RNA-protein complexes underlie numerous cellular processes, including basic translation and gene regulation. The high-resolution structure determination of the RNA-protein complexes is essential for elucidating their functions. Therefore, computational methods capable of identifying the native-like RNA-protein structures are needed. To address this challenge, we thus develop DRPScore, a deep-learning-based approach for identifying native-like RNA-protein structures. DRPScore is tested on representative sets of RNA-protein complexes with various degrees of binding-induced conformation change ranging from fully rigid docking (bound-bound) to fully flexible docking (unbound-unbound). Out of the top 20 predictions, DRPScore selects native-like structures with a success rate of 91.67% on the testing set of bound RNA-protein complexes and 56.14% on the unbound complexes. DRPScore consistently outperforms existing methods with a roughly 10.53–15.79% improvement, even for the most difficult unbound cases. Furthermore, DRPScore significantly improves the accuracy of the native interface interaction predictions. DRPScore should be broadly useful for modeling and designing RNA-protein complexes. RNA-protein docking is a very challenging area. Here, the authors develop a deep-learning based method, DRPScore, to evaluate RNA-protein complexes. DRPScore is robust and consistently performs better than existing methods on representative testing sets. It is recognized that RNA-protein complexes are involved in many human diseases ranging from neurologic disorders8 to cancer9. Understanding the biological roles of the RNA-protein complex requires a three-dimensional structure12. Unfortunately, the flexible RNA molecules are challenging to be well-crystallized and determined by X-ray crystallography13. Besides, electron microscopy is expensive and time-consuming14. The available RNA-protein experimental structures are few due to the technical limitations15. Some computational methods can predict the RNA-protein complex by homologous fragment modeling, docking, or molecular dynamics simulation21. However, it is still challenging to predict the highly accurate RNA-protein complex due to the limited RNA-protein scoring functions22.RNA regulates various biological functions by interacting with proteins, such as DNA repair, RNA splicing, protein synthesis, and gene regulation24. These methods can be divided into propensity-based and atomic-level statistical scoring functions25. The propensity-based scoring functions statistically analyzed the interface propensity of pairwise nucleotide-residue27. Then, a potential statistical formula was constructed based on the inverse Boltzmann formula. For example, DARS-RNP24 is one coarse-grained propensity-based scoring function introduced by Tuszynska and Bujnicki. DARS-RNP was developed by a reduced representation of protein and RNA. In this reduced representation, amino residues are represented by one to three united atoms29. For RNA representation, two united atoms are used for the backbone and one/two for pyrimidines/purines. DARS-RNP constructed the scoring function through the four terms: the steric clash penalty and dependencies on distances, angles, and sites. Subsequently, Xiao et al.30 constructed a novel scoring function, 3dRPC-Score, based on statistical potential energy using the conformation of nucleotide-residue pairs as statistical variables. Unlike DARS-RNP, it used relative RMSD (Root Mean Square Deviation) to assess conformational differences between nucleotide-residue pairs to reflect energy, thus using only one variable. The propensity-based scoring function can consider the pairwise-based nucleotide-residue interactions, but it is challenging to consider conformational changes24.Several computational methods have been developed to evaluate RNA-protein structures23 is one atomic-level statistical scoring function developed by Huang and Zou. The core idea of ITScore-PR is to improve the interatomic pair potential through iterations by comparing the differences between the predicted and native atomic pairs in the training set. However, a significant challenge for RNA-protein prediction is the conformational change upon binding. ITScore-PR is very effective at bound docking but challenging to deal with unbound docking. Deep learning strategies have been proved helpful for RNA and protein predictions36. But such techniques are not in widespread use for RNA-protein complex prediction yet. Moreover, the computational structure evaluation methods for unbound complex structures have not yet been developed.The atomic-level statistical scoring functions are the distance-dependent interaction potentials obeying the Boltzmann distribution, which is more discriminative than the propensity-based scoring functions in the native-like structure evaluation. For example, ITScore-PRHere, we propose a deep-learning-based RNA-protein complex scoring function to consider structural flexibility explicitly. We use physics-based simulations to generate the training decoys for training the deep-learning-based scoring function. Then, DRPScore is extensively evaluated on the RNA-protein testing sets, including unbound RNA-protein challenges. The results demonstrate significant improvements and are consistently better than the existing methods in selecting native-like RNA-protein complexes. We expect the technique described here to be useful for RNA-protein complex prediction.23, DARS-RNP24, and 3dRPC30, respectively. Supplementary Figs. The DRPScore was evaluated on the three generated bound-bound RNA-protein testing sets. Figure 37. At least one partner of each complex was taken from other complexes or homologous modeling.We further focused on the relatively difficult unbound cases to evaluate whether DRPScore consistently performs better in challenging cases. The unbound RNA-protein testing set was constructed by Huang and ZouFigure Irmsd = 1.98 Å for DRPScore, compared with Irmsd = 14.74 Å for ITScore-PR, Irmsd = 8.28 Å for DARS-RNP, Irmsd = 8.44 Å for 3dRPC, respectively  as an example Fig. . The low30. DRPScore used an alternative approach to evaluate the RNA-protein complex by deep learning. Thus, DRPScore can accurately extract more features from the individual frames in the training sets. The separate local features can infer the interface interactions. For example, we calculated the interface hydrogen bonds of the Bacillus subtilis YxiN protein complexed with a 23 S ribosomal RNA fragment (PDB ID: 3MOJ) using HBPULS38. The results show that the lowest RMSD structure in the top 5 predictions evaluated by DRPScore contains 8 hydrogen bonds at the RNA-protein interaction interface, compared with 17 for ITScore-PR, 10 for DARS-RNP, and 16 for 3dRPC, respectively  and inter-nucleotide/residue  information. This is done by adding convolutional layers at the sequence dimension, each layer gradually modeling a longer range of interactions between the nucleotides/residues. Therefore, our model can provide and learn both local and global structural features, including secondary structure interactions. Supplementary Fig. RNA-protein structure evaluation is still a relatively unexplored research field. The previous efforts focused on the RNA-protein rigid-body docking without consideration of the structural flexibility. A critical bottleneck is sampling the dynamical conformations that RNAs or proteins form when interacting. In the case of fully flexible unbound-unbound docking, the interaction interface has changed dramatically. And the scoring functions never learned similar structures before. Supplementary Fig. 44. DRPScore still shows lower RMSDs and performs better than the other three scoring functions  for all testing sets at different redundancy levels. Traditional scoring functions based on statistical potential energy use native structures to learn features of RNA-protein interactions. DRPScore samples many native-like decoys to consider the RNA-protein dynamical features implicitly. As a result, the performance of the DRPScore in the unbound test is better than the other three scoring functions. We will further consider the structural flexibility, such as adding molecular dynamics simulation to consider the flexibility of RNA-protein complexes explicitly.To test the robustness of DRPScore, we also provided the performance of DRPScore compared with ITScore-PR, DARS-RNP, and 3dRPC on the entire and 0.8 sequences redundancy cutoff for testing set II  for bound docking and 43.86% (56.14%) for unbound docking if the top 5 (20) predictions were considered. The significant improvements indicate that DRPScore resolves critical flexibility in the structural evaluation of an RNA-protein complex. We expect the method to be helpful for the RNA-related prediction and drug development.36. The X, Y, and Z axes were determined by Eqs. Previous RNA-protein complex scoring functions used statistical scoring functions to identify the native-like structures Fig. . The staL = 128 is the maximum length of RNA sequences, H, W, D denote height, width and depth of a 3D cube for each nucleotide in the RNA sequence. In this study, we set H = W = D = 3236.DRPScore used a 4D convolution neural network approach to train and identify the native-like structures. The input of DRPScore is the RNA-protein complex nucleotides/residues at the interaction interface with a 6 Å distance. The output of the DRPScore is the potential scores to evaluate the native-like RNA-protein structures. We compared the 4D convolution neural network with a 3D convolution neural network. In the pre-processing procedures, each RNA is represented by a tensor of shape C, 32x32x32 is the spatial dimension N layers of Conv3D modules, at the last layer, each nucleotide of the RNA, 3D convolution neural network approach. The 3D approach processes each nucleotide independently to generate local representations and then applies an average pooling at the end to generate a global representation for the sequence. Concretely, for a sequence comprised of 128 nucleotides, the shape of the input is 36, it importantly ignores the sequential nature of RNAs. By naively averaging the independent representations of individual nucleotides to generate a global representation, crucial information about the interactions of nucleotides may be lost. Our proposed 4D approach addresses this shortcoming by incorporating an additional convolution operation at the sequential dimension. In other words, our method captures not only the spatial information, but also the sequential information . Our Conv4D uses a non-overlapping moving window of size 3 nucleotides/residues  to capture interactions between the nucleotides/residues at each layer of the convolution. By using multi-layer CNNs, we can capture the interactions between more distant nucleotides/residues since the input to each layer is the output of the convolution of the last layer. As can be seen in the bottom left panel of Fig. 4D convolution neural network approach. Though 3D CNN for modeling RNA has shown success in some tasksSpecifically, our network has six layers, with the last being a fully connected layer for classification. Each of the first six layers has a Conv4d module, a BatchNorm module , and a MaxPooling module. The number of channels in these Conv4d modules are . The strides in these Conv4d modules are , i.e., the effective length of the RNA-protein complex features is reduced by half in each of the first three blocks (and kept the same in the last 2 blocks). All the pooling modules use a kernel size of two and a stride of two, i.e., each pooling module reduces the effective spatial dimensions  by half. The last pooling is a global average pooling that reduces the spatial dimensions to one. Thus, the final representation of an RNA-protein complex is a vector of size 8,192. For example, for an RNA-protein complex initially represented by a tensor Finally, at the last layer, we have a tensor The 4DCNN in this work captures the sequence, secondary structural characteristics, and tertiary structural characteristics of protein and RNA Fig. . The seq46. Second, we removed the short RNAs with lengths of <10 nucleotides. Third, we considered the cases with no more than six chains of protein or RNA as described in ITScore-PR23. Fourth, we removed the RNA redundancy by 0.95 sequence similarity cutoff as RASP47 and DRNA11 using CD-HIT44. Finally, we obtained a non-redundant RNA-protein dataset with 346 structures. We randomly selected 277 RNA-protein complex structures for training from the 346 non-redundant RNA-protein structures  with the search options “only RNA and Protein” and “Resolution cutoff 3.5 Å (X-ray)”48 to generate RNA-protein structural decoys. 3dRPC first generates the RNA-protein complex by the RPDOCK algorithm and then evaluates the structures by RPRANK. For each RNA-protein complex in the training set, 10,000 decoys were generated using the command of ‘3dRPC -mode 9 -system 8 -par RPDOCK.par’. Then, we calculated the RMSDs of the complex structures using the following command ‘3dRPC –mode 2 –system 0 –par RMSD.par’. Finally, we selected the top 500 structures from 10,000 decoys by RMSD ranking. Thus, there are 1 native structure and 500 docking structures for each RNA-protein complex.We used 3dRPC37. Thus, the definition of a “bound-bound” structure is that two binding partners are taken from the same complex structure. The “unbound-bound” structure is that one of the two binding partners (RNA or protein) is either in apo form or taken from another complex. The “unbound-unbound” refers to structures that both binding partners (RNA and protein) that are either in apo form or taken from a different complex.We tested DRPScore with two independent RNA-protein docking testing sets. The unbound structure is defined as a structure in free form or being a binding partner in a different complex48.Testing set I is the non-redundant bound-bound RNA-protein docking benchmark. We randomly selected 36 RNA-protein complexes from the remaining non-redundant RNA-protein complexes (Supplementary Data 37. We removed the redundancy between training and testing sets II by 0.95 sequence similarity cutoff using CD-HIT. Thus, this benchmark remains 57 RNA-protein unbound complex structures, which consist of 41 unbound-unbound complexes and 16 unbound-bound complexes (Supplementary Data 48 using the command of ‘3dRPC -mode 9 -system 8 -par RPDOCK.par’. The relative RMSDs between decoys and native complex structures were calculated using the command of ‘3dRPC –mode 2 –system 0 –par RMSD.par’. The maximum, minimum, and average RMSD were provided in Supplementary Data Testing set II is the non-redundant unbound RNA-protein docking benchmark provided by Huang and ZouFor RNA-protein complexes ranking, ITScore-PR uses the command of ‘itscorepr protein.pdb RNA.pdb -nomin’, DARS-RNP uses the command of ‘python DARS_potential_v3.py -s complex.pdb’, and 3dRPC uses the command of ‘3dRPC -mode 8 -system 9 -par scoring.par’, respectively.50. The 48. We define the RNA-protein complex as successfully predicted if the The quality of the RNA-protein complex prediction is evaluated by the CAPRI criterion38 to identify intermolecular hydrogen bonds between the RNA and protein molecules. A maximum donor-acceptor distance of 3.35 Å and a maximum hydrogen-acceptor distance of 2.7 Å were used to define a hydrogen bond. Protein often folds into the secondary structure of α-helix and β-sheet while RNA contains the single-strand, stem, pseudoknot, internal loop, hairpin loop, and junctions. The PSIPRED40 and forna41 were used to identify the protein and RNA secondary structures.We also analyzed the RNA-protein interface’s hydrogen bond and secondary structure interactions. We used HBPLUSFurther information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1-13Reporting Summary


Integrating different types of vaccines into a singular immunization regimen is an effective and accessible approach to strengthen and broaden the immunogenicity of existing coronavirus disease 2019 (COVID‐19) vaccine candidates. To optimize the immunization strategy of the novel mRNA‐based vaccine and recombinant protein subunit vaccine that attracted much attention in COVID‐19 vaccine development, we evaluated the immunogenicity of different combined regimens with the mRNA vaccine (RNA‐RBD) and protein subunit vaccine (PS‐RBD) in mice. Compared with homologous immunization of RNA‐RBD or PS‐RBD, heterologous prime‐boost strategies for mRNA and protein subunit vaccines failed to simultaneously enhance neutralizing antibody (NAb) and Th1 cellular response in this study, showing modestly higher serum neutralizing activity and antibody‐dependent cell‐mediated cytotoxicity for “PS‐RBD prime, RNA‐RBD boost” and robust Th1 type cellular response for “RNA‐RBD prime, PS‐RBD boost”. Interestingly, immunizing the mice with the mixed formulation of the two aforementioned vaccines in various proportions further significantly enhanced the NAb responses against ancestral, Delta, and Omicron strains and manifested increased Th1‐type responses, suggesting that a mixed formulation of mRNA and protein vaccines might be a more prospective vaccination strategy. This study provides basic research data on the combined vaccination strategies of mRNA and protein‐based COVID‐19 vaccines. The immunogenicity of vaccines can be enhanced by the optimization of immunization strategies. In this paper, we investigated the immunogenicity of different combined regimens with the mRNA vaccine RNA‐RBD and protein subunit vaccine PS‐RBD. The result showed that compared with homologous immunization, heterologous prime‐boost strategies for mRNA and protein subunit vaccines failed to simultaneously enhance NAb and Th1 cellular response, but immunizing the mice with the mixed formulation of the two aforementioned vaccines in various proportions further significantly enhanced the NAb responses and Th1 cellular response. Vaccination has always been considered the most effective approach to eliminate the coronavirus disease 2019 (COVID‐19) epidemic, and nearly all vaccine technologies have been used for the development of an “effective and safe” COVID‐19 vaccine candidate.A heterologous prime‐boost immunization strategy,Among the COVID‐19 vaccines under clinical trials, mRNA and recombinant protein vaccines are the two commonly studied platforms and account for 23% and 32% according to World Health Organization collected data.22.1p = 0.0169), indicating a stronger boosting potential of RNA‐RBD than PS‐RBD. For the IgG in heterologous prime‐boost groups, the IgG titers of the PS‐RBD > RNA‐RBD group showed to be significantly higher than that of the RNA‐RBD > PS‐RBD group, with GMTs of 2,152,747  and 360,276 , respectively (p = 0.0005). However, there was no significant difference between PS‐RBD > RNA‐RBD and homologous 2 × RNA‐RBD or 2 × PS‐RBD  as the specific antigen, were tested for homologous and heterologous prime‐boost immunization. To be specific, mice were divided into six groups and immunized with different vaccines at 2 weeks interval as shown in Figure D Figure . For IgAD Figure .p = 0.0021). The groups PS‐RBD and RNA‐RBD showed no significant difference in IgG2a, IgG2b, and IgG3. Similar to total IgG, after the homologous booster dose, the IgG1 level of group 2 × RNA‐RBD increased and was similar to that induced by 2 × PS‐RBD, whereas, the IgG2a level of 2 × RNA‐RBD exceeded that induced by 2 × PS‐RBD. The titers of the four IgG subtypes induced by PS‐RBD > RNA‐RBD were higher than those of RNA‐RBD > PS‐RBD in varying degrees but were lower than those in the 2 × RNA‐RBD groups  and significantly higher than that induced by one dose of RNA‐RBD  reached >86%, while IgG2a only accounted for 2.45%, 1.81%, and 7.89%, respectively. In comparison, groups with RNA‐RBD as the primary vaccine  had relatively lower ratios of IgG1 , while the ratios of IgG2a increased in varying degrees  Figure . The ratTaken together, RNA‐RBD induced a higher level of binding antibody titers following homologous booster compared to PS‐RBD. The heterologous prime‐boost immunization regimen didn't increase the IgG level of RNA‐RBD and PS‐RBD, but it regulated the constituent ratio of the IgG subtypes. RNA‐RBD priming induced higher ratios of IgG2a/IgG1.2.2To evaluate the serum‐neutralizing activity of the vaccinated mice, we measured the NAb titers against pseudovirusp = 0.0030; Delta: p = 0.1128), 2 × PS‐RBD , and RNA‐RBD > PS‐RBD , with GMT of 6001  against ancestral strain and 1306 (95% CI: 708–2455) against Delta strain. Thus, compared to the homologous regimens, the heterologous prime‐boost regimen PS‐RBD > RNA‐RBD improved the serum‐neutralizing activity to a certain extent   activity of the immunized mouse serum against ancestral and Delta SARS‐CoV‐2 strains. ADCC activity was detected by the luciferase reporter gene assay, and its main judgment index was the fold induction (FI) of the fluorescence. Our results showed that the ADCC activity against the ancestral strain in 2 × PS‐RBD was comparable to 2 × RNA‐RBD, with a FI (1:12 dilution) of 4.51 and 4.19, respectively. For the Delta variant, the FI in group 2 × PS‐RBD was higher than that in 2 × RNA‐RBD  Figure . Notably) Figure .p = 0.06). Meanwhile, all three groups containing RNA‐RBD, such as 2 × RNA‐RBD, PS‐RBD > RNA‐RBD, and RNA‐RBD > PS‐RBD, successfully induced a T cell‐mediated immune response, with 51.10, 15.58, and 33.97 spot forming units (SFUs)/2 × 105 cells, respectively ‐γ‐secreting lymphocytes by enzyme‐linked immunospot assay (ELISpot assay). Our results showed that one dose of RNA‐RBD induced higher T‐cell immune responses than PS‐RBD  and PS‐RBD > RNA‐RBD (4.36 pg/ml) (p < 0.0005) group  assay was conducted. Supernatants of splenic lymphocytes stimulated by spike peptide pool were collected for interleukin (IL)‐2 and IL‐4 measurement. IL‐2 is mainly secreted by Th1 cells, while IL‐4 is secreted by Th2 cells. IL‐2 levels were highly improved in all vaccinated groups (from 1.75 to 14.13 pg/ml) when compared to IL‐4 (0.00–0.22 pg/ml). Remarkably, 2 × RNA‐RBD and RNA‐RBD > PS‐RBD induced higher levels of IL‐2 (14.13 and 12.01 pg/ml) than 2 × PS‐RBD  , PS‐RBD+RNA‐RBD (1:3), and PS‐RBD+RNA‐RBD (1:1) Figure . The imm) Figure . A simil) Figure .p < 0.001) , PS‐RBD+RNA‐RBD (1:3), PS‐RBD+RNA‐RBD (1:1) were 3547 (95% CI: 2884–4365), 4,218 (95% CI 3548–5012), 3677 (95% CI: 2818–4898) respectively, and were significantly higher than that of PS‐RBD > RNA‐RBD   Figure . Further) Figure .5 cells, PS‐RBD+RNA‐RBD (1:3) induced 1.50 SFUs/2 × 105 cells, PS‐RBD+RNA‐RBD (1:1) induced 4.13 SFUs/2 × 105 cells, and PS‐RBD > RNA‐RBD induced 2.00 SFUs/2 × 105 cells  induced 0.38 SFUs/2 × 10s Figure . T cell In summary, the codelivery of PS‐RBD and RNA‐RBD, especially PS‐RBD+RNA‐RBD (1:1), brought a higher level of antibody titers than PS‐RBD > RNA‐RBD and strengthened the cellular immune responses.3Different vaccine platforms induce varying immune responses. The mRNA vaccine is delivered into host cells via a lipid nanoparticle system and subsequently translated into targeting immunogens,PS‐RBD and RNA‐RBD induced a different modality of immune status in this study. The total IgG titer induced by one dose of PS‐RBD was 1.92 times than that induced by one dose of RNA‐RBD, but after two doses, the total binding IgG titer of the RNA‐RBD group was significantly higher than that of the PS‐RBD group. RNA‐RBD showed a stronger immune‐boosting potential in a prime‐boost vaccination procedure. In addition, heterologous immunization with PS‐RBD followed by RNA‐RBD induced a higher level of IgG response than immunization with RNA‐RBD followed by PS‐RBD Figure . Thus, PStudies have confirmed the positive correlation between binding antibodies and NAbs,Due to the rapid mutation of the spike protein during the transmission of the SARS‐CoV‐2, the neutralizing activity against the prevalent virus strains induced by existing COVID‐19 vaccines of the first generation declined to varying extents.Taken together, we investigated the proper combined immunization strategy for the novel mRNA and recombinant protein subunit COVID‐19 vaccine candidates in a mouse model. We described that the heterologous prime‐boost regimen “PS‐RBD prime and RNA‐RBD boost” could enhance the neutralizing and ADCC activity induced by PS‐RBD and RNA‐RBD to a certain amount but with a limited Th1 cellular response. Interestingly, the mixed formulation of PS‐RBD and RNA‐RBD achieved a stronger and broader immune response by integrating the advantages of the two divergent forms of the vaccine. To be specific, the mixed formulation markedly improved the cross‐NAb responses against ancestral, Delta, and Omicron strains and elicited a more robust Th1‐type response than heterologous priming with PS‐RBD and boosting with RNA‐RBD. This study pointed out the importance of the vaccination order and formulation of mRNA and protein vaccines on immune response in a mouse model, but it should be noted that murine data do not always translate to humans, further investigations in non‐human primates and human beings are required. Nevertheless, the stability of the mixed formulation vaccine and the duration of the vaccine‐induced immune responses need to be further investigated.44.1Specific pathogen‐free (SPF) grade BALB/c female mice  were provided by the National Institute for Food and Drug Control. RNA‐RBD used in this study was an mRNA vaccine expressing the receptor binding domain of ancestral SARS‐CoV‐2. PS‐RBD was a protein subunit vaccine with a dimeric RBD as the immunogen and Alum as the adjuvant. In Figures 4.2Spike‐specific binding antibodies were assayed by enzyme‐linked immunosorbent assay: 96‐well EIA/RIA plates were coated with 1 μg of spike protein at 4°C overnight. The plates were washed three times with PBST  and then blocked using PBS with 10% fetal bovine serum and 5% sucrose for 2 h at 37°C. After incubation, plates were washed three times again with PBST and added to 10‐fold serially‐diluted test samples, and incubated for 37°C at 1 h. After incubation, plates were washed five times with PBST and added to a 1:10000 dilution of HRP‐labeled goat anti‐mouse IgG, IgG1, IgG2a, IgG2b, IgG3 or IgA secondary antibody . After 1 h of incubation at 37°C, the absorbance of the plates was detected with the substrate  at wavelengths of 450 and 630 nm. The endpoint of serum antibody titers was determined as the reciprocal of the highest dilution that was 2.1‐fold higher than the optical absorbance value of the negative control.4.3The neutralization capacity of the pseudo‐virus  was examined following a previous protocol.4 50% tissue culture infective dose, 50 μl) were co‐cultured with an equal volume of 3‐fold gradient diluted serum for 1 h at 37°C, and then 2 × 104/well Huh‐7 cells were added in 96‐well plates at 100 μl/well and incubated at 37°C with 5% CO2 for 20–28 h. After incubation, 100 μl of chemiluminescence detection reagent (PerkinElmer cat#6‐66769) was added to each well, and the substrate was mixed and transferred to the luminescence plate (XIAMEN YJM LABWARE CO. LTD. Cat#KH 25). The Reed‐Muench method was used to calculate the virus neutralization titer.Pseudovirus (104.4The micro cytopathogenic effect assay was used for the measurement of serum NAbs against live SARS‐CoV‐2.4.55 cells/well) were collected and co‐cultured with a peptide pool spanning the SARS‐CoV‐2 spike protein at 37°C in 5% CO2 for 20 h. A peptide pool was generated as follows: a panel of consecutive 15‐mer peptides overlapped with 9 amino acids were synthesized spanning the whole spike protein and pooled by the concentration of 5 μg/ml for each peptide. The supernatant was removed and IFN‐γ positive cells were detected using the antibody from the kit. Counts were performed with an ELISpot reader  and the final result was obtained after subtracting the background value from the raw value.Mouse IFN‐γ ELISpot kit  was used for IFN‐γ enzyme‐linked immunosorbent spot (ELISpot) assay. Mouse splenocytes  was added for color development, and relative light units (RLUs) were detected using a Micro2000. The FI was calculated by FI = RLU (induction − background)/RLU (negative serum control − background).Jurkat cells stably expressing murine FcRγIII and fluorophore reporter genes were used as effector cells , and 293T cells transfected with pcDNA3.1‐spike and transiently expressing SARS‐CoV‐2 spike were prepared as target cells before ADCC measurement. For experiments, serum samples were diluted with a 2.5‐fold gradient and incubated with 4.25 × 104.76 per well) were stimulated by the peptide pools spanning the SARS‐CoV‐2 spike protein at a concentration of 5 μg/ml for each peptide. Supernatants were collected after 20 h of culturing. Cytokines secreted by splenocytes were detected by a V‐PLEX Proinflammatory Panel 1 (mouse) Kit , and the final result was calculated by the experimental group minus background.Mouse splenocytes .After antibody titers were log‐transformed, comparisons between two groups of data were analyzed by student ZL, JW, MX and QH conceived this study. QH designed the study. QH, JZ and XY performed the majority of the experiments and analyzed the data. JZ wrote the original draft. QH reviewed and revised the original draft. JL, YB, CA, BC, QM and FG helped analyze the data and conduct some experiments. All authors reviewed and approved the manuscript.The authors declare that they have no conflict of interest.5All animal research protocols were approved by the Institutional Animal Care and Use Committee at National Institutes for Food and Drug Control, China (No. 2022‐B016), and these protocols were conducted in accordance with regulations regarding the management of laboratory animals  and “guidance notes on the treatment of experimental animals” (Chinese version (2006) no. 398). All institutional guidelines for animal care and use were strictly followed throughout the study.


The alphabet of building blocks for RNA molecules is much larger than the standard four nucleotides. The diversity is achieved by the post-transcriptional biochemical modification of these nucleotides into distinct chemical entities that are structurally and functionally different from their unmodified counterparts. Some of these modifications are constituent and critical for RNA functions, while others serve as dynamic markings to regulate the fate of specific RNA molecules. Together, these modifications form the epitranscriptome, an essential layer of cellular biochemistry. As of the time of writing this review, more than 300 distinct RNA modifications from all three life domains have been identified. However, only a few of the most well-established modifications are included in most reviews on this topic. To provide a complete overview of the current state of research on the epitranscriptome, we analyzed the extent of the available information for all known RNA modifications. We selected 25 modifications to describe in detail. Summarizing our findings, we describe the current status of research on most RNA modifications and identify further developments in this field. The discovery of the first modification of RNA occurred in 1957, when the “fifth nucleotide”, pseudouridine, was identified . In the It is now widely appreciated that RNA modifications are significantly more numerous and common in cellular RNA than thought initially—The current release of the main database for cataloging RNA modifications, MODOMICS, lists slightly more than 300 such modifications . These mMany RNA modifications are indispensable for the function of RNA species. They are required for proper folding , transcrThe significance of the research on RNA modifications is highlighted by their tight association with diseases of various natures . ParticuThe presented facts signify the importance of research on RNA modifications, but most of the attention in recent years has been paid to only a handful of well-established modifications. At the same time, our knowledge of other modifications is rapidly growing. Annotating modifications with data on their distribution and function is critical to obtain the global picture of the role and prevalence of RNA modifications. Accordingly, in this review, we analyzed the popularity of known modifications as evidenced by the number of relevant articles and provided a comprehensive description of the frequency of occurrence, distribution, biological function, connection to diseases, and detection methods for 25 modifications. Together, this information paints the role of RNA modifications as widely occurring in all RNA classes from all domains of life and as key players in cellular processes.To understand the current level of research on RNA modifications in total and per individual RNA modifications, we used the full name of each modification provided in the MODOMICS database  togetherThe research on RNA modifications is an actively developing area, as evidenced by a continuous increase in the number of relevant articles in recent years . Our seaTo provide a justified overview of the current state of the characterization of individual RNA modifications, we selected the top 25 RNA modifications with the most relevant articles in our list for further characterization. We collected all the available information regarding their frequency of occurrence, distribution in RNA, established biological function, association with various diseases, and specific methods designed to detect them in RNA . Our goaIn terms of the occurrence of RNA modifications across domains of life, most modifications are present in all three domains with a few exceptions, for instance, archaeosine, which is detected only in archaea . This isThe majority of modifications from In terms of importance, the selected modifications can be firstly classified as essential and nonessential. Essential modifications are shown to be necessary for important cellular processes, for example, translation, as is the case with dihydrouridine or 1-methylguanosine, for instance . Generally, a lack of these essential modifications significantly impairs cellular growth or causes death. We estimate that 10 modifications from our list (40%) represent essential modifications. It is possible that this percent is higher, since some of the nonessential modifications could have undiscovered essential roles. An additional indicator of the importance of a modification is its connection to human disease. Eleven of the modifications (44%) from our list are associated with various diseases, and nine of these (36%) are associated specifically with cancer. These results highlight the necessity of studying the epitranscriptome in detail, since RNA modifications are frequently essential for proper growth or linked to ailments of various natures.As for the methods for the detection of RNA modifications, they are impressively numerous and range from polymerase chain reaction (PCR) and mass spectrometry to RNA sequencing and even fluorescence in situ hybridization (FISH). In the table, the most popular methods are given for each modification, but this is an extensive and complex topic that goes beyond the scope of this review. For excellent coverage of the range and specifics, as well as the technical challenges of the methods for the detection of RNA modifications, readers can refer to the review by Huang et al. .Below, we describe the properties of several groups of biochemically and functionally related modifications, emphasizing the recent progress in establishing unique features that affect their distribution in RNA and their molecular function, as well as the specifics of the detection methods.N6-methyladenosine (m6A) is the most-studied modification in all life domains . m6A is A. thaliana, but the m6A sites are also detected near the start codons [In eukaryotes, the m6A modification is located in all mRNA regions: 5′UTR, CDS, and 3′UTR. The modification residues were enriched in 5′-UTRs around the stop codons and in 3′ UTRs adjacent to the stop codons. Approximately 35% of m6A residues are located within the CDS . This mot codons . The m6At codons .N6-methyladenosine is installed by “writer” proteins, including METTL3, METTL14, METTL16, WTAP, KIAA1429, ZCCHC4, RBM15, ZC3H13, and ZCCHC4 ,132. In The landscape of m6A in RNA was first uncovered by NGS. Nowadays, m6A can be detected by multiple methods, including m6A-seq , MeRIP-sN6-methyladenosine is involved in multiple human diseases, including obesity, heart failure, and especially cancers ,26. m6A Adenosine-to-inosine editing is a major pathway of RNA modifications that is present in all domains of life . InosineArg in bacteria, while eukaryotes have seven or eight tRNAs modified in this way [In tRNA, the adenosine at position 34 (the first base of the anticodon) is frequently modified to inosine to support the mechanism of wobble codon recognition, since inosine can hybridize with adenosine, guanosine, and uridine . This mothis way . This mothis way . Additiothis way  and 57  as guanosine . The mRN of mRNA . Notably of mRNA .Standard NGS protocols can reveal the locations of A-to-I editing, since inosine is interpreted as guanosine, and A-G genome mismatches arise in the final result . RecentlThe A-to-I editing mechanism has been repurposed in the last three years to introduce precise changes to specific mRNAs by a complex of ADAR and RNA-targeting catalytically dead Cas nucleases . This coPseudouridine (Ψ) was the first modified ribonucleoside discovered in 1951 [E. coli, this modification is catalyzed by the truB gene [ in 1951  and is t in 1951 . It is d in 1951 . In tRNA in 1951 . In E. cruB gene . In archruB gene , and in ruB gene . PseudouruB gene . In rRNAruB gene .Uridine is transformed into Ψ by a class of enzymes known as pseudouridine synthases. For each species, these enzymes are different. For instance, in humans, Ψ is installed by a methyltransferase complex, including RPUSD1/2/3/4, PUS10, PUS7L, and TRUB2, while in yeast, this complex includes PUS2/5/6/8/9 enzymes. Both species have similar pseudouridine synthases, for example, PUS1/4 and DKC1 . PseudouB. subtilis [The next uridine modification is 5-methyluridine (m5U), having a single methyl substituent at the 5-position on the uracil ring. It is frequently found in bacterial and eukaryotic tRNAs at position 54 in the T loop . m5U sitsubtilis ,165. Thesubtilis . Experimsubtilis , miCLIP-subtilis  can be uE. coli and S. cerevisiae, 5-methyluridine formation is catalyzed by various types of enzymes such as TrmA [In  as TrmA  and Trm2 as TrmA . In mamm as TrmA . This mo as TrmA  and syst as TrmA .S. cerevisiae [In short, 2′-O-methyl ribose is Nm, where N stands for any nucleotide. Such a modification can occur on any base by adding a methyl group (-CH3) to the 2′-hydroxyl (-OH) of the ribose moiety. Nm modifications  are found in multiple types of RNA, such as snRNA, snoRNA, lncRNA, rRNA, tRNA, and mRNA . 2′-O-merevisiae . The modrevisiae . Such a revisiae .Streptomyces actuosus by Nsr [E. coli [The Nm modification is produced by fibrillarin in eukaryotes and their homolog Nop1 in yeast . Differes by Nsr . Cm is c[E. coli .The modification can be detected by various methods, from liquid chromatography coupled with LC/MS and two-dimensional thin-layer chromatography (2D-TLC) to high-throughput sequencing  76,77,7,777,79.2′-O-methylation is involved in multiple human diseases, such as Prader–Willi syndrome, asthma, Alzheimer’s disease , and breAs another modification that involves the methylation of adenosine, in this case at N1, 1-methyladenosine is known to play an essential structural role in tRNA and rRNA in all organisms . The preMet [In tRNA, m1A has been identified at a total of six positions, one of which, m1A58, is conserved across all three domains of life . Other pMet . In otheMet . InteresMet . AnotherMet .C. elegans [N1-methyladenosine is structurally vital in ribosomal RNA . In the  elegans . The los elegans , as well elegans .As for mRNA, two initial studies utilizing an enrichment strategy with an m1A-specific antibody and subsequent RNA-sequencing identified large numbers (1000 for m(1)A-ID-seq  and 7000Until recently, dihydrouridine (D) was found only in transfer RNA, located in the appropriately named D loop, and very seldom in prokaryotic rRNA . HoweverIn tRNA, dihydrouridine is found at positions 16, 17, 20, 20a, 20b, and 47, although the specific positions may vary from species to species . PositioS. pombe, which were spread across 125 protein-coding genes and one lncRNA. It is reported that the modification of tubulin mRNA is essential for proper meiotic chromosome segregation and gamete viability. D-seq, another method developed to uncover the distribution of dihydrouridine in the transcriptome, was used to identify 48 novel D sites in 23 different snoRNAs and 130 sites in 112 mRNAs of S. cerevisiae [Two recently developed NGS-based methods have revealed the existence of dihydrouridine in the mRNA, lncRNA, and snoRNA of yeast and human cells. The first method, Rho-seq, utilizes rhodamine labeling to detect dihydrouridine transcriptome-wide . Besidesrevisiae . TogetheLeu) [Ser) [Ser and helps to increase the high fidelity of protein translation [N4-acetylcytidine (ac4C) is one of the most-studied modifications of tRNA and rRNA and is found in all domains of life . A recenLeu)  and the u) [Ser) . This monslation . In humanslation . These pnslation . In mRNAnslation . N4-acetnslation . Remarkanslation , but prenslation .Leu and tRNASer in S. cerevisiae [N-acetyltransferase 10 (NAT10), a member of the GCN5-related NAT (GNAT) family of histone acetyltransferases, catalyzes the formation of ac4C on rRNA, tRNA, and mRNA . Additiorevisiae .Nowadays, ac4C can be detected by various methods, including LC-MS/MS  and acRIE. coli contains it at position U1498 [3-Methyluridine  can be detected in the 23S rRNA of archaea; 16S and 23S rRNA of eubacteria ; and 18Son U1498 . The yeaon U1498 . The Bmton U1498 . This moon U1498 . The m3Uon U1498 . Additioon U1498 ,205.Methylation-sensitive RNA fluorescence can detect m3U in in situ hybridization (MR-FISH), which is sensitive to single methylations, and can characterize the composition of heterogeneous mixtures of cells that differ only in their RNA methylation . In addiThe methylation of the cytosine C5 atom has been identified in all three domains of life and many RNA types—aside from tRNA, rRNA, and mRNA, m5C is present in many classes of other noncoding RNAs .E. coli rRNA only contains three m5C residues, while T. thermophilus has five or six [In rRNA, m5C is found in small numbers that differ from one species to another . For inse or six . In humae or six . These se or six .Met, C34 is modified to 5-methylcytidine and subsequently to 5-formylcytidine, which is required for the proper decoding of the AUA codon in mitochondria [m5C has also been identified in tRNA, where it is usually located at several positions in the variable loop, anticodon loop, and T stem . Two enzchondria . The inschondria ,212. Remchondria .While the existence of 5-methylcytidine in mRNA has been known for the last 60 years, the distribution frequency and the exact locations of this modification have remained largely unknown until the last decade, when a variety of NGS-based methods were developed for this task . m5C hasThe m5C modification has also been found in other classes of RNA, such as lncRNA, where it can increase its stability ; vault R7-methylguanosine is a common and well-known RNA modification found in all studied organisms . Its recInternal m7G has been detected in rRNA, tRNA, mRNA, and miRNA ,43. RiboSeveral high-throughput methods aimed at detecting m7G modifications with a single-base resolution have been recently proposed. The first method, m7G-meRIP-seq, resulted in the detection of 3823 m7G peaks in mRNA shared between three human cell lines . ShortlyThe question of whether m7G exists in other classes of RNA remains controversial. Borohydride reduction sequencing (BoRed-seq) has been used to show that miRNA let-7 is methylated by METTL1 and the installed 7-methylguanosine is vital for the biogenesis of this miRNA. However, these findings have been disputed , and altThe largest fraction of RNA modifications in our selection were primarily implicated in structural roles in tRNA, where they maintained the correct folding and stability of the transfer RNA molecules and the desired parameters of the codon–anticodon interactions .Lys3, while another uridine modification, 5-methoxycarbonylmethyl-2-thiouridine (mcm5s2U), is essential for the proper decoding of NNR codons [Modifications of tRNA in the anticodon region, primarily in position 34, are widespread because they are responsible for the mechanism of wobble base pairing, which allows one tRNA to recognize multiple mRNA codons, thus reducing the overall number of tRNAs required for translation . For exaR codons .Phe ubiquitously in archaea and eukaryotes, and they ensure effective codon recognition by stabilizing the codon–anticodon interactions during decoding on the ribosome [Arg and tRNASer2 [Bases adjacent to the anticodon are also frequently modified to alter the specific parameters of translation. Two modified adenosines, N6-isopentenyladenosine (i6A) and N6-threonylcarbamoyladenosine (t6A) found in position 37 directly adjacent to the anticodon, positively affected the translation efficiency and prevented frameshifting ,112. Anoribosome . InterestRNASer2 . This detRNASer2 . The 32ntRNASer2 .Another set of modifications affects other regions of tRNA and usually performs structural functions. N2,N2-dimethylguanosine, which is almost always present in positions 10 and 26 of tRNA in archaea and eukaryotes, plays an essential role in the correct folding of tRNA . Only onS. cerevisiae mRNA in small quantities using LC/MS-MS and were also shown to affect the translation efficiency in a position-specific manner [Several modifications thought to be exclusive to tRNA were discovered in other RNA types, most notably mRNA. For instance, 1-methylguanosine and N2,N2-dimethylguanosine were identified in c manner . HoweverIn recent years, there has been an increased interest in the epitranscriptome. Among the existing RNA modifications, currently numbering ~300 variants, special attention has been focused on a few of them, such as m6A, I, Ψ, m7G, and Nm. This is caused by their high frequency of occurrence in all domains and sequences, a wide range of functions performed, and several options for relatively simple detection methods.Most RNA modifications are found in tRNA, and LC-MS/MS-based approaches are used for their detection. Sequencing-based methods are utilized mostly to detect modifications in other types of RNA molecules, in particular mRNA. Nowadays, protocols have been developed for processing data obtained using both nanopore-based long-read sequencing (direct RNA-seq) and more traditional short-read technology (RNA-seq).The gap in the popularity of research on epitranscriptomic events is significant. Our analysis of epitranscriptome studies revealed that other modifications are mentioned in less than 20% of articles compared with the ubiquitous top five modifications . This biAlthough their function is still unknown for most RNA modifications, it has been shown that in some cases, they play a key role in the development of a number of diseases and can be used as therapeutic targets. In particular, the study of the epitranscriptome initiated the development of mRNA vaccines. Although the first attempts did not reach the clinic, eventually, COVID-19 mRNA vaccines showed their high efficiency in preventing severe disease ,228,229.All of the above would have been impossible without the development of NGS technology, enabling it to detect modifications with a resolution of up to one nucleotide. However, not all RNA modifications can be detected by NGS-based methods, e.g., m3U and Q. The development of new experimental and bioinformatics methods will expand the list of modifications—“favorites”, paving the road for novel epitranscriptomic biomarkers, drugs, and vaccines.In conclusion, we believe that the present review will improve the recognition of the lesser-known modifications, many of which are currently being found in additional classes of RNA and have already been shown to perform important biological functions. Considering the increasing evidence of the dynamic and interconnected behavior and regulation of these modifications, it is essential to investigate the full functional capacity of all, not just most, prevalent RNA modifications to comprehensively understand the epitranscriptome. Furthermore, the integration of the known information regarding specific properties of certain RNA modifications can lead to improved and more universal methods for their detection.


Adenosine-to-inosine (A-to-I) editing is an important RNA posttranscriptional process related to a multitude of cellular and molecular activities. However, systematic characterizations of whether and how the events of RNA editing are associated with the binding preferences of RNA sequences to RNA-binding proteins (RBPs) are still lacking.With the RNA-seq and RBP eCLIP-seq datasets from the ENCODE project, we quantitatively survey the binding preferences of 150 RBPs to RNA editing events, followed by experimental validations. Such analyses of the RBP-associated RNA editing at nucleotide resolution and genome-wide scale shed light on the involvement of RBPs specifically in RNA editing-related processes, such as RNA splicing, RNA secondary structures, RNA decay, and other posttranscriptional processes.These results highlight the relevance of RNA editing in the functions of many RBPs and therefore serve as a resource for further characterization of the functional associations between various RNA editing events and RBPs.The online version contains supplementary material available at 10.1186/s13059-022-02741-8. RNA editing via nucleobase deamination is an important procedure of posttranscriptional RNA processing that results in adenosine-to-inosine (A-to-I) or cytidine-to-uridine (C-to-U) conversion . In mammDue to changes in the RNA sequence, RNA editing results in a multitude of consequences. Editing in mRNA-coding regions could lead to increased protein variability due to translation of the altered codons . AnotherApproximately 1000–2000 human proteins have been annotated as RNA-binding proteins (RBPs) , most ofBinding of an RBP to the target RNA could be dependent on the RNA sequence, secondary and higher order structures, and potentially other third-party molecules . Many ofThe ENCODE project has generated RNA deep sequencing data for different fractions of HepG2 and K562 cells . For theAs expected, most of the A-to-I RNA editing events identified from the RNA-seq data above were found in the Alu elements , their editing levels were generally consistent across different fractions  in the RBP-specific eCLIP data  and disfavoring (editing level in eCLIP lower than that in RNA-seq) RNA sequences for each RBP . Therefore, given that the structural information of most RNA editing sites is unknown, we rely on eCLIP-seq data as a trustworthy survey of the RNA sequences that directly bind with each RBP.Another major issue of the analysis is that the eCLIP-seq assays were performed with whole cell extracts, whereas the intracellular locations of RBPs are usually unbalanced between the nucleus and the cytosol , 61. TheFirst, the intracellular locations of the RNA molecules are highly unbalanced between the nucleus and the cytosol. Ninety percent of the nucleotides are spliced out as introns, and less than 10% of the nucleotides remain in the mature RNA . FurtherFurthermore, for the RNA editing sites detected in multiple different cellular fractions , the editing levels of the majority were mostly consistent across different fractions Fig. B. This mIn summary, given the discussions above, we are convinced that the issue of RBP associated with the different stages of RNA maturation or nuclear/cytosolic location of the RNA should have been largely circumvented. Nevertheless, we used the RNA editing events detected from the RNA-seq data of all three fractions  as the backgrounds to compare with the RNA editing levels in the RBP eCLIP-seq data. Approximately 50–70% of the RBP-editing associations identified with the cytosolic or nuclear fraction RNA-seq data were also found with the whole-cell RNA-seq data  or disfavored (editing level higher in the RNA-seq data) editing sites.For a particular RBP and each specific RNA editing site falling in the RBP-binding peaks, the Fisher exact test  was intrThe PARS-seq data of HepG2 cells was downloaded from GEO (GSE100210). The reads were aligned to human 3′ UTR sequences by bowtie v1.2.3 . As prevP-values lower than 0.05 were categorized into 5 types, i.e., SE (skipped exon), MXE , A3SS , A5SS , and RI (retained intron). For each of these 5 AS types, the RNA editing sites located within the event coordinates (reported by rMATS in the “Events specific columns”) were counted as the AS-related sites.Differential alternative splicing (AS) events upon knockdown of each RBP or ADAR1 were downloaded from the ENCODE project (ENCSR413YAF) . SpecifiEscherichia coli and induced overnight with 1 mM isopropyl--D-1 thiogalactopyranoside (IPTG) at 18°C. Cells were lysed in 25 mM Tris HCl pH 8.0, 150 mM NaCl, and 10 mM imidazole. The proteins were purified with GSH or Ni2+-NTA resin  at 4°C and eluted in 25 mM Tris HCl pH 8.0, 150 mM NaCl, 300 mM imidazole. The proteins were dialyzed into 25 mM Tris HCl pH 8.0, 150 mM NaCl overnight with 1 mg TEV protease to remove the tags. TEV protease and GST were then removed from the proteins using heparin sepharose or mono S cation exchange chromatography and eluted with a salt gradient to 1 M NaCl. The proteins were further purified by size exclusion chromatography using a Superdex S75 column .Two RNA-binding domains of human ILF3  and hnRNPC  were used for electrophoretic mobility shift assay (EMSA). ILF3 dsRBD was cloned into pGood6p vector with N-GST tag, and hnRNPC dsRBD was cloned into pSUMOH10 vector with 10*His tag. These two constructs were expressed in For EMSA assays, synthesized RNA labelled with a 5′fluorescent DY781 dye and containing a three base extension was used (5′-DY781-AGUGGCUUAUGCCUGUA/GAUCCCAACAC). Unlabeled RNA was used for competition assays.Protein-RNA-binding reactions, containing 1 μM labelled RNA and 3 μM ILF3 or hnRNPC in a 10 μl reaction volume, were carried out on ice for 45 min. Competition assays were also carried out in a 10-μl reaction volume. A 1:1 molar ratio complex of RNA and proteins was prepared by incubation on ice for 1 h. The complex at a final concentration of 0.5 μM was mixed with 50 μM unlabeled competitor RNA. Monoclonal antibodies specific for ILF3  and hnRNPC  were added to the binding reaction after complex formation. A 6% polyacrylamide gel in 0.5× Tris/Borate/EDTA buffer (TBE) was pre-run at 4°C for 1 h. Samples were then mixed with 2 μl of native gel loading buffer to a total volume of 6 μl and run on the gel for 2 h. The gel was scanned on a LICOR Odyssey fluorescent infrared scanner at 800 nm. Images were converted to greyscale by ImageJ.HEK293FT cells and HepG2 cells were cultured with DMEM high glucose with 10% fetal bovine serum (FBS). Forward transfection was carried out with lipofectamine 2000 (Invitrogen) according to the manufacturer’s instructions.The shRNA plasmids were obtained from Sigma-Aldrich (TRCN0000022258 for UPF1 and TRCN0000050849 for negative control). For packaging of lentivirus, 70% confluent HEK293FT cells in Flask T-175 were transfected with 10 μg of the shRNA vector with 15 μg and 10 μg of Δ8.9 envelope and Vsv-G packaging vectors, respectively. Six hours after transfection, the medium was replaced with a fresh medium. Infectious lentivirus supernatant was harvested at 72 h post-transfection. After centrifugation, lentivirus supernatant was filtered through 0.45-μm PVDF filters , aliquoted, and stored at −80°C. The lentivirus-containing supernatant supplemented with 8 μg/mL of polybrene  was used to infect the HepG2 cells. Twenty-four hours after infection, the HepG2 cells were selected in a medium containing 2 μg/mL puromycin  and passaged under continuous selection for at least 2 passages before the experiments.Reporter plasmid pZW4 was a kind gift from Zefeng Wang’s lab at the Partner Institute for Computational Biology Chinese Academy of Sciences and Max Planck Society, Shanghai. In this reporter construct, the cDNA sequence of GFP was divided into 2 exons. Arranged between these two exons were the exons and introns being tested for alternative splicing such as exon skipping. Site-directed mutagenesis was carried out to induce A to G mutations at particular intronic sites. All constructs were sequenced to confirmed correct insert before transfection.Total RNA was isolated and purified using TRIzol (Invitrogen) followed by DNase I treatment. The reverse transcription was carried out with High-Capacity cDNA RT Kit with random primers . The PCR primers used in this assay are GFP-F (AGTGCTTCAGCCGCTACCC) and GFP-R (GTTGTACTCCAGCTTGTGCC).Additional file 1: Fig. S1. Statistics of the RNA editing events. Fig. S2. Cell specificity of the A-to-I RNA editing events in HepG2 and K562. Fig. S3. Statistics of the RNA editing sites falling in the RBP-binding regions. Fig. S4. Distributions of the editing levels in RBP eCLIP and RNA-seq data. Fig. S5. Overlaps between the RBP-associated editing sites identified by comparing RBP eCLIP and RNA-seq of 3 cellular fractions. Fig. S6. Numbers of the RBP-associated RNA editing events. Fig. 7. Summary of the RBP-associated RNA editing events in the same RBP-binding regions. Fig. S8. RBP interaction networks reconstructed with two methods. Fig. S9. The RBP interaction network based on the RBP-associated editing sites, highlighting different RNA processes. Fig. S10. The RBP interaction network based on the overlapping eCLIP peaks, highlighting different RNA processes. Fig. S11. RNA secondary structure differences before and after ADAR knockdown in HepG2.Additional file 2: Table S1. Intracellular locations of RBPs.Additional file 3: Table S2. Summary of RNA editing sites detected in each fraction of K562 and HepG2.Additional file 4: Table S3. RNA editing sites covered in RBP eCLIP data.Additional file 5: Table S4. Comparison of the overall editing levels between RBP eCLIP and RNA-seq data in K562 and HepG2.Additional file 6: Distributions of RNA editing levels in RBP eCLIP and RNA-seq data.Additional file 7: Table S5. Annotations of the statistically significant RBP-associated RNA editing sites in K562 and HepG2.Additional file 8: Table S6. Survival-related RNA editing sites in cancers, data obtained from Han, L., et al., Cancer Cell, 2015.Additional file 9: Table S7. Statistics of 3’ UTR energy changes in HepG2 upon ADAR knockdown.Additional file 10. Uncropped images for the blots in figures Additional file 11. Review history.


Gag during genome packaging. Here, we developed functional analysis of RNA structure-sequencing (FARS-seq) to comprehensively identify sequences and structures within the HIV-1 5′ untranslated region (UTR) that regulate this critical step. Using FARS-seq, we found nucleotides important for dimerization throughout the HIV-1 5′ UTR and identified distinct structural conformations in monomeric and dimeric RNA. In the dimeric RNA, key functional domains, such as stem-loop 1 (SL1), polyadenylation signal (polyA) and primer binding site (PBS), folded into independent structural motifs. In the monomeric RNA, SL1 was reconfigured into long- and short-range base pairings with polyA and PBS, respectively. We show that these interactions disrupt genome packaging, and additionally show that the PBS–SL1 interaction unexpectedly couples the PBS with dimerization and Pr55Gag binding. Altogether, our data provide insights into late stages of HIV-1 life cycle and a mechanistic explanation for the link between RNA dimerization and packaging.RNA dimerization is the noncovalent association of two human immunodeficiency virus-1 (HIV-1) genomes. It is a conserved step in the HIV-1 life cycle and assumed to be a prerequisite for binding to the viral structural protein Pr55 Comprehensive functional and structural probing of the HIV-1 5′ untranslated region reveals novel interactions that regulate RNA dimerization, Pr55Gag binding and genome packaging into virions. Dimerization is assumed to be a prerequisite for genome packaging into virions, although the mechanistic relationship between dimerization and packaging is still under debate6. It also plays a role in genome integrity and evolution by bringing two genomes in close proximity for strand-switch recombination10. Finally, it is linked to a structural switch that may regulate genome packaging and translation within cells16 , like all retroviruses, packages two copies of its genome into viral particles. These genomes are noncovalently associated at an RNA motif called the dimerization initiation site (DIS). This association, known as dimerization, affects multiple steps of the HIV-1 life cycles16 Fig. .Fig. 1An19 . For example, dimerization is promoted by a long-range base pairing between nucleotides overlapping the gag start codon (AUG) and the unique 5′ element (U5)27  . Furthermore, intrinsic transcriptional start site heterogeneity, which produces transcript variants beginning with different counts of G residues , has been shown to regulate dimerization by shifting the equilibrium between mutually exclusive structures containing either an U5–AUG or a U5–SL1 interaction29: 1G transcripts expose the DIS for dimerization and sequester the 5′ cap, whereas 3G variants conceal the DIS while exposing the cap to enhance translation14. In addition to the U5–AUG and U5–SL1 conformations, over 20 structural models of the HIV-1 genome have been proposed, suggesting that the 5′ UTR may dynamically adopt multiple conformational states31. It seems therefore likely that other structural forms of the HIV-1 genome exist to regulate genome dimerization, or other critical aspects of HIV-1 biology.An extensive body of work maps the DIS to stem-loop 1 (SL1) of the HIV-1 5′ untranslated region (UTR)19 Fig. . SL1 con)27 Fig. . Alterna28) Fig. . The U5–34). All in all, our data provide a mechanistic explanation for how RNA dimerization can be a prerequisite for packaging.Sequences required for dimerization largely overlap with other conserved functional elements, such as those involved with genome packaging. Indeed, this genetic overlap between dimerization and packaging signals is a main reason why dimerization is considered to be a prerequisite for packaging, even though the precise molecular mechanism underlying this phenomenon is unclear. In this study, we disentangled genome dimerization from other steps of the viral life cycle and comprehensively mapped structure determinants of HIV-1 genome dimerization using a new high-throughput approach that we call functional analysis of RNA structure-sequencing (FARS-seq). Using FARS-seq, we found nucleotides throughout the HIV-1 5′ UTR influencing dimerization and identified distinct structural conformations in monomeric and dimeric RNA. The dimeric RNA folded into a ‘canonical’ structure of the 5′ UTR that displayed TAR, PolyA, primer binding site (PBS) and SL1–SL3 as stem loops, and contained a long-range U5–AUG interaction. In monomeric RNA, SL1 formed interactions with polyA and PBS. The PBS–SL1 interaction functionally couples primer binding with dimerization and the polyA–SL1 long-range interaction disrupts the major packaging motifs for Pr55Gag  and other viruses49. This setup also disentangles the effect of RNA structure on dimerization from other factors, such as the binding of protein or other cofactors. To assess the effect of transcription start site heterogeneity on the dimerization properties of the HIV-1 genome we tested three transcript variants beginning with 1G, 2G or 3G (refs. 50)  > 2  Fig. . For eac50) Fig. . After pKdimer values for each nucleotide position. This metric is related to the ratio of mutation frequencies in the monomer versus dimer RNA. For computational analysis, however, these ratios are corrected for errors introduced during library preparation and sequencing  values were heavily skewed toward positive values indicating that most mutations inhibited, rather than enhanced, dimerization indicating that the HIV-1 genome is highly optimized to dimerize as a key part of its life cycle  to calculate cle Fig. . By segrSL1 Fig. . AlthougSL1 Fig. .2(Kdimer) values at each nucleotide position for capped and uncapped transcript variants measured under the two buffer conditions  values of each variant to the mean values of the three transcripts. In high salt buffer, most positions were unchanged in the 1G, 2G and 3G variants (less than Δ0.25 log2(Kdimer) variant − mean)  as well as noncanonical and tertiary interactions that are otherwise impossible to predict from classical one-dimensional RNA structural probing experiments.Refinement of monomer and dimer structures. The incorporation of information from RNA structural probing experiments improves the accuracy of RNA structure predictions, but structural elements can still be incorrectly predicted because data from chemical probing experiments typically provide information on whether a nucleotide is base paired or not, but not its base-pairing partnertly Fig. . When muz-score normalized data along the diagonals  values 0.61–6.85 versus 0.31–3.49)  Fig. . Mutatio49) Fig. . Dimer eWhile two-dimensional structural probing identified SL1 as a short stem loop with an apical and basal stem separated by an internal loop (nucleotides 243–277), our data nevertheless reveal structural plasticity in SL1. This realization comes from mapping the functional data to different extended forms of SL1 that have been proposed in the literature: a two-internal loop model, a three-internal loop model (3IL) and three-way junction (3WJ) model Fig. . Even thInter-domain interactions regulate dimerization. Outside SL1, we found several structural domains and inter-domain interactions that affected dimerization Fig. . Our dat60. We found that mutation of two nucleotides G217 and C218 in the lower PBS stem very strongly enhanced dimerization, even though mutations to this stem were generally inhibitory . We therefore postulate that cPBS182-199 disrupts the TAR–PBS interaction detected by multidimensional structural probing, allowing TAR to dimerize independently of SL1.Because these results indicated a functional interaction between primer tRNA binding and dimerization, we next assessed whether disruption of the PBS with tRNA mimic oligos affected dimerization. cPBSoop Fig. . Both olGag binding by microscale thermophoresis (MST)  to Pr55Gag compared with wild-type (WT) RNA (Kd 38 nM). In contrast, PBS A220G–G221A, which was unable to dimerize, did not bind Pr55Gag at any of the concentrations tested . In polyA, dimerization enhancing mutation C84A–C85C bound Pr55Gag with higher affinity (17 nM) than WT, whereas dimerization disrupting mutation U86G–A89C bound Pr55Gag with lower affinity than WT (110 nM). By performing in solution DMS-MaPseq analysis in vitro, we established that mutations in polyA–SL1 and PBS–SL1 alter ensemble reactivities toward the profiles seen in the isolated monomer and dimer.  Fig. . None ofFig. Kd 1 nM to Prays Fig. . In PBS,than WT 1 nM. By pGag binding and packaging. We also show how changes to the PBS functionally link the tRNA binding region to packaging . Here, we found no direct evidence that SL1 is in an extended form in dimeric RNA and consistently observe signals for SL2 as a short imperfect stem containing a bulged adenosine. Nevertheless, functional profiling provides strong evidence that mutually exclusive extended forms of SL1 can be readily generated, either directly through stabilizing mutations or indirectly by destabilizing SL2. The fact that single point mutations could have such dramatic effects on dimerization provides evidence that the 5′ UTR is dynamic and metastable. In the context of viral infection, this is noteworthy because it provides a mechanism to regulate dimerization through the binding of viral or cellular factors to the genome  with polyA providing an additional packaging signal in cells69. The fact that SL1 and polyA are completely disrupted in the monomer population provides a mechanistic explanation for the long-postulated link between dimerization and packaging.The metastable nature of SL1 was revealed in monomeric RNA. In contrast to SL3, which was present in both monomer and dimer structures, SL1 was destructured in monomeric RNA. Instead of a stem loop, SL1 was reorganized into a short-range interaction with PBS and a long range interaction with polyA. These results are in agreement with the prevalent idea that RNA conformational switches regulate HIV-1 replication14). Thus, our results agree that 3G transcripts are preferentially monomeric, yet disagree with precise structural details, in particular the base-pairing partner of SL1. One way to reconcile these data is that the NMR structure was obtained with the Mal isolate, in contrast to the NL43 isolate used in the present study. The Mal isolate contains a 23-nucleotide duplication in the same region in PBS that we find as a regulator of dimerization. Moreover, this duplication leads to structural differences in the initiation of reverse transcription in Mal compared to the prototypic subtype B strain NL43 (refs. 53). It is therefore plausible that Mal and NL43 isolates use related, yet distinct, structural rearrangements to regulate dimerization. Nonetheless, both the polyA–SL1 and PBS–SL1 interactions are conserved among 800 curated sequences in the Los Alamos HIV-1 sequence database indicating regulation of dimerization by polyA and PBS is widespread  using the PCR cycling conditions: 98 °C for 30 s, followed by 32 cycles of 98 °C for 10 s, 60 °C for 30 s and 68 °C for 1 min. Products were visualized by electrophoresis on 1% agarose gels in 1× TAE buffer and column purified with NucleoSpin Gel and PCR Clean-up kit (Macherey-Nagel). The purified PCR products were used as template for error prone PCR using the Mutazyme II DNA polymerase (Agilent) and forward primer TAATACGACTCACTATA and reverse primer GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGATCTAAGTTCTTCTGATCCTGTCTG. The PCR reaction volume was 50 μl and consisted of 2 ng of template DNA, 1× buffer, 200 µM dNTPs, 0.25 mM for each primer and 2.5 U of Mutazyme II DNA polymerase. PCR cycling conditions were 95 °C for 2 min followed by 35 cycles of 95 °C for 30 s, 35–42 °C for 30 s and 72 °C for 1 min. Products were visualized by electrophoresis on 1% agarose gels in 1× TAE buffer. A final column purification was carried out with the NucleoSpin Gel and PCR Clean-up kit (Macherey-Nagel) kit.DNA templates were prepared by PCR using Taq DNA polymerase (NEB) with RNA expression plasmid pDRNL43- ΔEnv and forward primers containing T7 RNA polymerase promoter and 3G/2G/1G at the 5′ end AAAgaagacTTggggTAATACGACTCACTATA2, 10 mM DDT, 1 mM Spermidine), 5 mM NTPs, 40 U RNasin (Molox), 900 ng of DNA template, 0.05 U of Pyrophosphatase (NEB) and 5 μl of homemade T7 RNA polymerase. The reaction was incubated at 37 °C for 3 h, followed by DNase I treatment for 30 min at 37 °C. RNA was gel purified after electrophoresis on 1% agarose gels in 1× TAE buffer using the NucleoSpin Gel and PCR Clean-up kit (Macherey-Nagel) with NTC buffer (Macherey-Nagel). Half of the purified RNA was capped with Vaccinia Capping System (NEB). Briefly, 10 µg of RNA was mixed with nuclease-free H2O in a 1.5-ml microfuge tube to a final volume of 15 µl. The sample was heated at 65 °C for 5 min, then placed on ice for 5 min. Then 2 µl of 10× capping buffer, 1 µl of 10 mM GTP, 1 µl of 2 mM SAM and 1 µl of Vaccinia Capping Enzyme were added and the sample was incubated at 37 °C for 30 min. Capped RNA was column purified using the NucleoSpin Gel and PCR Clean-up kit (Macherey-Nagel) with NTC buffer (Macherey-Nagel).Purified WT and mutated PCR products (900 ng) were used as templates for RNA in vitro transcription with a homemade T7 RNA polymerase. Reaction contained 1× reaction buffer  or low salt buffer . Samples were loaded with native loading dye (0.17% Bromophenol Blue and 40% (vol/vol) sucrose) on 1% agarose gel prepared with 1× tris-borate magnesium (TBM) buffer  and fractionated at 100 V for 85 min at room temperature. In some experiments, 12 pmol of oligos cPBS(182–199)GTCCCTGTTCGGGCGCCA and/or cPBS(199–216)TTCCCTTTCGCTTTCAAG were added to the RNA before denaturing to assess the effect of disrupting the PBS on dimerization.RNA (600 ng) was denatured at 90 °C for 2 min followed by chilling on ice for 2 min. RNA was incubated at 37 °C for overnight (15–17 h) in high salt buffer (10 mM KH2) or low salt buffer . Samples were loaded with native loading dye (0.17% Bromophenol Blue and 40% (vol/vol) sucrose) on 4% acrylamide nondenaturing gel prepared with 1× TBM  and fractionated at 150 V for 4 h at 4 °C, including two reference samples with SYBR gold (Invitrogen), which could be visualized under blue LED light. The dimer and monomer bands in samples were cut from the gel according to the position of reference samples by scalpel.RNA (800 ng) was denatured at 90 °C for 2 min followed by chilling on ice for 2 min. RNA was then incubated at 37 °C for 30 min in high salt buffer  containing the equivalent volume of EtOH as the DMS treated sample, and incubated at 37 °C for 15 min. Gel slices were crushed into small pieces, soaked in 1× TBM  buffer at 4 °C overnight. RNA was extracted using NucleoSpin Gel and PCR Clean-up kit (Macherey-Nagel) with NTC buffer (Macherey-Nagel).Each gel piece from the polyacrylamide gel was divided into two parts. Half was soaked in 1× TBM containing 170 mM DMS (dissolved in EtOH), incubated at 37 °C for 15 min, followed by quenching with 50%  β-mercaptoethanol. The other half was soaked in 1× TBM , 0.1 µM reverse transcription primer GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGATCTAAGTTCTTCTGATCCTGTCTG, 0.5 mM dNTPs, 50 mM Tris·HCl, pH 8.0, 75 mM KCl, 6 mM MnClFor the functional probing MIME experiments, reverse transcribed complementary DNAs were amplified with 250-nM primers forward TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGGGTCTCTCTGGTTAGACC, reverse GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGATGGTTGTAGCTGTCCCAG, 200 µM dNTPs, 1× Q5 reaction buffer, Q5 polymerase (NEB) using the PCR cycling conditions: 98 °C for 30 s, followed by 32 cycles of 98 °C for 10 s, 55 °C for 30 s, and 72 °C for 30 s. The PCR products were visualized by electrophoresis on 1% agarose gels in 1× TAE buffer and column purified . Then 25 ng of purified products were used in the final sequencing library preparation with Nextera DNA Flex Library Prep (Illumina) and Nextera DNA CD Indexes , according to the manufacturer’s instructions. For structural profiling by DMS, we performed amplicon sequencing. PCR reaction volume was 25 μl, 200 μM dNTPs, 250 nM primer pair 1 (forward TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGggtctctctggttagacc and reverse GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGCGTACTCACCAGTCGCC) or primer pair 2 (forward TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGcgaaagtaaagccagaggag and reverse GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGCTCCCTGCTTGCCCATAC), 1× GXL reaction buffer, 0.625 U of PrimeSTAR GXL DNA Polymerase (Takara Bio). Two PCR amplifications were performed using the PCR cycling conditions: 98 °C for 30 s, followed by 34 cycles of 98 °C for 10 s, 60 °C for 15 s and 68 °C for 30 s. Amplified libraries were column purified using the NucleoSpin Gel and PCR Clean-up kit (Macherey-Nagel). Paired-end PE150 sequencing was carried out on an Illumina Novaseq instrument (Novogene).73 to generate Kdimer, which is a quantitative metric relating the effect of a mutation on dimerization . PCA was carried out using the PCA function of the python scikit-learn library (v.0.23.2). Variances in DMS reactivities were calculated using the var function from the python NumPy library (v.1.19.2). Pairwise comparison of DMS reactivities were carried out using a modified deltaSHAPE calculation75. This modified deltaSHAPE (v.1.0) analysis uses several criteria to identify statistically significant changes in reactivities. First, a z-factor test identifies nucleotides where DMS reactivities change by >1.96 standard deviations of the DMS errors. Second, a standard score threshold of 1.5 is applied, meaning that delta reactivity values are at least 1.5 standard deviations away from the mean reactivity change. To filter these statistically significant sites for biological meaning, we next applied an absolute and a relative threshold filter. The absolute difference threshold ensures that a minimum reactivity change of 0.2 is needed for the site to be considered biologically relevant. The relative threshold filter was set so that a relative change of at least 0.75-fold was needed to remove false positives where DMS reactivities are high in both conditions such that a large change in reactivity is unlikely to affect RNA structure. RNA structures were visualized using Visualization Applet for RNA (VARNA)76.Sequencing data relating to DMS structural probing were first preprocessed with ShapeMapper2 using parameters ‘--output-parsed-mutations--output-counted-mutations--render-mutation’. EtOH treated and DMS treated raw sequencing reads were passed to ShapeMapper2 via the modified and unmodified parameters, respectively46. Briefly, data were preprocessed by ShapeMapper into simple files that are string representations of mutations in each read. Simple files were converted into the rich and compact rdat format specific for RNA structure mapping experiments77. A two-dimensional matrix containing mutation rates at pairs of nucleotide positions was constructed. Mutation counts were subsequently normalized for total number of mutations along each row to give a true modification frequency. RNA structure signatures were further refined by calculating z-scores. A thresholding of zero was applied to remove negative values, and a convolution filter was applied to enhance cross diagonal features. RNA helices were finally identified in an unbiased manner by applying a filter for stems of Watson–Crick and G-U wobble base pairs of at least three base pairs in length. Best stems were predicted by eliminating conflicting stems by selecting the highest scoring stem. Bootstrapping analyses were performed using the rna_structure function of the Basic Inference Engine for RNA Structure (Biers) (https://ribokit.github.io/Biers/) using the default parameters (100 bootstrapping iterations).RNA structural interference by multidimensional structural probing was carried out using the M2-seq pipeline2PO4, pH 7.4, 1 mM MgCl2, 122 mM KCl). For each binding experiment, RNA was diluted to 10 nM in high salt buffer . A series of 16 tubes with Pr55Gag dilutions were prepared in high salt buffer, producing Pr55Gag ligand concentrations ranging from 30 pM to 1 μM. For measurements, each ligand dilution was mixed with one volume of labeled RNA, which led to a final concentration of 5 nM labeled RNA. The reaction was mixed by pipetting, incubated for 30 min at 37 °C, followed by 30 min on ice. Samples were then centrifuged at 10,000g for 5 min. Capillary forces were used to load the samples into Monolith NT.115 Premium Capillaries (NanoTemper Technologies). Measurements were performed using a Monolith Pico instrument (NanoTemper Technologies) at an ambient temperature of 25 °C. Instrument parameters were adjusted to 5% LED power, medium MST power and MST on-time of 1.5 s. An initial fluorescence scan was performed across the capillaries to determine the sample quality and afterward, 16 subsequent thermophoresis measurements were performed. Data from three independently pipetted measurements were analyzed for the ΔFnorm values and binding affinities were determined by the MO. Affinity Analysis software (v.2.3 NanoTemper Technologies). Graphs were plotted using GraphPad Prism v.8.4.3 software.For MST, RNA was labeled at the 3′ end using cytidine-5′-phosphate-3′-(6-aminohexyl) phosphate (Jena Biosciences) with T4 RNA ligase (NEB) overnight at 16 °C, followed by RNA Clean and Concentrator Kits (ZYMO). The 500-nM labeled and purified RNA was denatured at 90 °C for 2 min followed by chilling on ice for 2 min. RNA was folded at 37 °C for overnight (15–17 h) in high salt buffer . DMS was added to the RNA solution to final concentration 170 mM, incubated at 37 °C for 6 min, followed by quenching with β-mercaptoethanol and purification with ethanol precipitation. The purified DMS probed RNAs followed the same reverse transcription and library preparation process as the in-gel DMS probed RNA samples.RNA (300 nM) was denatured at 90 °C for 2 min followed by chilling on ice for 2 min. Next, RNA was refolded at 37 °C for overnight (15–17 h) in high salt buffer (10 mM KH7 human embryonic kidney 293T (HEK293T) cells were plated in 10 ml of DMEM media containing 10% FBS. Next, 4 µg of plasmids expressing HIV-1 WT or HIV-1 mutants were mixed with 48 µl of polyethylenimine , Polysciences) and 500 µl of DMEM, and incubated for 10 min at room temperature before being added dropwise on the cells. Then 24 h posttransfection, the cells were probed by replacing the media with 3 ml of DMEM containing 170 mM DMS and incubated at 37 °C for 6 min. Cells were then washed with 5 ml of PBS containing 140 mM β-mercaptoethanol to quench the DMS. Next, 1 ml of TRI-reagent (Sigma-Aldrich) was directly added on the cells to extract RNA according to the manufacturer’s instructions. DNA contaminants were removed by TurboDNase (Invitrogen) treatment for 30 min at 37 °C. RNA was purified using NucleoSpin Gel and PCR Clean-up kit (Macherey-Nagel) with NTC buffer (Macherey-Nagel) and eluted in 20 µl of 5 mM Tris-HCl pH 8.0. Then 7 µl of the purified DMS probed RNAs were used for the reverse transcription following the same reverse transcription and library preparation process as for the in-gel DMS probed RNA samples.Here, 24 h before transfection, 105 HEK293T cells were plated in 2 ml of DMEM media containing 10% FBS. For the cotransfection experiments, equal amounts of plasmids expressing WT or mutants  were mixed with 7.2 µl of polyethylenimine  and 100 µl of DMEM and incubated for 10 min at room temperature before being added dropwise on the cells. Cells and viral supernatant were collected at 24 h posttransfection. Cells were washed with 2 ml of PBS and RNA was extracted with 1,000 µl TRI-reagent (Sigma-Aldrich) according to the manufacturer’s instructions. Viral supernatant was first clarified for 2 min at 17,000g, followed by a filtration step through a 0.45-μm filter. The filtrate was then transferred into a new tube and the virus was pelleted for 2 h at 17,000g. The viral pellet was extracted with 500 µl of TRI-reagent (Sigma-Aldrich) and DNA contaminants were removed by TurboDNase (Invitrogen) treatment for 30 min at 37 °C. RNA was purified using NucleoSpin Gel and PCR Clean-up kit (Macherey-Nagel) with NTC buffer (Macherey-Nagel) and eluted in 20 µl of 5 mM Tris-HCl pH 8.0. Then 10 µl of purified RNA was heat denatured at 65 °C for 5 min together with 0.67 μM reverse primer (GATGGTTGTAGCTGTCCCAGTATTTGCC) and 1.67 mM dNTPs in 15 µl of total volume, then chilled on ice for 2 min. RNA was then reverse transcribed by adding 1× SSIV buffer, 5 mM DTT, 20U RNasin, 100 U of SSIV in 25 μl of total volume and incubating at 52 °C for 1 h. cDNAs were amplified with 250 nM primers forward TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGggtctctctggttagacc, reverse GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGCGTACTCACCAGTCGCC, 200 µM dNTPs, 1× Q5 reaction buffer and 0.02 U μl−1 of Q5 polymerase (NEB) using the PCR cycling conditions: 98 °C for 1 min, followed by 22 cycles of 98 °C for 10 s, 55 °C for 20 s and 72 °C for 30 s. The PCR products were visualized by electrophoresis on 1% agarose gels in 1× TAE buffer and column purified . 40 ng purified products were used in the final indexing PCR using 2.5 µl of Nextera DNA CD Indexes  in a 14 µl of reaction ). The PCR cycling conditions were 98 °C for 2 min, followed by five cycles of 98 °C for 30 s, 6,255 °C for 320 s and 72 °C for 1 min. Paired-end PE150 sequencing was carried out on an Miniseq instrument (Illumina) according to the manufacturer’s instructions.Here, 24 h before transfection, 7 × 10Details of the description of statistical methods are provided in the Further information on research design is available in the Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41594-022-00746-2.Supplementary InformationMathematical and statistical methods for analyzing mutational interference data in dimerization experiments.Reporting SummaryPeer Review FileSupplementary Data 1One-dimensional structure prediction for pooled, unpooled and mutant samples.Supplementary Data 2Two-dimensional structure prediction for unpooled samples.Supplementary Data 3Conservation of predicted interactions.Supplementary Data 4Statistical deltaSHAPE analysis.Supplementary Data 5DMS reactivities colored onto predicted and refined dimer and monomer structures.Supplementary Data 6Tables of DMS reactivities.Supplementary Data 7Kdimer values plotted onto predicted and refined dimer and monomer structures.Unpooled Supplementary Data 8Kdimer values.Tables of Supplementary Data 9Output of MIMEAnTo.Supplementary Data 10XML files of DMS reactivities for pooled, unpooled and mutant samples.


Crassostrea gigas. The increase of ADAR1 expression and hyper-editing activity parallel to OsHV-1 RNAs suggests a functional connection between dsRNA editing and antiviral responses. We analyzed 87 RNA-seq data sets from immuno-primed, resistant, and susceptible oysters exposed to OsHV-1 to compare the ADAR hyper-editing levels on host and viral transcripts and trace hyper-editing on the oyster genes. Host RNAs were more hyper-edited than viral RNAs, despite the increased editing of viral RNAs in late infection phases. A set of genes, representing ∼0.5% of the oyster transcriptome and including several tripartite motif-containing sequences, were constantly hyper-edited. Conversely, we identified genes involved in antiviral response, miRNA maturation, and epigenetic regulation that were hyper-edited in specific conditions only. Despite technical and biological bottlenecks that hamper the understanding of the bivalve “RNA editome,” available tools and technologies can be adapted to bivalve mollusks.Ostreid herpesvirus-1 (OsHV-1) RNAs are enzymatically modified by A-to-I conversions during the infection of IMPORTANCE Ostreid herpesvirus-1 (OsHV-1) is a harmful pathogen of bivalve species, such as oysters. However, knowledge is lacking about host–virus interactions at the molecular level, hampering the possibility of a correct management of viral outbreaks and related massive mortalities. Notably, OsHV-1 transcripts are massively modified by host RNA editing enzyme during infection, resulting in multiple A-to-I variations along RNAs assuming double-strand conformations. The impact of these modifications on host transcripts is, however, not completely clear. Analyzing RNA-seq data of oysters infected with OsHV-1, we revealed that ∼0.5% of the oyster transcriptome is always enzymatically modified by ADAR, whereas genes involved in antiviral response, miRNA maturation, and epigenetic regulation were hyper-edited in specific conditions only. Despite our results, relevant technical bottlenecks impair an accurate quantification of RNA editing events, making necessary an approach specifically dedicated to the progressive understanding of oyster “RNA editome.” Crassostrea gigas), scallops (Chlamys farreri), and clams (Scapharca broughtonii) , which includes the only two invertebrate herpesviruses known so far  (poly(I·C) (Ostreid herpesvirus-1 (OsHV-1) is pathogenic and even deadly to marine bivalves such as oysters  . Moreoveoly(I·C) . Adenosioly(I·C) . Althougoly(I·C) . Mechanioly(I·C) . The intoly(I·C) . Malacoholy(I·C) . HoweverC. gigas transcriptome. Then, we discuss the current limitations of this analysis and we trace future research lines, aiming to unravel the “RNA editome” at the edge of host–virus interactions in the marine environment.Following a dedicated analysis of RNA-seq data sets of immuno-primed, OsHV-1-susceptible (S) and OsHV-1-resistant (R) oysters infected with OsHV-1, we quantified host and viral ADAR hyper-editing and evaluated the editing impact on the C. gigas RNA-seq data sets referring to (i) a susceptible oyster family treated with the dsRNA analog poly(I·C) and, therefore, immuno-primed (IP) or with filtered seawater (FSW), and infected with OsHV-1 10 days later , the hyper-editing level in the virus is also reported. Download Table S1, DOCX file, 0.03 MB.Details of the RNA-seq datasets and related analyses. Library ID, description, number of total reads, percentage of reads mapped on the Copyright © 2022 Rosani et al.2022Rosani et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the KY242785.1) revealed a successful oyster infection, herein defined as samples with at least 0.1% of viral reads, in 6 EXP1 samples and 9 in EXP2 samples , only ADAR1 (G10242) expression was modulated in EXP1. It increased 14 times at 10 days in IP oysters and reached an expression level of 220 transcripts per million . Download FIG S1, DOCX file, 0.07 MB.ADAR and ADAR-mediated hyper-editing in EXP1. The expression levels of oyster ADAR1  together with the level of hyper-editing  are reported for EXP1 samples, averaged per experimental condition . Black and grey refer to samples from susceptible and resistant oysters, respectively. Download FIG S2, DOCX file, 0.05 MB.ADAR and hyper-editing in EXP2. The expression levels of oyster ADAR1  together with the level of hyper-editing  are reported for EXP2 samples, averaged per experimental condition   than the EXP2 samples (0.173% ± 0.014), possibly due to the different sequencing layouts (We used the editing)  to ident layouts  and S2.  layouts . The hyp layouts , followe layouts . In EXP2 layouts . S oyste layouts .10.1128/msphere.00011-22.5FIG S3FIG S3, DOCX file, 0.04 MB.The distribution of the nucleotide variations in hyper-edited reads, for EXP1 (a) and EXP2 (b) samples. Download Copyright © 2022 Rosani et al.2022Rosani et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the In the 15 samples with successful OsHV-1 infection, ADAR-editing impacted more oyster RNAs than OsHV-1 RNAs: 14.6 times at 12 hpi and 6 times at 24 hpi in EXP1; 47 times at 12 hpi, 7.6 times at 24 hpi, and 6.3 times at 60 hpi in EXP2. Notably, ADAR-editing directed on OsHV-1 RNAs increased between 12 and 24 hpi 2.3 times in EXP1 and 6 times in EXP2 .C. gigas genes consistently edited in the three biological replicates per condition, in EXP1 and EXP2, respectively. Intriguingly, 217 genes  and 158–241  s 18.8%,  were hyps 18.8%, . Some geIP group . In EXP2IP group . Among RIP group .10.1128/msphere.00011-22.2TABLE S2C. gigas genes. The table lists hyper-edited genes in EXP1 and EXP2 with gene ID, description, hyper-editing status in EXP1 and EXP2, and the corresponding normalized hyper-editing levels as reported averages for experimental conditions. [file .xlsx]. Download Table S2, XLSX file, 0.2 MB.Hyper-editing impact on Copyright © 2022 Rosani et al.2022Rosani et al.https://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the C. gigas, we demonstrated that in the early stage of infection host RNAs are more impacted than viral RNAs, with viral-directed editing increasing along the time course and suggesting that at later stages ADAR1 will mostly target viral dsRNAs. This increase is in agreement with data reported demonstrated for OsHV-1 infection in S. broughtonii (By tracing ADAR hyper-editing along OsHV-1 infection in ughtonii , and likughtonii . Next, wughtonii . The absughtonii  possiblyughtonii . Despiteughtonii . A consiughtonii , while sughtonii . The conSeveral bottlenecks currently limited the analysis of ADAR editing from bivalve RNA-seq data. Firstly, we rely only on a reference genome for the quantification of ADAR editing, not covering interindividual variability, which is extremely important in bivalves, compared to vertebrates, and involves presence–absence of hundreds to thousands of genes . Second,Several relevant questions regarding computational and biological aspects remain to be answered and challenge future research toward a deeper understanding of the bivalve ADAR editome. How precise is the quantification of ADAR edits and what is the penetrance of these variations? Are other RNA editors active in oysters and can their activity explain the abundance of G-to-A variations ? What isIn conclusion, available tools are ready to be adapted to bivalve mollusks for a deeper understanding of the significance of ADAR activity.