Urinary extracellular vesicles (EVs) and their RNA cargo are a novel source of biomarkers for various diseases. We aimed to identify the optimal method for isolating small (<200 nm) EVs from human urine prior to small RNA analysis. EVs from filtered healthy volunteer urine were concentrated using three methods: ultracentrifugation (UC); a precipitation-based kit (PR); and ultrafiltration (UF). EVs were further purified by size-exclusion chromatography (SEC). EV preparations were analysed with transmission electron microscopy (TEM), Western blotting, nanoparticle tracking analysis (NTA) and an Agilent Bioanalyzer Small RNA kit. UF yielded the highest number of particles both before and after SEC. Small RNA analysis from UF-concentrated urine identified two major peaks at 10–40 nucleotides (nt) and 40–80 nt. In contrast, EV preparations obtained after UC, PR or SEC combined with any concentrating method, contained predominantly 40–80 nt sized small RNA. Protein fractions from UF+SEC contained small RNA of 10–40 nt in size (consistent with miRNAs). These data indicate that most of the microRNA-sized RNAs in filtered urine are not associated with small-sized EVs, and highlights the importance of removing non-vesicular proteins and RNA from urine EV preparations prior to small RNA analysis. Extracellular vesicles (EVs) are membrane-bound particles which have many functions, including intercellular communication, immunomodulation and regulation of angiogenesis. They carry various cargo, including non-coding RNA (ncRNA), messenger RNA (mRNA), proteins, lipids and metabolites . EVs refTo date, studies investigating uEV-based small RNA biomarkers have focused on miRNAs. However, miRNAs constitute a relatively small percentage of total small RNA present in uEVs and urine, and the uEV preparations contain large amounts of other small RNAs, including tRNA and YRNA fragments ,12,13. FDifferent EV isolation methods have been compared and evaluated in several review articles ,24. UltrThe primary aim of this study was to analyse the small RNA profile of small uEVs obtained by different methods. Aliquots of 0.22 μm filtered urine were concentrated by ultracentrifugation (UC), precipitation (PR) and ultrafiltration (UF) prior to further purification by SEC. The small RNA profile of UF-concentrated urine (containing both EVs and proteins >100 kDa) was compared to the profile of the EV and protein fractions obtained after SEC. A secondary aim of the study was to determine the optimal method to concentrate uEVs before SEC purification, and identify if further SEC purification increases the purity of uEVs prepared by these methods.We compared the particle and protein yield obtained using three EV concentrating methods , both before and after SEC. The experimental procedure presented in p = 0.01) and 1.8 times that of PR pellet, although the latter did not reach statistical significance (p > 0.05) (p > 0.05). After SEC, UF+SEC had the highest normalised particle number (mean 1.55), with PR+SEC and UC+SEC yielding mean normalised particle numbers of 0.80 and 0.65, respectively; however, these differences were not statistically significant. Particle recovery within fraction 3 after SEC (compared with that of the corresponding pellet) was between 46% and 78% for UC, between 35% and 58% for PR, and between 37% and 67% for UF (Particle numbers (measured with NTA) and protein quantity within pellets and SEC fractions 2–8 from individual rounds are presented in  > 0.05) c. The no% for UF d. When the normalised mean protein content of the pellets was compared between methods, the UF pellet contained approximately 10 times more protein than the UC pellet, and the PR pellet contained approximately 15% less protein than the UC pellet e. It sho8 particles/μg protein  than the UC and UF pellets .The purity of vesicles in each sample was further evaluated using the particle/protein ratio f. For UC protein f. The UCWestern blot analysis verified the presence of the common EV proteins Alix, Tsg-101 and CD9 in SEC fraction 3 from all samples, thus confirming EV isolation . UromoduThe size distribution of EVs obtained from a single urine pool by the different methods are shown in g) prior to centrifugation at 17,000× g , regardless of the initial concentrating method used  contained two major peaks: the first at 10–40 nt and the second at 40–80 nt a. After hod used f.Our results indicate that the majority of microRNA-sized (10–40 nt) small RNAs in 0.22 μm filtered urine are neither within nor associated with small EVs, whereas the longer 40–80 nt sized small RNAs co-purify with small EVs. The UF concentrated urine RNA profile (representing concentrated 0.22 μm filtered urine) included two main peaks: one sized 10–40 nt and another sized 40–80 nt, which is consistent with the previously reported small RNA profiles obtained from urine . After SThe exact carrier of the small RNA in the non-vesicular protein fractions was not investigated in this study. Earlier studies have shown that the vast majority of plasma miRNAs are in the non-vesicular SEC fractions, associated with AGO2 instead of EVs ,17. AGO2Our analysis did not reveal what subtype of small RNA is included in the 40–80 nt peak that is associated mainly with small EVs obtained after SEC. The profile observed in our study is similar to that of mature tRNA molecules, which are ~75–80 nt in size  althoughWhen the three concentrating methods prior to SEC were compared, UF yielded the highest number of vesicles (based on NTA data). These results are not surprising, given that UF simply concentrates the urine, and NTA can also quantify non-EV particles, including larger protein aggregates ,45. HoweThe particle/protein ratio of SEC fraction 3 was higher than the corresponding pellet in UF samples, as expected. Accurate comparison of the particle/protein ratios of SEC fraction 3 obtained after PR and UC was not possible, because the protein concentration of the PR+SEC and UC+SEC samples was below the accurate limit of detection. However, our data suggest that the purity of these samples was at least similar to that of UF+SEC samples. In the present study, the particle/protein ratio of PR pellets (prior to SEC) was significantly greater than that of UC pellets; however, the UC protocol we used did not incorporate a wash step because we aimed to maximise particle yield, rather than purity, prior to SEC. Therefore, it is likely that the purity of samples obtained by UC would be higher (and particle yield would likely be lower) if an additional wash step was used. However, earlier studies have also reported a relatively low purity of the EVs prepared by UC even when a wash step was incorporated ,47.2+ or Na+ ions; hence, several methods have been developed to address the problem [One limitation of the present study was that RNAse inhibitors were not used. RNA is extremely prone to degradation especially in the extracellular space, and although EVs are considered to protect their RNA cargo, the small RNAs attached to the outside of EVs may be less protected. Previously, it was reported that adding RNAse inhibitor to the sample at an early stage helps to improve the integrity of the non-vesicular RNA, and interestingly, the authors noted that some of the small extracellular RNAs detected can be stable products of extracellular degradation instead of secretory products of the cells . To maxi problem ,50,51. HIn summary, based on our comparison of 50 mL urine aliquots, UF followed by SEC yields a similar number of particles compared to PR, and a higher number of particles than UC and SEC preparations prepared after concentration of the sample by either PR or UF. The purity of the EV preparations prepared by SEC following UF tended to be greater than those prepared by PR alone, and was significantly greater than those prepared by UC alone. The major disadvantage of UF is that it can be time- and labour-intensive, especially with larger starting volumes and a high number of samples. Therefore, when maximal EV recovery is not required, PR might be a more convenient way to concentrate the particles prior to the SEC. Small RNA analysis indicated that the majority of small RNA in urine are not associated with small EVs, and it is likely that mature tRNA-sized RNAs are the most abundant RNA associated within small uEVs of healthy humans. Most miRNA-sized small RNAs were associated with non-vesicular proteins, thus highlighting the importance of EV purification and robust separation of EVs from non-vesicular proteins if specific analysis of small EV-associated small RNA content is desirable; however, further studies of the RNA content of large sized EVs are warranted.g, 20 min, +4 °C) and filtered  in order to remove cells and bacteria; therefore, larger vesicles (>220 nm) would have been mostly excluded from the downstream analyses. The filtered urine was divided into 100 mL aliquots and used to concentrate EVs via one of three different methods: ultracentrifugation (UC), precipitation (PR) and ultrafiltration (UF).Urine samples were collected from healthy volunteers under Cambridgeshire Research Ethics Committee approval (08-H0306-62) and with the informed consent of participants. Fresh, first void, urine samples without urine dipstick abnormalities  were used. Subsequently, the urine from two different donors were combined to obtain 400 mLs of urine . Eight protease inhibitor tablets  were dissolved into each combined sample before the urine was processed. Since the focus of the study was on the study of small uEVs, fresh urine was centrifuged  and EVs were pelleted by UC . After removing the supernatant by pipetting, individual EV pellets were re-suspended in 100 μL of 0.22 μm filtered PBS, combined, and the total volume was adjusted to 300 μL. A 130 μL aliquot of the concentrated EVs was taken, diluted to 600 μL with 0.22 μm filtered PBS and named “UC-pellet”. Another 130 μL aliquot was used for SEC. As our main aim was to concentrate the maximum number of vesicles prior to SEC, we did not wash the pellet and perform a second ultracentrifugation step.g, 30 min, at room temperature (RT)), the supernatant was removed and an additional 5-min centrifugation was performed to allow removal of the residual supernatant. The pellets in individual tubes were re-suspended in 50 μL of resuspension buffer provided with the kit, combined together, and the total volume was adjusted to 300 μL with 0.22 μm filtered PBS. A 130 μL aliquot of the concentrated EVs was taken, diluted to 600 μL with 0.22 μm filtered PBS and named “PR-pellet”. Another 130 μL aliquot was used for SEC.Four 25 mL aliquots of filtered urine were combined with 10 mL of precipitation buffer  and placed on a roller mixer overnight (o/n) (16–18 h) at +4 °C. The following day, EVs were pelleted using centrifugation , before 15 mL of filtered urine was placed in two columns and centrifuged . The flow through was discarded and the procedure was repeated until the sample was concentrated to <250 μL. The concentrated urine was adjusted to 300 μL with 0.22 μm filtered PBS. A 130 μL aliquot of concentrated EVs was taken, diluted to 600 μL with 0.22 μm filtered PBS and named “UF-pellet”. Another 130 μL aliquot was used for SEC.Amicon ultra concentrators  were washed with 5 mL of filtered PBS . Prior to use, the storage buffer was removed and the column was washed with 5 mL of 0.22 μm filtered PBS. After inserting the 130 μL sample, eight fractions were collected. The volume of the first fraction was 400 μL and fractions 2–8 were 600 μL each. One 100 μL aliquot from each “pellet” sample and fractions 2–8 was stored at −70 °C for further analyses. The remaining 500 μL was mixed with 2.5 mL of Qiagen Qiazol lysis reagent , kept at RT for 5 min and stored at −70 °C prior to RNA isolation.7–9 × 108 particles/mL. Each 400 μL sample was injected into the sample chamber at a constant flow rate using the Malvern NanoSight syringe pump system. For all recordings, the camera level was adjusted to 15, and three 60 s videos were recorded for each sample. For analysis, the detection threshold was set to 5 and the other settings were “automatic”.The particle concentration and size distribution from pellets and individual SEC fractions (2–8) was measured using a NanoSight NS300 . The 100 nm latex beads  were used as a positive control and 0.22 μm filtered PBS as a negative control. Each sample was diluted 1:50 or greater to obtain a concentration between 5 × 10Protein concentration of pellets and SEC fractions were measured using the Pierce™ BCA Protein Assay Kit  according to the manufacturer’s instructions. The reliable detection limit of the kit is 20 μg/mL; therefore, samples with a protein concentration between 10–20 μg/mL were assigned an arbitrary value of 15 μg/mL and those with concentrations below 10 μg/mL were assigned an arbitrary value of 5 μg/mL.Copper-carbon film grids  were glow-discharged using a Quorum K100X glow discharger. Grids were then placed on a 5 μL droplet of sample  for 2 min. Buffer salts were removed by transferring the grids twice to a fresh drop of distilled water and incubated for 5 s each. The excess fluid was removed with filter paper and the grids were transferred to one drop of uranyl acetate (1.5% in distilled water) and incubated for 1 min. Excess fluid was removed with filter paper and the grids were air dried prior to imaging. Grids were imaged using an FEI/ThermoFisher Scientific Tecnai G2 TEM electron microscope run at 200 keV accelerating voltage and using a 20 μm objective aperture to improve contrast. Images were acquired with an ORCA HR high resolution CCD camera using a Hamamatsu DCAM board and running the Image Capture Engine, software version 600.323, from AMT Corp. .g, 15 min, 5 °C), to allow separation and collection of the aqueous phase, which was in turn combined with 2.5 volume of 96% ethanol. The sample mix was added to miRNeasy Mini Kit columns  and washed according to the manufacturer’s instructions. Finally, the total RNA was eluted in 30 μL of nuclease free water. The small RNA profile of isolated RNAs was measured from 1 μL of sample using the Agilent Bioanalyzer Small RNA kit . The concentration of 10–40 nt and 40–80 nt small RNA were calculated using the 2100 Expert software with custom ranges.RNA analysis was performed from each “pellet” sample, fraction 2 (pre-EVs), fraction 3 (EV fraction), combined fractions 4 + 5 (middle fractions) and combined fractions 6–8 (protein fractions). Qiazol-sample mixes were defrosted on ice, combined with 0.2 volume of chloroform and left for 5 min at RT. Solutions were centrifuged  and MOPS running buffer and transferred to nitrocellulose membranes using a BIORAD Trans-Blot SD semi dry transfer system. Membranes were blocked for 45 min  and primary antibodies against Tsg101 , Alix , Tamm-Horsfall protein  and CD9  were used at 1:1000 dilution o/n at 4 °C. Horseradish peroxidase (HRP)-conjugated secondary antibodies  and rabbit anti-mouse ) were used and chemiluminescence activated using the Amersham ECL Prime detection reagent kit . Bands were visualised using the signal accumulation mode with a Bio-Rad ChemiDoc™ MP System .Samples were mixed with 4× Pierce™ LDS Sample Buffer  including DTT (0.3M) and denatured for 5 min at 75 °C. For Western blot analysis, 5 μL (fractions 6–8 in UF+SEC) or 25 μL (other samples) of protein sample was separated using a 4–20% SDS-PAGE , version 8.2.1 (279). For normalised particle numbers and particle/protein ratio were analysed using the Friedman test followed by uncorrected Dunn’s test. For comparison of the percentage of total small RNA of size 10–40 nt and 40–80 nt present within fractions 3 and 6–8, the Wilcoxon matched-pairs signed rank test was used. The ratio of 10–40 nt sized small RNA was calculated for samples from UF and the ratio of 40–80 nt sized small RNA samples was calculated from all methods combined. We have submitted all relevant data of our experiments to the EV-TRACK knowledgebase (EV-TRACK ID: EV210150) .


No non-invasive method can accurately determine the presence of central cervical lymph node (CCLN) metastasis in papillary thyroid cancer (PTC) until now. This study aimed to investigate factors significantly associated with CCLN metastasis and then develop a model to preoperatively predict CCLN metastasis in fine-needle aspiration (FNA) reporting suspicious papillary thyroid cancer (PTC) or PTC without lateral neck metastasis.st, 2016 and June 30th, 2018 were included. The total eligible patients were randomly divided into a training set and an internal validation set with the ratio of 7:3. Univariate analysis and multivariate analysis were conducted in the training set to investigate factors associated with CCLN metastasis. The predicting model was built with factors significantly correlated with CCLN metastasis and validated in the validation set.Consecutive inpatients who were diagnosed as suspicious PTC or PTC in FNA and underwent partial or total thyroidectomy and CCLN dissection between May 1BRAF mutation, multifocality, size, and capsule involvement were found to be significantly correlated with the CCLN metastasis in univariate and multivariate analysis. A model used to predict the presence CCLN metastasis based on these factors and US CCLN status yielded AUC, sensitivity, specificity and accuracy of 0.933 , 0.816, 0.966 and 0.914 in the training set and 0.967 , 0.897, 0.959 and 0.936 in the internal validation set.A total of 770 patients were eligible in this study. Among them, 268 patients had histologically confirmed CCLN metastasis, while the remaining patients did not. Factors including age, BRAF mutation, multifocality, size, and capsule involvement were independent predictors of CCLN metastasis in FNA reporting suspicious PTC or PTC without lateral neck metastasis. A simple model was successfully built and showed excellent discrimination to distinguish patients with or without CCLN metastasis.Age,  Thyroid cancer ranks the seventh in the list of newly diagnosed cancers and occurs 3.2 times more often in women than in men in China according to the latest report . The morUltrasound (US) is the most important method to assess thyroid and cervical lymph nodes. However, the diagnostic efficiency of US in detecting CCLN metastasis is poor than that in lateral cervical lymph node (LCLN). A meta-analysis including 19 studies reported the pooled sensitivity, specificity, diagnostic odds ratio and area under curve of US in detecting CCLN metastasis to be 0.33, 0.93, 5.63, and 0.69, respectively; and LCLN metastasis to be 0.70, 0.84, 18.7 and 0.88, respectively . NeverthBRAF mutation is the most common genetic alteration in thyroid cancer, with incidence ranging from approximate 30% to more than 80%  cervical lymph node metastasis in thyroid cancer. Some studies demonstrated BRAF mutation was correlated with  cervical lymph node metastasis  histologically confirmed PTC, 2) detection of BRAF mutation for thyroid lesions with specimens from preoperative FNA, 3) underwent preoperative US examination of thyroid and neck. The exclusion criteria included: 1) imaging suspicious or FNA confirmed LCLN metastasis, 2) CCLN metastasis data was missing, 3) BRAF mutation data was missing, 4) US images were missing. The flow chart of patient selection was showed in Consecutive inpatients who had US-guided FNA reporting suspicious PTC or PTC  and undeUS examination of thyroid and neck was conducted with Logic E9 , IU22 , Aixplorer , Aplio 500 , and Mylab90  equipped with a 5–14-MHz linear-array transducer. First, the grey-scale images of the neck were obtained for thorough evaluation of the neck anatomy, including surrounding vasculature, major salivary glands, cervical lymph nodes, and thyroid. The cervical lymph node is divided into seven levels according to their anatomic location. The CCLNs refer to the level VI and VII, whereas the LCLNs include level I, II, III, IV, and V . Lymph nThe suspicious nodule in thyroid and neck was directed to US-guided FNA biopsy. Standard 21- or 23- gauge injection needles were used for FNA biopsy. Three to four passes per target nodule were made at the discretion of operator; two passes for cytologic diagnosis and one or two passes for BRAF mutation analysis. For both unilateral and bilateral multiple nodules in the thyroid, only the most suspicious was directed as target nodule. Specimens for cytologic diagnosis were evaluated by pathologist special for cytopathology and the results were reported according to The Bethesda system .BRAF V600E mutation analysis was conducted in the Pathology Department. Briefly, the DNA of specimens from FNA biopsy was extracted. Real-time PCR was performed using the BioRad-CFX96 real-time PCR system . The mutant BRAF gene (encoding BRAF V600E) was amplified with specific primers. Thermal cycling conditions were initial denaturation of 1 cycle for 5 minutes at 95°C, followed by 15 cycles of 95°C for 25 s, 64°C for 20 s, and 72°C for 20 s with a final step of annealing and elongation of 31cycles at 93°C for 25 s, 60°C for 35 s and 72°C for 20 s. The BRAF V600E mutation status of each primary PTC was determined using the Human BRAF Gene V600E Mutation Fluorescence Polymerase Chain Reaction (PCR) Diagnostic Kit (Amoy Diagnostics). The FAM signals of the mutation detection system indicate the mutation status of the sample. The HEX/VIC signals indicate the internal control status. The FAM Ct value was checked for each sample: a) If the sample FAM Ct value ≥27, the sample was classified as negative or below the detection limit of the kit. b) If the sample FAM Ct value < 27, the sample was classified as mutation positive. In the end, the run files were interpreted according to the manufacturer’s instruction.Total thyroidectomy and bilateral central neck dissection were performed in patients with bilateral thyroid cancer. For patients with unilateral cancer, the extent of thyroid surgery and ipsilateral central neck dissection were decided at the discretion of operator before the operation.p < 0.05 in the regression analysis were used to build the predicting model. The efficacy of this model was evaluated with area under the curve (AUC), sensitivity, specificity and accuracy and validated in the validation set. All tests were two-sided, and p < 0.05 indicated statistical significance. The SPSS version 19.0  software was used for all statistical analysis.The end-point event of this study was CCLN metastasis. For bilateral cancer, only the CCLNs ipsilateral to the FNA-targeted nodule was analyzed. The total group of patients was randomly divided into a training set and an internal validation set with the ratio of 7:3. The predicting model was developed using the training set. Univariate analysis with Chi-squared test was used to identify the variables correlated with CCLN metastasis. Factors significantly correlated with CCLN metastasis in the univariate analysis were included in the multivariate analysis, which was conducted with logistic regression analysis. Factors with BRAF mutation presented in 642 (83.4%) tumors. Only 79 (10.3%) patients presented suspicious CCLN metastasis in US. Total thyroidectomy was performed in 192 (24.9%) patients, partial thyroidectomy in the remaining patients. All patients accepted central neck dissection. After surgery, 268 (34.8%) patients had histologically confirmed CCLN metastasis, while the remaining (65.2%) patients did not. The mean number of removed lymph nodes was 4.2 (range 1 to 30), and mean number of positive lymph nodes was 2.1 (range 1 to 11). Five hundred and forty-six patients (70.9% of 770) were allocated into the training set. The patient characteristics in the training set were in good agreement with that in the validation set except slightly difference in the tumor location  females and 190 (24.7%) males, with median age of 44 years (range 22 - 80 years) were eligible for this study. Among them, 251 (32.6%) patients were accompanied with Hashimoto’s thyroiditis. Five hundred and fifty-three (71.8%) patients had one solitary tumor, whereas unilateral multiple tumors were found in 71 (9.2%) patients, and bilateral multiple tumors in 146 (19.0%) patients. The median size of tumors was 8 mm (range 3 -53 mm). location Table 1.BRAF mutation status, cytologic category, multifocality, size, location, component, echogenicity, margin, shape, ratio of tall to wide, calcification, capsule involvement, depth, and US CCLN status was analyzed . With the optimum cutoff of 0.465, the sensitivity, specificity and accuracy of this model were 0.816, 0.966 and 0.914, respectively. When validated in the internal validation set, the modal yielded the AUC of 0.967  Figure 2The supposed benefit of prophylactic CCLN dissection in thyroid cancer without metastasis to lateral neck includes local-recurrence prevention and improved long-term survival. However, although several meta-analyses showed reduction in the risk of local-recurrence in thyroid cancer with prophylactic central neck dissection, they all stated that the surgical morbidity should not be overlooked –29. FurtUnfortunately, to date no non-invasive imaging method can confidently classify cervical lymphadenopathy as metastasis, especially for CCLNs because they may be obstructed by the thyroid. In this study, the sensitivity of US alone for detection of CCLN metastasis was only 29.5% (79/268), though the specificity was 100% (502/502), which are comparable with previous report . A recenBRAF gene mutation. Thirdly, BRAF mutation status was determined with specimens from the preoperative FNA biopsy. Therefore, all factors included in this model could be determined preoperatively, making it a real tool to preoperatively predict the status of CCLN metastasis. Finally, this simple model showed more excellent discrimination in both the training set and internal validation set than most previous models.Several studies have developed various models to predict the CCLN metastasis in thyroid cancer –37. In cOne interesting finding in this study is that CCLN metastasis was less likely to present in older patients with PTC, which was similar to previous reports , 38. ThiBRAF mutation was significantly associated with CCLN metastasis in PTC. However, although gender and calcification were significantly correlated with CCLN metastasis in the univariate analysis, they did not show statistical significance in the multivariate analysis.Consistent with previous studies , 37–39, One confounder should be noted in this study is that only unilateral CCLN ipsilateral to the FNA-targeted nodule was analyzed in this study. However, for patients suffered from bilateral thyroid cancer, metastasis might come from contralateral disease; for patients with unilateral cancer, metastasis might skip to contralateral CCLNs. Although no study reports how often this would happen yet, we believe it occurs rarely and would result in little impact on the results. Other limitations of this study include lack of external validation and retrospective design.BRAF mutation, multifocality, size, and capsule involvement were independent predictors of CCLN metastasis in FNA reporting suspicious PTC or PTC without lateral neck metastasis. A simple model was successfully built based on these factors and US CCLN status and shown excellent discrimination to distinguish FNA reporting suspicious PTC or PTC with or without CCLN metastasis.In conclusion, this study demonstrated that age, The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.The studies involving human participants were reviewed and approved by Ethics Committee of Fudan University Shanghai Cancer Center. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.KZ, conception, writing and supervision. LQ, writing, data collection and analysis. JC, writing and data analysis. QZ and CC, data collection and review. All authors contributed to the article and approved the submitted version.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Secondary structure plays an important role in determining the function of noncoding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine learning (ML) technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on ML technologies and a tabularized summary of the most important methods in this field. The current pending challenges in the field of RNA secondary structure prediction and future trends are also discussed. Since its discovery, for a long time, RNA was regarded solely as a message carrier between DNA and protein. However, we are now beginning to understand its important roles, as increasing numbers of noncoding RNAs (ncRNA) are being discovered . AccordincRNA molecules often fold into higher-order structures, and functionally important ncRNA structures are typically conserved during evolution. Similar to protein, the ncRNA function is usually closely related to its structure. Currently, a wide variety of ncRNA sequences are publicly available, and their numbers keep dramatically increasing . By contUnlike the global folding of protein driven by hydrophobic forces, the RNA folding process is hierarchical  Fig 1)..Fig 1). Computational predictions are mainstream approaches for identifying RNA secondary structure. A number of prediction methods have been developed since the 1970s. Most of these methods attempt to identify a structure with a minimum free energy (MFE), in agreement with the hypothesis that an RNA molecule is likely to exist in an MFE state, just like protein . Many prIn this paper, we provide a comprehensive overview of ML-based methods for RNA secondary structure prediction, with a thorough discussion of their advantages and disadvantages. We also provide a tabularized summary  of the mThe RNA molecule is an ordered sequence of nucleotides that contain 1 of the 4 bases: adenine (A), cytosine (C), guanine (G), and uracil (U), arranged in the 5′ to 3′ direction. Pairing (via hydrogen bonds) of these 4 bases within an RNA molecule gives rise to the secondary structure. Typically, each base pairs with at most one other base. The canonical base pairs include the Watson–Crick base pairs (A–U and G–C) and the wobble base pair (G–U). These base pairs often result in the formation of a nested structure, in which multiple stacked base pairs form a helix, and one or multiple unpaired base pairs form a loop.It has to be noted that 3 kinds of special base pairs  commonlyn can be regarded as:Typically, the secondary structure of an RNA molecule with a length i,j),1≤i<j≤n}, where  indicates a base pair formed between the i-th and j-th nucleotide in the RNA sequence; or a set of compatible helixes [A set of base pairs {( helixes .i-th row and j-th column representing the interaction between the i-th and j-th nucleotides in the RNA sequence.A contact table (CT table), i.e., a square matrix, with elements in the A graph, where nodes represent nucleotides and edges represent base pairing relationships.n, e.g., “dot-parenthesis” notation, with matching parentheses for paired bases and dots for unpaired bases.A labeled sequence with the length A parse tree derived from context-free grammars, of which the leaf nodes comprise the RNA sequence .The above definitions form the basis of both traditional and ML-based RNA secondary structure prediction methods.RNA structure determination is a fast-evolving topic. Many different methods have emerged in the last 20 years. They can be divided into 2 categories, i.e., wet lab experimental approaches and computational predicting approaches.X-ray crystallography  and nuclO(n3), n being the RNA sequence length). To reduce the running time, Tahi and colleagues [O(n2 log n). DCfold searches for helices based on their lengths and mutation rates using a “divide and conquer” approach. Comparative sequence analysis can also predict the structures with pseudoknots [Comparative sequence analysis ,40 is thlleagues  implemenudoknots –46; howeudoknots –50, e.g.udoknots , KnetFoludoknots , and ILMudoknots . One greudoknots , which mScore-based methods are the most widely used methods and have dominated the field of RNA secondary structure prediction in the last 4 decades. These methods assume that the native RNA structure is a structure with a minimum/maximum total score, depending on the hypothesis of RNA folding mechanism or its simplification. Hence, the problem of RNA secondary structure prediction is transformed into an optimization problem. Since the RNA secondary structure can be recursively broken down into smaller elements with independent score contributions, the dynamic programming (DP) algorithm is often employed to identify the optimal structure. Evaluation of the score for structure elements requires a score scheme of many parameters. Nussinov and Jacobson  proposedO(n3) (where n is the RNA sequence length), neglecting the special base pairs and weak interactions. Hence, the computational cost is not acceptable, especially when analyzing an RNA molecule longer than 1,000 nucleotides. Moreover, predicting the special base pairs in RNA structures is still a difficult task. Since an RNA structure with special bases pairs is not a nested structure in general, score-based methods have to employ sophisticated algorithms to capture these special base pairs at the cost of higher time complexity. However, the performance of these methods needs to be further improved.However, the folding mechanism hypotheses of score-based methods do not always hold, e.g., the RNA molecule often folds into locally stable structural domains. Furthermore, almost all score-based methods use virtually the same DP algorithm to find the best-scoring structures. However, the running time of the DP algorithm is usually In fact, it is extremely difficult to fully understand the RNA folding mechanism. ML methods, in contrast, are data driven and requiring no knowledge of such mechanism. These methods can learn the underlying folding patterns from large amount of training data. In the last few decades, ML methods have been used for many aspects of RNA secondary structure prediction methods to improve the prediction performance (see Section 4). However, they did not replace the mainstream score-based methods with respect to accuracy and generalization. This situation has been changing in the last 2 years because of the development of ML techniques, especially DL.The ML-based methods for RNA secondary structure prediction can generally be divided into 3 categories  accordinEarly ML-based methods usually train an ML model that can generate a new score scheme  and replConsidering the score schemes, the free energy–focused approach is the most popular approach. Ever since Tinoco and colleagues  put forwSome ML techniques were adopted to refine the parameters in the energy model. These techniques can employ subtle models to estimate the scores for a richer and more accurate feature representation using known thermodynamic data or RNA secondary structure data. Xia and colleagues  first trOf note, the parameters determined by the above free energy parameter-refining approaches are thermodynamic and can be used directly in the algorithms embedded by the same energy model, such as miRNA target prediction  and RNA While ML-based free energy parameter approaches successfully improved the accuracy of the RNA secondary structure prediction, the energy model is still far from ideal. Actually, the above methods for the estimation of ML-based parameters can only substitute for some wet lab experiments geared toward obtaining the energy parameters. However, it is entirely possible to obtain an improved score scheme independent of free energy based on ML techniques. Several weighted approaches were proposed that consider the parameters of RNA structure elements as weights instead of free energy changes. By removing the thermodynamic meaning, the weighted approach can utilize ML models to determine thousands of weights for more comprehensive RNA structure elements instead of obtaining a few energy parameters from a large number of wet lab experiments.l1 regularization was used to relieve overfitting. Then, MXfold was built by combining ML-based weights with experimentally determined thermodynamic parameters, achieving better performance than a model based on thermodynamic parameters or ML-based weights alone. Most recently, MXfold2 [By combining a discriminative structured-prediction learning framework with an online learning algorithm, Zakov and colleagues  greatly  MXfold2  was propAn advantage of the weighted approach is that it decouples structure prediction from energy estimation, which is potentially beneficial for both tasks. However, learned weights are not explainable, partly because of the black-box attribute of ML algorithms. Hence, the obtained scores cannot be used to compute the partition function, base pair binding probabilities, or centroid structures, etc.Stochastic context-free grammars (SCFGs) are an important alternative for predicting RNA structures ,80–84. SSakakibara and colleagues  first apHowever, the probabilistic approach cannot replace MFE methods for secondary structure prediction, as the accuracy of the currently best SCFG has yet to match those of the best free energy–based models. In addition, SCFG cannot describe all RNA structures, e.g., a structure containing special base pairs.ML can be also used in pretreatment, for selecting an appropriate prediction method or a group of appropriate parameters Figs  and S1. Since different prediction methods return several different structures, the ML model can provide a means of determining the most likely structures among the outcomes Figs  and S1. ML techniques are also directly used to predict RNA secondary structure in an end-to-end fashion or combined with other algorithms as constraints, base state detector, or structure selector. The general framework is shown in Figs m interactional neurons (m is the number of edges). Liu and colleagues [h neurons in the hidden layer, whose activation function is based on the Kolgomorov’s theorem (h is the number of possible base pairs in an RNA sequence).To the best of our knowledge, the ML technique was first introduced into the RNA secondary structure predicting process by Takefuji and colleagues . The autlleagues  enhancedlleagues  made uselleagues  further lleagues  modifiedHowever, because of the relatively poor performance of the above ML models and a small amount of the available data, ML-based RNA secondary structure prediction models can only process tRNAs, with relatively low accuracy. Currently, the use of DL techniques is rising rapidly, and they are dramatically changing these circumstances. Singh and colleagues  proposedIn addition to the encoded RNA sequences being used as the input, other information can also be incorporated into the DL model. Calonaci and colleagues  trained Alternatively, ML can be combined with other methods for a hybrid approach for RNA secondary structure prediction. Consequently, the ML model is usually considered as a scoring machine, mapping a score to each (pair of) base(s) in an RNA sequence, whose output is then passed to an independent filter to identify a reasonable structure.Bindewald and Shapiro  combinedCompared with the end-to-end approach, the performance of the hybrid approach is relatively poor, perhaps because of a bias between the training objective of the ML part and the overall system objective. Most methods in the hybrid approach are trained and tested using small-scale datasets. Hence, generalization of their abilities requires further verification.s, including ncRNA detection [It is well known that transcript abundance helps to identify transcripts of interest under different conditions, while the RNA structure helps to explain how these transcripts function. An excellent RNA structure prediction method is not only important for inferring RNA function, but also relates to many downstream studieetection –111, foletection , hybridietection , and olietection ,115 or detection –120. It etection ,122.Compared with comparative sequence analysis and traditional score-based methods, ML-based methods have some advantages. First, ML-based methods do not necessarily rely on the biological mechanism, which is usually difficult to thoroughly understand. Instead, they can utilize the information contained in various types of data, and, therefore, performance limitation caused by the mechanism hypothesis can be circumvented. ML-based methods can also be easily coupled with known biological mechanisms. Further, in terms of prediction performance, where a large amount of data is available, models with no or little knowledge of biological mechanisms usually perform better than mechanism-dependent ones. This also suggests that the assumed mechanism of RNA folding may be incomplete or not accurate. Second, in contrast to traditional score-based methods, the end-to-end DL methods do not need to consider the difficulties caused by base matching rules. Traditional score-based methods employ sophisticated algorithms to satisfy base matching rules at the cost of high time complexity. However, without the constraint of these rules, end-to-end models  can traiToday, many public RNA structure databases and other related datasets are available online, which provide abundant data for model training. Generally, these databases can be classified into 2 types, i.e., comprehensive databases and dedicated databases. A comprehensive database often consists of RNA structures with different conformations and in different RNA species, for example, RNA Strand  , RCSB PrData are extremely important for building ML-based RNA secondary prediction models, especially DL-based models with a large number of parameters. One of the reasons that the recent DL-based methods ,97,99 ouAnother issue that may affect the model performance is the imbalanced RNA families in training sets, e.g., thousands of 16S rRNAs but only a small number of telomerases occur in one dataset. When the length of the input RNA is comparable, trained models tend to perform better on the RNA species that are more prevalence in the training set . How to Generally, the enhancement of predictive ability is associated with the relatively large scale of the ML model, which requires large amounts of data for parameter training. Although a large number of RNA structure data in various formats is available, these are insufficient in terms of training large-scale DL models, especially with respect to the availability of high-accuracy data. Hence, questions on how to effectively utilize the limited data and cope with overfitting of a large-scale DL model are also important issues that remain to be resolved.Enormous progress has been made toward predicting RNA secondary structure by using ML-based methods. These methods are state of the art when considering most indices of prediction performance. However, some issues still require resolving.First, the accuracy of prediction should be improved further. Sato and colleagues  used theThe RNA sequence length limitation is another intractable issue, which becomes quite problematic with the recently discovered long  ncRNA . AlthougFor ML-based RNA secondary structure prediction models, overfitting is a very important issue , especiaAt last, the folding mechanisms need further exploration. Traditional RNA secondary structure prediction is based on different RNA folding mechanism hypotheses , while dCurrently, RNA secondary structure prediction is successfully shifting toward ML-based approaches, away from traditional score-based methods, and DL will surely continue to improve the prediction performance. The subtle structure of the DL model is a prerequisite to this end. Since the DL model is being rapidly developed in the natural language processing and image processing fields, using mature DL blocks from these fields, or combining them in such fields constitutes a feasible way to generate an excellent DL model for RNA secondary structure prediction.Further, using a DL model to predict the free energy parameter is an inevitable trend for more accurate energy estimations, when additional wet lab experimental data become available. However, these parameters may not improve RNA secondary prediction accuracy because they have to be combined with traditional score-based methods. On the other hand, combing an ML-based method and an optimization method is a promising approach for improving prediction performance.RNA structure is one of the central pieces of information for understanding biological processes, and determining RNA secondary structure will continue to be a hot topic in the computation and biology fields. In this review, we focused on ML-based methods, which involve many aspects of RNA secondary structure prediction. ML techniques have greatly improved the performance of prediction methods, including accuracy, applicability, and running speed. However, to thoroughly resolve the RNA secondary structure prediction problem, a more subtle ML model is still needed. At the moment, ML-based methods cannot be used as substitutes for wet lab experiments for obtaining high-resolution structures. Nonetheless, the advent of DL technologies and high-performance hardware will foster a new generation of RNA secondary prediction tools with an improved accuracy and running speed.S1 Fig(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.


Saccharomyces cerevisiae, recent studies have examined in depth the biogenesis of RNA polymerases by characterizing some elements involved in the assembly of these multisubunit complexes, some of which are conserved in humans. This study reviews the latest studies governing the mechanisms and proteins described as being involved in the biogenesis of RNA polymerases in yeast.Eukaryotic RNA polymerases (RNA pols) transcriptional processes have been extensively investigated, and the structural analysis of eukaryotic RNA pols has been explored. However, the global assembly and biogenesis of these heteromultimeric complexes have been narrowly studied. Despite nuclear transcription being carried out by three RNA polymerases in eukaryotes (five in plants) with specificity in the synthesis of different RNA types, the biogenesis process has been proposed to be similar, at least for RNA pol II, to that of bacteria, which contains only one RNA pol. The formation of three different interacting subassembly complexes to conform the complete enzyme in the cytoplasm, prior to its nuclear import, has been assumed. In  Transcription is the most studied step of the gene expression catalyzed by RNA polymerases (RNA pols). Eukaryotes contain at least three RNA pols , while archaea and bacteria consist of a single enzyme . In addiDespite the fact that the transcription process and regulation have been extensively studied along with the structure of RNA pols , very liin vitro experiments. Assembly would start with the formation of the αα dimer, which would interact with the β subunit. Later, the ααβ module would associate with the β’ subunit, which probably forms a complex with the ω subunit  assembly has been proposed based on  subunit . Interes subunit .In yeast, a model for the biogenesis of RNA pol II based on bacterial RNA pol formation  has beenWhile the proposed model accounts for RNA pol II, the question of what occurs for RNA pol I and III arises. By taking into account the conservation of the different subunits ααββ’ω in RNA pol I and III, similar pathways may act for the assembly of all eukaryotic RNA pols, and this process is likely coordinated by the existence of five RNA pols common subunits: Rpb5, Rpb6, Rpb8, Rpb10, and Rpb12 . It is wIn line with previously proposed mechanisms for yeast, quantitative proteomic analyses in human cells have demonstrated the existence of a cytoplasmic RNA pol II subcomplex formed by subunits RPB2, RPB3, RPB10, RPB11, and RPB12 , which sin vitro, including stable trimer Rpc31/82/34 and the two heterodimers Rpc82/31 and Rpc17/25, as well as some free subunits like Rpb10, Rpc11, Rpc82, and Rpc34  . This coThe human R2TP complex interacts with components of the prefoldin (PFD) complex (PFDN2 and PFDN6) and the prefoldin-like complex  to form the R2TP/prefoldin-like complex (R2TP/PFDL) . URI yeaThese data suggest that R2TP, in both yeast and human, participates in the assembly of subassembly complex Rpb1 to the rest of the RNA pol II enzyme and likely occurs for RNA pol I and III.Yeast Hsp90 and human HSP90 are well-conserved molecular chaperones that participate in protein folding and avoid the non-specific aggregation of non-native proteins .As indicated above, yeast Hsp90 associates with RNA pol II  and humaBud27, and its human ortholog URI, are members of the PFD family of the ATP-independent molecular chaperones considered to function as scaffold proteins capable of assembling additional members of the PFD family in both human and yeast . Bud27 cSaccharomyces cerevisiae in an Rpb5-dependent manner before nuclear translocation, which probably occurs similarly for human URI, at least for RNA pol II  in not only RNA pol II but also in RNA pol III assembly and/or transport  leads to RPB1 cytoplasmic accumulation  functions by favoring a platform for other assembly factors like Gpn2 and Npa3 to sequentially mediate the association of RNA pol II subassembly complexes .in vivo (Rtr1 (“regulator of transcription” 1) has been described as a phosphorylated RNA pol II interactor by acting as an S5-P CTD phosphatase during the transition from the initiation of the transcription to elongation in vivo . Additioin vivo    .By considering the role of Rtr1 in only the transcription of RNA pol II  and the via Iwr1-independent pathways (RTP1 gene depletion in yeast leads to the cytoplasmic accumulation of Rpb1 and Rpb2 (rtp1 mutant shows no clear cytoplasmic accumulation of small RNA pol II subunits, such as Rpb3 and Rpb11, suggests the existence of passive nuclear diffusion of small RNA pol II subunits (Rtp1 has been proposed to be an important factor for nuclear RNA pol II localization pathways . In factpathways . Rtp1 phpathways . Mass sppathways . RTP1 geand Rpb2 . Interessubunits .S. cerevisiae (iwr1Δ mutant strains show cytoplasmic accumulation of both Rpb1 and Rpb3 (Iwr1 was initially reported as a protein that interacts with almost every RNA pol II subunit to regulate the transcription of some genes  and was revisiae . It has revisiae . Iwr1 birevisiae . Iwr1 isrevisiae . Neverthand Rpb3 , which sIn addition to Iwr1-dependent nuclear RNA pol II import, Iwr1-independent import pathways have been proposed as mechanisms that maintain cell viability when the main pathways are blocked, which involves the nuclear import of individual or partially assembled subunits, even by diffusion .RPB10 mRNA by interacting with its 3′ UTR region (Rbs1 was originally identified as a PAS kinase suppressor in a genetic high-copy suppressor study . More reR region .Despite the pre-existing knowledge about RNA pols assembly in yeast and their transport to the nucleus, mainly focused on RNA pol II and likely well-conserved for human RNA pol II, many interesting questions still need answers: although the assembly of RNA pols in eukaryotes seems to be similar to that in bacteria, why do RNA pols-specific processes exist? Why do RNA pol-specific assembly factors exist, while others seem to be general? Can different and additional mechanisms act for the assembly of RNA pols in yeast and other eukaryotes? Which mechanisms account for the nuclear transport of different RNA pols?Resolving these important questions and others must be the goal of future studies, to help us better understand the mechanisms governing the assembly and nuclear transport of different RNA pols.All authors wrote and reviewed this 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.


V-D-J genes. Somatic hypermutation (SHM) of immunoglobulin heavy chain variable (IGHV) genes are used as a prognostic marker in B-cell malignancies. Clinically, IGHV mutation status is determined by targeted Sanger sequencing which is a resource-intensive and low-throughput procedure. Here, we describe a bioinformatic pipeline, CRIS (Complete Reconstruction of Immunoglobulin IGHV-D-J Sequences) that uses RNA sequencing (RNA-seq) datasets to reconstruct IGHV-D-J sequences and determine IGHV SHM status.B cells display remarkable diversity in producing B-cell receptors through recombination of immunoglobulin (Ig) IGHV gene sequence. CRIS improves on existing tools that infer the B-cell receptor repertoire from RNA-seq data using a portion IGHV gene segment by de novo assembly. We show that the SHM status identified by CRIS using the entire IGHV gene segment is highly concordant with clinical classification in three independent chronic lymphocytic leukemia patient cohorts.CRIS extracts RNA-seq reads aligned to Ig gene loci, performs assembly of Ig transcripts and aligns the resulting contigs to reference Ig sequences to enumerate and classify SHMs in the https://github.com/Rashedul/CRIS.The CRIS pipeline is available under the MIT License from Bioinformatics Advances online. AdditioIGHV) genes are prognostic markers in different B-cell malignancies, including chronic lymphocytic leukemia (CLL), mantle cell lymphoma (MCL) and follicular lymphoma   were designed to reconstruct only the CDR3 region, representing only a portion of the IGHV gene, while the entire IGHV gene segment is required to determine the SHM status in B-cell malignancies. In addition, these tools have not been validated against gold standard PCR-Sanger datasets for SHM classification. To address these gaps in determining IGHV mutational status in B-cell malignancies, we developed a bioinformatic pipeline, CRIS (Complete Reconstruction of Immunoglobulin IGHV-D-J Sequences), which extracts RNA-seq reads aligned to putative Ig loci, assembles the complete IGHV gene, identifies the most abundant Ig transcript and enumerates SHMs by comparison with germline reference sequences. Classification of IGHV mutational subtypes by CRIS was validated against PCR-Sanger-based clinical classification in three independent cohorts of CLL patients and shown to be comparable.Several bioinformatic pipelines have been developed to infer BCR repertoire from RNA-seq data, including ABRA (n = 16) and used according to procedures approved by the Research Ethics Board (REB H12-01767) of the University of British Columbia (In the Centre for Epigenomic Technology (CEMT) cohort, peripheral blood samples were obtained from CLL patients undergoing treatment at BC Cancer (Columbia . RNA washttp://www.ihec-epigenomes.org). These guidelines as well as the standard operating procedures for RNA-seq library construction and sequencing are available at https://thisisepigenetics.ca/for-scientists/protocols-and-standards and by request. Additional CLL patient RNA-seq datasets with matching IGHV mutation status were collected from published datasets: GSE66228  with the germline IGHV sequences, we used blastn   and MiXCR (v3.0.3) were run on the RNA-seq bam file generated by STAR (v2.7.5a) aligner  for Trinity to assemble the Ig transcript. However, as expected, the overall number of Ig transcripts identified correlated with the sequencing depth. Based on this analysis, we developed a pipeline called CRIS and benchmarked its ability to call IGHV mutation status. In the CRIS pipeline, we automate the process of read extraction from our novel putative Ig coordinates, perform quality trimming of selected sequence reads, assemble transcripts, enumerate transcript abundances and identify somatic mutations using reference germline sequences for SHM classification.Having established that enrichment of sequences using our Ig feature set significantly reduced compute resources without a reduction in the sensitivity, we next examined the impact of RNA-seq sequencing depth. For this, we leveraged a set of 16 CLL RNA-seq libraries with an average of ∼28 million paired reads from GSE66228  is clinically defined by IGHV sequence alignments of >98% identity to the reference sequence  that did not change the SHM classification. In seven of the eight divergent cases, the percent IGHV identity reported by the Sanger-based test was higher compared to CRIS  spanning the CDR3 region. Thus, on average, V’DJer assembled 75.44% of the IGHV gene whereas CRIS reconstructed 99.74%  compared to V’DJer . Using deeper RNA-seq datasets (∼300 million reads) V’DJer took five times more time to run than CRIS . Using 16 threads, TRUST took 36 wall-clock minutes on average using GSE66228, an order of magnitude longer than CRIS. MiXCR (IGHV mutation status in CLL at a sensitivity and specificity equivalent to current Sanger-based clinical tests. Furthermore, CRIS was able to reconstruct the entire IGHV sequence thus increasing the accuracy of SHM classification. This is in contrast to a majority of existing pipelines designed to infer only CDR3-derived sequences (PCR-Sanger-based Ig SHM classification is resource-intensive, subject to PCR bias, and suffers from an ∼9% to 18% failure rate (IGHV mutations (IGHV genes is either not reported or partially reported. Furthermore, for a majority of publicly available RNA-seq datasets where SHM status is reported, detailed IGHV mutation reports with gene name, percent identity and clonal frequency are not available restricting the ability to assess mutational values. To meet this need, we developed CRIS and demonstrated its ability to rapidly classify IGVH mutational status with clinical accuracy. We anticipate that CRIS will prove to be useful in the mining of available B-cell RNA-seq datasets and that it will provide a framework to incorporate RNA-seq as a diagnostic tool to examine the BCR clonal rearrangement and SHM status.A registry of ∼1500 CLL patients showed that 90% of patients were not screened for vbab021_Supplementary_DataClick here for additional data file.


Mettl3 or Mettl14, which encode components of a central RNA methyltransferase complex. Importantly, MSR transcripts that are partially deficient in m6A RNA methylation display impaired chromatin association and have a reduced potential to form RNA:DNA hybrids. We propose that m6A modification of MSR RNA will enhance the functions of MSR repeat transcripts to stabilize mouse heterochromatin.Heterochromatin has essential functions in maintaining chromosome structure, in protecting genome integrity and in stabilizing gene expression programs. Heterochromatin is often nucleated by underlying DNA repeat sequences, such as major satellite repeats (MSR) and long interspersed nuclear elements (LINE). In order to establish heterochromatin, MSR and LINE elements need to be transcriptionally competent and generate non-coding repeat RNA that remain chromatin associated. We explored whether these heterochromatic RNA, similar to DNA and histones, may be methylated, particularly for 5-methylcytosine (5mC) or methyl-6-adenosine (m6A). Our analysis in mouse ES cells identifies only background level of 5mC but significant enrichment for m6A on heterochromatic RNA. Moreover, MSR transcripts are a novel target for m6A RNA modification, and their m6A RNA enrichment is decreased in ES cells that are mutant for  Constitutive heterochromatin spans the pericentric region of each mouse chromosome. It is defined by underlying repeat DNA sequences, such as the major satellite repeats (MSR) and is characterized by DNA methylation, H3K9me3 methylation and heterochromatin protein 1 (HP1) accumulation . The basSuv39h-dependent H3K9me3 also occurs over intact 5′ untranslated regions (5′UTR) of long interspersed nuclear elements (LINE), in particular the L1MdA subfamily . The 5′UMSR and LINE transcription are important after fertilization to ensure development of the early mouse embryo , and MSRCaenorhabditis elegans has shown that R-loops are suppressed by H3K9 methylation, which protects the genome from inappropriate repeat element expression and aberrant increase in RNA:DNA hybrid formation , in particular MSR and LINE L1MdA 5′UTR transcripts, are methylated, similar to DNA and histone methylation. 5-methylcytidine (5mC) was shown to be mostly present on tRNA and rRNA  and to stivation , and siltivation ,52.in vitro and is also relevant to direct m6A RNA methylation to hetRNA in mouse ES cells. Using Mettl14 and Metll3 null ES cell lines, we found that the decrease of m6A RNA methylation leads to impaired chromatin association of MSR transcripts and reduces their ability to form RNA:DNA hybrids. Our work identifies MSR repeat RNA as a novel target for m6A RNA modification and suggests that m6A methylation of MSR hetRNA strengthens the functions of MSR repeat transcripts in stabilizing heterochromatin integrity.In this study, we identified m6A as an abundant RNA modification of MSR and LINE L1MdA 5′UTR transcripts. We established that the Mettl3/Mettl14 complex deposits m6A methylation on hetRNA 2.Mouse embryonic stem (ES) cells were cultured on dishes coated with 2% gelatin in high glucose DMEM medium (Sigma-Aldrich) containing 15% Serum Replacement (Thermo Fisher Scientific), 100 U/ml penicillin, 100 μg/μl streptomycin (Sigma-Aldrich), 2 mM L-glutamine (Sigma-Aldrich), 0.1 mM beta-mercaptoethanol, 1× non-essential amino acids (Sigma-Aldrich) and 1 mM Na-pyruvate (Sigma-Aldrich). 1 ml of conditioned medium from COS-7 cells  expressing Leukemia Inhibitory Factor (LIF) was added to 500 ml of ES cell culture medium to prevent ES cell differentiation. Cells were cultured at 37°C in 5% CO7 ES cells were lysed in 1 ml TRIreagent (Sigma-Aldrich) with 5–10 strokes of a syringe using 20, 23 and 26 G needles. RNA was precipitated with isopropanol, washed with 75% ethanol, resuspended in nuclease-free water (Qiagen) and stored at –80°C. The quality of the RNA was controlled using a Bioanalyzer RNA 6000 Nano Kit (Agilent). 20 μg of RNA was digested for 1 h at 37°C with 7 U of TURBO DNase I (Thermo Fisher Scientific), RNA was purified using the RNeasy MinElute Cleanup kit (Qiagen) and the eluted RNA was subjected for a second round of DNase digestion and purification. The double DNase-digested RNA was stored at –80°C.Nuclear RNA preparation was done according to published protocols . Nuclei in vitro transcribed (IVT) EGFP RNA (see below), where either all cytidines are 5mC or all adenosines are m6A methylated, and the volume was adjusted to 60 μl. The RNA mixes were denatured at 70°C for 10 min and divided equally into three samples: (i) 20 μl input (stored at –80°C until cDNA synthesis), (ii) 20 μl immunoprecipitation (IP) and (iii) 20 μl beads control. The IP samples were incubated with 5 μg of antibody in 500 μl 1× MeRIP buffer  overnight (O/N) on a rotating wheel at 4°C. The following antibodies were used for MeRIP: α-5mC  or α-m6A . 40 μl of blocked (0.1% BSA in PBS for 2 h at 4°C) Magnetic Protein G Dynabeads (Thermo Fisher Scientific) were then added to the IP and the beads control samples, and incubated for another 2 h at 4°C. Samples were washed five consecutive times in 700 μl of fresh 1× MeRIP buffer for 10 min at room temperature (RT). After the last wash, magnetic Dynabeads were resuspended in 200 μl of Proteinase K buffer , and 70 μg of Proteinase K (Thermo Fisher Scientific) were added for 3 h at 50°C. RNA was purified from the supernatant using the RNeasy MinElute Cleanup kit (Qiagen) and eluted in 20 μl nuclease-free water (Qiagen). For reverse transcription, equal volumes (10 μl) of input, IP and beads control were used to generate cDNA  to generate RNA fragments with an average length of 500 nt. Nine microgram of sonicated RNA was spiked with 1 ng of see also .Reverse transcription (RT) was performed using the SuperScript II Reverse Transcriptase kit (Thermo Fisher Scientific), 0.5 mM of dNTP mix (Thermo Fisher Scientific) and 200 ng of random hexamer primers (Thermo Fisher Scientific). The cDNA was diluted 10x in water and stored at –20°C. 5 μl (37.5 ng) of the 10x diluted cDNA was mixed with 2× SYBR Select Master Mix (Thermo Fisher Scientific) and 200 nM of target-specific forward and reverse primers (Sigma-Aldrich) in a total volume of 10 μl. qPCR was performed with the QuantStudio 6 Flex machine (Applied Biosystems) with an annealing temperature of 60°C in a program using 40 cycles. Cycle threshold (Ct) values were used to calculate normalized expression (ΔΔCt method) and enrichment over input was calculated using the ΔCt method.9 (240 maxi dishes) J1 WT ES cells  with T7 RNA polymerase . For mutant IVT MSR and LINE L1MdA 5′UTR transcripts lacking either all adenosines or containing the RRACHmut motifs, corresponding DNA templates with a flanking T7 promoter sequence were purchased from Integrated DNA Technologies (IDT). The EGFP IVT transcript was generated from the pCR4-TOPO-EGFP-S1 plasmid (Jenuwein lab) that was linearized with NotI prior to in vitro transcription with T3 RNA polymerase. For the EGFP IVT transcripts containing either 5mC or m6A, CTP was replaced by 5mCTP or ATP was replaced by m6ATP (both TriLink Biotechnologies) in the in vitro transcription reactions. The IVT transcripts were purified using the RNeasy MinElute Cleanup kit (Qiagen) and their quality was assessed on a Bioanalyzer RNA 6000 Nano Kit (Agilent).PCR products were amplified from the pSAT-MSR-1-repeat or pEX-L1MdA-1-repeat plasmids ,56 using2, 1 mM dithiothreitol (DTT), 50 mM KCl, 4% glycerol) containing 40 μM S-adenosyl-l- methionine (SAM) plus 1.1 μCi S-[methyl-3H]-SAM (Perkin Elmer) for 3 h at 30°C. The RNA was then purified with an RNA Clean & Concentrator kit (Zymo Research) and added to Ultima Gold scintillation fluid (Perkin Elmer), and the methyl-3H incorporation  were measured with the Tri-Carb 2910 TR (Perkin Elmer).The RNA methyltransferase assay was adapted from a published protocol . 5 μM ofMettl3/Mettl14 CRISPR mutants. After 48 h, transfected cells were selected with 1.25 μg/ml puromycin for two days. Surviving cells where then collected (mixed population of cells) and an aliquot of cells was used to perform a surveyor assay (4)2SO4, 1 mM EDTA, 0.1% Triton X-100) and 0.1 mg/ml of Proteinase K (Thermo Fisher Scientific) for minimum 4 h at 50°C, followed by a 10 min 95°C inactivation of Proteinase K. Lysates, 10x diluted in water, were directly used for PCR (Terra Taq polymerase (Takara)) with primers amplifying exon 5 of Mettl3 and exon 10 of Mettl14. PCR amplicons were sequenced by Sanger sequencing. Out of 192 clones we identified only one clone (A10 clone) that is homozygous mutant for Mettl14 (see Mettl3.Single guide RNAs (sgRNA) targeting the MT-A70 domain of either Mettl3 and Mettl14 were designed using the CRISPR design online tool  and clonor assay , while ttl14 see . The A10Mettl14/Mettl3 KO ES cells was processed for m6A MeRIP as described above. The resulting material from input and IP samples of the three MeRIP experiments  was combined and concentrated with a Concentrator 5301 (Eppendorf) to a final volume of 12 μl. The quality of the RNA was controlled by Fragment Analyzer (Agilent) and the concentration was measured using the Qubit RNA High Sensitivity assay (Thermo Fisher Scientific). Between 10 and 100 ng of the IP samples were used to prepare ribosomal RNA-depleted cDNA libraries (random hexamers) using the TruSeq Stranded Total RNA Library Prep Gold protocol (Illumina), but without the fragmentation step. Paired-end, 75 bp reads were generated with the HiSeq3000 sequencer (Illumina) with a coverage of 30 million reads per sample. Two biological replicates of m6A MeRIP enrichment were analyzed.Three times 10 μg of double DNase-digested nuclear RNA of WT/RBC or http://www.repeatmasker.org) obtained from UCSC genome database  were annotated by ChIPseeker. Pathway enrichment analysis was performed on the differential m6A peaks using ReactomePA package  with an 18G needle (10 strokes). The lysate was rotated for 10 min at 4°C and centrifuged at 6000 g for 10 min at 4°C. The supernatant (cytoplasmic fraction) was aspirated, mixed with 1 ml of TRIreagent (Sigma-Aldrich) and stored at –80°C. The nuclear pellet was resuspended in 1 ml of ice-cold buffer B(0.4)  with an 18G needle (10 strokes). The lysate was rotated for 30 min at 4°C and centrifuged at 10 000 g for 10 min at 4°C. The supernatant (nucleoplasmic fraction) was aspirated, mixed with 1 ml of TRIreagent (Sigma-Aldrich) and stored at –80°C. The chromatin pellet was resuspended in 1 ml of ice-cold buffer B(2.0)  with an 18 G needle (10 strokes). The lysate was rotated for 30 min at 4°C, sonicated with 10 pulses  using a Bioruptor (Diagenode) until the viscosity was reduced and then mixed with 1 ml of TRIreagent (Sigma-Aldrich). The subsequent purification of TRIreagent isolated RNA from the cytoplasmic, nucleoplasmic and chromatin fractions and double DNase digestion was done as described above.RNA preparations from sub-cellular fractions were done according to published protocols  and with7 ES cells, which was then subjected to total NA isolation using phenol/chloroform (Extraction of chromatin-associated nucleic acids (NA) for RNA:DNA hybrid detection was performed with phenol/chloroform. This allowed for a more robust RDIP signal as compared with material that was isolated by TRIreagent (data not shown). A sonicated chromatin lysate was prepared from 1 × 10loroform . The pheTo perform RNase H digestion, we followed a protocol that allows comparison of RNase H treated and untreated NA at the same temperature . 10 μg oEscherichia coli strain as described (The hybrid binding domain (HBD) sequence, spanning amino acids 27–76 from mouse RNase H1 , and theFor the optimization of a RNA:DNA immunoprecipitation (RDIP) protocol to detect MSR and LINE L1MdA 5′UTR repeat sequences, we compared TRIreagent-isolated NA and phenol/chloroform-isolated NA and incubation with RNase H at 37°C or at 4°C. We obtained the most reproducible results with phenol/chloroform-isolated NA and RNase H incubation at 4°C. In addition, RNA:DNA hybrid detection by recombinant HBD(H1)-eGFP is more efficient at 4°C. We therefore used an RNase H enzyme (Roche) that has robust activity at 4°C.2 and 1 mM DTT) in a total volume of 30 μl. The untreated control (2.1 μg) was also incubated for 2 h at 4°C in 1× buffer in a volume of 30 μl. RNase H-treated and untreated samples were then diluted to a volume of 42 μl and the samples were immediately subjected to RDIP with the HBD(H1)-eGFP (20 μl) or with the eGFP control (20 μl). 2 μl were saved as input.2.1 μg of chromatin-associated, phenol/chloroform-isolated NA were incubated for 2 h at 4°C with 13 U of RNase H (Roche) in 1x buffer . The beads–protein–NA complexes were sequentially washed with for 5 min at RT in high salt buffer , low salt buffer , IP-washing buffer  and 10– TE buffer. After the last wash, the beads-protein-NA complexes were resuspended in 100 μl of RDIP-elution buffer  and incubated for 15 min at 65°C in a thermomixer (600 rpm). Input samples , that were stored on ice, were combined with 98 μl RDIP-elution buffer and also incubated for 15 min at 65°C in a thermomixer (600 rpm).The RNase H treated or the untreated material (20 μl each) was combined with 10 μl of GFP-Trap magnetic beads (Chromotek) that had been coupled to either 1 μg of recombinant HBD(H1)-eGFP or eGFP. Samples were then incubated for 90 min at 4°C in 2× EMSA buffer  or untreated for 2 h at 4°C. This material was then incubated with 25 μl of magnetic Protein G Dynabeads (Thermo Fisher Scientific) that had been coupled to 1 μg of the monoclonal S9.6 antibody  O/N at 4°C in a rotating wheel. The next day, the bead-protein-NA complexes were washed three times for 5 min at RT with ChIP-wash buffer ) and once for 10 min at RT with the ChIP-final wash buffer ). The bead-protein-NA complexes were then resuspended in 100 μl of RDIP-elution buffer and processed for detection of MSR and LINE L1MdA 5′UTR RNA:DNA hybrids by RNA amplification as described above.2, 12.5% glycerol, 0.05% Triton X-100 and 1 mM DTT) for 1 h at 4°C. Reaction products were resolved on a 4% polyacrylamide (60:1) gel  in running buffer (12.5 mM Tris–HCl and 100 mM glycine) and the Cy5 signal was detected using a Typhoon FLA 9500 fluorescence scanner .EMSA was performed as described . RNA:DNAt-test or the Wald test. P-values ≤0.05 were considered to be statistically significant.Where indicated, the data are presented as mean values ± SD. Statistical significance between groups was determined using paired or unpaired two-tailed Suv39h double null (dn) ES cells, as there is more transcripts of MSR and LINE L1MdA 5′UTR in Suv39h dn ES cells  in RNA samples from Suv39h dn ES cells to the level of ∼2% of input RNA  coupled to RT-qPCR  signal at a background level (10-fold) for both, MSR reverse-enriched . The existence of DNA 5-methyldeoxycytidine (dm5C) suggests some contamination of DNA coming either from genomic DNA or DNA oligonucleotides used for hybridization (6A) was detected at 10,000-fold higher signal for MSR reverse-enriched transcripts  or antisense (AS) orientation, were incubated with METTL3/METTL14 and S-[methyl-3H]-(SAM), and incorporation of m6A into RNA was measured by scintillation counting.The m6A modification found in RNA Pol II transcripts is mostly deposited by the Mettl3/Mettl14 methyltransferase complex ,46. We nin vitro.The METTL3/METTL14 complex methylated all RNA substrates  when compared to no substrate controls Figure . Notablyin vitro transcripts that have the RRACH motif mutated to RRUCH sequence (RRACHmut) or where all adenosines found in the MSR and LINE L1MdA 5′UTR RNA sequences were replaced with uridines . We examined these mutated RNA sequences as substrates for the METTL3/METTL14 complex. m6A methylation was decreased by half when comparing the MSR sense RRACHmut RNA to MSR-sense RNA. By contrast, the MSR-antisense RRACHmut RNA did not show any difference when compared to the MSR-antisense RNA (<20% A-rich)  ,35. Alth) Figure , left. F) Figure , right. ) Figure .in vitro.The MSR sense transcript contains six RRACH motifs  to generate knock-out (KO) cell lines. We chose to target the catalytically active MT-A70 domain of Mettl3 and the degenerated MT-A70 domain of Mettl14, which has been shown to be important for the dimerization of both proteins .Mettl3 and MT-A70 of Mettl14 were co-transfected to obtain double CRISPR/Cas9 Mettl3/Mettl14 KO in WT26 cells. Although no double KO was obtained, a single Mettl14 KO clone (A10 clone) was derived. It has a 4 bp homozygous deletion that causes a frame shift and generates a premature termination codon, resulting in a truncated 311 aa long Mettl14  were derived from engineered WT ES cells (129/Sv × C57Bl6 background), termed RBC  (Single guide RNAs (sgRNA) for MT-A70 of  Mettl14 . The A10nt study . The Metmbinase) .Mettl14 KO and Mettl3 KO cells display a similar morphological change: the loss of ES cell-like colonies and a more flattened cell shape  (Mettl14 KO and Mettl3 KO) and in signal transduction (Mettl14 KO) or in cell migration (Mettl3 KO)  tails and do not have a 5′ cap (P < 0.05). In addition, there are also no significant alterations in the expression of RNaseH1 or RNaseH2 genes. Although we have not analyzed transcription rates directly, these data suggest that there are no major differences for gene pathways involved in transcriptional regulation or heterochromatin formation between WT26/Mettl14 and RBC/Mettl3 ES cells.Both e Figure . HiSeq Rgulated) . Intriguttl3 KO) . No pathttl3 KO) ,74. Howea 5′ cap . We haveMettl14 KO and Mettl3 KO cells. A 10-fold reduction in the expression level of MSR transcripts and a 2-fold reduction for LINE L1MdA 5′UTR transcripts was observed in the Mettl14 KO in comparison to WT26  and 5-fold (LINE L1MdA 5′UTR) when compared to RBC controls . We could confirm that the highest percentage of called m6A peaks were present within the 3′UTR of protein coding poly(A) transcripts in both WT26  and RBC  cells  transcripts and only recently were there reports identifying m6A on some repeat RNA ,75 and os) cells . Motif as) cells . While tKO cells . Howeverata sets .We then analyzed m6A peak calling of transcripts that fall within the main repeat classes. In the mouse transcriptome, short interspersed nuclear elements (SINE) are the most abundant class of repeat transcripts (≥ 3 million reads), followed by LINE transcripts, long terminal repeats (LTR)/endogenous retroviruses (ERV) (both > 1 million reads) and satellite repeat transcripts Figure . m6A enrMettl14 KO or reduced by >50% in Mettl3 KO RNA samples  and reduced by half in Mettl3 KO samples  and L1MdGf 5′UTR  repeat transcripts showed m6A enrichment over input, and this m6A enrichment was modestly decreased in s Figure . By conts Figure . For thes Figure . In totas Figure ,52. Anots Figure . For sats Figure . Major ss Figure . Notablyn Figure .Although we observed significant differences for m6A RNA enrichment between the four main repeat classes and considerable variability among distinct repeat subtypes, the data suggest that some nuclear LINE 5′UTR, ERV-type, and major satellite repeat transcripts are enriched for m6A RNA methylation. In addition, m6A RNA methylation of major satellite repeat transcripts is dependent on Mettl14 and/or Mettl3 function.Mettl14 KO and Mettl3 KO ES cells  and Hprt (mostly nucleoplasmic)  . Chromatnscripts . This inMettl14 KO and also decreased in Mettl3 KO samples , and were probed for MSR and LINE L1MdA 5′UTR RNA in RT-qPCR . There are several described methods to detect RNA:DNA hybrids, but none of them are error-free . Thus, w−/−p53 and −/−;Rnaseh2−/−p53 MEFs -eGFP. For RNase H digestion, we used a protocol with an RNase H enzyme that allows processing of samples at 4°C, as detection of RNA:DNA hybrids by the recombinant HBD(H1)-eGFP is enhanced at 4°C . Upon enrichment with HBD(H1)-eGFP, we quantified MSR and LINE L1MdA 5′UTR RNA:DNA hybrids by RNA amplification using RT-qPCR -eGFP of chromatin-associated nucleic acids that were prepared from WT26/RBC and see also . MSR RNAs Figure , left. Rn Figure , right.Mettl14 KO but outside a significant difference in the Mettl3 KO samples -eGFP -eGFP and from Mettl14 KO ES cells as shown in the RDIP analysis with the S9.6 antibody. Together, these results indicate that m6A RNA modification can stabilize MSR RNA:DNA hybrids.Despite this variability in the enrichment and RNAse H sensitivity of RNA:DNA hybrids by using either HBD(H1)-eGFP or the S9.6 antibody, our analysis confirms that a significant fraction of chromatin-associated MSR transcripts forms RNA:DNA hybrids. MSR RNA:DNA hybrid formation is reduced in chromatin-associated nucleic acids prepared from m6A RNA modification has mainly been studied for its function in gene coding transcripts ,35,39. Oin vitro RNA methyltransferase assays  and LINE L1MdA 5′UTR (>70% of transcripts) repeat RNA  of MSR RNA is m6A-modified raised the question to its possible role in the formation and/or stabilization of heterochromatin. Based on the m6A-paradigm , and as paradigm ,52, m6A-paradigm  that hasparadigm . We prob of HP1α ,9 and of of HP1α  to m6A M of HP1α ,87. IGF2 of HP1α ,88,89. W of HP1α  for m6A Mettl14 KO and Mettl3 KO ES cells  to form RNA:DNA hybrids or to invade a double-stranded DNA target , with the accession number GSE156481.The MeRIP-seq and the RNA-seq data have been deposited to the GEO repository (gkab364_Supplemental_FileClick here for additional data file.


MicroRNA (miRNA)–based drugs are quickly taking the clinic by storm. Herein, we analyzed resistance-associated variants (RAVs) to the first miRNA inhibitors to make it to the clinic, namely miR-122 inhibitors for chronic hepatitis C virus (HCV) infection. We uncovered three distinct resistance mechanisms based on unique alterations to the structure of the viral RNA. Specifically, RAVs altered the structure of the viral RNA in a manner that promotes riboswitch activity, genome stability, or positive-strand viral RNA synthesis. Our findings support recent models of miR-122–mediated HCV RNA accumulation and provide mechanism(s) of resistance to antiviral therapy. These early insights into the mechanism(s) of resistance to miRNA-based therapies may be of importance as more miRNA-targeted therapies enter into the clinic. Hepatitis C virus (HCV) is a positive-sense RNA virus that interacts with a liver-specific microRNA called miR-122. miR-122 binds to two sites in the 5′ untranslated region of the viral genome and promotes HCV RNA accumulation. This interaction is important for viral RNA accumulation in cell culture, and miR-122 inhibitors have been shown to be effective at reducing viral titers in chronic HCV-infected patients. Herein, we analyzed resistance-associated variants that were isolated in cell culture or from patients who underwent miR-122 inhibitor–based therapy and discovered three distinct resistance mechanisms all based on changes to the structure of the viral RNA. Specifically, resistance-associated variants promoted riboswitch activity, genome stability, or positive-strand viral RNA synthesis, all in the absence of miR-122. Taken together, these findings provide insight into the mechanism(s) of miR-122–mediated viral RNA accumulation and provide mechanisms of antiviral resistance mediated by changes in RNA structure. Flaviviridae family. The ∼9.6 kb HCV genomic RNA consists of a single open reading frame, which gives rise to the viral polyprotein that is processed into the 10 mature viral proteins flanked by highly structured 5′ and 3′ untranslated regions (UTRs)  is a positive-sense RNA virus of the s (UTRs) . As a pos (UTRs) . To thisfe cycle –4. Specife cycle , 5–8. Ad cycle 59.alt), which results in the recruitment of an Ago:miR-122 complex to site 2 of the HCV genome. This results in an RNA chaperone-like switch in conformation, akin to a bacterial riboswitch , the first miRNA-based drugs to enter clinical trials, have been developed and used to treat chronic HCV infection in the clinic . Both Mi2422SI Appendix, Fig. S1). First, we assessed viral RNA accumulation in miR-122 replete conditions in WT Huh-7.5 cells . The C2GC3U RAV accumulated to the least extent, with a 1,166.5-fold reduction in luciferase activity, although this was still significantly above background. Thus, under miR-122 replete conditions, WT and U4C have a fitness advantage when compared with the other RAVs.Each of the HCV RAVs were isolated under conditions in which miR-122 was absent and/or limiting in cell culture or in miR-122 inhibitor–treated patients 22. Thus, w.5 cells B. In theSI Appendix, Fig. S1 A and B). However, it is possible that the other RAVs are also able to accumulate but to levels that are below the level of detection in this assay. Nonetheless, these results suggest that only G28A has a distinct fitness advantage in viral RNA accumulation in the complete absence of miR-122.Next, we asked whether we could detect viral RNA accumulation of any of the RAVs in the absence of miR-122 using miR-122 knockout (KO) Huh-7.5 cells C. In thiSI Appendix, Fig. S1C). Interestingly, while C3U accumulated to a similar extent as WT, the G28A and C37U RAVs accumulated to a greater extent than WT, while the U4C and C2GC3U RAVs accumulated to a lesser extent. In contrast, complementation of site 2 allowed for the greatest accumulation of G28A followed by similar levels of accumulation of the WT, C2GC3U, and U4C RAVs, while the C3U and C37U RAVs were substantially impaired under these conditions . Taken together, these results suggest that the G28A RAV is the most fit in the absence of miR-122, while the remaining RAVs can compensate for the loss of miR-122 at least at one of the two miR-122–binding sites. Moreover, the differential ability of the RAVs to accumulate either in the absence of miR-122 or when complemented specifically at site 1 or site 2, prompted us to further investigate the mechanism(s) of action of the RAVs.As the RAVs identified in both cell culture and miR-122 inhibitor-treated patients were selected under conditions in which miR-122 was limiting, but not necessarily completely absent, we decided to investigate HCV RNA accumulation under conditions in which miR-122 was limiting –25. To dSI Appendix, Fig. S2 and alt  . Like WT, the C3U and C37U RAVs favored the alternative  structure  structure, there is a very close equilibrium between the two structures , which is reflected in our in vitro SHAPE analysis, particularly at nucleotides 20 to 23 and 30 to 37  similar to WT HCV RNA .In contrast to WT, several recent studies have suggested that the G28A RAV is able to form the functional SLII structure, even in the absence of miR-122 , 17. Thuig. S2A) –17. Intetructure C and E. 30 to 37 C and E. alt and functional SLII conformations, structural prediction analysis suggests that this mutation also has the potential to increase base-pairing interactions at the 5′ terminus, reducing the single-stranded nucleotides at the 5′ end of the viral RNA . Thus, our structure prediction and SHAPE analyses suggest that the RAVs can be generally categorized into three overlapping groups based on changes to the structure of the viral RNA. Specifically, the C2GC3U, U4C, and G28A RAVs are functionally “riboswitched,” even in the absence of miR-122. The C2GC3U, C3U, and U4C RAVs all have additional base-pairing interactions at the 5′ terminus of the HCV genome. And finally, the C37U RAV does not appear to alter the structure of the 5′ UTR of the positive-strand viral RNA.In addition to the riboswitched conformation, we also noticed other subtle changes in RNA structure that might have functional consequences for the RAVs. Specifically, we noted that the C2GC3U and U4C RAVs had additional base-pairing interactions at the 5′ terminus that reduce the number of single-stranded nucleotides at the 5′ end of the viral RNA A–D. For SI Appendix, Fig. S3). We predicted that due to their riboswitched conformation, the C2GC3U, C3U, and U4C RAVs may have a slightly higher affinity for hAgo2:miR-122 complexes because they exist in a more open conformation. For WT 1 to 117 nucleotide HCV RNA, we predict site 2 likely has a higher affinity and is bound first followed by a binding to site 1 based on the SLIIalt conformation. In agreement with this, we observed dissociation constants of 33 ± 10 pM (Kd1) and 277 ± 130 pM (Kd2) in line with previous measurements of hAgo2:miR-122 complex interactions with HCV RNAs . In line with our predictions, the C2GC3U and U4C RAVs had slightly higher affinities for site 2 binding (Kd1) and reduced binding to site 1 (Kd2)  to explore the binding of hAgo2:miR-122 complexes to 1 to 117 nucleotide HCV RNAs (HCV RNAs . Importa 1 (Kd2) . While fPrevious studies have suggested that miR-122 protects the HCV genome from two cellular pyrophosphatases, decapping exonuclease  and dual specificity phosphatase 11 (DUSP11) as well as subsequent exoribonuclease-mediated decay by Xrn-1 and/or 2 19, 27, 28.SI Appendix, Fig. S2 B–D). Thus, we hypothesized that this additional base pairing could provide stability to these RAVs, even in the absence of miR-122. To investigate this, we performed in vitro exoribonuclease assays with each of the RAVs , as well as the G28A and C37U RAVs, that do not alter base pairing at the 5′ terminus, had a similar stability to WT HCV RNA . This is also in agreement with a previous study that suggests Xrn-1 requires a single-stranded 5′ overhang that is sufficiently long to reach the active site of the enzyme , when compared to WT HCV RNA (t1/2 = 2.4 h) in miR-122 KO cells . Taken together, our findings suggest that the increase in base pairing at the 5′ terminus of the HCV genome provided by the C2GC3U and U4C RAVs provides HCV genome stability, even in the absence of site 1–bound miR-122.To further demonstrate whether this increase in base pairing at the 5′ terminus of the HCV genome results in increased stability of the viral RNA in cell culture, we performed viral RNA stability assays in miR-122 KO cells . SpecifiKO cells . The remSI Appendix, Fig. S6), which we confirmed in solution using in vitro SHAPE analysis . We observed that while G28A had a positive to negative strand ratio of 37.4, combining G28A+C37U resulted in a higher positive to negative strand ratio of 83.8 at day 4 post-electroporation. Notably, combining the U4C+C37U RAVs also rescued replication of U4C in miR-122 KO cells . Taken together, these results suggest that the C37U RAV alters the structure of the 3′ terminus of the negative-strand replicative intermediate in a manner that promotes positive-strand RNA synthesis.To understand the impact of alteration of the structure of the 3′ terminus of the negative-strand RNA intermediate, we introduced the C37U mutation into the G28A RAV  the C2GC3U, U4C, and G28A RAVs are riboswitched, even in the absence of miR-122; 2) the C2GC3U and U4C RAVs have increased base-pairing interactions at the 5′ terminus of the HCV genome that provide stability in the absence of miR-122; and 3) the C37U RAV alters the structure of the 3′ terminus of the negative-strand RNA intermediate, which promotes positive-strand RNA synthesis .SI Appendix, Fig. S1). We suspect that this is likely explained by the close equilibrium that still occurs between the alternative  and functional  G28A structures when compared with the C2GC3U and U4C RAVs . Alternatively, this might also be due to a slight increase in affinity for Ago:miR-122 complexes at both sites, although our binding affinity measurements herein were not robust enough to reveal a significant difference in binding affinity when compared with WT HCV RNA. Specifically, since the G28A mutation converts a potential G-U wobble base pair to a A-U Watson–Crick base pair at this position, this likely provides an advantage in terms of recruiting miR-122 to site 2 but could result in a reduction in recruitment of a second Ago:miR-122 complex to site 1. In addition, human Ago2 possesses a solvated surface pocket that specifically binds adenine nucleobases in the 1 position (t1) of target RNAs that can help anchor Ago2 to t1A-containing targets   .As expected for the C2GC3U and U4C RAVs, which stabilize the viral RNA in the absence of miR-122, in the presence of miR-122, they accumulated to a lesser extent than WT, likely due to reduced auxiliary base-pairing interactions with site 1–bound Ago:miR-122 at the 5′ terminus of the HCV genome. However, the additional base pairing at the 5′ terminus did not afford them a significant viral RNA accumulation advantage over WT when either site was complemented. Nonetheless, in vitro stability assays as well as decay assays in cell culture suggest that they are indeed more stable than WT HCV RNA. This is likely explained by either a reduced ability to recruit Ago:miR-122 to site 1 or the increased stability of SLI of the HCV genome, which could interfere with the initiation of positive-strand RNA synthesis . Interestingly, the C3U RAV did not have a significant advantage, nor was it significantly more stable than WT, likely because it only creates one additional U-A Watson–Crick pair at the base of SLI.SI Appendix, Fig. S8). These results are truly astounding because they suggest that the evolutionary pressure to maintain miR-122 interactions results in a fitness cost to the virus in terms of production of progeny genomic RNAs. Thus, while the C2GC3U, U4C, and G28A RAVs alter the structure of the positive-strand RNA in a manner that compensates for the loss of one or more of miR-122’s activities, the C37U RAV overcomes miR-122 inhibition by creating a stronger promoter for positive-strand RNA synthesis.Finally, the C37U RAV was particularly interesting because it converts a C-G Watson–Crick pair to a U-G wobble base pair in the seed sequence of site 2 A, which 34SI Appendix, Fig. S10). Thus, although both miR-122–binding sites are 100% conserved across HCV isolates, and the remainder of the 5′ terminus is very highly conserved , and while the U4C, G28A, and C37U mutations were all identified in a genotype 2 background, C2GC3U was isolated from genotype 3 and 4 patients, and the C3U and A4C mutations were isolated from genotype 1 patients and replicons, respectively , 38. IntFinally, what are the clinical implications for the use of miR-122 inhibitors in the future? Firstly, our results do not suggest that HCV can easily develop resistance because all of the RAVs were relatively unfit when compared with WT, and the RAVs were only detected in a few patients. Furthermore, RAVs were detected ≥8 wk after a single dose of the miR-122 inhibitor (RG-101), presumably when miR-122 levels were on the rise in chronic HCV-infected patients , 38. MorHuh-7.5 cells were kindly provided by C. M. Rice and miR-122 KO Huh-7.5 cells were kindly provided by M. Evans , 40. BotT isolate , with three cell culture–adapted mutations, was provided by Rodney Russell, Memorial University, St. John’s, NL, Canada  according to the manufacturer’s instructions.To make full-length viral RNAs, all templates were linearized and in vitro transcribed as previously described . To geneEcoRI and BamHI restriction sites. The geneblock was subcloned into pUC18 using EcoRI/BamHI, and the RAVs were generated using the QuikChange II XL Site-Directed Mutagenesis Kit (Agilent Technologies) according to the manufacturer’s instructions. After linearization with BamHI, in vitro transcription reactions were carried out as described above. Negative-strand transcripts were generated after self-cleavage by the HDVr concurrently with the transcription reaction  of the HCV genome was in vitro transcribed using the T7 RiboMAX Express Kit (Promega) followed by gel purification as previously described . For negreaction , and subSI Appendix, Table S1). All microRNAs used in this study were synthesized by IDT .For Xrn-1 exoribonuclease activity assays, nucleotides 1 to 42 of the WT and each of the RAV HCV RNAs were synthesized by IDT  were coelectroporated with 10 µg WT, GNN, and RAV full-length HCV RLuc RNAs, 2 µg capped Firefly luciferase mRNA, and in some cases, 60 pmol of duplexed miR-122p3U. Cells were electroporated using 4 mm cuvettes at 270 V, 950 µF, and infinite resistance, optimized for the BioRad GenePulser XCell (BioRad). Cells were resuspended in 4.5 mL media and 500 µL per timepoint were plated in 12-well or 6-well plates for luciferase assays at 2 h to 7 d post-electroporation.Electroporations were carried out as previously described , 45. BriRenilla and Firefly luciferase activity according to the manufacturer’s protocol. Firefly luciferase was used to ensure similar electroporation efficiency across samples.Cells were washed with cold sterile PBS (Wisent) and harvested in 100 µL 1× Passive Lysis buffer (Promega). The Dual Luciferase Assay Reporter Kit (Promega) was used for all samples analyzed for both In vitro SHAPE analysis was performed as previously described . For poshttps://rna.urmc.rochester.edu/index.html  to remove the DNA template, followed by purification with Sephadex G-25 spin columns (Cytiva) to remove unincorporated NTPs and the degraded template. The flowthrough was treated with quickCIP (New England Biolabs) to remove the 5′ triphosphate followed by heat inactivation of the phosphatase (2 min at 80 °C). Viral RNAs were labeled with T4 PNK (New England Biolabs) and γ[32P]ATP followed by gel purification on a denaturing (8 M urea) 15% acrylamide gel run in 0.5× Tris-borate-ethylenediaminetetraacetic acid (TBE) buffer. Bands were imaged, excised, and crushed in 300 µL crush and soak buffer  followed by elution at 4 °C overnight. The gel was removed with a spin-X column (Sigma Aldrich), and the RNA was precipitated using 3M NaOAc, pH 5.2 and ice-cold 100% ethanol at −20 °C for >20 min. RNAs were pelleted by centrifugation, washed with 150 µL ice-cold 75% ethanol, dried, resuspended in RNase-free H2O, and quantified by nanodrop spectrophotometer. Extinction coefficients were calculated with the IDT online tool available at https://www.idtdna.com.In vitro transcribed HCV (1-117) RNAs were ethanol precipitated, resuspended in RNase-free HSI Appendix, Table S1) were end labeled using T4 RNA ligase and [γ-32P] ATP and purified using an RNA Clean & Concentrator-5 Spin Column (Zymo Research). A total of 2 pmol end-labeled RNA was refolded by denaturing at 95 °C for 3 min followed by incubation at 37 °C for 10 min. For experiments with miRNAs, 4 pmol of miR-122 or miR-124 (control) guide RNAs were added, and reactions were incubated for 20 min at 37 °C following the refolding step. Xrn-1 assays were carried out using 1 unit Terminator 5′-Phosphate-Dependent Exonuclease in 1× Terminator Reaction Buffer A (Lucigen) and 1 unit Ribolock (Life Technologies). The reactions were incubated at 30 °C and were quenched with 1 µL 100 mM EDTA after incubation for 1 to 60 min. At the indicated time points, 10 µL Gel Loading dye (Life Technologies) was added to each sample, and 5 µL each sample was resolved on a 15% denaturing polyacrylamide gel , dried at 80 °C for 1.5 h , and visualized by a phosphoimager . Image analysis was performed using the ImageJ software. For live cell stability assays, viral RNA half-lives were calculated using GraphPad Prism by fitting the data to a one-phase exponential decay model.A total of 100 pmol monophosphorylated 1 to 42 nucleotide WT and RAV HCV RNAs .Total RNA was harvested and extracted from cells using the TriZol reagent (Thermo Fisher Scientific) according to the manufacturer’s protocol. Strand-specific RT-qPCR was performed as previously described  with a fSupplementary File