clash was defined as a distance of fewer than 5 Å.13. To analyze the secondary structures of 7SK, we clustered HEK293T PARIS non-continuous alignments on 7SK using CRSSANT3.PARIS data from human and mouse cells were used to generate DGs for 7SK14 and analyzed as follows. First reads mapped to 7SK were extracted from the mapped bam files (chr6:52995620–52995951 in hg38 coordinates). Reads with CIGAR gap flags D and N are extracted. All reads with D flags are converted to N for consistency. Then all reads with “N” were divided into three groups based on read start using the script readspan7SK.py and short-span reads were used to construct local structures.LARP7 eCLIP data in HepG2 and K562 cells were downloaded from ENCODEFurther information on research design is available in the Supplementary InformationReporting Summary


The dynamic process of transcription termination produces transient RNA intermediates that are difficult to distinguish from each other via short-read sequencing methods.atxrn3, fpa, and met1 mutants. Our data reveal a wide range of termination windows among genes, ranging from ~ 50 nt to over 1000 nt. We also observe efficient termination before downstream tRNA genes, suggesting that chromatin structure around the promoter region of tRNA genes may block pol II elongation. 5′ Cleaved readthrough transcription in atxrn3 with delayed termination can run into downstream genes to produce normally spliced and polyadenylated mRNAs in the absence of their own transcription initiation. Consistent with previous reports, we also observe long chimeric transcripts with cryptic splicing in fpa mutant; but loss of CG DNA methylation has no obvious impact on termination in the met1 mutant.Here, we use single-molecule nascent RNA sequencing to characterize the various forms of transient RNAs during termination at genome-wide scale in wildtype Arabidopsis and in Our method is applicable to establish a comprehensive termination landscape in a broad range of species.The online version contains supplementary material available at 10.1186/s13059-021-02543-4. Transcription termination is the critical final step in RNA synthesis, during which nascent RNA is released from the complex of RNA Polymerase II (Pol II) and DNA template . TerminaThe majority of the termination RNA intermediates are transient and rapidly degraded after cleavage, and are therefore difficult to capture and characterize. Several short-read Illumina-based methods have been developed to analyze nascent RNAs , but theRecent advances in third-generation long-read sequencing technologies, such as PacBio and Nanopore , 27, havOur group recently developed a single-molecule nascent RNA sequencing method named FLEP-seq (Full-Length Elongating and Polyadenylated RNA sequencing)  by usingDRM2 (AT2G33830) as an example  transcripts Fig. , Fig. S2ple Fig. . Moreoveple Fig. .Genome-wide analysis showed that the poly(A) transcripts are the most abundant forms of the four, followed by readthrough transcripts and 5′ cleavage products Fig. . The 3′ R = 0.91) for individual genes between the two biological replicates of FLEP-seq libraries  site with a TW size of 85 nt  has many longer readthrough transcripts with a TW size of 686 nt  site , 49. We ies Fig. . Hence, ies Fig. , with th nt Fig. , while g nt Fig. . Even usThe transition from transcription elongation to termination requires the slow down or pause of the elongating Pol II . The pauDuring the termination process, many factors are associated with the Pol II elongation complex . We founLeishmania major also found tRNA gene can serve as terminator for Pol II transcription in the trypanosomatida [The tRNA genes are transcribed by Pol III . In thissomatida .Fig. 4DWe also analyzed the correlation between termination window size and intergenic distance, direction, and transcription level of downstream protein-coding and/or noncoding genes, respectively. The genome-wide analysis showed that the termination window size of genes arranged in tandem is similar to genes arranged in convergent direction  site compared to wildtype [atxrn3 and how atxrn3 affects readthrough, due to the limitation of short-read sequencing. We set out to address these questions by taking advantage of our full-length nascent RNA sequencing method, which can capture the 3′ cleavage products, particularly those without poly(A) tails, enriched in the partial loss-of-function allele of atxrn3. In order to maximize the capture rate of 3′ cleavage products which might not be closely associated with chromatin, we used nuclear RNA instead of chromatin-bound RNA as the nascent RNA input of FLEP-seq. It turned out that both the nuclear fraction and the chromatin-bound fraction can efficiently capture the cleaved and readthrough transcripts  site . These results illustrated that AtXRN3 is specifically responsible for the degradation of 3′ cleavage products in vivo.Compared to wildtype, the 3′ cleavage products drastically accumulated in the ant Fig. , consistary Fig. . Similarite Fig. , Fig. S8oci Fig. . The teratxrn3, we also characterized the fpa and met1 mutants by FLEP-seq. FPA is a component of the 3′ end processing complex, and MET1 is the key DNA methyltransferase in Arabidopsis [fpa mutant FLEP-seq data showed a global impact on termination window size and a prolonged 3′ end distribution compared to WT  site selection, as well as on poly(A) tail length [In addition to bidopsis . The fpa WT Fig. , consista mutant , 56. Forase MET1 , in whicl length .atxrn3 mutant  and ORRM6 (AT1G73530) can continue elongation and pass through the entire downstream gene, and then be cleaved and polyadenylated  sites in a row  site as cleavage products of upstream transcriptional readthrough, instead of their own initiation  + RNA-seq signal downstream of poly(A) site, including a role for transcription activation of downstream genes by readthrough transcription [Furthermore, we identified 14 genes with cleaved readthrough transcripts entering their immediate downstream genes in ted Fig. . Strikinrow Fig. . This caion Fig. . A reordype Fig. . It remacription . Our sinAT1G73530-AT1G73510 fusion poly(A) transcripts cannot be solely explained by the loss of 5′ to 3′ exonuclease activity as AtXRN3 should not affect the cleavage at the poly(A) site. It is possible that AtXRN3 may affect chromatin status that in turn determines poly(A) site selection and the readthrough phenomena. To check if DNA methylation status is altered at poly(A) sites in the atxrn3 mutant, we performed whole-genome bisulfite sequencing (WGBS) in seedlings of atxrn3 and corresponding Col-0 control. At the AT1G73530-AT1G73510 loci, there is little CG methylation in the wildtype, and it remains mostly unmethylated in the atxrn3 mutant  site selection [atxrn3 mutant.However, the election . We propHere we applied single-molecule Nascent RNA sequencing to investigate the transcription termination landscape at a genome-wide level in Arabidopsis. The single-molecule feature of Nanopore sequencing enables our FLEP-seq to analyze the termination window of genes Fig. , regardlMany powerful Illumina-based methods have been applied to characterizing transcription termination, including GRO-seq, NET-seq, 4tU-seq, and TT-seq , 21, 24.atxrn3 mutation. We do not have a clear answer yet, but it is worth noting that the atxrn3 alleles we and other previous studies sequenced are weak alleles, and the strong allele of atxrn3 is embryo lethal [Pol II pausing after poly(A) site is a common feature in plant transcription , 41. Preo lethal , which sFLEP-seq approach has its limitations . For exaOur single-molecule full-length nascent RNA sequencing approach presented here has the unique advantage of distinguishing the full-length readthrough, cleaved, and polyadenylated forms of transient RNA intermediates in the termination process and enables the characterization of transcription termination patterns at the single-gene level in the model plant Arabidopsis.Arabidopsis thaliana wildtype (accession Columbia-0) and T-DNA insertion line of atxrn3 (SALK_116909C), fpa (SALK_011615), and met1-1 [d met1-1  were use2, 0.5% (w/v) Triton X-100, 1 mM dithiothreitol (DTT), 1× protease inhibitor (Roche), and 100 ng μl−1 tRNA). The samples were homogenized and filtered through Miracloth, and the remaining samples on filter were washed with another 10 ml Honda buffer. After centrifuged at 2000g for 5 min at 4 °C, the nuclear pellets were resuspended and washed twice with 15 ml Honda buffer. The pellets were transferred to a 1.5-ml RNase/DNase-free microcentrifuge tube in 1 ml Honda buffer and centrifuged at 8000g for 1 min at 4 °C. Then the supernatant was completely removed. For RNA extraction, the nuclei pellet was resuspended with 1 ml TRIzol and vortexed to mix thoroughly. After 10 min incubation at room temperature, 0.2× volume of chloroform was added, mixed, and incubated at room temperature for 5 min. The mixture was centrifuged at 14,000g for 10 min at 4 °C. The supernatant was collected and added with one volume of 100% (w/v) ethanol. The RNA extraction was performed according to the manufacturer’s instructions . RNA concentration was measured by Nanodrop and Qubit 3.0 fluorometer with Equalbit RNA HS assay kit .Nuclear RNA extraction is performed according to the previously reported method , 38. In −1 Murine Rnase Inhibitor , and 1 μl T4 RNA ligase 2, truncated K227Q  were added to the RNA and mixed thoroughly. The ligation reaction was performed at 16 °C for 10 h. After that, the RNA was purified and concentrated to 6 μl using the ZYMO RNA Clean & Concentrator kit . Reverse transcription and template-switching were performed using the SMARTer PCR cDNA Synthesis Kit  with minor modification. The SMART CDS Primer II A was replaced by the custom 3′ cDNA RT primer (5′-AAGCAGTGGTATCAACGCAGAGTACATTGATGGTGCCTACAG-3′), which is complementary to the 3′ adapter sequence. To minimize the PCR bias resulting from over-amplification, PCR cycle optimization was performed to determine the best cycle number for cDNA amplification. The cDNA for Nanopore library construction was amplified using the optimized cycle number and purified twice with the VAHTS DNA Clean Beads . DNA concentration and quality were measured by Qubit 3.0 and Agilent 2100 Bioanalyzer. Then, 200 fmol DNA was used to construct the Nanopore library using the Ligation Sequencing Kit  according to the instruction. Libraries were loaded onto R9.4 flow cell (Oxford Nanopore Technologies) and sequenced on MinION device for ~ 48 h.At least 2 μg nuclear RNA was used for FLEP-seq library preparation. Ribosomal RNA (rRNA) was depleted using riboPOOL kit (siTOOLs Biotech) following the manufacturer’s instructions. After purification with the ZYMO RNA Clean & Concentrator-5 kit , the nuclear RNA was mixed with 50 pmol 3′ adapter  at 65 °C for 5 min, and then placed on ice for more than 1 min. Then, 2 μl 10× T4 RNA ligase reaction buffer , 10 μl 50% PEG 8000 , 1 μl 40 U μl--c dna_r9.4.1_450bps_hac.cfg, --qscore_filtering. The basecalled reads were mapped to Arabidopsis TAIR10 genome using Minimap2 v2.10-r761 [-ax splice, –secondary = no, -G 12000. The unmapped reads, not primary alignment reads, or supplementary alignment reads were removed using SAMtools [-F 2308. The 5′ or 3′ unmapped sequences (soft-clip sequences) plus the flanking 20 nt mapped sequences were used to search the template-switching oligo sequence (5′-AAGCAGTGGTATCAACGCAGAGTACATGGG-3′) and the 3′ adapter sequence (5′-ATTGATGGTGCCTACAG-3′) using our custom script adapterFinder.py. The template-switching oligo and the 3′ adapter can characterize the integrity of 5′ and 3′ end of reads and provide the strand information. Therefore, only reads containing both sequences were used for subsequent analysis. PolyAcaller was used to estimate poly(A) tail lengths of Nanopore reads [The Nanopore data analysis workflow is provided in Additional file .10-r761  with parSAMtools  view witre reads .The identification of poly(A) sites was performed as previously described . Brieflyhttps://github.com/pysam-developers/pysam) was used to extract the 5′ and 3′ end coordinates of reads from BAM files. Non-poly(A) reads with 5′ ends located at gene body ( > 50 nt upstream of poly(A) site) and 3′ end located more than 50 nt downstream of the poly(A) site are considered as “readthrough transcripts.” Non-poly(A) reads with 5′ ends located at gene body as well as 3′ ends within 50 nt upstream and downstream of poly(A) sites are considered as “5′ cleavage products.” Non-poly(A) reads with 5′ end within 500 nt downstream of poly(A) sites are regarded as “3′ cleavage products.” Of note, when two neighboring genes are arranged closely on the same strand, the “3′ cleavage products” is considered only if the 5′ end is 100 nt before the TSS of the downstream gene.The schematic of RNA intermediates is shown in Fig. The readthrough distance was defined as the distance between poly(A) site and 3′ end of each read Fig. . Genes wxrn3 mutant using Hi-DNAsecure Plant Kit . The whole-genome bisulfite sequencing (WGBS) libraries were prepared and sequenced at the BGI Group on the MGISEQ-2000RS. For each library, adapters were trimmed by using fastp v0.20 [Genomic DNA was extracted from 12-day-old seedlings of wildtype and tp v0.20 , and reatp v0.20 . The DNAtp v0.20 .-dovetail, and with parameters -X 1000 for paired-end sequencing data to set the maximum insert size. The duplicate reads were removed by using Picard v2.2.4 in MarkDuplicates mode (http://broadinstitute.github.io/picard/). For ChIP-seq data, input-normalized log2 fold enrichment was computed using deepTools bamCompare [--binSize 10 --scaleFactorsMethod SES. ChIP-seq peaks were called on input and control BAM files with MAC2 [--binSize 10 --normalizeUsing RPGC --effectiveGenomeSize 119481543. The normalized coverage was directly used to produce meta-profile without smoothing.Illumina reads were first trimmed the adapter sequences using fastp v0.20 . For themCompare , with paith MAC2 . Normaliith MAC2 , with paU test was used to obtain the significance between conditions. All information about statistical testing can be found in figure legends. This includes the number of observations, statistical tests used, and significance level.Pearson’s correlation coefficient is used to measures the statistical relationship. Mann–Whitney Additional file 1: Figures S1-13. Supplementary figuresAdditional file 2: Table S1.The termination window size and poly(A) site identified by FLEP-seq data.Additional file 3: Table S2. Closely spaced genes with tRNA located immediately downstream.Additional file 4: Table S3. Genes with cleaved readthrough transcripts enter the downstream genes in atxrn3.Additional file 5. Review history.


The extraction of high-quality ribonucleic acid (RNA) from tissues and cells is a key step in many biological assays. Guanidinium thiocyanate-phenol-chloroform (AGPC) is a widely used and efficient method to obtain pure RNA from most tissues and cells. However, it is not efficient with some cells like sperm cells because they are resistant to chaotropic lysis solutions containing guanidinium thiocyanate such as Buffer RLT+ and Trizol. Here, we show that disulfide bonds are responsible for the chemical resistance of sperm cells to RNA extraction reagents. We show that while β-mercaptoethanol (βME) can increase sperm lysis in Buffer RLT+, it has no effect in Trizol and leaves sperm cells intact. We measured the reduction of disulfide bonds in 2,2′-dithiodipyridine (DTDP) and observed that βME has a pH-dependent activity in chaotropic solutions, suggesting that pH is a limiting factor. We identified tris(2-carboxyethyl)phosphine (TCEP) as an efficient lysis enhancer of AGPC solutions that can retain reducing activity even at acidic pH. Trizol supplemented with TCEP allows the complete and rapid lysis of sperm cells, increasing RNA yield by 100-fold and resulting in RNA with optimal quality for reverse transcription and polymerase chain reaction. Our findings highlight the importance of efficient cell lysis and extraction of various macromolecules for bulk and single-cell assays, and can be applied to other lysis-resistant cells and vesicles, thereby optimizing the amount of required starting material and animals. Ribonucleic acid (RNA) is a macromolecule essential for many biological processes across all known species. It exists in different forms and length, and has numerous functions. In eukaryotes, messenger RNA (mRNA) is a form of coding RNA that is transcribed from genes and serves as template for translation into proteins . Non-codPurification of high-quality RNA is an important step to investigate biological processes, cellular functions and phenotypes in molecular assays. The successful extraction of short and long RNA relies on the separation from DNA, proteins and cellular debris contained in cell lysate. Reverse transcription of purified RNA results in cDNA that can be amplified by polymerase chain reaction (PCR) for quantification of gene and transcript expression for classical qPCR or digital droplet PCR. Next generation sequencing is another method to quantify RNA, now commonly used to identify molecular signatures of tissues or individual cells and assess differential expression .RNA can be purified from lysed cells by mainly two methods: acid guanidinium thiocyanate-phenol-chloroform (AGPC) and silica-based extraction columns . AGPC exIn this study, we report that some cells, particularly mouse sperm cells, are resistant to commercially available AGPC and lysis solutions used for silica-based columns. Sperm cells cannot be properly lysed, which results in poor RNA yield and significant sample loss. Sperm lysis in AGPC is not improved by addition of the reducing agent β-mercaptoethanol (βME) or dithiothreitol (DTT), while βME added to the lysis solution in silica-based column protocols results in cell lysis. We observed that reduction of protein disulfide bonds is necessary for the lysis of sperm heads, and that reduction efficiency is pH-dependent, potentially explaining the difference in sperm lysis efficiency of various solutions. We identify tris(2-carboxyethyl)phosphine (TCEP) as a potent enhancer of AGPC RNA extraction and show that it allows complete lysis of mouse sperm cells.n = 32, 5 months old) for experiments. Mice were housed in groups of 3–5 animals in individually ventilated cages. Animals were kept in a temperature- and humidity-controlled facility on a 12 h reversed light/dark cycle  with food  and water ad libitum. Cages were changed weekly.Animal experiments were conducted in strict adherence to the Swiss Law for Animal Protection and were approved by the cantonal veterinary office in Zürich under license number 57/2015 and 83/2018. C57Bl/6J mice were obtained from Janvier (France) and bred in-house to generate male mice . Sperm cells were incubated at 37°C for 30 min. 1 ml of supernatant containing motile sperm was collected and centrifuged at 2,000 rcf for 5 min. The supernatant was again collected, mixed with 1 ml of somatic cell lysis buffer (0.1% sodium dodecyl sulfate and 0.05% Triton X-100 in MilliQ water) and incubated at 4°C for 10 min. Sperm samples were centrifuged at 2,000 rcf for 5 min, sperm pellets were washed twice in phosphate buffer saline  then snap-frozen and stored at −80°C until further processing. For lysis experiments, sperm samples were immediately resuspended in 250 μl water, lysis solutions were added and sperm observed under a light microscope.Sperm pellets and small pieces of testis, liver or epididymis were lysed in 1 ml of Trizol  or 1 ml of Buffer RLT+  depending on the experiments. Lysis buffer was supplemented with 100 mM β-mercaptoethanol , 100 mM dithiothreitol  or 100 mM tris(2-carboxyethyl)phosphine . Sperm samples were either resuspended in lysis buffer by passage through a 30 G syringe or in the presence of 0.2 mm steel beads  at 20 Hz for up to 10 min in a TissueLyser II . Since the lysis of sperm cells depends primarily on chemical lysis, we recommend future users a simple homogenization to sufficiently break up the cell pellet to obtain a single-cell suspension using 5 mm steel beads for 2 min . We did not observe any differences between sperm lysis using small or large steel beads. Tissue samples were homogenized in a TissueLyser II using 5 mm steel beads at 20 Hz for 2 min.For RNA extraction by phenol chloroform phase separation, 100 μl of sample lysed in Buffer RLT+ were added to 900 μl Trizol and extracted using the standard Trizol protocol. All centrifugation steps were at 4°C and samples kept on ice unless noted otherwise. In brief, 200 μl chloroform was added to 1 ml lysate, shaken for 15 s and then incubated at room temperature for 3 min. Samples were centrifuged at 12,000 rcf for 15 min. The aqueous phase was transferred to a fresh tube, 10 μl glycogen was added and the tube inverted 4 times for mixing. 500 μl isopropanol was then added and the tube inverted again 4 times. After an incubation at room temperature for 10 min, samples were centrifuged at 12,000 rcf for 10 min. The supernatant was removed and the pellet washed twice in 75% ethanol. After a final centrifugation at 7,500 rcf for 5 min, the supernatant was carefully removed. The pellet was dried in a concentrator  at 45°C for 4–6 min. Sperm RNA was resuspended in 20 μl nuclease-free water and tissue RNA in 100 μl nuclease-free water . Finally, the solution was incubated at 55°C for 15 min. RNA was stored at −80°C.RNA concentration and integrity were analyzed on the 2100 Bioanalyzer  with the RNA 6000 Pico Kit  according to manufacturer instructions. DNA content was quantified by fluorometry with the Qubit double-strand DNA high sensitivity assay  according to manufacturer instructions. For RT-PCR of long RNAs, RNA samples were first DNase treated with the DNA-free Kit  and reverse-transcribed with the GoScript Reverse Transcription System  using random hexamers according to manufacturer instructions. A testis sample without GoScript reverse transcriptase (noRT) and a sample without RNA (no Input) was processed in parallel and served as negative controls. For qPCR of cDNA from long RNAs, 1 μl of cDNA per well was quantified using SYBR Green I Master  and primers for β-actin (Forward (5′–3′): CGATGCCCTGAGGCTCTTTT, reverse (5′–3′): TAGAGGTCTTTACGGATGTCAACG). For end-point PCR of cDNA from long RNAs, 5 ng of cDNA was amplified by GoTaq G2 HS Polymerase  for 30 cycles using specific primers for Malat1 (Forward (5′–3′): ATCGATTTAAAGTAAATGGGCTA, reverse (5′–3′): TTACATGCAGGAACATTGACA) and Pgk2 (Forward (5′–3′): AAGTTTGATGAGAATGCTAAAGT, reverse (5′–3′): CCTCCTCCTATAATGGTGACA). PCR products size was assessed by agarose gel electrophoresis. In n = 3).The reduction of disulfide bonds was performed as described previously . 1 mM 2,t-test (p < 0.05). For comparisons of qPCR data, groups were analyzed by ordinary one-way analysis of variance . For comparisons in the DTDP assay, groups were analyzed by repeated measures one-way analysis of variance . Significant effects in ANOVA were further analyzed by multiple comparison with Tukey’s post hoc test . Graphs and statistics were prepared using the software GraphPad Prism 9. Contrast in microscopy pictures of Data are represented as mean ± standard error of mean (SEM). Two groups were compared by unpaired A sperm cell is composed of a head containing a nucleus that carries the paternal genome and RNA, and a flagellum prolonging the head through a mitochondria-rich midpiece, that provides motility . We incuWe examined if other commercially available RNA extraction solutions are more efficient. Buffer RLT+ is a proprietary lysis solution of the Qiagen RNAeasy Mini kit, whose composition is unknown. However, the material safety data sheet suggests that it contains up to 50% guanidinium thiocyanate, thus resembles chaotropic properties of Trizol. Buffer RLT+ did not allow sperm lysis either  even witWe extracted RNA from Trizol and Buffer RLT+/βME sperm lysates using a standard phenol-chloroform protocol. To exclude contamination by testicular somatic cells, we prepared sperm samples using a swim-up method followed by treatment with a somatic cell lysis buffer. This approach has previously been shown to yield sperm samples free of somatic cells . We obseThese results indicate that the standard protocol for AGPC RNA extraction is inappropriate to completely lyse mouse sperm cells and is the main reason for poor RNA yield from these cells, and that using Buffer RLT+/βME instead of AGPC solutions circumvents this issue.We next examined the reason for the lysis-resistance of sperm outer membrane. Sperm cells are lysed by Buffer RLT+ only if supplemented with βME despite the presence of guanidinium thiocyanate as main chaotropic agent. βME is a widely used reducing agent that disrupts disulfide bonds between thiol groups such as cysteine residues, within and between proteins . During We then examined if the pH of the lysis solution influences the efficiency of sperm lysis. The pH primarily affects the reactivity of reducing agents and an alkaline pH is necessary for efficient reduction of disulfide bonds by βME and DTT . In contF = 31.6, p = 0.0014]. When added to Trizol, 50 mM βME led to only slow and incomplete reduction of DTDP, with 25% remaining after 60 s .Finally, we confirmed the pH-dependent activity of reducing agents by measuring the amount of 2-thiopyridione (2-TPD) formed after reduction of 2,2′-dithiodipyridine (DTDP) in lysis solutions. DTDP mimics protein disulfide bonds between cysteines and absorbance at 343 nm of 2-TPD in solution indicates the amount of disrupted bonds . Trizol,In summary, the supplementation of acidic AGPC solutions with βME or DTT is not sufficient to lyse mouse sperm, likely because guanidinium thiocyanate cannot reduce disulfide bonds present in the sperm head membrane, and the reactivity of βME is pH dependent.F = 28.96, p = 0.0002].Tris(2-carboxyethyl)phosphine (TCEP) was previously shown to effectively reduce disulfide bonds across a wide pH range from 1.5 to 8.5 . TherefoF = 18.55, p < 0.0001]. Therefore, all samples were DNAse treated before further use. To exclude RNA degradation, somatic RNA samples were processed in parallel and no substantial decrease in RNA integrity values was observed  = 30.42, p < 0.0001], miR-101b , and miR-200c . Lower cycle values in qPCR indicate faster amplification and therefore higher RNA concentration in the sample. We observed significantly faster amplification of RNA samples obtained from lysis in Trizol/TCEP and RLT+/TCEP when compared to incomplete lysis . To determine the differences in RNA yield by quantitative PCR (qPCR), all sperm samples were processed similarly from lysis to final reverse transcription without any adjustment to volumes at the different processing steps. This ensured that initial differences in RNA quantity remained until qPCR and were quantified by reporting the cycle threshold number (Cq). First, we conducted qPCR on miR-141 . Furthermore, we conducted end-point PCR for two less abundant genes, metastasis associated lung adenocarcinoma transcript 1  and germ-cell specific phosphoglycerate kinase 2 (Pgk2). Malat1, a long non-coding RNA expressed in many mouse tissues  and thiocyanate (SCN–) ions, and additional factors such as mechanical shearing, degrading conditions and osmotic pressure. Gdm+ and SCN– ions are strong protein denaturants that partially disrupt accessible hydrophilic bonds, and are commonly used in studies of protein stability and folding  during the estrous phase . This isSince sperm cells are transcriptionally silent, a low RNA yield is expected from these cells. In sperm cells, protein synthesis is suppressed by cleavage of ribosomal RNA and most of the RNA in the cytoplasm is expulsed during spermatogenesis. A single sperm cell likely contains only 10–100 fg RNA . Thus, aWhile we focused on the lysis of mouse sperm cells, similar issues and resistance to AGPC or other chaotropic solutions have been reported for sperm from other species such as human , bovine The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.The animal study was reviewed and approved by cantonal veterinary office in Zürich.MR designed, performed, analyzed and interpreted all experimental work. IM supervised and acquired funding for the study. MR and IM wrote the manuscript. Both authors discussed the results and approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Management of the coronavirus disease 2019 (COVID-19) pandemic requires widespread testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A main limitation for widespread SARS-CoV-2 testing is the global shortage of essential supplies, among them RNA extraction kits. precipitation-enhanced analyte retrieval) that addresses this limitation. PEARL uses a lysis solution that disrupts cell membranes and viral envelopes while simultaneously providing conditions suitable for alcohol-based precipitation of RNA, DNA, and proteins. PEARL is a fast, low-cost, and simple method that uses common laboratory reagents and offers performance comparable to that of commercial RNA extraction kits. PEARL offers an alternative method to isolate host and pathogen nucleic acids and proteins to streamline the detection of DNA and RNA viruses, including SARS-CoV-2.Management of the coronavirus disease 2019 (COVID-19) pandemic requires widespread testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A main limitation for widespread SARS-CoV-2 testing is the global shortage of essential supplies, among them RNA extraction kits. The need for commercial RNA extraction kits places a bottleneck on tests that detect SARS-CoV-2 genetic material, including PCR-based reference tests. Here, we propose an alternative method we call PEARL ( The coronavirus disease 2019 (COVID-19) pandemic has had a devastating social and economic impact worldwide. As the disease continues to spread, the need for global severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing is more urgent than ever. The most widely used reference test for SARS-CoV-2 detection relies on the isolation of viral genetic material followed by PCR-based amplification . These kTRIzol, a phenol- and guanidine-based reagent routinely used for isolation of RNA, DNA, and proteins, has been used to isolate SARS-CoV-2 RNA –6. Howev–8–precipitation-enhanced analyte retrieval). PEARL is fast, is easy to perform, and uses common laboratory reagents. Moreover, PEARL allows the downstream detection of specific SARS-CoV-2 viral sequences with sensitivity comparable to that afforded by commercial RNA extraction kits. PEARL can be coupled to nucleic acid amplification or immunodetection methods to detect host and viral RNA, DNA, and proteins from multiple sources. PEARL does not require specialized equipment or highly trained personnel, and it offers a low-cost straightforward alternative to facilitate virus detection.To address the aforementioned shortcomings, we developed a simple technique to isolate nucleic acids and proteins from cells and viruses we call PEARL , 5 μg/μl, 15 min at 37°C; DNase I (New England Biolabs), 0.2 U/μl, 30 min at 37°C; proteinase K (Macherey-Nagel), 2 μg/μl, 30 min at 37°C. Total RNA was isolated from PEARL extracts using TRIzol (Thermo Fisher) following the manufacturer’s recommendations.Samples were mixed in a 1:2 (vol/vol) sample-lysis solution  ratio and incubated for 5 min at room temperature. Next, nucleic acids and proteins were precipitated on ice, for 10 min, using 1 volume of cold isopropanol. The precipitated material was collected by centrifugation at 19,000 × Residual nasopharyngeal, nasal, and oropharyngeal samples previously tested for SARS-CoV-2 were obtained from the Santa Barbara County Department of Public Health and the University of California San Francisco Clinical Laboratories at China Basin.l-glutamine, and antibiotics  and were maintained in a humidified incubator at 37°C and 5% CO2. iSLK-219 cells are latently infected with KSHV.219 (All cell lines were maintained in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum (FBS), KSHV.219 . This reqC) values were determined by regression. Data analysis and statistical tests were performed using Graph Pad Prism 6.0 software.PEARL extracts were obtained from deidentified human samples or cultured cells. SARS-CoV-2-positive human samples were heat inactivated by incubation at 56°C for 30 min before RNA extraction. RNA from these samples was obtained with the QIAamp Mini Elute virus spin kit (Qiagen) following the manufacturer’s protocol, using 200 μl of sample input and eluting the purified RNA in 50 μl. PEARL extracts were prepared using 250 μl of SARS-CoV-2-positive human samples or a fixed number of cultured infected cells suspended in 250 μl of PBS. PEARL extracts from cultured cells were treated with either DNase I  or with RNase A  in a final volume of 10 μl for 30 min at 37°C. Five microliters of DNase-treated samples was reverse transcribed in a final volume of 10 μl using the iScript cDNA synthesis kit (Bio-Rad) following the manufacturer’s protocol and diluted 5-fold in nuclease-free water before qPCR. Target detection by qPCR was carried out with SYBR Select master mix (Applied Biosystems) using 2 μl of diluted cDNA as the template, and following the manufacturer’s protocol. The entire 10 μl from RNase-treated samples (genomic DNA) were diluted 5-fold with nuclease-free water, and 2 μl of diluted genomic DNA was used as the template for detection of specific genes with the SYBR Select master mix (Applied Biosystems) following the manufacturer’s protocol. Detection of SARS-CoV-2 N1 gene sequences and host RNase P mRNA from deidentified SARS-CoV-2-positive samples was carried out with the one-step TaqMan RNA-to-Ct 1-Step kit (Thermo Fisher), using 2 μl of undiluted PEARL extract and following the manufacturer’s protocol. All qPCR data were collected using a CFX96 Touch real-time PCR instrument (Bio-Rad) and analyzed with the CFX Maestro 1.1 software (Bio-Rad). Quantification cycle (Nuclease-treated PEARL extracts were separated on 10% SDS-PAGE gels and transferred onto nitrocellulose membranes (Bio-Rad) for Western blot analysis. The membranes were blocked in 0.5% bovine serum albumin–Tris-buffered saline with Tween (BSA-TBST) for 30 min. Primary antibodies were diluted in 0.5% BSA-TBST as follows: anti-HSP-70 , 1:1,000; anti-LANA/ORF73 (Advanced Biotechnologies 13–210-100), 1:3,000; anti-GFP (Invitrogen A11122), 1:3,000; and anti-NS2B (GeneTex GTX133308), 1:1,000. The membranes were incubated with primary antibodies for 30 min at room temperature. Following primary-antibody incubation, the membranes were washed with TBST 3 times before the addition of horseradish peroxidase (HRP)-conjugated secondary antibodies. The membranes were incubated for 30 min with secondary antibody diluted 1:3,000 in 0.5% BSA-TBST. Immunoreactivity was detected with Radiance Plus HRP substrate (Azure Biosystems). All images were captured with an Azure Biosystems C300 gel imaging system. Image postprocessing was carried out in Photoshop CC (Adobe) using automatic contrast. For dot blot-based immunodetection, nitrocellulose membranes (Bio-Rad) were spotted with 1 μl of PEARL extract and allowed to dry completely at room temperature for 30 min. For the remainder of the procedure, membranes were processed and imaged as described for Western blotting.−5 × rmax × rpm2, where rmax is specified in centimeters.Our hand-powered centrifuge was designed in SolidWorks 2018 (Dassault Systèmes), sliced (0.2-mm layer height) in Cura (Ultimaker), and printed on an Ender3 3D printer  using a 1.75-mm polylactic acid filament (Hatchbox Inc.). To actuate our device, we used Brutal Strong 135-test braided fishing line . Approximately 1 m of line was threaded through holes designed for the string-driven system in the hand pulls and in the rotor, and the line was secured to itself with a double uni knot forming a loop. Maximum angular speed was determined by affixing reflective tape to the rotor, and revolutions per minute were measured with a laser tachometer (Neiko) over a 1-s sampling time. The maximum relative centrifugal force (RCF) was calculated as 1.118 × 10https://3dprint.nih.gov/discover/3dpx-014683. After PEARL precipitation, the samples were spun at maximum speed with the hand-powered centrifuge or at 19,000 × g in a benchtop centrifuge. RNA and protein recovery for both centrifugation methods was determined by RT-qPCR and dot blotting.3D print files can be found at We designed PEARL to provide a low-cost, column-free approach for the isolation of nucleic acids and proteins. PEARL uses common laboratory reagents  to recover target analytes by precipitation. Briefly, a sample is mixed with PEARL lysis solution, which disrupts cell membranes and viral envelopes, liberating DNA, RNA, and proteins. These analytes are subsequently recovered by alcohol-based precipitation and centrifugation . The PEAqC values . Next, we compared the RT-qPCR detection sensitivity obtained with PEARL extracts to that of RNA purified using a commercial kit. We found strong concordance between extraction methods using deidentified clinical specimen samples (P < 0.0001 for N1 SARS-CoV-2 and RNase P)  . HoweverWe reasoned that because DNA and protein coprecipitate with RNA upon addition of isopropanol during extraction , PEARL cACTB) and viral transcripts. These viral mRNAs included those for the KSHV latency-associated nuclear antigen (LANA) and KHSV-encoded GFP  by Western blotting  and 3C ahttps://www.thingiverse.com/thing:1946291). We engineered a safety lid and chamfered all edges to avoid abrasion of the string, and we increased the distance between the pull points to augment torque around the rotational axis . We 3D printed our device with thermoplastic polyester, measured its angular velocity using a laser tachometer, and found that we could achieve centrifugal forces of approximately 3,900 × g . To test whether our hand-powered centrifuge could replace a benchtop centrifuge in PEARL, we compared the RNA and protein extraction efficiency achieved with our device and with a benchtop centrifuge set at 19,000 × g. Despite the substantial difference in centrifugal force, we found that viral RNA and proteins can be easily detected using PEARL extracts prepared with our hand-powered centrifuge . Thus, this device can enable the deployment of PEARL in the field without a significant loss in detection sensitivity.While we designed PEARL to be accessible, it still uses a high-speed centrifuge, which is expensive, requires AC power to operate, and is typically restricted to professional laboratories. We reasoned that we could make PEARL field deployable by using a hand-powered centrifugation device. Inspired by the work of Byagathvalli et al. and of the Prakash lab, who pioneered these types of devices –22, we m–The primary tool to combat the COVID-19 pandemic is widespread and accessible testing to monitor SARS-CoV-2 prevalence and spread, which informs deployment of containment and mitigation measures . GloballOur data also show that PEARL extracts can be used to efficiently detect host and viral transcripts, genomic DNA, and proteins regardless of the nature of the infection—PEARL was equally useful in detecting DNA and RNA viruses with different tropism. Coupling PEARL to different downstream analyses for detection of nucleic acids and proteins can provide a powerful tool for detection of diverse viruses. Moreover, because RNA, DNA, and proteins are extracted at once, PEARL reduces sample handling time, allowing streamlined diagnostic procedures. Thus, it may enable both nucleic acid- and antigen-based SARS-CoV-2 testing. PEARL’s minimal handling requirements also make it scalable, which is desirable for high-volume testing operations, as is needed for SARS-CoV-2 testing.It is possible that the collection medium used to store samples before processing may influence the performance of PEARL. For example, the viral transport medium recommended by the CDC to store and inactivate samples for SARS-CoV-2 testing   has compIt is also possible that PEARL may introduce extraction bias, as short RNAs, including tRNAs, snoRNAs, and microRNAs (miRNAs), are more difficult to precipitate than longer RNA and DNA molecules . Though Finally, since PEARL uses common reagents and does not require expensive equipment or highly trained personnel, it can provide an accessible alternative for streamlining diagnostics in geographic areas that lack access to capital, specialized reagents, and professional laboratories. Moreover, PEARL is field deployable, given that a hand-powered centrifugation device can be used. In view of these considerations, coupling PEARL to our recently developed CRISPR-based protocol for detection of SARS-CoV-2 genetic material called CREST   could al


The current methods of RNA extraction from berry fruits are either time-consuming or expensive. To simplify the conventional phenol-chloroform based RNA extraction method, we modified the protocol with less steps as well as the removal of the use of phenol. In this protocol, the extraction buffer is composed of hexadecyltrimethyl ammonium bromide (CTAB), polyvinylpyrrolidone (PVP), and Dithiothreitol (DTT). The method facilitates efficient removal of polysaccharides and phenolic compounds from both fruit pulp and fruit peel. Additionally, the protocol is phenol-free and less toxic than traditional phenol-containing method. High-quality RNA, with RNA Integrity Number value > 8, isolated by this method is applicable for RNA sequencing and qPCR. Only 3–4 working hours are required for one batch of RNA isolation.  Specifications TableRNA isolation is a vital step to ensure data quality for RNA sequencing and transcriptomic study. Berry fruits are rich in polysaccharides and phenolic compounds, which affect the quality and/or quantity of isolated RNA Bilberry fruits were collected from wild-grown forestry (60 °20′33.1"N 25 °03′54.6"E) in Southern Finland, and were separated into pulp and peel, which were directly kept in liquid nitrogen and stored at -80°C until needed. Frozen pulp and peel samples were milled for 60 s at 22 Hz in a Mixer Mill MM400  in 2 ml Eppendorf tube with 5 mm sterile steel ball. Three replicates (∼100 mg/replicate) for both pulp and peel samples were applied.1.Add 900 µL preheated CTAB buffer (65 °C) and 9 μl DTT (1 mol/L) to each sample (∼80 mg powder in 2 ml Eppendorf tube) and vortex for 3 s. Incubate the tubes for 20 min at 65 °C with 1000 rpm shaking .2.Centrifuge the tubes at 11,000 rpm g for 10 min at room temperature.3.Pipette the supernatants to new 2 ml tubes and extract twice with an equal volume of chloroform:IAA (24:1), separating the phases at 12,000 rpm for 10 min at room temperature.4.Add 1/4 v of 10 M LiCl (42.4 g/mol) to the supernatant and mix gently. The RNA is precipitated overnight at + 4 °C.5.Centrifuge the tubes at 13,000 rpm for 20 min at + 4 °C.6.Wash the pellet with 500 µL 70% cold ethanol. Centrifuge the tubes 5 min and decant the ethanol. Dissolve the RNA pellets in 100 µL TE buffer.7.Add 300 µL 100% cold ethanol and centrifuge 5 min. Dissolve the RNA pellets in 20 µL TE buffer.The procedure details are as follows:Total RNA concentration was measured with NanoDrop 2000C and RNA integrity (RIN) was assessed with Agilent 2100 bioanalyzer based on manufacturer's instructions.Both pulp and peel RNA with high amount and high quality were harvested. RNA concentration measured with NanoDrop ranged from 43–84 ng/µl. The 260/280 nm and 260/230 nm ratios from NanoDrop ranged between 2.12–2.16 and 2.00–2.41 respectively , A. The Compared to previously reported berry RNA isolation method In this study, we performed two steps of ethanol washing (70% and absolute) to ensure the removal of salts and final RNA precipitation. We obtained about 1 µg total RNA from each 2ml Eppendorf tube. To gain more yield or a higher concentration, simple combination of multiple technique replicates after Step 4 is beneficial, as stated in previously reported method In addition to bilberry, we equally successfully tested this protocol with spruce needles. The needle RNA concentration measured with NanoDrop was 109–885 ng/µl, with 260/280nm and 260/230 nm ratios that ranged between 1.93–2.13 and 1.70–2.34 respectively. Thus, this protocol could be extended to other plant materials such as spruce and pine needles. This method will significantly save time or cost, especially when performing large-scale RNA isolation from fruit tissues such as bilberry. In conclusion, we provide a simplified and effective RNA isolation method especially for fruits of bilberry and it can equally be used for conifer tissues like spruce and pine needles.


Our minimal patchy particle simulations suggest that the strikingly different effect of RNA length on homotypic LLPS versus RBP–RNA complex coacervation is general. Phase separation is RNA-length dependent whenever the relative contribution of heterotypic interactions sustaining LLPS is comparable or higher than those stemming from protein homotypic interactions. Taken together, our results contribute to illuminate the intricate physicochemical mechanisms that influence the stability of RBP condensates through RNA inclusion.Biomolecular condensates formed via liquid–liquid phase separation (LLPS) play a crucial role in the spatiotemporal organization of the cell material. Nucleic acids can act as critical modulators in the stability of these protein condensates. To unveil the role of RNA length in regulating the stability of RNA binding protein (RBP) condensates, we present a multiscale computational strategy that exploits the advantages of a sequence-dependent coarse-grained representation of proteins and a minimal coarse-grained model wherein proteins are described as patchy colloids. We find that for a constant nucleotide/protein ratio, the protein fused in sarcoma (FUS), which can phase separate on its own—i.e., via homotypic interactions—only exhibits a mild dependency on the RNA strand length. In contrast, the 25-repeat proline-arginine peptide (PR Liquid–liquid phase separation of proteins and other biomolecules into condensed phases has emerged as a fundamental mechanism to organize biological matter in living cells. Nucleic acids, which are ubiquitously found in condensates, can act as critical modulators in the stability of protein condensates. RNA strands can promote or inhibit phase separation of RNA-binding proteins in a concentration-dependent manner. In vivo, RNAs vary enourmously in sequence, structure, and length. Here, we focus on single-stranded disordered RNAs and uncover the role that RNA strand length has in promoting phase separation of RNA-binding proteins. To do so, we develop a multiscale computational strategy that integrates a sequence-dependent protein/RNA coarse-grained model and minimal representation of proteins as patchy particles. Our simulations reveal that phase separation of RNA-binding proteins is RNA-length dependent only when heterotypic protein–RNA interactions have a comparable or higher contribution towards the connectivity of the condensate liquid network than the homotypic protein–protein interactions. Overall, our work sheds light on the molecular and physicochemical mechanisms by which condensate stability is impacted by RNA of different lengths. Cells require precise compartmentalization of their material into different organelles in order to function. While some of these organelles and compartments are shaped by physical membranes, many others are thought to be sustained by liquid–liquid phase separation (LLPS) –4. Like Phase-separation of RBPs can be both promoted or inhibited by the presence of RNA in an RNA-concentration, and sometimes RNA-structure, dependent manner , 40–50. Computer simulations have been instrumental in advancing the characterization of biomolecular condensates from a thermodynamic, molecular and mechanistic perspective , 59–61. 25 that undergo RNA-dependent complex coacervation , increasing RNA length at constant total nucleotide concentration significantly promotes condensate stability. Next, we use minimal coarse-grained simulations to look at the problem from a soft condensed matter perspective. Our minimal simulations reveal that the striking differences in the impact of RNA length on complex coacervation versus homotypic LLPS originates in the diversity of intermolecular connections that biomolecules employ in the different scenarios to sustain the liquid network of the condensates.In this work, we focus on single-stranded disordered RNA and ask: What is the function of RNA strand length in biomolecular LLPS? For this, we use our recently developed residue/nucleotide-resolution coarse-grained protein/RNA model , which pBiomolecular LLPS entails the self-assembly of thousands of different proteins and other biomolecules into liquid-like condensates. Although experiments and simulations have begun to approach condensates at the atomistic level ,61,110, π interactions are modelled through a mid-range pairwise potential , and elotential . Bonded Complementary to the residue-resolution sequence-dependent model, we employ a minimal coarse-grained model , 91 to iNVT MD simulations until equilibrium is reached. Once the simulations have converged, we measure the equilibrium coexisting densities of both phases along the long side of the box, excluding the fluctuations of the interfaces and keeping the center of mass of the system fixed. We repeat this procedure at different temperatures until we reach supercritical temperatures, where no phase separation is observed any longer. Then, to avoid finite system-size effects close to the critical point, we evaluate the critical temperature (Tc) and density (ρc) using the law of critical exponents and rectilinear diameters [To measure the stability of the RBP–RNA condensates, we compute phase diagrams of the different systems in the temperature–density plane by means of Direct Coexistence (DC) simulations , 118. Wiiameters  . Fi25, which only undergoes LLPS at physiological conditions in the presence of RNA  and has been shown to ensure the maximum condensate stability of FUS as a function of RNA concentration [For the different FUS/RNA systems, regardless of the length of the RNA strands in each case, we always add a total amount of U nucleotides to get a constant U/FUS mass ratio of 0.096; that is because this ratio enhances phase separation with respect to the pure FUS system. Importantly, the net charge of the system at this RNA/protein mass ratio is very low , which maximizes the size of the coexistence region for the smallest length of polyU used (20 nucleotides). We then test five different polyU lengths: (i) 120 polyU chains of 20 nucleotides each, (ii) 60 polyU chains of 40 nucleotides each, (iii) 30 polyU chains of 80 nucleotides each, (iv) 6 polyU chains of 400 nucleotides each, (v) 3 polyU chains of 800 nucleotides each  for FUS  [25–polyU mixtures , the number of contacts significantly decreases   than FUSversus RNA–protein complex coacervation.To test the universality of these observations, we now employ our minimal protein model , 98–100,T* = 0.09 , the critical temperature of the mixture considerably increases  than 3-fold shorter RNA strands. Moreover, it has been recently shown that length and charge segregation in the IDR domain of VRN1-like proteins has a critical impact on modulating DNA-induced VRN1 phase separation, where liquid-like, gel-like or no phase-separation behaviour can be favoured depending on the IDR length and the presence of neutral vs. charged residues [et al. [et al. [In agreement with the preceding results, Zacco et al.  found thresidues . Another [et al.  showed t [et al. , 135. In [et al.  showed t [et al. . Nonethe [et al. . Since t [et al. , 41, 50, [et al. .Using a multiscale simulation approach we demonstrate how variations in RNA length can yield non-trivial effects in the stability of RBP condensates. We find that in condensates sustained by homotypic protein–protein interactions, RNA behaves as a LLPS enhancer that subtly augments the stability of the condensates irrespective of its length. In contrast, in condensates sustained by heterotypic protein–RNA interactions, we find that RNA acts as a LLPS enabler that increases the stability of the condensates in a RNA length-dependent manner.25 polyU systems using sequence-dependent coarse-grained simulations in parellel with our results for the miminal protein/RNA model suggest that when protein–protein LLPS-stabilising interactions are substantially higher than protein–RNA contacts, like in FUS or in our archetypal scaffold protein model, it is the RNA concentration rather than its chain length what critically modulates the condensate stability (at least for strands larger than 50–80 nucleotides or of comparable length to that of the proteins). Nevertheless, when protein–RNA intermolecular contacts contribute similarly or even higher than homotypic protein–protein interactions, like in PR25 peptides or in our minimal cognate–RNA model, not only the RNA concentration, but also the RNA chain length plays a major role in controlling RBP condensate stability. Our study demonstrates that RNA participation in biological phase transitions is not uniform and argues that RNA parameters should be considered as important as those of proteins with respect to the regulation of the stability and mesoscale properties of condensates.Our findings for FUS and PRS1 TextTable A. System sizes and simulation details. Summary of the simulation details of the employed systems: Total number of proteins (NP), total number of RNA nucleotides , total number of RNA chains , length of the RNA chains (LRNA), net charge of the system, box dimensions , and estimated critical temperature . 25 in absence of RNAFig A. Direct Coexistence simulation of PR. Snapshot of a pure PR25 Direct coexistence simulation at T/Tc,FUS = 0.5. As it can be seen, in absence of RNA, PR25 cannot undergo LLPS (even at low temperatures). The same colour code employed in Fig B. Direct Coexistence simulation of cognate proteins in absence of RNA. Snapshot of a pure cognate system Direct Coexistence simulation at T/Tc,Scaffold = 0.6. As it can be seen, in absence of RNA, the cognate protein cannot undergo LLPS (even at low temperatures). The same colour code employed in 25Fig C. Radius of gyration of FUS and PR. Radius of gyration (Rg) distribution function for: a) FUS within a FUS-polyU(400-nt) condensate (green curve) and FUS in the dilute phase (black curve) at T/Tc,FUS = 0.96. b) PR25 within a PR25-polyU(400-nt) condensate (green curve) and PR25 in the dilute phase (black curve) at T/Tc,FUS = 0.85. The polyU/FUS mass ratio was kept constant at a value of 0.096, while the polyU/PR25 mass ratio at a value of 1.20. 25-polyU mixtures using the HPS and KH modelsFig D. Phase diagrams of FUS-polyU and PR. Temperature–density phase diagrams of FUS with polyU of different lengths at a constant polyU/FUS mass ratio of 0.16, and for a pure system of FUS (black curve). (b) Temperature–density phase diagrams of PR25 with RNA at different lengths at a constant RNA/PR25 mass ratio of 0.57. In both (a) and (b) panels, filled circles represent the coexisting densities evaluated from DC simulations while empty circles depict the critical temperatures estimated from the law of rectilinear diameters and critical exponents near the critical temperature. Temperature in both panels has been normalized by the critical temperature of pure FUS, Tc,FUS = 309K (black empty circle in (a)). 25-polyU condensates using the HPS and KH modelsFig E. Intermolecular contacts of FUS-polyU and PR. Density of LLPS-stabilizing intermolecular contacts within condensates as a function of RNA length plotted separately for protein–protein interactions (black symbols) and protein–RNA interactions (green symbols) for FUS-polyU (a) and PR25-polyU mixtures (b). The temperature at which the intermolecular contacts were computed was T/Tc,FUS = 1.13 for FUS–RNA systems and T/Tc,FUS = 0.924 for PR25–RNA mixtures . (c) Critical temperature versus RNA length for FUS–RNA (black) and PR25–RNA (blue) systems. 25-polyU condensates as a function of temperature (Mpipi model)Fig F. Intermolecular contacts of FUS-polyU and PR. Density of LLPS-stabilizing intermolecular contacts within the condensates  as a function of temperature  for: a) FUS+polyU(400-nt), and b) PR25+polyU(400-nt) condensates. The polyU/FUS mass ratio was kept constant at a value of 0.096 for all FUS simulations, while the polyU/PR25 mass ratio was kept constant at a value of 1.20 for all PR25 simulations at every studied temperature. vs. non-electrostatic interactions in FUS-polyU and PR25-polyU condensatesFig G. Electrostatic . Electrostatic (black symbols) vs. non-electrostatic (red symbols) contribution to the potential attractive interactions (molecular contacts sustaining phase-separated condensates) as a function of RNA length for FUS+polyU condensates (filled circles) and PR25+polyU condensates (empty squares). The temperature at which the intermolecular contacts were computed was T/Tc,FUS = 0.99 for FUS–RNA systems, and T/Tc,FUS = 0.85 for PR25–RNA mixtures. Error bars depict the computed standard deviation in the percentage contribution of electrostatic vs. non-electrostatic interactions. The polyU/FUS mass ratio was kept constant at a value of 0.096 for FUS-polyU simulations, and at a polyU/PR25 mass ratio of 1.20 in PR25-polyU simulations.Mpipi model, full-FUS sequence, patchy particle protein/RNA model, Direct Coexistence calculations for extracting phase diagrams, and calculation of protein/RNA molecular contacts. (PDF)Click here for additional data file.


Viruses are extraordinary biological entities that can only thrive as obligate intracellular parasites, exploiting other living cellular components in order to reproduce. Despite the diversity in the virosphere, a central component of the viral replication machinery is the nucleic acid polymerases that are responsible for copying the viral genome and for generating genetic diversity. In the RNA virus world, with the exception of retroviruses, the RNA-dependent RNA polymerases (RdRPs) catalyze RNA synthesis, functioning either as single polypeptides, or in a complex with other viral or host components to transcribe and replicate viral RNA genomes. In this Special Issue, we seek to highlight recent progress in our understanding of the structure and function of different viral replication complexes. Four original studies and seven reviews contributed to the issue to increase our general knowledge of RNA virus replication.From the original articles, Ferrero and colleagues describe a remarkable series of X-ray structures of the non-canonical RdRP of the (+) ssRNA Thosea asigna virus in a complex with RNA, nucleotide substrates and the ions involved in its catalytic activity . The binAlbentosa-González and colleagues explain the effects of phosphorylation on the modulation of RdRP activity in the Usutu, Zika and West Nile viruses  . The flaSong and colleagues provide new insights into therapeutic intervention against AIDS, using viral antigen-specific iPSCs to suppress HIV replication in a murine model .Finally, we dive into the conformational dynamics of the poliovirus multifunctional 3CD polyprotein, guided by the work of Winston and Boehr . The picThe reviews in this Special Issue leave no doubt that exciting new discoveries in the regulation of genome replication in (+) and (−) ssRNA viruses are far from over. The seven reviews in this section cover a broad range of topics. Domingo and colleagues consider the error rates displayed by the RdRPs during template copying as an important parameter to understand the genetic and antigenic variability of RNA viruses. However, there is a limit to the number of mutations that can be accepted to preserve de-encoded genetic information. Authors also review how, in some viruses , mutation rates may be modulated by proofreading repair activity . This SpFor the (−) ssRNA virus world, the review by Cao and colleagues updates the molecular insights into the replicative complex of the respiratory syncytial virus (RSV), reviewing the fundamentals of RNA synthesis and proposing a model of multiprotein coordination during different RNA synthesis processes . In addiFinally, we would like to acknowledge all the authors for their contributions to this Special Issue. It has been a pleasure to read their work and learn from it. We will encourage our colleagues to continue with their exceptional research, even during the difficult times of the SARS-CoV-2 pandemic. Because of this pandemic, we are more conscious of the transcendence of understanding viral replication and the multiple mechanisms that viruses use to regulate this essential activity during infection.