Gly(GCC) in ginseng root was purified and four modified nucleosides, including m7G, D, T, and Ψ, were identified by LC-MS/MS. The results also revealed that the m7G occurs at a novel position 18, which may be related to the deformation of D-loop. PARI is the first enzyme-assisted technique for RNA isolation from plants, which could fundamentally solve the problem of polysaccharide contaminations. By using the PARI method, more individual tRNAs could be isolated easily from polysaccharide-rich plant tissues, which would have a positive impact on the feasibility of research on structure and function of tRNA in plants.Transfer RNAs (tRNAs) are the most heavily modified RNA species in life entities. Post-transcriptional modifications severely impact the structure and function of tRNAs. To date, hundreds of modifications have been identified in tRNAs, mainly from microorganisms and animals. However, tRNAs in plant roots or tubers that have been widely used for food and medical purpose for centuries are rarely studied because isolation of RNA from plants still remains a challenge. In this paper, a polysaccharase-aided RNA isolation (PARI) method for extraction of high-quality RNA from plants containing large quantities of polysaccharides is developed. This method presents a new strategy of “digesting” polysaccharides that is completely different from the conventional method of “dissolving” the contaminants. By using this method, RNA of high integrity and purity were successfully extracted from ginseng roots because polysaccharide contaminations were removed efficiently with α-amylase digestion. Ginseng tRNAs were first sequenced by NGS and a total of 41 iso acceptors were identified. ChloroplastictRNA RNA extraction of high integrity and purity is a prerequisite for downstream application. Robust and generic methods for isolating RNA from animal species cells have been well established . HoweverTransfer RNAs (tRNAs), a class of highly conserved small non-coding RNA of typically 76 to 90mer, play a crucial role in messenger RNA decoding in nearly all domains of life. In addition to their well-known roles in translation, tRNAs have been shown to perform diverse regulatory functions in other cellular processes, such as apoptosis modulating and stress response programs ,9. For aPanax ginseng C.A. Mey is a globally well-known traditional Chinese medicine (TCM). Ginseng roots are rich in polysaccharides mainly composed of starch-like glucans and pectins Gp19 with the highest confidence (p-score = 87.20), indicating a methylguanosine (mG) in position 18 of tRNAGly(GCC)  by matching the retention time of modified nucleoside to that of the corresponding standard substance in the HPLC separation system (The modified nucleosides in ginseng tRNAGly(GCC) . Ion m/zGly(GCC) A,C. The n system B.m/z 1579.52 (the corresponding mass = 4741.82 Da) in peak 10 was consistent with the expected values for 20UAAAAUUUCUCUUUGp34 + 2H (mass = 4739.78 + 2 Da), which indicated a unique mass shift of 2 Da exclusively generated by a dihydrouridine (D). Ionm/z 646.07 and 1293.16 in peak 5 was consistent with the theoretical precursor ions of 54UUCGp57 + CH2 at a charge state of –2 and –1, respectively. In p-scores better than 90, whichconfirmed the two modified digestion products as 20[D]AAAAUUUCUCUUUGp34 and 54[mU]UCGp57. The structure of mU is assigned as m5U based on its retention time  and 54[m5U]UCGp57 + C3H3N (m/z 672.59), give a mass increment of 53 Da compared with the untreated digestion products, indicating that each of them harbors one Ψ. A pseudouridine was assigned to U55 because it is the only nucleoside that could be turned into Ψ in 54[m5U]UCGp57. Positions of Ψ in the other digestion products were determined by LC-MS/MS analysis. The CID mass spectra in 20[D]AAAAUU[Ψ]CUCUUUGp34 (p-scores = 63.30). At this point, the identity and location of modified nucleosides, including m7G, D, T, Ψ, in ginseng tRNAGly(GCC) were affirmatively identified from the combination of intact mass and sequence confirmation (Pseudouridine (Ψ), a mass-silent modified nucleoside that cannot be detected by MS directly, was distinguished from uridine bycyanoethylation with acrylonitrile. When comparing the MS1 spectra in irmation A. In this study, polysaccharases were unprecedentedly integrated into the process of RNA extraction of ginseng roots. Three enzymes, including α-amylase, pectinase, and glucoamylase were selected based on the types of ginseng polysaccharides. It can be expected that α-amylase will work better for ginseng roots than other polysaccharases because starch-type polysaccharides readily hydrolyzed by α-amylase make up over 60% of water-soluble polysaccharides of ginseng roots, while substrates for pectinases including various pectins with different pectic domains account for only a small portion (<9%) of ginseng’s polysaccharides  19]. Ac. Ac19]. v/v) of alcohol. CTAB naturally holds the power to segregate RNA from polysaccharide. However, severe degradation of ginseng RNA in CTAB extraction buffer was observed in this study , which could be attribute to the high evolutionary conservation of tRNAs across plant species .,28.Gly(G T-stem) ,30. HereGly(GCC) (Gly(GCC) without any base modifications in anticodon regions mainly participates in transcriptional regulation and transcription. Instead, the tRNAGly(UCC), which was also identified in ginseng chloroplasts  . tRNAs lroplasts , more of


SLC transporters are emerging key drug targets. One important step for drug development is the profound understanding of the structural determinants defining the substrate selectivity of each transporter. Recently, the improvement of computational power and experimental methods such as X-ray and cryo-EM crystallography permitted to conduct structure-based studies on specific transporters having important pharmacological impact. However, a lot remains to be discovered regarding their dynamics, transport modulation and ligand recognition. A detailed functional characterization of transporters would provide opportunities to develop new compounds targeting these key drug targets. Here, we are giving an overview of two major human LeuT-fold families, SLC6 and SLC7, with an emphasis on the most relevant members of each family for drug development. We gather the most recent understanding on the structural determinants of selectivity within and across the two families. We then use this information to discuss the benefits of a more generalized structural and functional annotation of the LeuT fold and the implications of such mapping for drug discovery. Any dysregulation of these proteins function induces various disorders and diseases. For example, mutations in the Naal delay . Additioal delay . For exaal delay . Thus, Sal delay .Despite their pharmacological importance, SLCs have been understudied for many years . HoweverMolecular modeling has been proven to be a useful tool to conduct structure-based ligand discovery studies on SLC transporters . A key sAlthough very useful, homology modeling captures static conformations of the studied transporter. However, understanding how the transport cycle is achieved is of the utmost importance to design conformation specific modulators. In fact, a wide range of computational methods have been recently used to characterize the transport cycle at the molecular level . For insInterestingly, the available structures reveal a significant variety of folds . Among tPh-like fold, associated to the elevator mechanism. In this case, one mobile domain moves up and down across the membrane while the scaffold domain remains static.The three main folds comprise: i) the LeuT fold, associated to the gated-pore mechanism of transport . In this case one scaffold domain remains static while the mobile bundle domain opens and closes to capture and release the substrate. ii) Major facilitator superfamily (MFS), associated to the rocker switch mechanism. In this fold, the transporter is divided in two domains oscillating back and forth along an axis perpendicular to the membrane. iii) The GltIn this review, we will focus on the first type, i.e., transporters presenting a LeuT-fold.The prokaryotic leucine transporter LeuT shares 20–30% of sequence identity with the human neurotransmitter sodium symporters (NSS) and has been studied as a model transporter of the NSS superfamily . The NSSMost drug discovery studies on SLCs have been focused on one specific transporter or a subgroup of transporters within a family. These studies have improved tremendously our understanding of how ligand recognition is achieved. Yet, the important question of how the conservation of a similar fold and transport mechanism allows substrate selectivity remains mostly unanswered. This review aims at shedding the light on the importance of collecting the most recent understanding on structure-function relationships of individual transporters to create a general knowledge on a specific fold and transport mechanism. We will focus on two human SLC transporters families sharing the LeuT-fold, i.e., SLC6 and SLC7, with an emphasis on the transporters with an important pharmacological impact.+, as well as 1 or 2 Cl−. Sequence similarity profiles and substrate specificities allow the division of the family into four subgroups  subgroup that comprises the serotonin SERT (SLC6A4), dopamine DAT (SLC6A3) and norepinephrine NET (SLC6A2) transporters. The pharmacology of these transporters has been studied for many years, due to their high therapeutic impact . ParticuThe GABA transporters (GAT) subgroup. GABA is the main inhibitory neurotransmitter in the brain and is therefore a target for treating epilepsy. In humans, four distinct transporters are responsible for the transport of this neurotransmitter, namely GAT1 (SLC6A1), GAT3 (SLC6A11), BGT1 (SLC6A12), GAT2 (SLC6A13). This subgroup also comprises the transporters of the osmolyte taurine and betaine TauT (SLC6A6), as well as creatine CreaT (SLC6A8), which is a storage compound for high energy phosphate bonds.0,+.Amino acid (AA) I—including the glycine transporters. Glycine is another important inhibitory neurotransmitter of the central nervous system and is transported by two distinct transporters—GlyT1 (SLC6A9) and GlyT2 (SLC6A5), mainly located in the brain and the spinal cord, respectively. These transporters are targeted for the treatment of pain and schizophrenia. This group also contains two other amino acid transporters, the proline transporter PROT and neutral and amino acid transporter ATBAmino acid II. The fourth group contains amino acid transporters most probably responsible for amino acid homeostasis.The SLC7 family comprises cationic amino acid transporters (CATs) and L-type amino acid transporters (LATs) . LATs arvia LAT1. Thus, this transporter can be targeted by substrate compounds that can serve as prodrugs with optimal BBB permeability  in particular has been subject of interest because of its pharmacological importance. Specifically, LAT1 is a neutral amino acid exchanger located in the blood brain barrier (BBB) and has thus been investigated to deliver drugs into the brain. Interestingly, L-DOPA  and gabaeability . Furthereability . Thus, aeability . ConsequAs mentioned above, the LeuT-fold is one of the three main folds observed in SLC transporters, together with the MFS and elevator folds . LeuT foThe distinct crystal structures of LeuT . as wellRecently, structures of human transporters SERT  and LAT1Importantly, these various structures also shed the light on some diversity occurring in this conserved fold.The dynamic of transport requires concerted global and local movements of the bundle domain. Specifically, the extracellular side is constituted of conserved gating residues i.e., (R30–Q404 in LeuT) and a hydrophobic lid . In fact, F253 is an aromatic gating residue described in various LeuT fold transporters that flips open simultaneously to a tilting of the extracellular regions of TM1b and 6a . The intInterestingly, the different oligomerization states observed for LeuT fold transporters has been suspected to modulate the transport dynamics. Consequently, distinct helices involved in the oligomerization interfaces have restricted mobility from one transporter to the other .Furthermore, some variability has been observed in which transmembrane helix accomplishes the gating function. In fact, in the prokaryotic transporters BetP  and vSGLAdditionally, while the inverted repeat core of the LeuT fold is constituted of 10 transmembrane helices, there is a variability in the total number of helices from 11 (such as the prokaryotic tyrosine transporter Tyt1 ) to 14 ).+ ions, the LATs subgroup of the SLC7 family, to which LAT1 belongs are mostly exchangers.The number of co-transported ions is another variable feature. While the SLC6 family co-transports 2 to 3 NaThis suggests that many factors contribute to the broad dynamic and functional plasticity within a conserved fold.The primary binding site has been largely described for the prokaryotic transporter LeuT, the closest prokaryotic homolog of the SLC6 family (20–30% identity). This site is located in between transmembrane helices 1, 3, 6, 8, and 10 approximately halfway across the transmembrane bilayer , as wellThe functional role of these subpockets can be expanded to the other subgroups of the SLC6 family. For instance, the corresponding area of the so-called subpocket A and C similarly has a ligand anchoring function. However, some variability takes place due to the different nature of the substrates between the subgroups. For example, the substrates of the GAT subgroup are constituted of a carboxylate moiety, interacting with a conserved glycine that substitutes the conserved aspartate within the monoamine subgroup  has been shown to be deprotonated and suspected to be involved in ligand binding specificity . Interes+.Furthermore, multiple sequence alignment of the GAT subgroup with the rest of the SLC6 members revealed an additional residue in TM10. This multiple insertion has been discussed by our group and others as being a π-helix signature . In partOverall, several key features contribute to the various substrate selectivities of the SLC6 transporters. For instance, residue substitutions influence the physico-chemical properties of the binding site. Building on the functional annotation of the monoamine subgroup, a general trend can be drawn: i) conserved (within each subgroup) residues anchor the substrates in TM1 and 6, ii) while the residue variability occurring in TM3, 8, and 10 confers selectivity.In an effort to find compounds with optimal affinity/transport properties for LAT1, integrative multidisciplinary studies have been conducted, comprising computational modeling and experiments . The recvs. W202 in AdiC) and broken helices facilitating transport , while integrating variability of residues at key positions allowing specific protein-ligand interactions  . This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation program and EFPIA.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Accurate predictions of RNA secondary structures can help uncover the roles of functional non-coding RNAs. Although machine learning-based models have achieved high performance in terms of prediction accuracy, overfitting is a common risk for such highly parameterized models. Here we show that overfitting can be minimized when RNA folding scores learnt using a deep neural network are integrated together with Turner’s nearest-neighbor free energy parameters. Training the model with thermodynamic regularization ensures that folding scores and the calculated free energy are as close as possible. In computational experiments designed for newly discovered non-coding RNAs, our algorithm (MXfold2) achieves the most robust and accurate predictions of RNA secondary structures without sacrificing computational efficiency compared to several other algorithms. The results suggest that integrating thermodynamic information could help improve the robustness of deep learning-based predictions of RNA secondary structure. Accurately predicting the secondary structure of non-coding RNAs can help unravel their function. Here the authors propose a method integrating thermodynamic information and deep learning to improve the robustness of RNA secondary structure prediction compared to several existing algorithms. Because it is well-known that the functions of ncRNAs are deeply related to their structures rather than their primary sequences, discovering the structures of ncRNAs can elucidate the functions of ncRNAs. However, there are major difficulties in determining RNA tertiary structures through experimental assays such as nuclear magnetic resonance and X-ray crystal structure analysis, because of the high experimental costs and resolution limits on measurements of RNA. Although considerable advances in cryo-electron microscopy research on RNA tertiary structure determination have been achieved in recent years2, these limitations have not yet been completely overcome. Therefore, instead of conducting such experimental assays, we frequently perform computational prediction of RNA secondary structures, defined as sets of base-pairs with hydrogen bonds between the nucleotides.Recent studies have revealed that functional non-coding RNAs (ncRNAs) play essential roles, including transcriptional regulation and guiding modification, participating in various biological processes ranging from development to cell differentiation, with defective functionality being involved in various diseases4, in which a secondary structure is decomposed into several characteristic substructures, called nearest-neighbor loops, such as hairpin loops, internal loops, bulge loops, base-pair stackings, multi-branch loops, and external loops, as shown in Fig. 3. The free energy of an entire RNA secondary structure is calculated by summing the free energy of the decomposed nearest-neighbor loops. We can efficiently calculate an optimal secondary structure that has the minimum free energy using a dynamic programming (DP) technique, the well-known Zuker algorithm5. A number of tools, including Mfold/UNAfold7, RNAfold9, and RNAstructure11, have adopted this approach.The most popular approach for predicting RNA secondary structures is based on thermodynamic models, such as Turner’s nearest-neighbor model13, ContextFold14, and other methods, and has enabled us to more accurately predict RNA secondary structures. However, rich parameterization can easily cause overfitting to the training data, thus preventing robust predictions for a wide range of RNA sequences15. Probabilistic generative models such as stochastic context-free grammars (SCFGs) have also been applied to predicting RNA secondary structures. Recently, TORNADO15 implemented an application of SCFGs to the nearest-neighbor model, thus achieving performance comparable with that of other machine learning-based methods.An alternative approach utilizes machine learning techniques, which train scoring parameters for decomposed substructures from reference structures, rather than experimental techniques. This approach has successfully been adopted by CONTRAfold17 more accurately estimates thermodynamic parameters from training data, including RNA sequences and their known secondary structures, as well as the free energy of their known secondary structures. We previously proposed MXfold18, which combines thermodynamic energy parameters and rich-parameterized weight parameters. The model learns more precise parameters for substructures observed in training data and avoids overfitting rich-parameterized weight parameters to the training data by resorting to the thermodynamic parameters for assessing previously unobserved substructures.Hybrid methods that combine thermodynamic and machine learning-based approaches to compensate for each other’s shortcomings have been developed. SimFold19 and E2Efold20 have been developed for RNA secondary structure prediction using deep neural networks (DNNs). Both algorithms formulate RNA secondary structure prediction as multiple binary classification problems that predict whether each pair of nucleotides forms a base pair or not, using a deep neural network trained with a large amount of training data. As with the other machine learning-based methods described above, concerns about overfitting caused by rich parameterization remain.In recent years, deep learning has made remarkable progress in a wide range of fields, including bioinformatics and life sciences. SPOT-RNAInspired by MXfold and the DNN-based RNA secondary structure prediction methods, in this paper, we propose an algorithm for predicting RNA secondary structures using deep learning. Similar to MXfold, we integrate folding scores, which are calculated by a deep neural network, with Turner’s nearest-neighbor free energy parameters. The deep neural network is trained using the max-margin framework with thermodynamic regularization, which is proposed in this paper to make our model robust by maintaining that the folding score calculated by our method and the free energy calculated by the thermodynamic parameters are as close as possible. To confirm the robustness of our algorithm, we conducted computational experiments with two types of cross-validation: sequence-wise cross-validation, with test datasets structurally similar to training datasets, and family-wise cross-validation, with test datasets structurally dissimilar to training datasets. The resulting method, called MXfold2, achieved the best prediction accuracy based not only on the sequence-wise cross-validation, but also on the family-wise cross-validation, indicating the robustness of our algorithm. In addition, the computational experiment conducted on a dataset with sequence-structure-energy triplets shows a high correlation between folding scores predicted by MXfold2 and the free energy derived from optical melting experiments.5. The deep neural network is trained using the max-margin framework, also known as structured support vector machine (SSVM), to minimize the structured hinge loss function with thermodynamic regularization, which prevents the folding score of the secondary structure from differing significantly from the free energy of the thermodynamic parameters.Our algorithm computes four types of folding scores for each pair of nucleotides by using a deep neural network, as shown in Fig. 15. Note that TrainSetA and TestSetB are structurally dissimilar, whereas TrainSetA and TestSetA have some structural similarity. All the models were trained with TrainSetA and were then evaluated using TestSetA and TestSetB. The results in Table To investigate the effectiveness of the thermodynamic-integrated folding scores and thermodynamic regularization, which are the main contribution of this study, we conducted an experiment to compare the cases with and without the thermodynamic-related techniques. We implemented a base model that employs only the deep neural network to compute the folding score; that is, it does not use the thermodynamic parameters for the folding score and the thermodynamic regularization. Then, we compared the base model with the use of the thermodynamic-integrated folding score and/or the thermodynamic regularization. For this experiment, we employed TrainSetA, TestSetA, and TestSetB, which have been established by Rivas et al.18, LinearFold-V 21, CONTRAfold version 2.0213, ContextFold version 1.0014, CentroidFold version 0.0.1623, TORNADO version 0.315 with ViennaRNA grammar, RNAfold in ViennaRNA package version 2.4.149, SimFold version 2.117, and RNAstructure version 6.211. The parameters of four trainable algorithms, namely, MXfold, CONTRAfold, ContextFold and TORNADO, were trained with TrainSetA. For the other algorithms, default parameters were used.We compared our algorithm with nine available folding algorithms: MXfold version 0.0.2γ, and therefore, their accuracies are shown as the curves for multiple γ ∈ {2n∣ − 5 ≤ n ≤ 10}.Figures F = 0.761), followed by ContextFold (F = 0.759). While the difference in F-value between MXfold2 and ContextFold was not significant , MXfold2 was significantly more accurate than the other methods, including TORNADO (F = 0.746 at γ = 4.0), MXfold (F = 0.739), and CONTRAfold (F = 0.719 at γ = 4.0) (p < 0.001). On the other hand, for the predictions on TestSetB, which is structurally dissimilar from TrainSetA, we observe that ContextFold achieved the worst accuracy (F = 0.502), consistent with the possible proneness of ContextFold to overfitting, as noted by Rivas et al.15. In contrast, we do not observe heavy overfitting, as occurred with ContextFold, with MXfold2 (F = 0.601), which is also significantly more accurate than the other methods, including MXfold (F = 0.581), CONTRAfold (F = 0.573 at γ = 4.0), and TORNADO (F = 0.552 at γ = 4.0) (p < 0.001).For the predictions on TestSetA, our algorithm, MXfold2, achieved the best accuracy  achieved the best accuracy, followed by the trainable methods, namely, MXfold (F = 0.673), TORNADO (F = 0.664 at γ = 4.0), CONTRAfold (F = 0.658 at γ = 4.0), and ContextFold (F = 0.651), and MXfold2 also outperformed the thermodynamics-based methods .Figures 19 and E2Efold20. We trained MXfold2 using the available SPOT-RNA and E2Efold training datasets and tested the prediction accuracy on their test datasets for direct comparison because SPOT-RNA does not provide training modules. Furthermore, because their datasets were designed only for sequence-wise cross-validation, for the purpose of family-wise cross-validation, we developed the bpRNA-new dataset, which does not contain any families present in their training datasets.We compared our algorithm, MXfold2, with two recently published DNN-based methods: SPOT-RNA24. In contrast, SPOT-RNA used the pre-trained model trained on the TR0 dataset and an additional dataset for transfer learning. All of these methods were tested using the TS0 dataset, which is also a subset of the bpRNA-1m dataset, for sequence-wise cross-validation, after which the bpRNA-new dataset was used for family-wise cross-validation.Table p = 0.31, one-sided Wilcoxon signed-rank test). On the other hand, in terms of family-wise cross-validation, MXfold2 is significantly more accurate than SPOT-RNA (p < 0.001). In addition, as shown in Supplementary Fig. We observe that MXfold2 performs slightly worse in sequence-wise cross-validation relative to SPOT-RNA, but the difference is not significant , whereas MXfold2 accurately predicted their secondary structures (F = 0.628). Furthermore, E2Efold trained with TrainSetA also failed to predict secondary structures for TestSetB (F = 0.0322), which is outside of Fig. We also compared MXfold2 with E2Efold, TORNADO, ContextFold, and RNAfold  between the free energy of the reference structure and the folding score of the predicted structure for MXfold2 with and without thermodynamic regularization compared with CONTRAfold and RNAfold. The folding score of MXfold2 is highly correlated with the true free energy, though it is not as high as that of RNAfold. In contrast, even though MXfold2 without thermodynamic regularization is able to predict secondary structures as accurately as MXfold2 with thermodynamic regularization, its folding score is not highly correlated with the true free energy. This suggests that high accuracy in the secondary structure prediction does not directly lead to the reliable evaluation of thermodynamic stability.We investigated the correlation between the free energy and the predicted folding score using the T-Full datasetWe have proposed a deep learning-based algorithm for predicting RNA secondary structures with thermodynamic integration. Our experiments showed that thermodynamic integration, consisting of thermodynamic-integrated folding scores and thermodynamic regularization combined, substantially improved the robustness of predictions. We conducted benchmarks that compare our algorithm with conventional algorithms, using not only sequence-wise cross-validation, but also family-wise cross-validation, which assumes one of the most practical situations for RNA secondary structure prediction from single sequences. In this comparison, our algorithm achieved the best prediction accuracy without overfitting to the training data, as was the case with other rich-parameterized methods such as ContextFold and E2Efold. The proposed MXfold2 algorithm should be useful for improving RNA structure modeling, especially for newly discovered RNAs.15 have already pointed out that rich-parameterized methods might easily tend towards overfitting. Such secondary structure prediction methods would be useful if we could know a priori whether the sequence we want to predict is a member of a family included in the training data. However, if we have knowledge about the family of the sequences whose structures are to be predicted, homology searches for RNA sequences, such as Infernal27, are a better choice. In other words, the lack of prior knowledge about sequences is a major reason the prediction of secondary structures from single sequences is an important goal. In principle, regardless of whether a given sequence is within a family included in a training dataset or not, robust methods should be able to properly predict its secondary structure because its secondary structure is formed under the same physical laws. In general, machine learning-based methods can be expected to improve prediction accuracy by increasing the number of training samples. Although the development of sequencing techniques has made it easier to obtain sequence data, it is still not easy to increase the amount of training data because of the significant effort required to obtain those empirically determined secondary structures. Therefore, it is impractical to predict secondary structures of newly discovered sequences using methods that are not generalizable to a wide variety of sequences, such as those methods that rely on only memorizing sequences and their secondary structures within a large number of neural network parameters. We designed our experiments to predict secondary structures of newly discovered RNAs using test datasets structurally dissimilar from training datasets, e.g., TestSetB for TrainSetA  and the bpRNA-new dataset for the TR0 dataset. These conditions are among the most important circumstances for RNA secondary structure prediction from single sequences, and our method, which is able to achieve high accuracy under these conditions, is useful in practice.Our experiments showed that several methods with rich parameterization achieved excellent performance for test datasets structurally similar to training datasets, but poor performance for test datasets structurally dissimilar from training datasets. Rivas et al.Deep neural network-based methods, including our method, improve prediction accuracy by optimizing a larger number of parameters for training data relative to conventional methods. For example, our algorithm employs 803k parameters, whereas SPOT-RNA and E2Efold use 1746k and 719k parameters, respectively. Therefore, as shown so far, such rich-parametrized models need to be trained more carefully because they are prone to overfitting. SPOT-RNA mitigates overfitting by building an ensemble of five deep models with transfer learning. To do that, our MXfold2 integrates the folding scores, which are calculated by a deep neural network, with Turner’s nearest-neighbor free energy parameters, and employs training using thermodynamic regularization. Whereas MXfold2 is limited to pseudoknot-free secondary structure prediction owing to its use of the nearest-neighbor model, SPOT-RNA and E2Efold are able to predict pseudoknotted structures because their RNA secondary structure prediction is formulated as multiple binary classification problems that predict whether each pair of nucleotides forms a base pair or not.28. Table Estimation of free energies is important for applications other than structure prediction, such as small interfering RNA selection using hybridization thermodynamicsFigure L. Each base  is encoded into d-dimensional embeddings using a trainable embedding function.The input of the network is an RNA sequence of length L × d-dimensional sequence embedding is entered into 1D convolution blocks (Block 1) repeated N1 times. Each 1D convolution block consists of two convolutional layers, with kernel sizes of 5 and 3, respectively, and a depth of d. Layer normalization29 and continuously differentiable exponential linear units (CELU) activation functions30 are employed. A dropout rate of 50% is used to avoid overfitting31. To efficiently train deep neural networks, a residual connection32 that skips each layer is employed.The N2 layers of the bidirectional long short-term memory (BiLSTM) network33. The number of hidden units in each direction of the BiLSTM network is d/2. Each BiLSTM layer is followed by the layer normalization and CELU activation functions. A dropout rate of 50% is also used in BiLSTM layers.The next block (Block 2) contains L × d matrix obtained so far is divided into two matrices of size L × d/2; one of the two matrices expands in the column direction and the other in the row direction, and then the two matrices are concatenated into an L × L × d matrix, as shown in the “Concat 2D” operation in Fig. N3 times. Each 2D convolution block consists of two convolutional layers, each with a kernel size of 5 × 5 and 3 × 3, respectively, and a depth of d. The layer normalization and CELU functions with a dropout rate of 50% are again utilized.The i-th and j-th nucleotides are calculated by 3-layer multilayer perceptron (MLP) with h hidden units.Finally, four types of folding scores for each pair of d = 64, N1 = 4, N2 = 2, N3 = 4, and h = 32.We used the following values for the above hyperparameters: 34 as an RNA secondary structure model without pseudoknots. As an alternative to the thermodynamic parameters, our algorithm calculates the free energy of the nearest-neighbor loops using four types of folding scores computed by the deep neural network described in the previous section. The four types of the folding scores are helix stacking scores, helix opening scores, helix closing scores, and unpaired region scores  is the contribution of the thermodynamic parameters  and fW is the contribution of the deep neural network, which is calculated as the sum of the folding scores over the decomposed nearest-neighbor loops as described above. The thermodynamic-integrated folding scores defined by Eq. 35. Given a training dataset xk)( is the k-th RNA sequence and yk)( is the reference secondary structure for the k-th sequence xk) and 5 modified by the use of loss-augmented inference35. The second and third terms of Eq.  and the sensitivity (SEN) of the base pairs, defined asei and i-th sequence, respectively. We also evaluated the Spearman’s rank correlation coefficient (ρ) between e and For the experiment on the T-Full dataset, we evaluated the root mean square error (RMSE)Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


A pivotal problem in NGS based small RNA analysis is identifying and quantifying the small RNA’ome constituent components. For example, small RNAs in the circulatory system (circulating RNAs) are potential disease biomarkers and their function is being actively investigated. Most existing NGS data analysis tools focus on the microRNA component and a few other small RNA types like piRNA, snRNA and snoRNA. A comprehensive platform is needed to interrogate the full small RNA’ome, a prerequisite for down-stream data analysis. We present COMPSRA, a comprehensive modular stand-alone platform for identifying and quantifying small RNAs from small RNA sequencing data. COMPSRA contains prebuilt customizable standard RNA databases and sequence processing tools to enable turnkey basic small RNA analysis. We evaluated COMPSRA against comparable existing tools on small RNA sequencing data set from serum samples of 12 healthy human controls, and COMPSRA identified a greater diversity and abundance of small RNA molecules. COMPSRA is modular, stand-alone and integrates multiple customizable RNA databases and sequence processing tool and is distributed under the GNU General Public License free to non-commercial registered users at  One of the most important applications is to quantify small RNAs in the circulatory system (circulating RNAs). The human circulatory system contains various types of RNA molecules, including fragmental mRNA, miRNA, piRNA, snRNA, snoRNA, and some other non-coding sequences4. Studies have shown the biomarker potential of circulating RNAs in cancer5, cardiovascular disease6, and asthma7. Moreover, other types of DNA and RNA fragments discovered in the human circulating system have been implicated as potential causes of chronic disease9.Small RNA sequencing (RNA-seq) technology was developed successfully based on special isolation methods and the RNA-seq technique, which facilitates the investigation of a comprehensive profile of small RNAs10. sRNAtoolbox is a web-based small RNA research toolkit11 and SeqCluster has started to focus on non-miRNAs by comparing the sequence similarity12. Some efforts have begun to characterize the full spectrum of small RNAs of a biological system , such as Oasis2, miRMaster and exceRpt15. Oasis2 and miRMaster are web servers for small RNA-seq data analysis. ExceRpt, maintained by the Extracellular RNA Communication Consortium (ERCC), is an extensive and commonly used web-based pipeline for extra-cellular RNA profiling. Oasis2 contains the pathogen detection module to detect the potential pathogenic infections or contaminations. miRMaster also permits the detection of potential exogenous miRNAs, but couldn’t handle case and control samples. ExceRpt provides few microbiome annotations. All the tools need users to upload the original sequencing files, which is un-workable for big data.In anticipation of a continued growing number of circulating RNAs studies, a comprehensive and stable platform is needed to identify the RNA classification, RNA read counts, differential expression between case and control samples, including both human and non-human (e.g. microbiome) small RNAs (<200 nucleotide length). Previous efforts to characterize small RNAs have focused primarily on microRNAs (miRNAs). For instance, sRNAnalyzer is a comprehensive and customizable pipeline for the small RNA-seq data centred on microRNA (miRNA) profiling16 and BLAST17 for alignment and sequence comparison. It takes FASTQ file as inputs and outputs the counts profile of each type of RNA molecule type per FASTQ file . When case and control files are marked, COMPSRA can perform a differential expression analysis with the p value from the Mann-Whitney U test as default.To profile intracellular and extracellular small RNA’omes through the small RNA-seq data, we built a comprehensive platform COMPSRA to identify and quantify diverse RNA molecule types, including miRNA, piRNA, snRNA, snoRNA, tRNA, circRNA and the fragmental microbial RNA. COMPSRA is built using Java and works as stand-alone providing detailed annotation for each type of small RNAs including microbial constituents. It currently uses STARCOMPSRA is built using Java and composed of five functionally independent and customizable modules: Quality Control (QC), Alignment, Annotation, Microbe and Function see Fig. . Users c18. The read quality of the remaining sequence is evaluated using its corresponding PHRED score. Poor quality reads (average PHRED ≤ 20) are removed according to quality control parameter set in the command line (−rr 20). The bases with bad quality (PHRED ≤ 20) in the head and tail ends of the read can also be removed with the related parameters (−rh 20 −rt 20). Users can specify qualified reads of specific length intervals for input into subsequent modules.FASTQ files from the small RNA-seq of biological samples are the default input. First, the adapter portions of a read are trimmed along with any randomized bases at ligation junctions that are produced by some small RNA-seq kits 16 as its default RNA sequence aligner and it will be updated with the latest version of STAR in the further release. Qualified reads from the QC module output are first mapped to the target genome , and then aligned reads are quantified and annotated in the Annotation Module. Reads that could not be mapped to the human genome are saved into a FASTA file for input into the Microbe Module. COMPSRA uses local type of read ends alignment because of the existence of miRNA isoforms (isomiRs)19. In order to make the alignment more accurate, only one mismatch is allowed in the default alignment parameter. For more detailed settings, users can refer to the online manual.COMPSRA uses STAR v2.5.3aUGAGGUAGUAGGUUGUAUAGUU) and piRNA has_piR_008113 (UGAGGUAGUAGGUUGUAUAGUUUUAGGGUC) have significant sequence overlaps. In this case, if the read can map to both, each small RNA will be assigned with one count.There are two scenarios where multi-aligned reads may exist when aligned against a reference genome. First, one small RNA read could be aligned to multiple distinct genomic locations. For example, the miRNA hsa-miR-1302 can derive from 11 potential pre-miRNAs. In this scenario, COMPSRA will only count once with the multi-aligned read. Second, two or more distinct small RNAs could have overlapping sequences. For example, miRNA has-let-7a-5p  in these databases, we use an automatic LiftOver created by the UCSC Genome Browser Group. All the databases used are pre-built to enable speedy annotation. For each RNA molecule, COMPSRA provides both the read count and indicates the database items that support its annotation. Using the command line parameter (-abam), COMPSRA will output all the reads that are annotated to a specific type of RNAs. COMPSRA still supports the annotation of small RNAs in the mouse genome and more species will be added in the latest release.COMPSRA currently uses several different (and expandable) small RNA reference databases for annotating human genome mapped reads: miRBaseLolp denotes the number of overlapped bases and Lread denotes the number of bases of the target read. In COMPSRA, the default Rolp equals to 1 (-ann_overlap 1), which means the read should lie completely within the annotation.The annotation depends on the degree of read support (DRS), which was measured by the overlap between the gene locations and read mapped coordinates. We define a ratio 27 from UCSC using BLAST. Because of the homology between species, one read may be aligned to many species and COMPSRA will list all the potential taxa with read count according to the phylogenetic tree as default. The four major microbial taxons archaea, bacteria, fungi and viruses are supported. To optimize processing the BLAST results, a fast accessing and parsing text algorithm is used28.The qualified reads that were not mapped to the human genome in the Alignment Module are aligned to the nucleotide (nt) databaseThe read count of each RNA molecule that is identified in the Annotation Module is outputted as a tab-delimited text file according to RNA type. With more than one sample FASTQ file inputs, the output are further aggregated into a data matrix of RNA molecules as rows and samples as columns showing the read counts of an RNA molecule across different samples. The default normalization method is Count-per-Million (CpM), which normalizes each sample library size into one million reads. The user can mark each sample FASTQ file column as either a case or a control in the command line, and perform a case versus control differential expression analysis for each RNA molecule using the Mann-Whitney rank sum test (Wilcoxon Rank Sum Test) as the default statistical test.15. Serum samples were prepared using NEXTflex Small RNA Kit and sequenced through the Illumina platform.We processed small RNA-seq FASTQ files from the serum of 12 healthy human subjects in a performance study through COMPSRA to evaluate its performance on diverse types of RNA molecules, and compare it to a previously published web-based pipeline exceRptWe run COMPSRA on server with 30 GB RAM and set the same parameters as exceRpt. COMPSRA takes ~10 minutes per sample, but exceRpt will cost ~20 minutes for each sample , Name (name of the RNA), ID  and Count (counts of reads). A summary count file including all samples can be obtained from the function module (-fun_merge).29. The 42 nt (or trimmed maximum read length in this study) might represent snRNAs, mRNA fragments and microbial RNAs. The snoRNA was overlapped with miRNA in a great measure. In addition, there were still large part of short RNA fragment around 12 nt, which may come from some RNA degradation products or even some unknown RNA classes.The read length distribution of 12 serum samples was described in Fig. 30. 7285 circRNAs were identified, which was much higher than other small RNAs. It might be because that the total number of circRNAs in human genome is huge. According to the statistics in CIRCpedia (v2), the human genome v38 (hg38) may contain 183,943 circRNAs31. The species of microbe were still large in number, which may be caused by the cross species homology. If one sequence read aligned to multiple homologous species, COMPSRA will output all the species without bias.COMPSRA identified diverse types of RNA molecules in this study including miRNAs, piRNAs, snRNAs, snoRNAs, tRNAs, circRNAs and RNAs in microbes see Fig. . We used33. Compared with exceRpt, COMPSRA may have a more robust results in snoRNAs detection. In the comparison of tRNAs, we reclassified tRNAs according to the amino acid it carries as exceRpt did.Compared to exceRpt outputs of the same study data See Fig. , COMPSRAThe comparisons of piRNAs, snRNAs, snoRNAs and tRNAs at the sample level were shown in Supplementary Tables 34. We take all the mature miRNAs in miRBase (v21) as a background and the miRNAs in the literature as benchmark, COMPSRA has a sensitivity of 85% and specificity of 87%. When comparing both the top 30% miRNAs identified, the sensitivity can reach 93% and specificity can reach 98% (see Supplementary Fig. We downloaded the dataset SRP120169 from SRA database in NCBI as an extraneous reference. There were 42 small RNA-seq samples in the dataset and we run COMPSRA according to the parameters they set in the literatureWe also check the 17 COMPSRA unique miRNAs and 24 exceRpt unique miRNAs above Fig.  in the SCOMPSRA can align the reads that fail to map to the human genome with the microbial genomes and output the counts for each kinds of species. Because of the different parameters, databases and nomenclatures of species used, it is difficult to compare the results between tools in microbiome. In this paper, we attempt to run the 12 test samples on miRMaster and compared with COMPSRA among the top five commonly existed viruses. All the five viruses were also detected by COMPSRA. Pearson’s correlations were calculated across the 12 samples between COMPSRA and miRMaster (see Supplementary Table COMPSRA is a comprehensive modular stand-alone platform for the small RNA-seq data analysis. As a stand-alone platform, it bypasses data transfer effort/time/risk offsite that web-based tools need. Its modularity allows the user to run all modules together as a complete basic small RNA analysis pipeline or specific modules as needed. Its pre-built RNA databases and sequence read processing tools enable turnkey basic small RNA analysis from identification, quantification to basic differential analysis. These pre-built databases/tools are customizable and expandable.Supplementary File.


We further show that NCBP3 competes with the RNA degradation factor ZC3H18 for binding CBC-bound transcripts, and that NCBP3 positively impacts the nuclear export of polyadenylated RNAs and the expression of large multi-exonic transcripts. Collectively, our results place NCBP3 with the EJC and TREX complexes in supporting mRNA expression.The nuclear Cap-Binding Complex (CBC), consisting of Nuclear Cap-Binding Protein 1 (NCBP1) and 2 (NCBP2), associates with the nascent 5′cap of RNA polymerase II transcripts and impacts RNA fate decisions. Recently, the C17orf85 protein, also called NCBP3, was suggested to form an alternative CBC by replacing NCBP2. However, applying protein–protein interaction screening of NCBP1, 2 and 3, we find that the interaction profile of NCBP3 is distinct. Whereas NCBP1 and 2 identify known CBC interactors, NCBP3 primarily interacts with components of the Exon Junction Complex (EJC) and the TRanscription and EXport (TREX) complex. NCBP3-EJC association  While providing protection against 5′-3′ exonucleolysis, the cap also serves as a binding site for the Cap-Binding Complex (CBC), which is composed of the Nuclear Cap-Binding Proteins 1 and 2  and considered a hallmark of all nuclear RNAPII transcripts (All eukaryotic RNA polymerase II (RNAPII) transcripts undergo processing and protein-RNA packaging events, that are essential for their downstream cellular fates . Shortlynscripts . Attachenscripts , RNA splnscripts ,10 to RNnscripts , and is nscripts .The impact of the CBC on RNP identity is diverse. Early in the RNP assembly process, the CBC interacts with the ARS2/SRRT protein to form the CBC-ARS2 (CBCA) complex, which constitutes a central platform for dynamic protein interactions determining RNA fate ,16. For In addition to its effects on RNP production and function, the CBC may also promote RNA nuclear degradation. In the case of such short-lived transcripts, the CBCA complex can link to the zinc-finger protein ZC3H18, which contributes to connect the RNA to either of two exosome adaptors, the Nuclear EXosome Targeting (NEXT) complex ,28 or th7G cap through its putative N-terminal RRM  screening of NCBP1, NCBP2 and NCBP3, we demonstrate a distinct protein interaction network of NCBP3 as compared to its canonical CBC counterparts. Our results identify components of the EJC and TREX complexes as abundant NCBP3 interactors and we show that NCBP3 accumulates in nuclear speckles together with these components. Interestingly, NCBP3 associates with the assembled EJC core and binds RNAs in proximity to the EJC. NCBP3 further aids the TREX complex in mRNA export and positively regulates the expression of multi-exonic RNAs. We conclude that NCBP3 helps to promote the productive fate of mRNPs, which is supported by its ability to compete with ZC3H18 for RNP association.2.HeLa Kyoto cells stably expressing LAP-tagged proteins  and control LAP cells were kindly provided by Anthony Hyman  see Sup. NCBP1-,RNA interference (RNAi) was performed with Lipofectamine 2000 (Invitrogen) according to the manufacturers’ instructions. The sequences of the used siRNAs are shown in The interaction screening was performed essentially as described in ,37. BrieWhere RNase treatment was applied, the beads were, after three times of washing, resuspended in 40 μl extraction solution with 1 μl of RNase A/T1 mix (Thermo Scientific # EN0551), and left shaking for 20 min at room temperature, and washed once before elution.NuPAGE 4-12% Bis–Tris gels (Invitrogen) were run with MOPS or MES buffers (Invitrogen), depending on the sizes of the target proteins, and following the manufacturer's manual. Proteins were subsequently transferred to 0.45 μm PVDF membranes (Immobilon-P membrane) with the iBlot2 transfer system (Thermo Fisher), and blocked with 5% BSA in Tris-buffered saline and 0.1% Tween 20 (TBST). Membranes were then incubated with primary antibody (in TBST with 1% BSA)  < 0.01, S0 (log2 fold changes) > 1 were considered as significantly enriched. Using significantly enriched proteins, stoichiometric abundances were calculated as follows (2(stoichiometric abundance × 103) with Euclidean distances under Perseus default settings. The Venn diagram was generated with the BioVenn tool (Details of MS sample processing is described elsewhere . Briefly follows : (i) meaenn tool .7G affinity beads  and 200 μl of BB-250 (250 mM NaCl) were added. After gentle rotation for 2 h at 4°C, the beads were washed three times with BB-150 (150 mM NaCl) and eluted with 1× SDS loading dye. Eluates were boiled and loaded on 4–12% SDS-PAGE gels with a protein marker . Proteins were visualized by Coomassie staining.The purified recombinant proteins and the performance of interaction assays were previously described ,42. Brie2O once before mounting on slides with ProLong Gold antifade reagent. pA+ RNA fluorescent in situ hybridization (FISH) analysis was performed as previously described (70 probe (DNA Technology A/S). Images were obtained with a ZEISS Axio Observer 7 microscope equipped with an Axiocam 702 camera and Plan-APOCHROMAT 63 x/1.4 oil objective, and Zen 2 software.For protein immunofluorescence, cells were grown on 18 mm cover slips, washed twice with PBS and fixed with 4% paraformaldehyde in PBS for 12 min. They were then washed twice with PBS, permeabilized and blocked with 0.5% Triton X-100 and 3% FBS in PBS for 15 min and washed three times with PBS. Cells were then incubated with primary antibodies for 1 h and washed three times with PBS before incubation with secondary antibodies for 45 min followed by three times of washing with PBS  following the manufacturer's instructions. RNA was DNase treated with TURBO DNA-free kit (Invitrogen) before reverse transcription with SuperScript III Reverse Transcriptase (Invitrogen). The cDNA obtained was used for qPCR with SYBR Green qPCR SuperMix (Invitrogen) and AriaMx Real-time qPCR System (Agilent). Sequence of primers used is listed in ftp://ftp.ccb.jhu.edu/pub/infphilo/hisat2/data/hg38_tran.tar.gz). Only proper pairs with both reads mapping to the genome were used for further analysis. Exon read counts were collected using the featureCounts tools from the subread software suite (v 2.0.0)   with sethttps://jgi.doe.gov/data-and-tools/bbtools/) using parameters k=13; ktrim=r; useshortkmers=t; mink=5; qtrim=t; trimq=10; minlength=20; ref=barcodes.fa, where the ‘barcodes.fa’ file contains the 2 adapter sequences. Remaining reads were mapped to GRCh38 using the STAR aligner (v2.5.2b) (Processed CLIP data for NCBP3 and EIF4A3 were obtained from . Data shv2.5.2b)  togetherv2.5.2b)  with setin vitro studies showing that NCBP3 can bind NCBP1 in the absence of NCBP2  analysis using HeLa cells stably expressing LAP-tagged versions of the three NCBPs at near endogenous level , or the Taken together, our results demonstrate that NCBP3 harbors a partially distinct interaction profile and suggest that the protein only associates with NCBP1 and NCBP2 in their CBC context.Further analyses of the stoichiometric interaction profiles of NCBP proteins revealed that NCBP1 and NCBP2 primarily interacted with each other and with PHAX . In contWe next sought to confirm the interactions of NCBP3 with EJC and TREX components by IP/western blotting analysis. While all three NCBP proteins were able to co-IP EIF4A3, RBM8A and MAGOH (EJC core components) as well as ALYREF and DDX39B (TREX components), NCBP3 did this most efficiently; this was even more striking considering that NCBP3-LAP expression and capture levels were discernibly lower than those of NCBP1-LAP and NCBP2-LAP Figure . It was We conclude that the most abundant interactors of NCBP3 are EJC and TREX components. Unlike NCBP1 and NCBP2, NCBP3 does not copurify PHAX and exosome adaptors, suggesting that it neither functions in sn(o)RNA transport nor in exosomal RNA decay.To further characterize the interaction dependence of the EJC and TREX complexes with NCBP3, we conducted NCBP3-LAP IPs upon depletion of selected EJC and TREX components. As previously reported, only partial depletion was achieved for the EJC components EIF4A3 and RBM8A , presumain vitro protein binding assays of recombinant EJC core proteins and NCBP3. Taking advantage of the affinity of NCBP3 to m7-GTP, we used m7-GTP Sepharose to purify NCBP3-bound proteins  data sets for NCBP3 ,51, EIF463We conclude that NCBP3′s physical interactions with EJC and TREX complexes are also reflected by cellular localization and RNA binding, with the RNA binding profile being particularly similar between NCBP3 and EIF4A3.The relationship between NCBP3 and EJC/TREX prompted the question whether NCBP3 might function in the context of EJC and/or TREX. To probe a possible functional relation of NCBP3 to the EJC, we took advantage of the circumstance that depletion of EJC core components, or the peripheral component RNPS1, affects certain alternative splicing decisions ,65. HoweWe then asked whether NCBP3 impacts global RNA expression levels by sequencing total RNA (RNA-seq) from HeLa cells depleted of NCBP3 . DiffereWe conclude that NCBP3 does not partake in core EJC functions, yet the protein positively impacts the expression of multi-exonic transcripts. Whether this exon number-dependent effect relates to transcription, RNA decay or RNA localization remains unclear.+) RNA export defect (+ RNA (in situ hybridization (FISH) for pA+ RNA upon single- and double-depletion of NCBP3 and THOC3  under the accession number GSE99059.gkaa744_Supplemental_FilesClick here for additional data file.


Dysregulation of small non‐coding RNA (ncRNA) is associated with various human diseases including cancer. This study aimed to evaluate the circulating exosomal small RNAs including microRNAs (miRNAs) and P‑element‑induced wimpy testis (PIWI)‐interacting RNAs (piRNAs) as sensitive and specific non‐invasive biomarkers for gastric cancer (GC) diagnosis. Serum exosomal small RNA transcriptome was examined using unique molecular identifiers (UMI) small RNA sequencing. Dysregulated miRNAs and piRNAs were verified in 70 GC patients and 60 healthy controls (HC) by reverse transcription quantitative PCR. The expressions of miR‐1307‐3p, piR‐019308, piR‐004918 and piR‐018569 in serum exosomes were significantly increased in GC group as compared to those in HC group. Moreover, GC patients with metastasis had significantly higher expression levels of piR‐004918 and piR‐019308 than GC patients without metastasis. The area under the curve (AUC) for miR‐1307‐3p, piR‐019308, piR‐004918 and piR‐018569 in the GC group was 0.845, 0.820, 0.754 and 0.732, respectively. The combination of carcinoembryonic antigen (CEA) and carbohydrate antigen (CA) 199 can improve the AUC of miR‐1307‐3p to 0.902 and piR‐019308 to 0.914 for GC diagnosis. In conclusion, our findings indicate that serum exosomal piRNAs are promising non‐invasive diagnostic biomarkers for GC patients and potential markers for monitoring metastasis. Our data showed that in addition to miR‐1307‐3p, serum exosomal piR‐019308, piR‐004918 and piR‐018569 are promising non‐invasive diagnostic biomarkers for GC patients. Moreover, serum exosomal piR‐019308 and piR‐004918 are potential markers for monitoring metastasis.22.1Serum samples from 70 GC patients and 60 HC who had no history of basic or chronic diseases were collected from Jinling Hospital of Nanjing University using gel barrier tubes. Samples were stored at −80°C until exosomal RNA isolation. All GC patients were diagnosed on the basis of the histopathology by biopsy or endoscopic examination, and serum samples were collected at the time of diagnosis before surgery or radiochemotherapy. Informed consent was obtained for all individuals. Ethics approval was obtained from the Ethics Committee of Jinling Hospital of Nanjing University. The demographic characteristics of participating patients and the HC are described in Table 2.2Frozen serum samples were incubated at 37ºC in a water bath until samples were completely thawed. The thawed samples were excluded cellular material, including thrombocyte fragments and particles larger than 0.8 µm using syringe filters . Then removing some of the larger vesicles by passing through 0.22 µm filter (Millipore). Exosomes were isolated from pre‐filtered serum by exoRNeasy Serum/Plasma Kits (Qiagen).2.3Serum exosomes eluted in 100 µL Buffer XE using exoRNeasy Serum/Plasma Kits were diluted in PBS to a final volume of 1 ml. The size distribution and concentration of exosomes were analysed by nanoparticle tracking analysis using ZetaView particle tracker (Particle Metrix). Ideal measurement concentrations were found by pre‐testing the ideal particle per frame value (140 ~ 200 particles/frame). The manufacturer's default software settings for exosomes were selected accordingly. Measurement data from the ZetaView were analysed using the corresponding software (ZetaView 8.02.28).2.4Freshly isolated exosomes suspensions were fixed with 4% paraformaldehyde in 0.1 M phosphate buffer (pH 7.4). A drop of each sample was placed on a carbon‐coated copper grid and negative staining with 1% uranyl acetate for 1 minute. The preparations were examined with a transmission electron microscope  at an acceleration voltage of 120 kV.2.5Serum exosomes eluted in 100 µL Buffer XE were concentrated to 10 µL using ultrafiltration . Concentrated exosomes were lysed with 40 µL of RIPA buffer. Then, 10 µg of total protein were prepared for Western blot analysis performed as previously described.2.6Caenorhabditis elegans miR‐39  were added into the lysate for further external control. Subsequently, 90 µl of chloroform was used for phase separation. The upper aqueous phase/ethanol mix, buffer RWT and buffer RPE were added into RNeasy MinElute spin column in sequence as specified in the manufacturer's instructions. Finally, RNA was eluted in 20 µl RNase‐free water. RNA integrity and size distributions were evaluated by the Agilent 2100 Bioanalyzer Instrument.Exosomal RNA was isolated by exoRNeasy Serum/Plasma Kits (Qiagen) according to the manufacturer's instructions.2.7Considering the quantity of RNA extracted from serum exosomes was extremely low ,Library was prepared with 6.5 μL total RNA for each sample . Total RNA was purified by electrophoretic separation on a 15% urea denaturing polyacrylamide gel electrophoresis (PAGE) gel, and small RNA regions corresponding to the 18 ~ 30 nt bands in the marker lane  were excised and recovered. Then, the 18 ~ 30 nt small RNAs were ligated to adenylated 3’ adapters annealed to UMI, followed by the ligation of 5’adapters. The adapter‐ligated small RNAs were subsequently transcribed into cDNA by SuperScript II Reverse Transcriptase (Invitrogen) and then several rounds of PCR amplification with PCR Primer Cocktail and PCR Mix were performed to enrich the cDNA fragments. The PCR products were selected by agarose gel electrophoresis with target fragments 110 ~ 130 bp and then purified by QIAquick Gel Extraction Kit (Qiagen). The library was quality and quantitated in two methods: check the distribution of the fragments size using the Agilent 2100 bioanalyzer and quantify the library using real‐time quantitative PCR (QPCR) (TaqMan Probe). The final ligation PCR products were sequenced using the BGISEQ‐500 platform .2.8http://pirnabank.ibab.ac.in) for piRNA with Bowtie 2. The small RNA expression level was calculated by counting absolute numbers of molecules using UMI. Differential expression analysis was performed using the PossionDis, FDR ≤ 0.001 and the absolute value of Log2Ratio ≥ 1 as the default threshold to judge the significance of expression difference.The raw tags were processed to remove the tags with low quality, with 5’ contamination, without 3 primer, without insertion, with poly A, shorter than 18 nt. After filtering, the clean tags were mapped against miRBase for miRNA,piRNABank , and other forward primers of targeted miRNAs and piRNAs were listed in Table Briefly, 4 μL of RNA extracted from serum exosomes was reverse transcribed to cDNA using the miRcute Plus miRNA First‐Strand cDNA Kit (TIANGEN). Quantitative real‐time PCR using TB Green™ Premix Ex Taq™ II (Takara) was performed on a CFX96 Touch™ Real‐Time PCR Detection System (Bio‐Rad). The cycle parameters were as follows: 95°C for 15 min followed by 40 cycles of 94°C for 20 seconds, 60°C for 34 s and 65°C for 5 seconds. We calculated ΔCq by subtracting the Cq value of the exogenous reference cel‐39 from the raw Cq value. The expressions of targeted genes in each sample were normalized to the cel‐39. The absolute expression levels were calculated by the 22.10t tests for normally distributed data. Otherwise, the data were analysed by nonparametric Mann‐Whitney tests. A P value < .05 was considered statistically significant.Statistical analysis was performed with IBM SPSS Statistics 25.0, GraphPad Prism version 7.0, and MedCalc version 17.1. Between group differences were analysed by parametric two‐tailed unpaired Student's 33.1Transmission electron microscopy showed the isolated particles have a typical size and morphology of exosomes Figure . NanoparThe integrity and RNA size distributions of serum exosomal RNA were analysed by use of the Agilent 2100 Bioanalyzer Instrument. Results showed that the RNA integrity number (RIN) of almost all serum exosomal RNA samples were below 2.0, meanwhile, exosomal RNA samples from serum contained high proportions of small RNAs, which was consistent with the characterization of exosomal RNA Figure . These r3.2Libraries established from the serum exosomal RNA samples of 4 GC patients and 4 HC individuals were subjected to UMI small RNA sequencing. The sequence data showed 306 different miRNAs and 161 different piRNAs were found to be present in serum exosomes Figure . Heatmap3.3The expressions of the 7 candidate miRNAs and 7 candidate piRNAs were evaluated by RT‐qPCR in serum exosomal RNA samples from 15 GC patients and 15 HC individuals in the training phase. Firstly, we excluded 3 candidate miRNAs  and 1 candidate piRNA (piR‐002528) because Cq values of these candidates were greater than 32 in majority of samples. Among the remaining candidates, the expression of miR‐1307‐3p and three piRNAs  was significantly up‐regulated in GC group compared with HC group  in GC patients (n = 70) as compared to those in HC (n = 60)  expression levels of piR‐004918 and piR‐019308 in serum exosomes than GC patients without metastasis , 0.820 , 0.754  and 0.732 , respectively. The results showed that the diagnostic values of miR‐1307‐3p, piR‐019308, piR‐004918 and piR‐018569 were much better than that of CEA, CA199 and AFP, which had the AUCs of 0.689, 0.687 and 0.634, respectively  curve was plotted to identify a cut‐off value. As shown in Figure y Figure .3.6P < .0001), 0.914 , 0.859  and 0.868 , respectively. The optimal cut‐off values for miR‐1307‐3p/piR‐019308/piR‐004918/piR‐018569 combined with CEA and CA199 were 5.5576 , 8.5607 , 21.8673  and 7.8026 , respectively , we found mRNA expressions of PIWIL1 and PIWIL4, two of the major PIWI proteins, were significantly up‐regulated in GC tumour tissues compared with gastric normal mucosa tissues  by the use of Kaplan‐Meier plotter . We found PIWIL4‐high expression group significantly correlated with poor OS ; Data curation (lead); Formal analysis (lead); Methodology (lead); Writing‐original draft (lead). Nan Zhang: Data curation ; Formal analysis (supporting). Dandan Li: Data curation (supporting); Formal analysis (supporting). Yingmin Wu: Data curation (supporting); Formal analysis (supporting). Hongsheng Wang: Funding acquisition ; Project administration ; Supervision ; Writing‐review & editing . Junjun Wang: Funding acquisition (lead); Supervision (lead); Writing‐review & editing (supporting).Table S1‐S5Click here for additional data file.


Cooperatively, advances in m5C detection techniques have enabled transcriptome mapping of RNA methylation at single-nucleotide resolution, thus stimulating m5C-based investigations. In this review, we summarize currently available approaches for detecting m5C distribution in RNA as well as the advantages and disadvantages of these techniques. Moreover, we elucidate the regulatory mechanisms of RNA m5C modification by introducing the molecular structure, catalytic substrates, cellular distributions and biological functions of RNA m5C regulators. The functional consequences of m5C modification on mRNAs, tRNAs, rRNAs and other RNA species, including viral RNAs and vault RNAs, are also discussed. Finally, we review the role of RNA m5C modification in cancer pathogenesis and progression, in hopes of providing new insights into cancer treatment.As an important posttranscriptional modification of RNA, 5-methylcytosine (m DNA ine base . In eukaiewed in ). In rec5C detection techniques, including bisulfite sequencing, m5C RNA immunoprecipitation sequencing (m5C-RIP-seq), 5-AZAcytidine-mediated RNA immunoprecipitation sequencing (Aza-IP-seq) and methylation-individual nucleotide resolution crosslinking immunoprecipitation sequencing (miCLIP-seq), over 10,000 potential sites of m5C modification were detected within the whole human transcriptome [5C was found in diverse RNA species from various organisms, not only in ribosomal RNA (rRNA), messenger RNA (mRNA) and transfer RNA (tRNA) but also in viral RNA, vault RNA, small nuclear RNA, small nucleolar RNA, pseudogenes, natural antisense transcripts, enhancer RNA, long noncoding RNA (lncRNA) and microRNA (miRNA) [5C differs among different organisms, as it has been common to find m5C methylation substrates in the tRNA and mRNA of eukaryotes and archaea, while no m5C methylation substrates have been detected in bacteria [5C is regulated by “writers”, “erasers”, and “readers”, referring to RNA m5C methyltransferases (RCMTs), which are enzymes of the ten-eleven translocation (TET) family and RNA binding proteins [5C modification as well as its regulators has been proven to influence RNA stability, gene expression, and protein synthesis and thus has a great impact on various cellular and physical processes [5C modification and its regulators have been confirmed to play crucial roles in the pathogenesis of bladder cancer [5C modification in cancer treatment.With the advances in RNA mcriptome , and the (miRNA) –15. The bacteria . It has proteins . The altrocesses –22. Addir cancer , hepatocr cancer , glioblar cancer  and leukr cancer , indicat5C mapping methods as well as their advantages and disadvantages. Next, we summarize the mechanisms and biological functions of m5C modification of RNAs. Finally, by discussing the role of m5C in tumorigenesis and cancer progression, we propose m5C-based approaches for cancer diagnosis, prognosis and clinical treatment. Although some statements in our study may briefly touch on other reviews [5C methylation in depth, we focus on literature published in the past five years and are the first to thoroughly discuss the clinical prospect of RNA m5C in cancer treatment.Herein, we introduce the principles of m reviews , 16, 25,5C was discovered in RNA almost 60 years ago, little progress has been made regarding the distribution and biological roles of RNA m5C due to a lack of reliable and sensitive methods for RNA methylation detection [5C patterns, including bisulfite sequencing, m5C-RIP-seq, Aza-IP-seq and miCLIP-seq. Additionally, their advantages and limitations are discussed to provide a more comprehensive view of RNA m5C mapping . Not surprisingly, Aza-IP enables identification of RCMT-specific m5C sites at single nucleotide resolution. However, it is also limited for some reasons, including the toxicity of 5-AZAcytidine to cells, the possible absence of RNAs that are not stably replaced by 5-AZA-C, and random incorporation of cytidine analog into DNAs [Aza-IP-seq and miCLIP-seq are designed to investigate specific catalytic sites of RCMTs by immunoprecipitation. All characterized mammalian RCMTs form a reversible covalent intermediate with their cytosine substrate—a covalent linkage that is trapped when formed with the cytosine analog 5-AZAcytidine (5-AZA-C) . Replacento DNAs . Remarka5C sites are mapped at the + 1 site of the sequencing reads, which is distinct from the C-to-G transversion signatures in Aza-IP. Despite its high specificity, miCLIP requires a large amount of time and money.In contrast to Aza-IP, due to the mutation of the conserved cysteine at the catalytic domain (C271A) of the RNA methyltransferase, an irreversible covalent bond is formed between the enzyme and catalytic site during the miCLIP process . Followi5C binding proteins (readers), such as ALYREF and YBX1, exert biological effects by recognizing and binding to m5C sites [5C regulators in detail, emphasizing their molecular structures, catalytic substrates, cellular distributions and biological functions  subgroups of methyltransferases and DNA methyltransferase homologue DNMT2, are recognized as writers for catalyzing cytosine-5 methylation . Additio5C sites . The eraThe DNA methyltransferase homologue DNMT2 contains a Rossman-fold catalytic domain and an S-adenosyl methionine (SAM) binding site. DNMT2 and NSUN2 have complementary target specificities . DNMT2 mSimilar to DNMT2, the NSUN family of proteins was also found to contain a Rossman-fold catalytic domain and a SAM binding site. Using SAM as a methyl group donor, RCMTs covalently bind with the RNA cytosine pyrimidine ring through the conserved cysteine residue and catalyze 5-cytosine methylation. Structurally, the NSUN family of proteins contains two cysteine residues in motif IV and motif VI within the catalytic domain . The thi5C in mitochondrial transfer RNAs (mt-tRNAs) at position C48–50 within mammalian mitochondria [5C regulator that is highly involved in HCC progression [The NSUN family of proteins comprises 7 members, including NSUN1, NSUN2, NSUN3, NSUN4, NSUN5, NSUN6 and NSUN7, among which NSUN2 was the first-discovered and is the most well-elucidated member. Dynamic cellular locations of NSUN2 were demonstrated to be associated with cell cycle progression of human epidermal cells, and it was observed to be enriched in the nucleolus in G1, move to the position between nucleoli and nucleoplasm during S phase, and localize to cytoplasm in G2 and centrioles during M phase . Moreovechondria . Originachondria , 43–45. chondria , and it chondria , Epsteinchondria  and tumochondria , 48, 49.chondria . Recent chondria , cell cychondria , chromatchondria  and HIV-chondria . NSUN4 ichondria . By intechondria . Notablygression . Anothergression . The knogression .Met) at the “wobble base C34” within the anticodon loop, and the methylated cytosine is further oxidized to 5fC for normal mitochondrial translation of the respiratory chain complex and oxidative phosphorylation. The mutation of NSUN3 is tightly linked to mitochondrial disease and skewed embryonic stem cell differentiation towards the meso- and endoderm lineages instead of neuroectoderm [Cys and tRNAThr at position C72, which affects tRNAs in a late step in their biogenesis. Both tRNA substrates share a 3′-CCA sequence in the structure, which can be recognized by NSUN6 to carry out cytosine methylation [Asp-GTC, tRNAGly-GCC and tRNAVal-AAC, all of which contain 3′-GCG and 5′-CA sequences around the methylated C38 position within the anticodon loop [5C methylation rate in mRNA, indicating that DNMT2 also catalyzes the methylation of other RNA types aside from tRNA [RCMTs exert broad regulation not only of rRNAs but also of tRNAs, which are predominantly modified by NSUN3, NSUN6 and DNMT2. NSUN3 is localized to mitochondria and is required for the methylation of mitochondrially encoded transfer RNA methionine  are Fe (II)- and α-ketoglutarate (α-KG)-dependent dioxygenases and were initially found to catalyze the oxidation of DNA 5-methyl-2′-deoxycytidine (5mdC) to form 5-hydroxymethyl-2′-deoxycytidine (5hmdC), 5-formyl-2′-deoxycytidine (5fdC), and 5-carboxyl-2′-deoxycytidine (5cadC) . Members5C sites [5C sites and facilitates mRNA nuclear-cytoplasmic shuttling, and the RNA-ALYREF binding affinity as well as the export process are mediated by NSUN2 [5C remain largely unknown; thus, further investigations are urgently needed.Aly/REF export factor (ALYREF), the THO subcomplex and the RNA helicase UAP56, are the 3 major components of the transcription-export complex, which plays an essential role in mRNA nuclear export . Mechani5C sites , 20. In by NSUN2 . In addi5C reader protein that was confirmed to regulate mRNA stability in the cytoplasm [5C-modified mRNAs through an indole ring of W65 within its cold-shock domain (CDS) [5C-modified mRNAs. Similarly, it was also reported in zebrafish that YBX1 contains a crucial residue, Trp45, in its CDS, through which it can recognize and bind to m5C-modified mRNAs via π-π interactions, thus stabilizing mRNA by recruiting poly(A)-binding protein cytoplasmic 1a [In addition to ALYREF, Y-box binding protein 1 (YBX1) is a newly discovered mytoplasm , 69. In in (CDS) . Then, b5C is now regarded to broadly occur in mRNAs as well as other noncoding RNAs, including viral RNAs, vault RNAs, lncRNAs and microRNAs. Herein, we summarize the functional consequences of m5C modification on coding and noncoding RNAs, elucidating the underlying mechanisms and providing a novel view for further investigations.Posttranscriptional methylation of 5-cytosine was previously mapped only in tRNAs and rRNAs; however, with the development of high-throughput sequencing technology, m5C modifications on mRNA metabolism have recently become a prevalent issue because of the high abundance of m5C sites in mRNAs. The functional role of m5C regulation on mRNA can be classified into 3 aspects, which refer to the influence on mRNA translation, transport and stability.The regulatory effects of m5C modification on mRNA translation is variable due to distinct the methylation profiles of mRNAs , and the sequences located immediately after start codons and before stop codons seemed to have peak m5C modifications [5C in the CDS was negatively associated with mRNA translation efficiency, as the results showed that a high m5C level was correlated with the presence of ribosomal subunits (40S and 60S) and monosomes (80S), which strongly impaired mRNA translation efficiency [5C site contents in the CDS were negatively correlated with mRNA translation efficiency [5C methylation, together with methyltransferase like protein 3 (METTL3)/METTL14-induced m6A methylation, synergistically mediated p21 mRNA methylation in the 3′-UTR and facilitated p21 mRNA translation efficiency. Furthermore, the increase in p21 expression profoundly affected the outcome of oxidative stress-induced cellular senescence [5C profiles, as a recent study showed that the negative correlation between m5C modification and mRNA translation efficiency was significantly associated with methylation site enrichment in the CDS, whereas enhanced methylation in the 3′-UTR was associated with a positive correlation between these features [The effect of mNAs Fig. . In Arabications . Furtherficiency . Similarficiency . Howeverficiency . NSUN2-inescence . The confeatures .Fig. 1UA. thaliana  plays an essential role in preventing the decay of root development-associated transcripts, thus maintaining mRNA stability and promoting plant growth [5C modification is also involved in the early embryogenesis of zebrafish [5C modification exhibit higher stability than unmodified mRNAs. This could be explained by the following mechanism: YBX1, together with its potential partner Pabpc 1a, specifically recognize and interact with m5C-modified mRNAs, thus maintaining maternal mRNA stability during MZT [5C level and mRNA stability [Recent studies have also uncovered the significant role of mt growth . Additioebrafish . During ring MZT . In conttability , 72, and5C modifications mediated by NSUN2 at position C48–50 in mt-tRNA have also been detected [5C modifications mediated by NSUN2 and DNMT2 have been broadly demonstrated to maintain tRNA stability and regulate cell metabolism [5C sites in tRNALeu-CCA is increased to facilitate the translation of survival proteins in response to hydrogen peroxide, suggesting that reprogramming of tRNA modifications is required for the cellular stress response [5C methylation by TRM4/NSUN2 prevents tRNA degradation from oxidative stress, and reduced tRNA stability and hypersensitivity to oxidative stress were observed in TRM4/NSUN2 mutants [Asp-GTC and tRNAGly-GCC stability for accurate polypeptide synthesis during hematopoiesis [Asp-GTC and tRNAGly-GCC, as double-knockout cells exhibited a robust reduction in abundance in comparison with either DNMT2−/− or NSUN2−/− single-knockout cells [tRNAs were the first identified substrates of RCMTs, with methylation sites mapped at positions C34, 38, 40, 48–50 and 72 within the anticodon loop. NSUN3 mediates cytosine methylation at position C34 of mitochondrial tRNAs, while NSUN6, NSUN2 and DNMT2 catalyze cytoplasmic tRNA methylation at the following positions: NSUN7-C72, DNMT2-C38, NSUN2-C34, 40, 48–49 . Notablydetected , 73. tRNtabolism , 58, 72, mutants , 74. Simopoiesis . Additiout cells .Fig. 3N5C at position 911 of mammalian 12S rRNA [5C2278, together with the loss of ribose methylation at G2288, resulted in a structural change in 25S RNA and the consequent loss of various ribosome proteins from the 60S large ribosomal subunit under low-salt conditions, thus impairing ribosome biogenesis [rRNA methylation can affect the structure of ribonucleoproteins and is essential for ribosome biogenesis. It was confirmed that Nop2/NSUN1 and Rcm1/NSUN5 mediates the methylation of 5-cytosine in domains V and IV of 25S rRNA in yeast  and that12S rRNA . The funogenesis . Moreoveogenesis . Metodieogenesis  proved t5C modification in viral RNAs is quite controversial. It was reported that DNMT2-mediated m5C methylation promotes HIV-1 genome RNA stability and ensures its survival in the host cell [5C modification on viral RNA replication and stability have also been reported recently. NSUN1 catalyzes the methylation of HIV-1 TAR RNA at the 5′-UTR by competing with HIV-1 Tat protein, thus suppressing HIV-1 replication and facilitating its latency [5C methylation of Epstein-Barr virus-encoded RNA 2 (EBER2), NSUN2 promotes RNase angiogenin-induced cleavage of EBER2 and leads to a decrease in its level [5C in viral RNAs.Cytosine-5 methylation sites are also detected in viral RNAs; however, the regulatory role of most cell . Moreoveost cell . In cont latency . Additiots level . Therefo5C was found in VTRNA1.1 and VTRNA1.3 [5C methylation sites were mapped within or near the region mediating interaction with chromatin-modifying complexes in lncRNA HOTAIR and XIST and affected XIST function by influencing the binding of XIST to chromatin-associated protein complex PRC2 [Vault RNAs were originally derived from large ribonucleoprotein vault particles and participate in various biological processes ; NSUN2-cVTRNA1.3 . NSUN2-mVTRNA1.3 . CytosinVTRNA1.3 , 15, 77.lex PRC2 . These r5C modification in cancer pathogenesis and progression, below, we discuss the essential role of m5C modification as well as the underlying mechanisms in human cancers and propose m5C-based clinical approaches for cancer treatment.Since accumulating evidence has suggested the emerging role of RNA m5C modification landscape provides insights into dissecting the etiology of bladder cancer pathogenesis. It was reported that the overexpression of NSUN2 and YBX1 in cancer tissues promotes tumorigenesis by maintaining mRNA stability and is significantly correlated with poor prognosis in bladder cancer patients [5C methylated sites and recruiting ELAVL1, YBX1 maintains HDGF mRNA stability and therefore exerts an oncogenic role in bladder cancer development through the activation of HDGF [5C mRNAs in tumor-related pathways, such as the PI3K-AKT [5C hypermethylation might initiate tumorigenesis through the activation of oncogenic signaling pathways [The RNA mpatients . More sppatients . By bind of HDGF . In addiPI3K-AKT  and ERK-PI3K-AKT , has alspathways .5C on HCC progression has been uncovered, and evidence has indicated that the elevated m5C regulators NSUN4 and ALYREF are negatively correlated with poor survival outcome in HCC patients and exhibit great value as significant diagnostic and prognostic biomarkers for HCC [5C peaks in HCC tissues was significantly higher than that in adjacent tissues, with a more widespread distribution [5C regulators in cancer cell malignant phenotypes and the related molecular mechanisms remain to be further investigated.As one of the most common malignances worldwide, HCC remains poorly understood in terms of its pathogenesis and development. Recently, the regulatory role of RNA m for HCC . Using 6ribution . Togethe5C substrates, and the methylation of mature miR-181a-5p results in the abolishment of its tumor suppressor effect and is negatively correlated with poor prognosis in GBM. Mechanistically, mediated by the complex comprising DNMT3a and AGO4 (a miRNA-induced silencing complex), miR-181a-5p methylation impedes the formation of the miRNA-181a-5p/mRNA BIM duplex, which was originally defined to induce apoptosis by interacting with antiapoptotic Bcl-2 or Bcl-xl [5C methylation and restoring normal miRNA function for cancer treatment [miRNAs are also common mr Bcl-xl . As a rereatment .5C and RCMTs have been reported to participate in drug response/resistance not only in solid tumors but also in leukemia by interacting with distinct partners to regulate the formation of different chromatin structures. NSUN1 specifically interacts with BRD4 and then directly binds to RNA-pol-II CTD-S2P to form a unique NSUN1/BRD4/RNA-pol-II CTD-S2P complex in 5-azacitidine (5-AZA)-resistant leukemia cells, thus mediating the formation of a 5-AZA-resistant chromatin structure and inducing 5-AZA resistance in leukemia [5C mRNA in 5-AZA-resistant bone marrow compared with the 5-AZA-sensitive bone marrow. The expression levels of hnRNPK, NSUN1 and BRD4 were correlated with the course of leukemia and were involved in 5-AZA resistance and cancer development [5C application in the treatment of hematologic malignancies.RNA mleukemia . In contleukemia . Mechanileukemia . Using celopment . These r5C levels than adjacent normal tissues [5C peaks was higher in drug-resistant cancer tissues than in drug-sensitive cancer tissues [5C modification may facilitate cancer diagnosis. In addition, there is evidence suggesting the feasibility of using m5C and m5C regulators as prognostic biomarkers, which are critical for treatment planning and patient life expectancy [5C regulators and the enrichment of m5C in RNA were correlated with the growth, proliferation, invasion, and drug resistance of cancer cells [5C exhibits promising prospects in the diagnosis, prognosis and treatment of cancer, studies on RNA m5C are limited, and the cancer types studied are too few to construct a thorough review.Searching for efficient biomarkers for the diagnosis and prognosis of cancer and developing suitable targets for cancer treatment have always been the mainstream approaches against cancer. In HCC, it was confirmed that cancer tissues have higher RNA m tissues . Similar tissues . These rpectancy , 23, 51.er cells , 23, 69,5C methylation is essential for maintaining the normal physiological functions of cells and organisms, and its abnormal distribution and abundance are closely related to various diseases, including cancers. Therefore, it is proposed that RNA m5C methylation has great potential in the diagnosis, prognosis and treatment of cancer. Additionally, the mechanisms leading to the dysregulation of various RNA m5C regulators and the pathogenic mechanisms of RNA m5C methylation need to be further explored.In summary, existing evidence has revealed that RNA m


Adult rodent skeletal muscles are composed of four types of myofibers  mice revealed that Vgll2 regulates skeletal muscle fiber composition by repressing the neonatal expression of sex-determining region Y (SRY)-box 6 (Sox6), trans-acting transcription factor 3 (Sp3), and purine-rich element-binding protein B (Purβ), which are transcriptional repressors of slow-twitch fiber-related genes  under Accession No. DRA008472.In humans, long-term resistance training not only induces muscle hypertrophy but also increases the proportion of slower muscle fibers by increasing and decreasing the number of type IIA and type IIX fibers, respectively  under anesthesia (2.5% isoflurane). Mice that underwent sham surgeries were used as controls . To explore the results of long-term effects of MOV, 6 weeks after surgery, the mice were sacrificed, and the plantaris muscles were collected and submerged in RNAlater  for RNA isolation.MOV was performed as previously described  according to the manufacturer's instructions and quantified using a NanoDrop spectrophotometer (Thermo Fisher Scientific). Poly(A)+ RNA was purified from 1 μg of total RNA using the NEBNext Poly(A) mRNA Magnetic Isolation Module  and used for RNA sequencing (RNA-Seq) library preparation with the NEBNext Ultra RNA Library Prep Kit for Illumina (New England Biolabs), according to the manufacturer's protocol. The quantity of the libraries was assessed using a Library Quantification Kit  and a Thermal Cycler Dice Real Time System TP800 (Takara); their quality was assessed with a DNA 1000 Kit and a Bioanalyzer 2100 .DRA008472. DDBJ accession IDs for each sample are listed in https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and the command “–Q 33 –t 20 –l 30”. Trimmed reads were aligned to the mouse reference genome (mm10) using HISAT2 ver. 2.0.5  at Fujita Health University. Two biological replicates were performed per sample. Base calling was conducted using bcl2fastq ver. 1.8.4 software. The RNA-Seq raw data have been deposited in the DDBJ Sequence Read Archive under Accession No. P-value, padj) <0.001 and log2-fold change >1 or <-1 were considered significant. The expression levels of all genes in each experimental group are listed in Statistical analysis of RNA-Seq data was performed as previously described , including a majority of genes whose expression was altered by MOV in wild-type mice, were significantly altered in Vgll2 KO mice compared with wild-type mice subjected to the sham operation  after MOV in wild-type mice. Although in the absence of MOV, statistically significant expression changes were observed in only 18 genes (11 upregulated and 7 downregulated) in peration . Intriguperation , Vgll2 dperation . TherefoVgll2 have been identified in the spindle cell variant of rhabdomyosarcoma .The RNA-Seq raw data have been deposited in the DDBJ Sequence Read Archive under Accession No. The animal study was reviewed and approved by the Animal Care and Use Committee of the National Cerebral and Cardiovascular Center in Japan.KH, HI, HK, HO, and KT conducted the experiments. KH and MH designed the experiments and prepared the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Closteroviridae have a mono-, bi- or tripartite positive-sense RNA genome of 13–19 kb, and non-enveloped, filamentous particles 650–2200 nm long and 12 nm in diameter. They infect plants, mainly dicots, many of which are fruit crops. This is a summary of the ICTV Report on the family Closteroviridae, which is available at ictv.global/report/closteroviridae.Viruses in the family  Virions are long, helically constructed filamentous particles; the primary helix has a pitch of 3.4–3.8 nm, about 10 protein subunits per turn and a central hole of 3–4 nm . The coal-Pro), methyltransferase (Met), helicase (Hel) and RNA-directed RNA polymerase (RdRP). Downstream ORFs form a conserved five-gene module encoding a 6K small hydrophobic protein, HSP70h, a ~60 kDa protein, CP and CPm [The genome consists of 1–3 molecules of 5′-capped, linear, positive-sense RNA that lack a 3′-terminal poly(A) or tRNA-like structure . The gen and CPm .Replication occurs in the cytoplasm, possibly in association with endoplasmic reticulum-derived membranous vesicles and vesiculated mitochondria induced by the 1a and 1b polyproteins . EssentiClosterovirus is monopartite with CPm encoded upstream of CP. Transmission is by aphids in a semi-persistent manner [The genome of members of the genus t manner . AmpelovClosteroviridae: ictv.global/report/closteroviridae.Current ICTV Report on the family