The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report assessments of RNA structure predictions in CASP15, the first CASP exercise that involved RNA structure modeling. Forty two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics  and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third ; predictions from deep learning approaches were significantly worse than these top ranked groups, which did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and X-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global fold of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as non-canonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography. After 1995, following progress in chemistry and X-ray technology, a steady stream of RNA structures with sizes equivalent to or larger than tRNAs, culminating with fully functional ribosome structures, revealed the many intricacies of RNA architectures. In parallel, computer programs for RNA modeling appeared . However, it was not until 2011 that a regular assessment of models, called RNA-Puzzles, was set up.8 The models for the RNA sequence of each RNA-Puzzle were collected prior to publications of the X-ray structures Since not enough targets were available for a short CASP-like season, the Puzzles were organized to occur right as the structures were solved . Since then, several additional publications have reported the results of the RNA-Puzzles assessments.11 In 2021, it became clear that accelerations in RNA structure determination12 would allow enough targets for a single CASP season. Here we report on the first collaborative effort between CASP and RNA-Puzzles teams on a set of RNA targets. Following the success of AI-based tools in protein structure prediction13 and a surge of interest in RNA during the COVID pandemic,14 the hope of the organizers and assessors was to generate motivation and attention from protein modeling groups to develop and evaluate methods for RNA.Soon after the establishment of the cloverleaf structure of transfer RNA,19), two assessments were carried out for RNA: one assessment was performed by the RNA-Puzzles team (Z. Miao & E. Westhof) and a completely independent analysis was performed by assessors nominated by the CASP organizers (R. Das and team). During a dedicated assessors’ meeting in October 2022, the two assessments’ results were critically compared, revealing a striking consensus in rankings and choice of top predictors, despite the use of distinct metrics and ranking schemes. Further analysis based on visual inspection of RNA-protein targets, direct comparison to cryogenic electron microscopy (cryo-EM) maps, and molecular replacement trials for targets solved by X-ray diffraction – catalyzed by the general CASP15 conference in December 2022 – revealed additional insights into the limitations and potential of current RNA 3D modeling, which are described here. The identification of accurate models also led to insights by CASP15 RNA experimental contributors and development of novel methods for cryo-EM model refinement, described in two separate papers co-submitted to the CASP15 special issue.21Between April and July of 2022, sequences of twelve RNA targets were received from experimental contributors and disseminated on the CASP website. Models were submitted by over 40 groups, and a double-blind assessment was carried out. Inspired by prior joint assessments by CAPRI and CASP for protein complexes  of the two superimposed structures. The DI score complements the RMSD values by introducing features specific to RNA in the metric in the following way. The pairs formed by the nucleotides are identified, counted, and annotated in the experimental structure. They are broadly classified as either of the Watson-Crick complementary type  or of the non-Watson-Crick type (NWC). The base–base network, i.e. WC, NWC, and stacking interactions in both reference and predicted models are extracted using the MC-Annotate23 tool. We then compute, for each of the three types of base-base interactions, the number of correctly predicted pairs, the true positive (TP), the number of predicted pairs with no correspondence in the reference model, the false positive (FP), and the number of pairs in the reference model that are not present in the predicted model, the false negative (FN). The Interaction Network Fidelity (INF) is then computed as the Matthews Correlation Coefficient, the geometric mean of the positive predictive value and sensitivity as in Gorodkin25:The RNA-puzzles-style assessment relied mainly on the Root Mean Square Deviation (RMSD) measure complemented by the Deformation Index (DI)WC), the non-Watson-Crick (NWC) base pairs (INFNWC), both WC and NWC base pairs (INFBPS), or the stacking interactions (INFSTACK). Finally, the Deformation Profile is a distance matrix computed as the average RMSD between the individual bases of the predicted and the reference models while superimposing each nucleotide of the predicted model over the corresponding nucleotide of the reference model one at a time. It is computed using the ‘‘dp.py’’ command from the ‘‘SIMINDEX’’ package22. For simplification, we also calculate the sum, mean and median of the deformation profile to account for the general accuracy of the prediction. The stereochemical correctness of the predicted models was evaluated with MolProbity26, which provides quality validation for 3D structures of proteins and nucleic acids. For the latter, MolProbity performs several automatic analyses, from checking the lengths of H-bonds present in the model to validating the compliance with the rotameric nature of the RNA backbone.27 As a single measure of stereochemical correctness, we chose the clash score, i.e., the number of all types of steric clashes per thousand residues.28 The assessment also considered the coordinate comparison metric TM-score as computed in RNA-Align29 and the Mean of Circular Quantities30 to assess accuracy in the torsion angle space. All the source codes and an example notebook are available at: https://github.com/RNA-Puzzles/RNA_assessment.The DI is then computed as: RMSD/INF. Several partial INF values (and respective DI) can be computed considering only the Watson-Crick (WC) base pairs  US-align31, which was used to compute TM-score through a heuristic alignment approach improving on the original RNA-align29; (2) Local-Global Alignment32 which yielded GDT_TS, the average percentage of aligned C4’ atoms (rather than that of C in proteins) at cutoffs of 1 Å, 2 Å, 4 Å, and 8 Å; (3) rna-tools33, a toolkit used to determine the accuracy of contact classifications among base stackings, Watson-Crick interactions, and non-canonical interactions. INF scores were calculated from interaction predictions dependent on ClaRNA34; (4) OpenStructure36, a framework used to find lDDT, a metric that measures structural similarity ; and (5) PHENIX, which reports a clashscore metric for all non-hydrogen bonded atom pairs that overlap worse than 0.4 Å37. For TM-score and GDT_TS, superposition of models and experimental models were calculated with default atoms for those packages, C3’ and C4’, respectively . Two alignment modes were considered for GDT_TS: a fixed residue-residue correspondence approach and an automated search for the best superposition, ignoring sequence; these gave nearly identical group rankings, so we opted for the former approach. INF scores were computed with ClaRNA to help increase robustness of base pair assignment for low resolution models; these values were slightly different than but highly correlated with INF scores computed with MC-Annotate, the tool normally used by RNA-Puzzles  was calculated by taking the mean and standard deviation for the best model from each group with respect to each considered metric. To prevent distortion from very poor outlier predictions, models with initial Z-scores that fall under a tolerance threshold of −2 were discarded, and the Z-scores were recomputed with the new mean and standard deviation. After this second pass, models with Z < −2 were re-assigned Z = −2. For Z-scores that involved linear combinations of multiple components , the Z-score values for individual components were then summed. To prevent penalization of novel methods that might give poor models for some targets, the sums of just the positive ZRNA over all targets were used to make final rankings. For targets where experimentalists provided multiple conformations to either represent experimental uncertainty or bona fide conformational diversity , predictor models were compared to all available experimental models. Groups were rewarded based on their best score. Code for the analysis of submitted models, assessment tools, and documentation using casp-rna are available as an open-source repository at https://github.com/DasLab/casp-rna. Metrics are also available for interactive viewing on the CASP15 website at https://predictioncenter.org/casp15/results.cgi?tr_type=rna.Similar to the assessment of protein models in past CASP assessments, we employ a two-pass procedure for Z-scores2.340 For the CPEB3 ribozymes (R1107 and R1108), we generated template-based structures using the HDV ribozyme structure (PDB ID: 3NKB). We used residues 2–9, 11–39, 43–47, and 57–72 in this HDV ribozyme structure to model residues 3–8, 10–43, and 54–69 in the CPEB3 ribozymes, avoiding loop residues that were not homologous between the structures. For the class I Pre-Q1 riboswitch, we compared the type III structure R1117 to template-based structures derived from the type I structure (PDB ID: 3Q50). We used residues 2–5, 7–20, and 23–33 in the type I Pre-Q1 riboswitch structure to model residues 2–30 in the target type III Pre-Q1 riboswitch structure, again avoiding loop residues that were not homologous between the models. Non-homologous residues were left out of these simple template-based structures.As baselines for the accuracy of predicted models, we prepared template-based structures generated using homology models with the rna_thread application in Rosetta 3 (version tag v2019.27-dev60818–134-g04678680f9c).2.431 and then fit locally using the command fitmap in ChimeraX41. We also tested an iterative phenix.dock_in_map42 procedure. For the well-fitting models, there was very little difference between these two methods and thus the fitmap method was selected. The following programs were used to measure the listed metrics, in all cases using default parameters (1) Phenix42, for cross-correlation of the map and model masked by the area around the model (CCmask), cross-correlation of the N highest density peaks in the model-generated map to the map (CCvolume), cross-correlation of the N highest density peaks in the model-generated map and N highest density peaks in the map (CCpeaks), and map-to-model Fourier shell correlation (FSC) values (N is the number of grid points inside the molecular mask); (2) TEMPy43, for cross-correlation coefficient (CCC), mutual information (MI), least-square fit (LSF), envelope score (ENV), and segment-based Mander’s overlap coefficient (SMOC); (3) ChimeraX41 and in-house script for atomic inclusion44 and density occupancy; and (4) MapQ45, for Q-score. An RMSD filter was selected for each target based on visual inspection. Ranking of all the models was carried out by Z-score, following the two-pass procedure described in https://github.com/DasLab/CASP15_RNA_EM.All models for the 6 targets determined by cryo-EM  were assessed directly against the experimental maps. The RNA-protein targets (R1189 and R1190) were excluded from this analysis because none of the predicted models for these targets fit sufficiently well into the density to give robust alignments, but in principle this analysis is compatible with RNA-protein targets. First, models were fit into maps using two approaches. Models were aligned to the reference models  using US-align2.529 and calculating the Log Likelihood Gain (LLG) with respect to the diffraction data using Phaser46. For R1108 and R1117, with two RNA molecules in the asymmetric unit, the LLG was calculated for a single copy of the model ideally placed on chain A. A ranking of groups was derived from Z-scores computed from equal weighting of LLG, TFZ (translation-function Z-score from the model search), and CC . These ranking Z-scores were based on the same two-pass procedure as described in All models for the four targets determined by X-ray crystallography  were assessed directly against the X-ray data by superimposing them on the target structure with RNAalign47 via CCP4 Cloud48 and specifically the programs Phaser46 and MOLREP49. Map correlation coefficients were calculated with the phenix.get_cc_mtz_pdb tool42. MR strategies were chosen with reference to the accuracy achieved for different targets: highly accurate predictions typically succeed unmodified while extensive manual intervention can be required with poorer predictions. For R1117, the models were used unedited from all groups. For the other targets, where overall modeling was less accurate, different editing approaches were used with the models from group TS232 (AIchemy_RNA2). For R1107 and R1108, RNA model superposition was carried out with Theseus50 and nucleotides with higher structural variance values were removed in 10% intervals. The group 232 model_1 after removal of 10, 20, 30, 40 or 50% of nucleotides with highest structural divergence across the models was then used as a search model. MR also made use of models of the U1 small nuclear ribonucleoprotein A protein (U1ABD) component, which were generated using the AlphaFold 251 network in its local ColabFold implementation52. For R1116, a version of Slice’N’Dice53 modified to work with RNA inputs was used to split model 1 from group TS232 into three structural segments using the Birch algorithm from the SciKit toolbox54.Molecular Replacement was carried out using the CCP4 package33.1a priori whether these cases would be easy or difficult to model. The majority of targets (8) were solved by cryo-EM, with the rest (4) by X-ray crystallography.In 3.2The RNA-Puzzles assessment recognizes that RNA architecture results from a set of coherent interaction networks stabilizing a given fold. There are several interaction networks: the set formed by all Watson-Crick pairs, the set of contacts formed by the stacking between the bases, and finally the set formed by the non-Watson-Crick pairs, the interactions characteristic of tertiary folding. In a 3D structure, the set of Watson-Crick is not always the one predicted because in the folded structure, pairs at the extremities of the helical segments can either disappear or new ones can be formed. The correct choice of stacking between nucleotides or helices is critical for the overall global fold of the RNA. A wrong choice in the helices of the core can lead to very different folds from the native one. Finally, the appropriate positions and orientations of several elements allow for specific non-Watson-Crick pairs to form and lock in the native structure. An approximate association of helices may yield a molecular shape or envelope roughly similar to the native structure, but generally more open and much less compact than the native fold. In such cases, the key sequence conservations that maintain the actual native RNA fold are neither observed nor understood from the modeled structure. Therefore, in addition to using RMSD as a major metric for assessment, the analyses also included distinct metrics that are more sensitive to the interaction networks that comprise RNA.3.331) and the global distance test (GDT32). For the latter, we focused on the GDT score for tertiary structure (GDT_TS) rather than the high-accuracy GDT score (GDT_HA55) since the RNA models lacked nucleotide-level, much less atomic accuracy. To evaluate models’ local quality, to complement the RNA-specific INF score described in 35) score, which compares distances between atoms that are nearby in the experimental structure to the distances between those atoms in the predicted structure and may generalize well between proteins and nucleic acids.In a second assessment fully independent of the assessment based on RNA-Puzzles above, we explored the use of distinct metrics, largely drawn from assessment methods developed for proteins in previous CASP events and expanded here to RNA. For evaluating the global fold of predicted RNA structures, we computed the template modeling score  is small with only 30 nucleotides, and the TM-score values, which involve a length-dependent distance parameter, are much lower than GDT_TS values , d0 = 13.59 Å and most of the residues in a visually good model like R1138TS232_4 align within this threshold in the TM-score calculation. In contrast, GDT_TS uses fixed distance cutoffs of 1 Å, 2 Å, 4 Å, and 8 Å, and most of the RNA residues for the large molecules R1138TS232_4 do not align to the cryoEM structure within these thresholds  suggest that all targets, aside from the two RNA-protein complexes R1189 and R1190, elicited some predicted models that recovered correct global folds, based on criteria that have been previously discussed in the context of RNA template identification , the apo and holo structures of the aptamer Apta-FRET60 (R1136), the young and mature conformations of 6HBC61 (R1138), the four cryo-EM classes for the SL5 domain of the bat coronavirus HKU5 (R1156), and finally the RNA structures for the RsmZ-RsmA RNA-protein complexes with six vs. four proteins bound (R1189 vs R1190). In addition, for two cases, we measured the deviation between different models derived from the same experimental data (black lines in 62 (R1107 and R1108 vs PDB ID 3NKB), and the class I type III Pre-Q1 riboswitch versus the class I type I Pre-Q1 riboswitch63 (R1117 vs PDB ID 3Q50). In all cases with available homologous structures , predicted models surpassed TM-score, GDT_TS, lDDT, INF, and INF_WC values of models derived by directly using homologous structures. In some cases , predicted models reached TM-score, GDT_TS, and lDDT values comparable to the deviation between distinct experimentally determined conformations  compared to those that recover local details  or produce correct nucleotide geometries (low weight for clashscore). Each group’s Z-score for a given target was computed using their best predicted model, and groups’ total scores were calculated as the sum of all positive Z-scores across all targets . The top64; and https://predictioncenter.org/casp15/doc/presentations/Day3/).Interestingly, the top four groups did not include any server submissions; the top-ranked servers  placed at positions 8 and 9, and gave Z-scores that were more than three-fold lower than the top two predictor groups. We note that these top server submissions additionally exhibited secondary structures (Watson-Crick base-pairing) with lower accuracy than some other top predictors, as measured by INF_WC  alone or in combination, or using RMSD, gave rankings with the same top four groups, up to some switching of third and fourth place . Use of 3.4The poorer predictions and the presence of RNA-protein contacts for the two RNA-protein complexes RT1189 and RT1190 largely precluded useful accuracy rankings from the metrics described above, so we carried out a detailed visual assessment for these targets. This assessment involved checking whether predictions had the right nucleotide – amino acid contacts and then visually assessing whether the fold was correct. For the contact-based analysis, a contact was defined as any pair of nucleotide and amino acid containing atoms within 5 Å of one another. The Matthews Correlation Coefficient (MCC) was used to score the contacts made by the predictions against those of the targets. The distribution of scores is shown in For the RNA folding pattern analysis, we needed to establish a well-defined descriptor for the RNA-protein binding arrangement that was not dependent on superposition . This was achieved by coloring each protein by the regions of interaction in the RNA with the lowest order. Region order was determined by RNA sequence position (where 5’ is low). Using this scheme, the colors blue (B), then red (R), then green (G) were assigned to the three RsmA homodimers in RT1189 – and this pattern was compared for each model against the experimental structure (folding pattern: BRGRGB). In the case of RT1190, which involved only two RsmA homodimers, not all six regions of the RNA were bound; in particular, the regions of the RNA at approximately nucleotides 25 and 50 should not interact with a dimer. For RT1189, no models exhibited the correct folding pattern for interacting with the 6 RsmA proteins . For RT13.521.The ‘native’ experimental models built from RNA cryo-EM maps may be particularly susceptible to biases from computational procedures or biases in human interpretation due to the generally low resolution of these maps 37 penalizes density that is not accounted for by a prediction. We attempted to find a combination of scores to balance these problems; however, in the end, we decided that no weighted combination of metrics was sufficient to enable ranking of all available models and predictors. Although overall correlation of map-to-model metrics to model-to-model metrics was high  and unmodeled regions (CCpeaks), and scores developed or shown to be most discriminatory for medium-resolution maps, atomic inclusion (AI), mutual information (MI), and Segment based Manders’ Overlap Coefficient (SMOC)67. We note that no metrics tested were RNA specific and can be used to assess any macromolecular complex. We used Z-score-based ranking, previously described, with uniform weight of the selected metrics:For ranking, we selected a set of metrics that correlated well with visual inspections of fit and chose the standard measures of cross-correlation, accounting for modeled , GeneSilico (TS128), and RNApolis (TS081), and then others  achieved the highest Zn others . This ran others . This ovn others ; howeverOverall, the results show that assessing models based on direct comparison to cryo-EM maps, appears feasible and that results are consistent with rankings based on model-to-model comparisons. Direct map-to-model assessments may be particularly important in future CASP events as prediction accuracy increases and approaches the level of detail obtained at typical cryo-EM map resolutions.3.629 and then calculating a Log Likelihood Gain (LLG) and translation-function Z-score (TFZ) with Phaser’s RNP search46 and a global map CC with phenix.get_cc_mtz_pdb42. We used a Z-based ranking after a round of outlier removal . Similar to above, the only use of the experimentally derived model was to align predictor models. All predictor models were compared directly to the crystallographic data by first ideally placing the model using RNAalignoval see  with a uMX scores for the other targets were relatively uniform and comparatively poor .One of the highly successful models is that of the paranemic crossover triangle (PTX) R1128, a molecule with no natural homologs whose difficulty for modeling was unclear before the CASP15 results helices –F. The m helices . The tig helices . The goo helices . The ori helices . In the  helices . DespiteIndeed, for all four non-natural RNA targets in CASP15 , the AIc60. The ‘young’ and ‘mature’ structures do not differ in their Watson-Crick-Franklin helices but, to interconvert, would require breaking of a kissing loop interaction, twisting of the two kissing elements about their helical axes, and then reformation of the kissing loop.61 None of the CASP models produced models close to the ‘young’ structure. Other natural and designed RNA systems are known to display similar kinetic traps and topological isomers70, and it will be interesting to see if in future CASPs, such conformations can be blindly predicted.Interestingly, for the same R1138 six-helix-bundle, cryo-EM also captured a distinct ‘young’ structure for the RNA  that is A common theme was that the model ordering as submitted by the predictor groups generally did not correspond to the ranking based on RMSDs (or other metrics) between experimental and model structures. This was the case for the R1108 and R1138 targets noted above, where the fourth models from group TS232, and not the first models, were most accurate. Overall, in 63% of the sets of CASP15 predictor submissions across all 12 RNA targets, a model submitted as 2–5 was better than model 1 by GDT_TS, and the difference in GDT_TS between model 1 and the top scoring model for each group was no lower than if model 1 had been randomly selected . The modFurther inspection of TS110_2 helps illustrate the requirement of paying attention to the non-Watson-Crick pairs beyond the standard Watson-Crick pairs of the secondary structure, both in prediction and in assessment of RNA targets resolved by cryo-EM. The presence of alternative structures, noted above for the non-natural six helix bundle R1138, was a common theme in RNA targets in CASP , and was20In summary, inspection of top-ranked CASP15 RNA models confirms, in each case, good prediction of global fold but also reveals fine details and/or aspects of conformational heterogeneity that have not been captured by the models. Ordering each set of five models by the predictor groups also typically did not correlate with the models’ accuracy. Similar conclusions for R1108, R1128, R1138, R1149 and R1156 based on alternative analyses by RNA experimental groups, are described in a separate paper prepared for the CASP issue.3.871. While we began these explorations in studies described above to rank models based on agreement with X-ray data . Across the up to five models submitted by groups, we found that 3 out of 34 groups succeeded with at least one model, using global map correlation coefficient CC > 0.2 as the criterion of success . Among tMX values in 73 was used as an indication of local prediction reliability and divergent regions removed before the edited model_1 was used as a search model. This approach borrows from that taken for proteins by the MR pipeline AMPLE74. R1107 was successfully solved by first placing the protein chain then the edited RNA search model, both with Phaser. The result  we could place the protein component, but the RNA component was reversed, providing only a partial solution. The truncated group TS232 models for R1108 were of a sufficient quality to solve R1107 and the resulting protein/RNA complex could then be used to solve R1108 with an Rfree of 41%.For the other three CASP targets solved by X-ray diffraction, visual inspection and the Ze result  has an R53 was therefore used to split model_1 into three structural units. A portion comprising nucleotides 1–24;125–157 could then be placed with MOLREP which indicated a partial solution after refinement . Three copies of a second fragment comprising 38–63 could then be placed to largely complete the structure with Phaser scores of LLG: 1324 and TFZ: 9.6. These values are unambiguously indicative of successful Molecular Replacement: for example, TFZ > 8 corresponds to ‘Definitely’ solved according to Phaser software guidance.75 The result  could be challenging, especially at 3.0 Å or worse resolution.Inspection of the Group 232 models for R1116 showed that more extensive model editing would be required. A modified version of Slice’N’Dicee result  has an R4CASP15 enabled a timely assessment of 3D RNA structure prediction, with 8 RNA targets solved by cryo-EM and 4 by X-ray crystallography. Forty two predictors from 25 research centers made submissions for at least one of these targets, many of whom had not published studies on RNA prior to CASP but explored deep learning approaches that were novel for the RNA field. The twelve RNA targets ranged in difficulty from “easy”, with clearly identifiable templates in the structure database, to “difficult”, with no templates. When looking at all five submissions for each target, visually good predictions were submitted for all 10 RNA-only targets, including 4 non-natural RNA targets that had no global homology to previously solved structures. Two protein-RNA complexes were not modeled accurately.Quantitative rankings of predictor groups were carried out by independent teams, based on RMSD and INF metrics developed in the RNA-Puzzles trials and based on TM-score, GDT_TS, and lDDT more familiar to protein structure assessments in prior CASP. Both rankings agreed in placing TS232 (AIchemy_RNA2) first, TS287 (Chen) second, and TS128 and TS081 (GeneSilico and RNAPolis) as tied for third. These rankings were also confirmed by analyses comparing predicted models to maps (for cryo-EM targets) and statistics related to molecular replacement . The top-ranked models for the 10 RNA-only targets captured global folds well, as assessed by visual inspection and by achievement of GDT_TS values greater than 45 and/or TM-score values greater than 0.45. Nevertheless, fine details such as non canonical pairs and hydrogen bonding at junctions were inaccurate in these models, even when taking into account sources of uncertainty for the experimental structures. Conformational heterogeneity in some targets, R1136 and R1156, was indicated by the presence of multiple structures captured by cryo-EM but was not captured by any group in their range of submitted models . Despite76 uses an end-to-end differentiable network inspired by AlphaFold 251. However, these AI-based predictions did not perform as well as expected and did not surpass prediction methods previously tested in RNA-Puzzles , which have been continuously improving for the past decade. The AI-based approaches77 also failed to demonstrate the accuracy claimed in their preprint papers, perhaps due to the limited amount of training data.Has there been improvement in RNA modeling in CASP15 compared to prior RNA-puzzles? Achieving accurate positioning of helices with respect to each other by modeling is often feasible when the single-stranded segments are short and unpaired because RNA helices are bulky and the interconnecting strands each have a polarity, leading to a reduced search space for modeling helix arrangements. The good helix positioning observed here in CASP15 was also regularly observed during previous RNA-Puzzles assessments and in previous RNA modeling efforts. During this CASP15 experiment, some research groups tried to use prediction approaches that were similar to AI-based methods for predicting the structure of proteins. For example, AlChemy_RNA64), they appeared to still make use of human intuition. While there were cases where server models were more accurate than ‘human’ models from the same laboratory , generally server models were worse in quality than the top 4 human predictor groups. Going forward, an important frontier for the RNA structure prediction field to focus on will be automation, so that methods can be more widely used and applied at the genomic scale, as is now the case for protein structure prediction methods. While the sparser data available for RNA structure, compared to protein structure, has complicated development of robust deep learning algorithms, recent accelerations in RNA structure determination – particularly from cryo-EM12 – and the availability of high-throughput sequencing-based methods sensitive to RNA structure78 may help close the gap between RNA and protein computational methods. Interestingly, secondary structures from even the top server predictions were poorer than those from ‘human’ groups, highlighting an area of potentially immediate improvement.In addition to not using deep learning, the top four RNA predictors shared the property that they were not servers and, based on their own accounts , rather than separate single-structure models, would better incentivize development of methods for predicting conformational ensembles of macromolecules, including molecular dynamics methods that have been previously difficult to assess. Furthermore, scoring of these ensembles directly against data should be feasible; e.g., log-likelihood frameworks and GPU-enabled software79 might enable predicted multi-model cryo-ensembles to be compared to the entire collection of electron micrographs collected for a target.Third, many and perhaps most of the CASP15 RNA targets showed conformational flexibility, e.g., as evidenced by differences in conformations of different monomers in crystallographic asymmetric units or, in cryo-ensembles captured by electron microscopy as classes of conformations separable by automated subclassification and/or 3D variability analysisSupplement 1


Combinatorial RNA Engineering via Scaffold Tagged gRNA (CREST), which can simultaneously execute multiple RNA modulation functions on different RNA targets. In CREST, RNA scaffolds are appended to the 3’ end of Cas13 gRNA and their cognate RNA binding proteins are fused with enzymatic domains for manipulation. Taking RNA alternative splicing, A-to-G and C-to-U base editing as examples, we developed bifunctional and tri-functional CREST systems for simultaneously RNA manipulation. Furthermore, by fusing two split fragments of the deaminase domain of ADAR2 to dCas13 and/or PUFc respectively, we reconstituted its enzyme activity at target sites. This split design can reduce nearly 99% of off-target events otherwise induced by a full-length effector. The flexibility of the CREST framework will enrich the transcriptome engineering toolbox for the study of RNA biology.RNA processing and metabolism are subjected to precise regulation in the cell to ensure integrity and functions of RNA. Though targeted RNA engineering has become feasible with the discovery and engineering of the CRISPR-Cas13 system, simultaneous modulation of different RNA processing steps remains unavailable. In addition, off-target events resulting from effectors fused with dCas13 limit its application. Here we developed a novel platform,   Post-transcriptional regulation controls gene expression at the RNA level, and its dysfunction is involved in many diseases . LeveragA variety of tools based on RNA scaffolds were developed for transcriptome engineering in the last two decades. An RNA scaffold is an RNA motif with a specific sequence or structure which can be recognized by a given RNA binding domain (RBD). Several pairs of RNA scaffolds and their RBDs are well-characterized, including PBS (PUF binding site) for PUF domain derived from Pumilio proteins, MS2 for MCP (MS2 coating protein) and PP7 for PCP (PP7 coating protein) . FusionsRecent discoveries of novel CRISPR-Cas13 systems overcome the limitations of RNA scaffold-based tools and enable efficient and precise targeting of endogenous RNAs . The CRIAn additional challenge in RNA manipulation is the poor substrate selectivity of the effectors. For example, adenosine deaminase acting on RNA (ADAR) binds to double-stranded RNA and then converts adenosine (A) to inosine (I) via deamination. The resultant inosine is functionally equivalent to guanosine (G) . Fusion Combinatorial RNA Engineering via Scaffold Tagged gRNA (CREST) by combining RNA scaffold with CRISPR-Cas13. As proof-of-principle, we engineered orthogonal MCP-, PCP- and PUF-based CREST modules for splicing modulation. We then created CREST base editing modules for A-to-G and C-to-U editing. We further generated high-efficient split RNA editing modules using CREST architecture and showed the significant reduction of off-target events in the split version compared with full-length effector. Finally, we demonstrated that orthogonal CREST modules could be used for multifunctional transcriptome engineering, specifically manipulating simultaneous alternative splicing and base editing. The CREST platform will enable complex operations within RNA regulatory networks, especially for fundamental research in the RNA biology field.Here, we report a novel platform called A4 or PUFc by TEDA. pC0079-pCMV-dCas13b6-mapkNES-GS-dADAR2   was introduced into the coding region of mScarlet by PCR to enable the easy readout with flow cytometry. For C-to-U base editing reporter, we ordered the oligos with the previous reported targeting region ‘CTGATCTGCCTGTCCCACATCAATCGA’ and inserted it into the downstream of mScarlet (All coding sequences used in this study were cloned into pmax expression vector (Lonza) by TEDA as previously reported . IDT gBlg Zhang)  was usedmScarlet  by anneagRNA expression cloning plasmids were cloned into a pCR8 vector with U6 promoter and a ccdbCam selection cassette flanked by two BbsI restriction enzyme cut sites as described previously . Oligos 2. For transfection, HEK293T cells were plated in 12-well, 24-well or 96-well plate at the density of 1.5–2 million cells per plate while HeLa, U-2 OS and HCT-116 cells were plated at 3 million cells per plate one day prior to transfection. All transfection experiments were done with Lipofectamine™ 3000 Transfection Reagent  according to manufacturer's instructions. Cells were harvested 48-hour after transfection for flow cytometry or RNA extraction.HEK293T, HeLa, U-2 OS cells were grown in Dulbecco's modified Eagle's medium  with 10% fetal bovine serum (FBS) , 4% glutamax , 1% sodium pyruvate . HCT-116 was cultured in McCoy's 5a Medium Modified medium  or NucleoSpin RNA Plus Kit . RNA concentration was measured by nanodrop and 500–1000 ng total RNAs were used for reverse transcription in 10 ul reaction volume by High-Capacity RNA-to-cDNA kit . Equal amount of RNAs were used in the same experiment.For RT-PCR, 2-μl cDNA was used for each reaction by Phusion® HighFidelity DNA Polymerase (NEB #M0530L) using specific primers for 35 cycles. PCR products were then analyzed on a 3% agarose gel.-exclusion – Ct-inclusion) and then normalized to the condition of control gRNA in the same experiment (displayed as first column in each figure) to determine the fold change of inclusion/exclusion ratio. All primer sequences are listed in For qPCR, 4-ul of diluted cDNA (1:40 dilution rate) was used for each reaction with 5 ul SsoAdvanced Universal SYBR® Green Supermix  and 1 ul of primer mix . qPCR reaction was run in ViiA 7 or QuantStudio™ Real-Time PCR System (ThermoFisher) with standard program. The inclusion/exclusion ratio was calculated by 2^ for 35 cycles. PCR products were loaded on a 1% agarose gel, run at 140 V for 50 min, and purified by QIAquick PCR Purification Kit . Sanger sequencing was done in Quintarabio at UConn Health. All primer sequences were listed in https://moriaritylab.shinyapps.io/editr_v10/ (-score/(A-score + G-score) × 100% and CtoU = T-score/ (C-score + T-score) × 100%.Data (.ab1 format) from sanger sequencing were uploaded to the online tool EditR for quantification  were added into each well for incubation (8 min at 37°C). Then 80 ul of fresh media were directly added on the top of trypsin and mixed by pipetting up and down. All cells were transferred to a V bottom 96-well plate to run flow cytometry analysis on LSRFortessa X-20 (BD Bioscience). mScarlet/Clover ratio was calculated as Q2/(Q2 + Q4) based on the percentage of each gate .HEK293T cells were transfected with given plasmids and then harvested for RNA extraction by NucleoSpin RNA Plus Kit (Takara) 48 hour after transfection. RNA samples were sent to GENEWIZ for next generation sequencing using polyA selection strategy for cDNA library generation and targeting 15–20 million paired-end reads for each sample.FASTQ reads were aligned to the GRCh38.p1 human reference assembly  by the nf-core/rnaseq (v3.0) pipeline using default parameters. Briefly, FASTQ files were processed by FastQC (0.11.9) for quality control and adapter sequences were trimmed by TrimGalore (0.6.6). After pre-processing of raw reads, alignment and quantification were done by STAR (2.6.1d) and Salmon (1.4.0), respectively. To exclude the effect of library size, all bam files from nf-core/rnaseq were downsampled to 15 million paired-end reads per sample, then used as input for off-target analysis.REDItoolDnaRna.py from REDItools (v 1.2) with default parameters was used to analyze RNA editing sites. Nearly 30–40 million sites were assessed for base change in each sample. All sites were then filtered by coverage (Coverage-q30 > 10) in every sample and desired base substitution (AllSubs = = ‘AG’ or ‘CT’). Plain HEK293T line was used as a negative control and all significant editing events were calculated by Fisher Exact test followed by by Benjamini Hochberg correction then filtered with false discovery rate (FDR) ≤0.05 and change of editing percentage ≥10%.For base editing, n = 3). The inclusion level difference (Δψ) of each candidate exon skipping (SE) event was calculated using reads mapping to the body of exons as well as to splice junctions. Considering that low coverage exons and splicing junctions lead to low confidence inclusion levels, we filtered out the cases in which average counts of two replicates for inclusion or skipping were <10. To discover off-target SE event, we set the threshold parameters at | Δψ | ≥0.2 and FDR ≤0.01.For alternative splicing, rMATs (v 4.1.0) with default parameters was used to quantify splicing events at transcriptome-wide. Each group was compared with negative control transfected non-targeting gRNA (A4) and RNA binding domain (RBD) fused with effector. The 5’ of the gRNA (spacer) is designed to be complementary to the target transcript while its 3’ end is appended with RNA scaffolds, acting as the bridge between dCas13bA4 and RBD-effector fusions . We. WeSMN2 ocessing . The resocessing . We nextocessing . TherefoThe orthogonality between different RNA scaffolds is key to multifunctional RNA engineering. Using alternative splicing as a readout, we evaluated orthogonality of CREST by co-transfecting all pairwise combinations of gRNA with different RNA scaffolds and RBD-RBFOX1. We showed that MCP-RBFOX1 but not PUFc-RBFOX1 activated the inclusion of exon 7 in the presence of gRNA-MS2 Figure . SimilarA4, these gRNAs would recruit PUFc-ADAR2-DD to the target RNA. Given that ADARs selectively deaminate adenosines of A-C mismatches in the context of double-stranded RNA, we placed a cytidine in the gRNA at the position facing the target adenosine on the target transcript  to convert adenosine (A) to inosine (I), which is recognized as guanosine (G) by translation and splicing machineries, making the A-to-I conversion equivalent to A-to-G editing . Given ianscript . A previanscript . We usedanscript . To examanscript , we trananscript . Though y Figure , indicaty Figure .A4 or PUFc. We speculated that CREST is able to reconstitute ADAR2-DD catalytic activity at the target locus in two scenarios. In the first scenario, PUFc is fused with either the N-or C-terminal fragments of ADAR2-DD and tandem PBSc on the gRNA recruits multiple PUFc, which stochastically includes both N- or C-terminal fragments of ADAR2-DD for reconstitution. In the second scenario, different fragments of ADAR2-DD are fused with dCas13bA4 and PUFc respectively and the gRNA functions as a bridge to bring them together. By screening all possible combinations, we found two optimal configurations, PUFc-N/PUFc-C and dCas13bA4-N/PUFc-C, which were termed as SP1 and SP2 respectively  were reported to be corrected by direct fusion of dCas13 and ADAR2-DD at high efficiency but others (CFTR and HBB) were edited less than 10%  but not in direct fusion, indicating the advantage of CREST system in specific contexts  as used for our split ADAR2-DD above  and direct fusion of dCas13b-RBFOX1 induced the percentage of spliced-in (PSI) of exon 23 from 20% to 40%  lead to significant A-to-G editing events without dCas13bA4, their efficiency was reduced, indicating that dCas13bA4 may help stabilize the CREST complex for editing  allows the appended gRNA scaffolds to recruit effectors on gRNA for CREST functionality.A4, we simultaneously modulated alternative splicing and base editing by providing function-matching gRNAs that can specifically recruit corresponding effectors at the respective targets. CREST dCas13bA4 and RBD-effectors can be ‘pre-installed’ into the cells to which the addition of gRNAs alone can induce combinatorial editing and modulations of multiple different transcripts.We demonstrated the orthogonality of different RNA scaffolds in our CREST platform and concurrently multifunctional RNA modulation for the first time. Though RNA scaffold and RBD-ADAR2-DD was shown to be effective in A-to-G editing , we founAn additional application of our CREST system is its utility in potentially reducing dCas13-indpendent off-target effects encountered when full-length effectors are utilized . RestoriIn sum, we developed a new platform (CREST) to overcome the challenges in CRISPR-Cas13 mediated RNA engineering. By tagging gRNAs with orthogonal RNA scaffolds, CREST fills the gap of simultaneously modulating multiple transcripts for different functions, such as alternative splicing and base editing. This architecture allows the introduction of additional functions into the RNA modulation toolbox via orthogonal scaffolds and effectors. In addition, CREST enables us to reconstitute the enzymatic activity of split ADAR2 with high efficiency and specificity. Overall, the CREST system may benefit a wide range of RNA-centric research and applications.gkad547_Supplemental_FileClick here for additional data file.


RNA modification includes four main types, N6‐methyladenosine, N1‐methyladenosine, alternative polyadenylation (APA), and adenosine‐to‐inosine (A‐to‐I) RNA editing, involving 41 enzymes that serve as “writers”, “readers” and “erasers”. By collecting RNA modifying enzyme information in 1759 hepatobiliary malignancy (HBM) samples from 11 datasets, an RNA modification HBM Score (RH_score) was established based on unsupervised cluster analysis of RNA modification‐associated differentially expressed genes (DEGs). We identified the imbalanced expression of 41 RNA modification enzymes in HBM, which was scientifically categorized into two groups: RH_Score high and RH_Score low. A high RH_Score was associated with a worse prognosis and more immature immune cells in the tumor microenvironment (TME), while a low RH_Score was associated with a better prognosis and more mature immune cells in the TME. Further analysis using single‐cell databases showed that the high RH_Score was immune exhaustion in the TME. RH_Score was involved in transcriptional regulation and post‐transcriptional events in HBM. Additionally, resistant and sensitive drugs were selected based on RNA modification, and anti‐PD‐L1 therapy responded better with low RH_Score. In conclusion, our study comprehensively analyzes RNA modification in HBM, which induces TME changes and transcriptional and posttranscriptional events, implying potential guiding significance in prognosis prediction and treatment options. Based on RNA modification‐associated differentially expressed genes from 1659 hepatobiliary malignancy (HBM) samples from 10 datasets, an RNA modification HBM Score (RH_score) is scientifically established and tested in 100 hepatocellular carcinoma clinical samples, which demonstrates excellent efficacy in terms of prognosis and targeted treatment. A high RH_Score means a worse prognosis and more immature immune cells, while a low RH_Score has the opposite result. Furthermore, we designed an RH score model to predict the clinical prognosis, response to molecular targeted drugs and immunotherapy, and transcriptional and posttranscriptional events based on RNA modification‐regulated DEGs, thereby providing a novel panel of next‐generation sequencing for clinical translation.22.1In this study, we selected 41 RNA modification regulators Table  to perfoFirst, the prevalence of nonsilent somatic mutations in 41 regulators was assessed in HBM. Consistent with a previous study,Next, genome ontology (GO) analysis, genome set variation analysis (GSVA), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to compare the mutant and non‐mutant groups of regulatory genes in the TCGA cohort Figure . We defi2.2To comprehensively understand the expression patterns of RNA modification regulators in HBM, a total of 424 samples from TCGA with clinical data were chosen for further study Table . A univap < 0.001). Next, we performed a KEGG enrichment analysis to evaluate the biological significance of these two clusters . We used this model to calculate the scores of various classifications, finding that the score in cluster1 was higher than that in cluster2 , discovering that patients with high RH_Score had poorer survival Figure . The AUCIn this study, HBM included three pathological types of liver cancer: HCC, intrahepatic cholangiocarcinoma (ICC), and mixed type (HCC+ICC). Among the three types of liver cancer, ICC is reported to have the highest malignant degree and the worst prognosis. To understand the relationship between the RH score and HBM pathological subtypes, the RH score of different HBM subtypes in the TCGA cohort was analyzed. We found that different HBM subtypes showed a significant difference in RH_Score, the RH_Score of the ICC group was the highest Figure . Additio2.4To comprehensively elaborate the immune environment in RNA modification molecular signatures, we performed a global niche atlas with cell classification and marker gene identification using a single‐cell sequencing dataset (GSE125449) without anti‐PD‐L1 treatment from the NCBI Public Data Platform Tables  and 10. Unsupervised clustering of macrophages (an important component of myeloid cells) and T cells were then performed with 11 clusters found, including four clusters for macrophages  and seven clusters for T cells  Figure . Usually2.5To further evaluate the role of the RH score in interpreting transcriptional and posttranscriptional events, we focused on the processes associated with RNA modification, including APA and A‐I editing. It has been reported that RNA modification patterns are related to different miRNA signatures with the effect of APA modification.p < 0.001) and CES2  exhibited significant lengthening, which showed shorter survival in patients with HBM  and POLH  were related to poorer survival time in HBM patients , ML323 , dihydrorotenone , alisertib , erlotinib , Wnt‐C59 , AGI‐6780 , while the others showed resistance, including AZD8186 , AMG‐319 , MK‐2206 , dasatinib , and BDP < 0.001 . In addition, drug sensitivity to RNA modification regulators was closely related to specific signaling pathways  with the anti‐PD‐L1 treatment Table . Consist2.7Given that the RH_Score appears to correlate with the TME Figure , we expl36A, m1A, APA, and A‐I RNA editing, are the most common and the most studied types and they are primarily mediated by the “writer”, “reader” and “eraser” enzymes and constitute key mechanisms for epigenetic regulation of tumorigenesis.As a rising research hotspot, the function of RNA modifications in certain cancers has been revealed. More than 100 different types of modifications have been shown to be present in RNA. Among them, four RNA modifications, namely, m+ T cells, which regulates durable neoantigen‐specific immunity.RNA modification enzymes also influenced immune infiltration of the TME, which played a significant role in tumor progression and prognosis. Researchers found that KIAA1429 (an m6A methyltransferase) resulted in less TME dendritic cell (DC) infiltration and decreased expression of MHC molecules.6Sig score according to 23 m6A regulators, aiming to estimate prognosis and immunotherapy. The WM_score presented by Chen et al.More importantly, a reliable predictive RH_Score model was established based on RNA modification enzymes to provide a brighter and more precise landscape for targeted drugs and immunotherapy applications. Many researchers have concentrated on this field. Chong et al.From the perspective of RNA modification enzymes, TME analysis may be a ray of dawn for hepatobiliary tumor precision medicine. Although we enrolled 41 RNA‐modifying enzymes, other participants in the RNA modification process required inclusion. In addition, the predictive performance of the RH score still needs testing in other tumors and immunotherapy regimens, such as anti‐PD‐L1, anti‐cytotoxic T‐lymphocyte antigen‐4, therapeutic antibody, and adoptive T cell transfer.4In this study, we comprehensively analyzed RNA modifications in HBM that induce TME changes and transcriptional and posttranscriptional events, and the results may have potential guiding significance in prognosis prediction and treatment options.55.1https://gdc‐portal.nci.nih.gov/) and UCSC Xena data website (https://xenabrowser.net/datapages/). We acquired mRNA expression data of liver cancer samples from 5 GEO datasets . Since these five datasets were gathered from different chip platforms with unclear clinical information and various confounding factors, we analyzed each data set individually to avoid false positive results.The mRNA expression data, miRNA expression data, mutation data, CNV data, clinical information, and survival data of liver cancer and cholangiocarcinoma were collected from the TCGA database (https://10xgenomics.com) was used to retain cells with cell gene numbers and UMI numbers within the mean ± 2 SD and with a mitochondrial gene ratio of less than 10% as high‐quality cells and then ready for downstream analysis. Seurathttps://satijalab.org/seurat/) was used for cell filtration, standardization, clustering, cell subgroup classification, and marker gene screening.The single‐cell dataset (GSE125449) was obtained from the NCBI Public Data Platform, which is single‐cell transcriptome profiling of HBM biospecimens from nine hepatocellular carcinoma and ten intrahepatic cholangiocarcinoma patients. Cell Ranger . Based on this dataset, we determined the relationship between the RH score and various cancer immunotherapies.Detailed clinical immunotherapy data were downloaded from the IMvigor210 dataset5.2https://cibersort.stanford.edu/). For chip expression, data after quantile normalization were collected, and for RNA‐seq expression, nonnormalized data were selected. The number of permutation tests was set to 1000.The relative abundance of 22 immune cell types in cancer samples was computed by the CIBERSORT, RRID:SCR_016955 algorithm .Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.


Surprisingly, LinearCoFold and LinearCoPartition’s predictions have higher PPV and sensitivity of intermolecular base pairs. Furthermore, we apply LinearCoFold to predict the RNA–RNA interaction between SARS-CoV-2 genomic RNA (gRNA) and human U4 small nuclear RNA (snRNA), which has been experimentally studied, and observe that LinearCoFold’s prediction correlates better with the wet lab results than RNAcofold’s.Many RNAs function through RNA–RNA interactions. Fast and reliable RNA structure prediction with consideration of RNA–RNA interaction is useful, however, existing tools are either too simplistic or too slow. To address this issue, we present LinearCoFold, which approximates the complete minimum free energy structure of two strands in linear time, and LinearCoPartition, which approximates the cofolding partition function and base pairing probabilities in linear time. LinearCoFold and LinearCoPartition are orders of magnitude faster than RNAcofold. For example, on a sequence pair with combined length of 26,190   RNA strands can interact via intermolecular base pairing and form RNA–RNA complexes. In nature, many RNAs function through these RNA–RNA interactions Table . For insO(n3) algorithm to compute the structure of the target sequence (n is the sequence length), resulting in a substantial slow down compared to RNAhybrid and RNAplex.Some existing systems are used to predict RNA–RNA interaction and structures. We categorize these systems into three categories, and summarize them in Figure D. aromatica 3) time complexity and O((n + m)2) space complexity (NUPACK predicts pseudoknotted structure in O((n + m)5) time and O((n + m)4) space), where n and m are the lengths of the two strands. Due to the cubic or worse time complexity, such traditional systems are slow or even impossible to run on long sequences (nt) and 23S (∼3000 nt) ribosomal RNA, viral RNA genomes such as HIV (∼10 000 nt), RSV (∼15 000 nt), Ebola (∼18 000 nt) and SARS-CoV-2 (∼30 000 nt), and even longer transcript sequences   and Thrent), LinearCoFold is 86.8× faster than RNAcofold MFE mode, and LinearCoPartition is 642.3× faster than RNAcofold partition function mode. Notably, RNAcofold cannot run on any combined sequences longer than 32 767 nt, but our LinearCoFold and LinearCoPartition have no limitation of sequence length internally, and can scale up to sequences of length 100 000 nt in 2.2 and 6.9 minutes, respectively. With respect to accuracy, LinearCoFold and LinearCoPartition’s predictions are more accurate in sensitivity (the fraction of known pairs correctly predicted) and Positive Predictive Value . Compared with RNAcofold (MFE mode), the overall PPV and sensitivity of LinearCoFold increase by +4.0% and +11.6%, respectively; compared with RNAcofold + MEA, LinearCoPartition + MEA gains improvement of +2.9% on PPV and +5.7% on sensitivity; compared with RNAcofold + TheshKnot, LinearCoPartition + TheshKnot increases by +1.5% and +5.2% on PPV and sensitivity, respectively. Furthermore, we demonstrate that our predicted interaction correlates better to the wet lab results of the RNA–RNA interaction between SARS-CoV-2 gRNA and human U4 snRNA, showing that our algorithms can be used as a fast and reliable tool in genome-wide studies.We compare the efficiency and scalability of our algorithms to Vienna RNAcofold, and confirm that the runtime and memory usage of LinearCoFold and LinearCoPartition scale linearly against the combined sequence length, while RNAcofold scales cubically in runtime and quadratically in memory usage. LinearCoFold and LinearCoPartition are orders of magnitude faster than RNAcofold. On the longest data point in the benchmark dataset that RNAcofold can run  as ξ, which includes both intramolecular base pairs and non-intermost intermolecular base pairs; we denote the free energy change of the innermost intermolecular base pair  as k is denoted as δ. Thus, the free energy change x and its structure y can be decomposed as:After this transformation, the classical dynamic programming algorithm for single-strand folding ,40 can bwhereNote that if no base pair covers the nick point, i.e. the two strands do not interact with each other, two-strand cofolding is simply single-strand folding of two strands separately.i, j) covers the nick point, the span  can be further categorized into three types: nicked hairpin, nicked interior loop and nicked multiloop, based on the type of loops it enclosed  where i < p < q < j, shown in the third row of Figure Such nicked loops are considered to be exterior loops when calculating their free energy change. Note that the nick point only affects the innermost loop that directly covers it; the loops are still normal interior loops and multiloops in the case that the nick point is covered by another base pair  structure of double-strand RNAs in linear runtime without imposing a limit on base pair length. Formally, LinearCoFold finds the MFE structure b states with lower folding free energy change at each step j. As a result, the time complexity of LinearCoFold is O((n + m)blogb), where b is the beam size and the default value is 100. It is clear in the pseudocode that LinearCoFold does not impose any constraints on base-pairing distance, which is different from the local folding approximation. To extend to two-strands cofolding, LinearCoFold applies the Inspired by LinearFold , LinearCentclass1pt{minimaE, P, M1 and M2 retain the MFE structure for the span , where P requires i paired with j, M1 has at least one branch with i as the 5’ end of the leftmost branch, and M2 contains at least two branches with i and j as the 5’ end and the 3’ end of the leftmost and rightmost branches, respectively , and also adds two states Compared to the Nussinov–Jacobson energy model, the Zuker system based on the Turner energy mode defines more states to represent different types of loops. Formally, for single-strand folding, state Q(x) sums the equilibrium constants of all possible secondary structures in the ensemble. Using the revised Nussinov-Jacobson energy model, the partition function of two interacting RNAs can be formalized as:Beyond the MFE structure, a partition function and base-pairing probabilities of cofolding two RNA strands, and their assembled structure from the ensemble  are desired in many cases. A partition function R is the universal gas constant and T is the absolute temperature, and where entclass1pt{minimaQi, j in a left-to-right order. Supplementary Figures S2 and S3 shows simplified pseudocode based on the Nussinov-Jacobson model. LinearCoPartition consists of two major steps: partition function (‘inside phase’) and base-pairing probability (‘outside phase’) calculations, where the outside phase is symmetrical to the inside phase but in a ‘right-to-left’ order. The base-pairing probability pi, j can be derived if position i can be paired with j.We further extend LinearCoFold to LinearCoPartition based on the inside-outside algorithm following LinearPartition , calculaet al. discussed the distinguishability issue of multi-strand folding, i.e. some of the permutations of strands are indistinguishable due to physical symmetries . Therefore, we place the shorter strand in front of the longer one by default, and provide the options for users to run in a customized order.Dirks mmetries , which net al. also discussed the distinguishability issue for identical input sequences, which results in algorithmic overcounting in partition function even when ruling out indistinguishable permutations. In our case, we assume the input sequences are different.It is worth mentioning that Dirks nt to 3651 nt. We refer to this dataset as the Meyer dataset, and use it for the efficiency and accuracy benchmarks. The second dataset contains 16 miRNA-mRNA pairs from the TargetScan database  ranging from 2432 to 100 297 nt. We refer this dataset as the TargetScan dataset in the paper, and use it for the efficiency benchmark only. For benchmarks, we used a Linux machine (CentOS 7.9.2009) with 2.40 GHz Intel Xeon E5-2630 v3 CPU and 16 GB memory, and gcc 4.8.5. We used the default setting of RNAcofold; the beam size b of LinearCoFold and LinearCoPartition is set to 100  for all experiments. Note that the default value of b is inherited from LinearFold . For one of the longest combined sequences with length of 3651 (255+3396) nt, LinearCoFold is 3.7× faster than RNAcofold MFE mode (4.7 s versus 17.2 s), and LinearCoPartition is 12.7× faster than RNAcofold partition-function mode (14.9 s versus 189.1 s).We first investigated the efficiency of LinearCoFold and LinearCoPartition by plotting the runtime against the combined sequence length, and compared them to Vienna RNAcofold on the Meyer dataset, whose sequences are relatively shorter than the TargetScan dataset. Figure nt in 2.2 and 6.9 min, respectively, while RNAcofold cannot process any sequences with combined sequence length longer than 32 767 nt.Figure nt) in the dataset that RNAcofold can run, LinearCoFold is 86.8× faster than RNAcofold MFE mode (0.6 min versus 52.1 min), and surprisingly, LinearCoPartition is 642.3× faster than RNAcofold partition-function mode (1.8 min versus 1156.2 min).For the longest sequence pair  and sensitivity (the fraction of annotated pairs predicted) to measure the accuracy only for intermolecular base pairs across the two families in the Meyer dataset, and got the overall accuracy averaged on the two families.γ  from 1 to 4; compared to RNAcofold MEA, LinearCoFold MEA shifts to the top-right corner, which means that it has higher PPV and sensitivity. For γ = 1, the overall PPV and sensitivity of LinearCoPartition MEA increase 2.9% and 5.7%, respectively. In addition, for the ThreshKnot structures  from 0.2 to 0.35; compared to RNAcofold ThreshKnot, LinearCoFold ThreshKnot also shifts to the top-right corner. For θ = 0.3, the overall PPV and Sensitivity of LinearCoPartition ThreshKnot increase 1.5% and 5.2%, respectively. Figure Figure ructures , we plotnt; csgD mRNA, 951 nt), we illustrated the MFE structures, the base-pairing probabilities, the MEA structures (γ = 1) and the ThreshKnot structures (θ = 0.3) generated from RNAcofold MFE mode, partition-function (-p) mode, as well as LinearCoFold and LinearCoPartition  and a linear-time computation of partition function and base pairing probabilities (LinearCoPartition). Unlike other nt, LinearCoFold is 86.8 × faster than RNAcofold MFE mode, and LinearCoPartition is 642.3× faster than RNAcofold partition function mode. See Figure LinearCoFold and LinearCoPartition both run in linear time and space, and are orders of magnitude faster than Vienna RNAcofold. On a sequence pair with combined length of 26190 Evaluated on the Meyer dataset with annotated intermolecular base pairs, LinearCoFold and LinearCoPartition’s predictions have higher PPV and sensitivity. The overall PPV and Sensitivity of LinearCoFold increase +4.0% and +11.6% over RNAcofold MFE, respectively; LinearCoPartition MEA increases +2.9% on PPV and +5.7% on sensitivity over RNAcofold MEA, and LinearCoPartition TheshKnot increases +1.5% on PPV and +5.2% on sensitivity over RNAcofold TheshKnot. See Figure LinearCoFold can predicts interaction between viral genomes and host RNAs. For the SARS-CoV-2 gRNA interacting with human U4 snRNA confirmed by a previous wet lab study, LinearCoFold correctly predicts 11 out of 18 intermolecular base pairs, while RNAcofold predicts 0 out of 18. See Figure Our algorithm has several potential extensions.O(n3) folding algorithms, and are slow for long sequences (Multiple RNAs can form into complex conformations, but current algorithms and tools are built on the classical equences . Our LinFollowing LinearSampling , a lineagkad664_Supplemental_FilesClick here for additional data file.


Immune checkpoint inhibitors (ICIs) combined with angiogenesis inhibitors (AGIs) have become increasingly available for multiple types of cancers, although the cardiovascular safety profiles of this combination therapy in real-world settings have not been elucidated to date. Therefore, we aimed to comprehensively investigate the cardiovascular toxicity profiles of ICIs combined with AGIs in comparison with ICIs alone.st quarter of 2014 to the 1st quarter of 2022 was retrospectively queried to extract reports of cardiovascular adverse events (AEs) associated with ICIs alone, AGIs alone and combination therapy. To perform disproportionality analysis, the reporting odds ratios (RORs) and information components (ICs) were calculated with statistical shrinkage transformation formulas and a lower limit of the 95% confidence interval (CI) for ROR (ROR025) > 1 or IC (IC025) > 0 with at least 3 reports was considered statistically significant.The Food and Drug Administration Adverse Event Reporting System (FAERS) database from the 1025/ROR025 = 0.559/1.478), showing stronger signal strength than those taking ICIs alone (IC025/ROR025 = 0.118/1.086) or AGIs alone (IC025/ROR025 = 0.323/1.252). Importantly, compared with ICIs alone, combination therapy showed a decrease in signal strength for noninfectious myocarditis/pericarditis (IC025/ROR025 = 1.142/2.216 vs. IC025/ROR025 = 0.673/1.614), while an increase in signal value for embolic and thrombotic events (IC025/ROR025 = 0.147/1.111 vs. IC025/ROR025 = 0.591/1.519). For outcomes of cardiovascular AEs, the frequency of death and life-threatening AEs was lower for combination therapy than ICIs alone in noninfectious myocarditis/pericarditis (37.7% vs. 49.2%) as well as in embolic and thrombotic events (29.9% vs. 39.6%). Analysis among indications of cancer showed similar findings.A total of 18 854 cardiovascular AE cases/26 059 reports for ICIs alone, 47 168 cases/67 595 reports for AGIs alone, and 3 978 cases/5 263 reports for combination therapy were extracted. Compared to the entire database of patients without AGIs or ICIs, cardiovascular AEs were overreported in patients with combination therapy (ICOverall, ICIs combined with AGIs showed a greater risk of cardiovascular AEs than ICIs alone, mainly due to an increase in embolic and thrombotic events while a decrease in noninfectious myocarditis/pericarditis. In addition, compared with ICIs alone, combination therapy presented a lower frequency of death and life-threatening in noninfectious myocarditis/pericarditis and embolic and thrombotic events. Over the past decade, immune checkpoint inhibitors (ICIs), including programmed death-1 (PD-1) inhibitors, programmed death ligand-1 (PD-L1) inhibitors and cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitors, have revolutionized the therapeutic strategy in a wide variety of tumours –9. HowevTherefore, we aimed to comprehensively investigate the cardiovascular toxicity profiles for ICIs in combination with AGIs in terms of frequency, spectrum and outcomes to provide new insights into the cardiovascular safety profiles of combination therapy.This retrospective pharmacovigilance analysis is based on the United States (US) Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database, which is a publicly available postmarketing safety surveillance database containing millions of real-world spontaneous AE cases/reports submitted by healthcare professionals, individual patients and drug manufacturers. The large quantity of the data collected at a national level from a large population around the world makes FAERS robust for conducting pharmacovigilance studies in the real-world settings.In the FAERS, AEs are coded using the preferred terms (PTs) according to the Medical Dictionary for Regulatory Activities (MedDRA) (Version 24.0), and all PTs representing symptoms, signs, and investigations likely to be relevant can be grouped into narrative categories using the Standardized MedDRA Queries (SMQs) to define a medical condition of interest. In this study, cardiovascular AEs were grouped into 9 narrow categories of SMQs   may have more than one AE report. Thus, three-step data cleaning was conducted before analysis  in China  as of December 31, 2021, was applied  is a widely used signal detection method in pharmacovigilance studies based on a two-by-two contingency table Table 2 observed (a) is the observed number of reports of target drug AEs, Nexpected is the expected number of reports of target drug AEs, Ndrug (a+b) is the total number of reports of a target drug, Nevent (a+c) is the total number of reports of target AEs, and Ntotal (a+b+c+d) is the total number of reports in the entire database.where NMoreover, the calculation formulas for the 95% confidence intervals (CIs) of ROR and IC are as follows:025) > 1 or a lower limit of the 95%CI for IC (IC025) > 0 with at least 3 reports was considered statistically significant and deemed a potential signal.Generally, a lower limit of the 95%CI for ROR .st quarter of 2014 to the 1st quarter of 2022, a total of 15 138 129 AE cases and a total of 44 964 609 AE reports were extracted from the database. After the data cleaning, 18 854 cardiovascular AE cases and 26 059 reports for ICIs alone without AGIs, 47 168 AE cases and 67 595 reports for AGIs alone without ICIs, and 3 978 cases and 5 263 reports for ICIs combined with AGIs were included for the final analysis  with cardiovascular AEs reported for AGIs alone, ICIs alone and combination therapy are presented and compared in Regarding the reported indications, the most frequently reported types of cancers were RCC (20.6%), colorectal cancer (CRC) (13.6%) and HCC (6.6%) for AGIs alone; NSCLC (25.5%), melanoma (16.5%) and RCC (7.6%) for ICIs alone; and RCC (28.1%), endometrial cancer (16.7%), HCC (12.0%) and NSCLC (11.4%) for combination therapy.Notably, with respect to chronological trends, the proportion of reported cardiovascular AEs for AGIs alone did not change significantly from 2014 to 2021, ranging from 10.0% to 12.4%; the reported proportion for ICIs alone gradually increased from 2014 to 2019 (from 1.3% to 17.5%) and then remained relatively stable from 2019 to 2021 (from 16.5% to 18.7%); and the reported proportion for combination therapy increased year by year from 2014 to 2019 (from 0.0% to 8.9%), with a rapid increase from 2019 to 2021 (from 8.9% to 32.8%).025/ROR025 = 0.323/1.252), ICIs alone (IC025/ROR025 = 0.118/1.086), and combination therapy (IC025/ROR025 = 0.559/1.478), showing stronger signal strength for combination therapy , autoimmune myocarditis  and myocarditis . In contrast, in comparison to AGIs alone, combination therapy demonstrated an increase for some PTs such as embolism  and portal vein thrombosis .Of note, 109, 102 and 59 PTs were detected as signals for AGIs alone, ICIs alone and combination therapy, respectively, based on the IC025/ROR025 = 1.744/3.354); using ICIs alone was associated with cardiomyopathy (SMQ), embolic and thrombotic events (SMQ), noninfectious myocarditis/pericarditis (SMQ) and pulmonary hypertension (SMQ), with the strongest signal value for noninfectious myocarditis/pericarditis (SMQ) (IC025/ROR025 = 1.142/2.216); and combination therapy was associated with cardiomyopathy (SMQ), embolic and thrombotic events (SMQ), hypertension (SMQ), ischaemic heart disease (SMQ) and noninfectious myocarditis/pericarditis (SMQ), with the strongest signal value for hypertension (SMQ) (IC025/ROR025 = 1.947/3.885). Similarly, combination therapy showed a decrease in signal strength for noninfectious myocarditis/pericarditis (SMQ) compared with ICIs alone , whereas there was an increase in signal value for embolic and thrombotic events (SMQ) (IC025/ROR025 = 0.591/1.519) compared with AGIs alone (IC025/ROR025 = 0.282/1.218) or ICIs alone (IC025/ROR025 = 0.147/1.111) , cardiomyopathy (SMQ), embolic and thrombotic events (SMQ), hypertension (SMQ) and pulmonary hypertension (SMQ), with the strongest signal value for hypertension (SMQ) , embolic and thrombotic events (SMQ), hypertension (SMQ), ischaemic heart disease (SMQ), and noninfectious myocarditis/pericarditis (SMQ), which were detected as significant signals are depicted in Notably, compared with ICIs alone, combination therapy showed a lower frequency of death and life-threatening events in all 5 cardiovascular AEs based on SMQs. Furthermore, in noninfectious myocarditis/pericarditis (SMQ), the frequency of death and life-threatening events was 38.4% and 10.8% (accounting for 49.2%) for ICI alone, and 33.2% and 4.5% (accounting for 37.7%) for combination therapy, respectively. Moreover, in embolic and thrombotic events (SMQ), the frequency of death and life-threatening events was 39.6% for ICIs alone, and 29.9% for combination therapy.025/ROR025 = 0.280/1.218) with a stronger signal strength than those using AGIs alone (IC025/ROR025 = 0.148/1.108) or ICIs alone (IC025/ROR025 = 0.144/1.106)  compared with AGIs alone (IC025/ROR025 = 0.132/1.097) (025/ROR025 = 0.298/1.244) compared with ICIs alone (IC025/ROR025 = 0.702/1.630)  in combination therapy   .Although overall increased cardiovascular risks were observed for combination therapy, the present study revealed some specific variances in the cardiovascular safety profiles of combination therapy. Interestingly, compared to ICIs alone or AGIs alone as a class of agent, combination therapy mainly raised the risk of embolic and thrombotic events, whereas it decreased the risk of noninfectious myocarditis/pericarditis, based on specific PTs or SMQs. These findings were further confirmed by analysis among indications of malignant tumours. Moreover, with respect to outcomes of cardiovascular AEs, the present study revealed that combination therapy presented a lower frequency of death and life-threatening AEs than ICIs alone or AGIs alone overall. In particular, combination therapy showed a lower frequency of death and life-threatening noninfectious myocarditis/pericarditis (SMQ) than ICIs alone but a slightly higher frequency of death and life-threatening embolic and thrombotic events (SMQ) than AGIs alone. To the best of our knowledge, this is also the first report that has revealed such specific cardiovascular safety profiles of combination therapy.We believe that these findings have great clinical implications. Noninfectious myocarditis is the most important cardiovascular AE for ICIs. Despite its low occurrence, the clinical course of ICI-associated myocarditis is often fulminant, and mortality rates up to 50% have been observed , 28, whiNotably, there is an intricate interplay between angiogenesis and immunity in tumours. In addition to its pivotal role in mediating tumour angiogenesis, a growing amount of evidence has revealed that VEGF also plays a critical role in the establishment of an immunosuppressive tumour microenvironment (TME) via several mechanisms , 9, 13. P = 0.003)  . Given t= 0.003) , 31, whi= 0.003) . Hence, = 0.003) . Further= 0.003) . ConsequAlthough the mechanisms underlying ICI-induced myocarditis remain elusive to date, infiltration of active CD8+ T lymphocytes in the cardiac tissue and T-cell-induced autoimmunity have been implicated in the pathogenesis of ICI-associated myocarditis. Importantly, a previous clinical study revealed that atezolizumab plus bevacizumab can increase the number of intratumoral CD8+ T cells and tumour antigen-specific T-cell migration . Thus, iOf note, Dr. Jain proposed the concept that an appropriate dose of antiangiogenic treatment can lead to normalization of the tumour vasculature, which can enhance cancer immunotherapy –38, whilWe acknowledge that there are some limitations to be considered. Firstly, as a spontaneous reporting database with safety reports from various countries and different types of reporters, some biases may exist in the FAERS database due to the incompleteness of data reported. Secondly, due to the lack of baseline clinical characteristics or prior cardiovascular risk profiles, the FAERS database may suffer from reporting biases or confounding issues. Thirdly, the FAERS database does not contain data on the total number of patients treated with drugs, thus, the incidence of suspected drugs and AEs could not be calculated. Finally, a causal relationship cannot be confirmed in a retrospective study. Nevertheless, despite the intrinsic limitations mentioned above, the current study still provides some valuable clues of cardiovascular AE profiles for combination therapy that warrant further prospective studies.Overall, ICIs combined with AGIs were associated with overreported cardiovascular AEs, which mainly increased the risk of embolic and thrombotic events while decreasing the risk of noninfectious myocarditis/pericarditis compared to ICIs alone. Moreover, combination therapy demonstrated a lower frequency of death and life-threatening noninfectious myocarditis/pericarditis and embolic and thrombotic events than ICIs alone.Of note, these findings on specific cardiovascular safety profiles may provide some valuable insights for the individual choice of combination therapy in clinical practice given that ICIs combined with AGIs have demonstrated promising synergistic antitumour effects and are likely to become essential treatments for multiple types of cancers. Above all, further studies to confirm appropriate dosing and scheduling of combination therapy to optimize the synergistic effect with reduced cardiovascular toxicity are highly warranted.The original contributions presented in the study are included in the article/Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the participants’ legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.YW was responsible for the study design. CC and LD were responsible for writing the article. XR was responsible for data acquisition and processing analysis. CC and LW were responsible for data checking and interpretation. All authors contributed to the article and approved the submitted version.


Lymphatic metastasis is recognized as the leading manner of metastasis in bladder cancer (BLCa), but hematogenous metastasis accounts for a majority of cancer-associated deaths. The past two decades have witnessed tremendous attention in long non-coding RNAs (lncRNAs), which are a new hope for the development of targeted drug therapy for metastatic cancers; however, the underlying mechanism of lncRNAs involved in BLCa hematogenous metastasis remains to be elucidated. Here, we identified BLCa-associated transcript 3 (BLACAT3), a lncRNA, which was aberrantly upregulated in BLCa and corelated with poor prognosis of patients with muscle-invasive bladder cancer. Methodologically, m6A epitranscriptomic microarray, RNA sequencing and mass spectrometry (MS) were used to screen the key molecules of the regulatory axis. Functional assays, animal models and clinical samples were used to explore the roles of BLACAT3 in BLCa in vitro and in vivo. Mechanistically, m6A modification contributes to BLACAT3 upregulation by stabilizing RNA structure. BLACAT3 recruits YBX3 to shuttle into the nucleus, synergistically enhances NCF2 transcription, and promotes BLCa angiogenesis and hematogenous metastasis by activating downstream NF-κB signaling. Our findings will develop prognosis prediction tools for BLCa patients and discover novel therapeutic biological targets for metastatic BLCa. Bladder cancer (BLCa) is the second most common genitourinary malignancy with approximately 549,000 new confirmed cases and 200,000 deaths worldwide each year . PatientPrevious studies have primarily focused on the mechanism of lymphangiogenesis and lymphatic metastasis in BLCa, because lymphatic metastasis is considered the leading metastatic manner in BLCa –13. In mLncRNAs are a class of transcripts that contain 200–100,000 nucleotides and cannot be translated into peptides , 30. LncTo identify novel potential vulnerabilities associated with BLCa progression, we screened the differential m6A modification and expression level profile of lncRNAs between three pairs of fresh tumor tissues and adjacent normal tissues by human m6A epitranscriptomic microarray Fig. , 8. We tWe detected the BLACAT3 expression in 104 pairs of BLCa and adjacent normal tissues using qRT-PCR. It was found that BLACAT3 expression in BLCa tissue was significantly higher than in normal tissues Fig. . MoreoveMeRIP and qRT-PCR analyses verified BLACAT3 m6A modification and expression level were consistently higher than those of SV-HUC-1 in most BLCa cell lines, with the most significant differences in T24, 5637, and UM-UC-3 Fig. . Dot bloMETTL3, METTL14, WTAP, FTO and ALKBH5 are five well-known m6A modification related enzymes, of which the first three are methyltransferases and the last two are demethylases . Using sTo investigate the effect of m6A modification on BLACAT3 expression, we overexpressed or silenced ALKBH5 in 5637 and T24 cells respectively Fig. . Dot bloWe also explored the effect of m6A modification on the malignant behaviors of BLCa cells. CCK-8 assay showed that up- or down-regulation of m6A modification could promote or inhibit the BLCa cell proliferation sFig. , respectTo explore the effect of BLACAT3 on the malignant behaviors of BLCa cells, we constructed the stable T24 strain with BLACAT3 knockdown and 5637 strain with BLACAT3 overexpression for establishing the subcutaneous xenograft model sFig. . Both ceHUVEC tube formation assay showed that the culture supernatant of BLCa cells with BLACAT3 knockdown could inhibit tube formation Fig. , while BWe then used the T24 strain with BLACAT3 knockdown and 5637 strain with BLACAT3 overexpression to establish the hematogenous metastasis models. Both cell strains also stably expressed the luciferase gene. In vivo imaging on the 28th day after tail vein injection Fig.  and the 2(FC)) > 0.585, P < 0.05). GO analysis showed that BLACAT3 was involved in the regulation of multiple biological processes related to cell movement and localization  is activated in several cancers –41. To vM6A RNA methylation, as a reversible epigenetic modification that differentially exists on mRNAs as well as ncRNAs, is involved in tumorigenesis and progression of multiple human malignancies . PreviouIt is reported that RT4 is the only luminal papillary NMIBC-derived cell line, with all other BLCa cell lines derived from MIBC . InteresIn vivo, we randomly chose shBLACAT3#2 to investigate the biological function of BLACAT3, based on our observation that shBLACAT3#1 and shBLACAT3#2 exhibit similar inhibitory effects on the malignant biological behavior of BLCa cells in vitro. For constructing tumor models, we chose NSG mouse, which was reported to lack mature T, B, and NK cells and thus exerted a high rate of tumorigenesis . InteresNCF2/p67phox, the activating subunit of NADPH oxidase, is aberrantly expressed in several genitourinary tumors with clinical significance, including ccRCC , 53–55, The subcellular localization of lncRNAs affects their regulatory patterns in tumors , 58. In Y-box binding protein 3 (YBX3) is a member of YBX family, which is also known as cold shock domain protein A (CSDA), DNA binding protein A (DBPA) orzonula occludens 1 (ZO-1)-associated nucleic acid binding protein (ZONAB) . IncreasDual luciferase reporter gene and ChIP assays displayed that YBX3 can bind directly to NCF2 promoter. In addition, BLACAT3 knockdown can decrease the binding of YBX3 to NCF2 promoter. WB analysis further confirmed the cooperative regulation of BLACAT3/YBX3 on NCF2. It has been reported that lncRNA and TF act together on the regulation of target gene promoters , 68. We As emerging biological and chemical means have been successfully used to solve the problems of RNA stability, delivery and immunogenicity, RNA therapeutics exhibits broad clinical application scenarios . We are Taken together, our data showed that m6A modification contributes to BLACAT3 upregulation by stabilizing RNA structure. We have found that BLACAT3 recruits YBX3 to shuttle into the nucleus, which synergistically enhances NCF2 transcription, and promotes BLCa angiogenesis and hematogenous metastasis by activating downstream NF-κB signaling. Our novel findings will help to develop prognostic prediction tools for BLCa patients and discover new therapeutic biological targets for metastatic BLCa.Clinical samples and patient information were obtained from two sources for this study. First, publicly available transcriptome sequencing data and corresponding patient clinical data from TCGA. Second, paired tumor and adjacent normal tissues from 107 patients with MIBC who underwent RC in Shanghai Tenth People’s Hospital. Among them, 3 pairs of tumor and adjacent normal tissues were collected from May to September 2020  test was used for statistical analysis. *P < 0.05, **P < 0.01, ***P < 0.001 and ****P < 0.0001 were set as statistically significant, and P > 0.05 means no significance.Under R version 4.0.2 environment, multiple regression analysis and nomogram construction were carried out by rms package, forest chart was drawn by forestplot package, and the ROC curve was produced by pROC package. The other statistical analyses were conducted by SPSS 24.0 and GraphPad Prism 8. The two-tailed Student Supplementary information


For many years, the RNA world of eukaryotic cells remained stable and predictable, organized by a few families of functionally different molecules. Thus, ribosomal RNAs structured the translational machinery; hnRNA/mRNAs transmitted the genetic information from nucleus to the cytoplasm; and a plethora of small RNAs  played additional structural or regulatory roles in translation, splicing, etc. In this context, the DNA/RNA sequencing revolution at the turn of the XXIth century revealed a surprising fact, namely that the RNA world  was far richer and more complex than anticipated. In fact, in 2007 the ENCODE pilot project demonstrated that more than 90% of the human genome was pervasively transcribed , produciThis Special Issue, entitled “ncRNAS in Therapeutics”, focuses on this important topic of research, providing examples of this use of ncRNAs, as well as on the vehicles used for specific RNA delivery to cells and tissues. In this context, De Benedittis and cols.  performeOn the lncRNA side, Kuwara and cols.  discusseThis Special Issue also includes two technical notes. Lee and cols.  reviewed