Transcription-wide mapping of dihydrouridine reveals that mRNA dihydrouridylation is required for meiotic chromosome segregation.

The epitranscriptome has emerged as a new fundamental layer of control of gene expression. Nevertheless, the determination of the transcriptome-wide occupancy and function of RNA modifications remains challenging. Here we have developed Rho-seq, an integrated pipeline detecting a range of modifications through differential modification-dependent rhodamine labeling. Using Rho-seq, we confirm that the reduction of uridine to dihydrouridine (D) by the Dus reductase enzymes targets tRNAs in E. coli and fission yeast. We find that the D modification is also present on fission yeast mRNAs, particularly those encoding cytoskeleton-related proteins, which is supported by large-scale proteome analyses and ribosome profiling. We show that the α-tubulin encoding mRNA nda2 undergoes Dus3-dependent dihydrouridylation, which affects its translation. The absence of the modification on nda2 mRNA strongly impacts meiotic chromosome segregation, resulting in low gamete viability. Applying Rho-seq to human cells revealed that tubulin mRNA dihydrouridylation is evolutionarily conserved.

Exploring a unique class of flavoenzymes: Identification and biochemical characterization of ribosomal RNA dihydrouridine synthase.

Dihydrouridine (D), a prevalent and evolutionarily conserved base in the transcriptome, primarily resides in tRNAs and, to a lesser extent, in mRNAs. Notably, this modification is found at position 2449 in the Escherichia coli 23S rRNA, strategically positioned near the ribosome's peptidyl transferase site. Despite the prior identification, in E. coli genome, of three dihydrouridine synthases (DUS), a set of NADPH and FMN-dependent enzymes known for introducing D in tRNAs and mRNAs, characterization of the enzyme responsible for D2449 deposition has remained elusive. This study introduces a rapid method for detecting D in rRNA, involving reverse transcriptase-blockage at the rhodamine-labeled D2449 site, followed by PCR amplification (RhoRT-PCR). Through analysis of rRNA from diverse E. coli strains, harboring chromosomal or single-gene deletions, we pinpoint the yhiN gene as the ribosomal dihydrouridine synthase, now designated as RdsA. Biochemical characterizations uncovered RdsA as a unique class of flavoenzymes, dependent on FAD and NADH, with a complex structural topology. In vitro assays demonstrated that RdsA dihydrouridylates a short rRNA transcript mimicking the local structure of the peptidyl transferase site. This suggests an early introduction of this modification before ribosome assembly. Phylogenetic studies unveiled the widespread distribution of the yhiN gene in the bacterial kingdom, emphasizing the conservation of rRNA dihydrouridylation. In a broader context, these findings underscore nature's preference for utilizing reduced flavin in the reduction of uridines and their derivatives.

The Dihydrouridine landscape from tRNA to mRNA: a perspective on synthesis, structural impact and function.

The universal dihydrouridine (D) epitranscriptomic mark results from a reduction of uridine by the Dus family of NADPH-dependent reductases and is typically found within the eponym D-loop of tRNAs. Despite its apparent simplicity, D is structurally unique, with the potential to deeply affect the RNA backbone and many, if not all, RNA-connected processes. The first landscape of its occupancy within the tRNAome was reported 20 years ago. Its potential biological significance was highlighted by observations ranging from a strong bias in its ecological distribution to the predictive nature of Dus enzymes overexpression for worse cancer patient outcomes. The exquisite specificity of the Dus enzymes revealed by a structure-function analyses and accumulating clues that the D distribution may expand beyond tRNAs recently led to the development of new high-resolution mapping methods, including Rho-seq that established the presence of D within mRNAs and led to the demonstration of its critical physiological relevance.

Identification of D Modification Sites Using a Random Forest Model Based on Nucleotide Chemical Properties.

Dihydrouridine (D) is an abundant post-transcriptional modification present in transfer RNA from eukaryotes, bacteria, and archaea. D has contributed to treatments for cancerous diseases. Therefore, the precise detection of D modification sites can enable further understanding of its functional roles. Traditional experimental techniques to identify D are laborious and time-consuming. In addition, there are few computational tools for such analysis. In this study, we utilized eleven sequence-derived feature extraction methods and implemented five popular machine algorithms to identify an optimal model. During data preprocessing, data were partitioned for training and testing. Oversampling was also adopted to reduce the effect of the imbalance between positive and negative samples. The best-performing model was obtained through a combination of random forest and nucleotide chemical property modeling. The optimized model presented high sensitivity and specificity values of 0.9688 and 0.9706 in independent tests, respectively. Our proposed model surpassed published tools in independent tests. Furthermore, a series of validations across several aspects was conducted in order to demonstrate the robustness and reliability of our model.

Accurate identification of RNA D modification using multiple features.

As one of the common post-transcriptional modifications in tRNAs, dihydrouridine (D) has prominent effects on regulating the flexibility of tRNA as well as cancerous diseases. Facing with the expensive and time-consuming sequencing techniques to detect D modification, precise computational tools can largely promote the progress of molecular mechanisms and medical developments. We proposed a novel predictor, called iRNAD_XGBoost, to identify potential D sites using multiple RNA sequence representations. In this method, by considering the imbalance problem using hybrid sampling method SMOTEEEN, the XGBoost-selected top 30 features are applied to construct model. The optimized model showed high Sn and Sp values of 97.13% and 97.38% over jackknife test, respectively. For the independent experiment, these two metrics separately achieved 91.67% and 94.74%. Compared with iRNAD method, this model illustrated high generalizability and consistent prediction efficiencies for positive and negative samples, which yielded satisfactory MCC scores of 0.94 and 0.86, respectively. It is inferred that the chemical property and nucleotide density features (CPND), electron-ion interaction pseudopotential (EIIP and PseEIIP) as well as dinucleotide composition (DNC) are crucial to the recognition of D modification. The proposed predictor is a promising tool to help experimental biologists investigate molecular functions.

Dihydrouridine synthesis in tRNAs is under reductive evolution in Mollicutes.

Dihydrouridine (D) is a tRNA-modified base conserved throughout all kingdoms of life and assuming an important structural role. The conserved dihydrouridine synthases (Dus) carries out D-synthesis. DusA, DusB and DusC are bacterial members, and their substrate specificity has been determined in Escherichia coli. DusA synthesizes D20/D20a while DusB and DusC are responsible for the synthesis of D17 and D16, respectively. Here, we characterize the function of the unique dus gene encoding a DusB detected in Mollicutes, which are bacteria that evolved from a common Firmicute ancestor via massive genome reduction. Using in vitro activity tests as well as in vivo E. coli complementation assays with the enzyme from Mycoplasma capricolum (DusBMCap), a model organism for the study of these parasitic bacteria, we show that, as expected for a DusB homolog, DusBMCap modifies U17 to D17 but also synthetizes D20/D20a combining therefore both E. coli DusA and DusB activities. Hence, this is the first case of a Dus enzyme able to modify up to three different sites as well as the first example of a tRNA-modifying enzyme that can modify bases present on the two opposite sides of an RNA-loop structure. Comparative analysis of the distribution of DusB homologs in Firmicutes revealed the existence of three DusB subgroups namely DusB1, DusB2 and DusB3. The first two subgroups were likely present in the Firmicute ancestor, and Mollicutes have retained DusB1 and lost DusB2. Altogether, our results suggest that the multisite specificity of the M. capricolum DusB enzyme could be an ancestral property.

The Role of Active-Site Plasticity in Damaged-Nucleotide Recognition by Human Apurinic/Apyrimidinic Endonuclease APE1.

Human apurinic/apyrimidinic (AP) endonuclease APE1 hydrolyzes phosphodiester bonds on the 5' side of an AP-site, and some damaged nucleotides such as 1,N6-ethenoadenosine (εA), α-adenosine (αA), and 5,6-dihydrouridine (DHU). To investigate the mechanism behind the broad substrate specificity of APE1, we analyzed pre-steady-state kinetics of conformational changes in DNA and the enzyme during DNA binding and damage recognition. Molecular dynamics simulations of APE1 complexes with one of damaged DNA duplexes containing εA, αA, DHU, or an F-site (a stable analog of an AP-site) revealed the involvement of residues Asn229, Thr233, and Glu236 in the mechanism of DNA lesion recognition. The results suggested that processing of an AP-site proceeds faster in comparison with nucleotide incision repair substrates because eversion of a small abasic site and its insertion into the active site do not include any unfavorable interactions, whereas the insertion of any target nucleotide containing a damaged base into the APE1 active site is sterically hindered. Destabilization of the α-helix containing Thr233 and Glu236 via a loss of the interaction between these residues increased the plasticity of the damaged-nucleotide binding pocket and the ability to accommodate structurally different damaged nucleotides. Nonetheless, the optimal location of εA or αA in the binding pocket does not correspond to the optimal conformation of catalytic amino acid residues, thereby significantly decreasing the cleavage efficacy for these substrates.

Identification of D Modification Sites by Integrating Heterogeneous Features in Saccharomyces cerevisiae.

As an abundant post-transcriptional modification, dihydrouridine (D) has been found in transfer RNA (tRNA) from bacteria, eukaryotes, and archaea. Nonetheless, knowledge of the exact biochemical roles of dihydrouridine in mediating tRNA function is still limited. Accurate identification of the position of D sites is essential for understanding their functions. Therefore, it is desirable to develop novel methods to identify D sites. In this study, an ensemble classifier was proposed for the detection of D modification sites in the Saccharomyces cerevisiae transcriptome by using heterogeneous features. The jackknife test results demonstrate that the proposed predictor is promising for the identification of D modification sites. It is anticipated that the proposed method can be widely used for identifying D modification sites in tRNA.

Mapping of ribosomal 23S ribosomal RNA modifications in Clostridium sporogenes.

All organisms contain RNA modifications in their ribosomal RNA (rRNA), but the importance, positions and exact function of these are still not fully elucidated. Various functions such as stabilizing structures, controlling ribosome assembly and facilitating interactions have been suggested and in some cases substantiated. Bacterial rRNA contains much fewer modifications than eukaryotic rRNA. The rRNA modification patterns in bacteria differ from each other, but too few organisms have been mapped to draw general conclusions. This study maps 23S ribosomal RNA modifications in Clostridium sporogenes that can be characterized as a non-toxin producing Clostridium botulinum. Clostridia are able to sporulate and thereby survive harsh conditions, and are in general considered to be resilient to antibiotics. Selected regions of the 23S rRNA were investigated by mass spectrometry and by primer extension analysis to pinpoint modified sites and the nature of the modifications. Apparently, C. sporogenes 23S rRNA contains few modifications compared to other investigated bacteria. No modifications were identified in domain II and III of 23S rRNA. Three modifications were identified in domain IV, all of which have also been found in other organisms. Two unusual modifications were identified in domain V, methylated dihydrouridine at position U2449 and dihydrouridine at position U2500 (Escherichia coli numbering), in addition to four previously known modified positions. The enzymes responsible for the modifications were searched for in the C. sporogenes genome using BLAST with characterized enzymes as query. The search identified genes potentially coding for RNA modifying enzymes responsible for most of the found modifications.

Major reorientation of tRNA substrates defines specificity of dihydrouridine synthases.

The reduction of specific uridines to dihydrouridine is one of the most common modifications in tRNA. Increased levels of the dihydrouridine modification are associated with cancer. Dihydrouridine synthases (Dus) from different subfamilies selectively reduce distinct uridines, located at spatially unique positions of folded tRNA, into dihydrouridine. Because the catalytic center of all Dus enzymes is conserved, it is unclear how the same protein fold can be reprogrammed to ensure that nucleotides exposed at spatially distinct faces of tRNA can be accommodated in the same active site. We show that the Escherichia coli DusC is specific toward U16 of tRNA. Unexpectedly, crystal structures of DusC complexes with tRNA(Phe) and tRNA(Trp) show that Dus subfamilies that selectively modify U16 or U20 in tRNA adopt identical folds but bind their respective tRNA substrates in an almost reverse orientation that differs by a 160° rotation. The tRNA docking orientation appears to be guided by subfamily-specific clusters of amino acids ("binding signatures") together with differences in the shape of the positively charged tRNA-binding surfaces. tRNA orientations are further constrained by positional differences between the C-terminal "recognition" domains. The exquisite substrate specificity of Dus enzymes is therefore controlled by a relatively simple mechanism involving major reorientation of the whole tRNA molecule. Such reprogramming of the enzymatic specificity appears to be a unique evolutionary solution for altering tRNA recognition by the same protein fold.

From bacterial to human dihydrouridine synthase: automated structure determination.

The reduction of uridine to dihydrouridine at specific positions in tRNA is catalysed by dihydrouridine synthase (Dus) enzymes. Increased expression of human dihydrouridine synthase 2 (hDus2) has been linked to pulmonary carcinogenesis, while its knockdown decreased cancer cell line viability, suggesting that it may serve as a valuable target for therapeutic intervention. Here, the X-ray crystal structure of a construct of hDus2 encompassing the catalytic and tRNA-recognition domains (residues 1-340) determined at 1.9 Šresolution is presented. It is shown that the structure can be determined automatically by phenix.mr_rosetta starting from a bacterial Dus enzyme with only 18% sequence identity and a significantly divergent structure. The overall fold of the human Dus2 is similar to that of bacterial enzymes, but has a larger recognition domain and a unique three-stranded antiparallel ß-sheet insertion into the catalytic domain that packs next to the recognition domain, contributing to domain-domain interactions. The structure may inform the development of novel therapeutic approaches in the fight against lung cancer.

Stack-DHUpred: Advancing the accuracy of dihydrouridine modification sites detection via stacking approach.

Dihydrouridine (DHU, D) is one of the most abundant post-transcriptional uridine modifications found in tRNA, mRNA, and snoRNA, closely associated with disease pathogenesis and various biological processes in eukaryotes. Identifying D sites is important for understanding the modification mechanisms and/or epigenetic regulation. However, biological experiments for detecting D sites are time-consuming and expensive. Given these challenges, computational methods have been developed for accurately identifying the D sites in genome-wide datasets. However, existing methods have some limitations, and their prediction performance needs to be improved. In this work, we have developed a new computational predictor for accurately identifying D sites called Stack-DHUpred. Briefly, we trained 66 baseline models or single-feature models by connecting six machine learning classifiers with eleven different feature encoding methods and stacked different baseline models to build stacked ensemble learning models. Subsequently, the optimal combination of the baseline models was identified for the construction of the final stacked model. Remarkably, the Stack-DHUpred outperformed the existing predictors on our new independent dataset, indicating that the stacking approach significantly improved the prediction performance. We have made Stack-DHUpred available to the public through a web server (http://kurata35.bio.kyutech.ac.jp/Stack-DHUpred) and a standalone program (https://github.com/kuratahiroyuki/Stack-DHUpred). We believe that Stack-DHUpred will be a valuable tool for accelerating the discovery of D modifications and understanding their role in post-transcriptional regulation.

Modified Nucleotides and RNA Structure Prediction.

Nucleotide modifications are occurrent in all types of RNA and play an important role in RNA structure formation and stability. Modified bases not only possess the ability to shift the RNA structure ensemble towards desired functional confirmations. By changes in the base pairing partner preference, they may even enlarge or reduce the conformational space, i.e., the number and types of structures the RNA molecule can adopt. However, most methods to predict RNA secondary structure do not provide the means to include the effect of modifications on the result. With the help of a heavily modified transfer RNA (tRNA) molecule, this chapter demonstrates how to include the effect of different base modifications into secondary structure prediction using the ViennaRNA Package. The constructive approach demonstrated here allows for the calculation of minimum free energy structure and suboptimal structures at different levels of modified base support. In particular we, show how to incorporate the isomerization of uridine to pseudouridine ( Ψ ) and the reduction of uridine to dihydrouridine (D).

Structure of dihydrouridine synthase C (DusC) from Escherichia coli.

Dihydrouridine (D) is one of the most widely conserved tRNA modifications. Dihydrouridine synthase (Dus) is responsible for introducing D modifications into RNA by the reduction of uridine. Recently, a unique substrate-recognition mechanism using a small adapter molecule has been proposed for Thermus thermophilus Dus (TthDusC). To acquire insight regarding its substrate-recognition mechanism, the crystal structure of DusC from Escherichia coli (EcoDusC) was determined at 2.1 Å resolution. EcoDusC was shown to be composed of two domains: an N-terminal catalytic domain and a C-terminal tRNA-binding domain. An L-shaped electron density surrounded by highly conserved residues was found in the active site, as observed for TthDus. Structure comparison with TthDus indicated that the N-terminal region has a similar structure, whereas the C-terminal domain has marked differences in its relative orientation to the N-terminal domain as well as in its own structure. These observations suggested that Dus proteins adopt a common substrate-recognition mechanism using an adapter molecule, whereas the manner of tRNA binding is diverse.

D-Seq: Genome-wide detection of dihydrouridine modifications in RNA.

In addition to A, C, G and U, RNA contains over 100 additional chemically distinct residues. An abundant modified base frequently found in tRNAs, dihydrouridine (D) has recently been mapped to over 100 positions in mRNAs in yeast and human cells. Multiple highly conserved dihydrouridine synthases associate with and modify mRNA, suggesting there are many D sites yet to be found. Because D alters RNA structure, installation of D in mRNA is likely to effect multiple steps in mRNA metabolism including processing, trafficking, translation, and degradation. Here, we introduce D-seq, a method to chart the D landscape at single nucleotide resolution. The included protocols start with RNA isolation and carry through D-seq library preparation and data analysis. While the protocols below are tailored to map Ds in mRNA, the D-seq method is generalizable to any RNA type of interest, including non-coding RNAs, which have also recently been identified as dihydrouridine synthase targets.

iDHU-Ensem: Identification of dihydrouridine sites through ensemble learning models.

Background: Dihydrouridine (D) is one of the most significant uridine modifications that have a prominent occurrence in eukaryotes. The folding and conformational flexibility of transfer RNA (tRNA) can be attained through this modification. Objective: The modification also triggers lung cancer in humans. The identification of D sites was carried out through conventional laboratory methods; however, those were costly and time-consuming. The readiness of RNA sequences helps in the identification of D sites through computationally intelligent models. However, the most challenging part is turning these biological sequences into distinct vectors. Methods: The current research proposed novel feature extraction mechanisms and the identification of D sites in tRNA sequences using ensemble models. The ensemble models were then subjected to evaluation using k-fold cross-validation and independent testing. Results: The results revealed that the stacking ensemble model outperformed all the ensemble models by revealing 0.98 accuracy, 0.98 specificity, 0.97 sensitivity, and 0.92 Matthews Correlation Coefficient. The proposed model, iDHU-Ensem, was also compared with pre-existing predictors using an independent test. The accuracy scores have shown that the proposed model in this research study performed better than the available predictors. Conclusion: The current research contributed towards the enhancement of D site identification capabilities through computationally intelligent methods. A web-based server, iDHU-Ensem, was also made available for the researchers at https://taseersuleman-idhu-ensem-idhu-ensem.streamlit.app/.

DPred_3S: identifying dihydrouridine (D) modification on three species epitranscriptome based on multiple sequence-derived features.

Introduction: Dihydrouridine (D) is a conserved modification of tRNA among all three life domains. D modification enhances the flexibility of a single nucleotide base in the spatial structure and is disease- and evolution-associated. Recent studies have also suggested the presence of dihydrouridine on mRNA. Methods: To identify D in epitranscriptome, we provided a prediction framework named "DPred_3S" based on the machine learning approach for three species D epitranscriptome, which used epitranscriptome sequencing data as training data for the first time. Results: The optimal features were evaluated by the F-score and integration of different features; our model achieved area under the receiver operating characteristic curve (AUROC) scores 0.955, 0.946, and 0.905 for Saccharomyces cerevisiae, Escherichia coli, and Schizosaccharomyces pombe, respectively. The performances of different machine learning algorithms were also compared in this study. Discussion: The high performances of our model suggest the D sites can be distinguished based on their surrounding sequence, but the lower performance of cross-species prediction may be limited by technique preferences.

Self-attention enabled deep learning of dihydrouridine (D) modification on mRNAs unveiled a distinct sequence signature from tRNAs.

Dihydrouridine (D) is a modified pyrimidine nucleotide universally found in viral, prokaryotic, and eukaryotic species. It serves as a metabolic modulator for various pathological conditions, and its elevated levels in tumors are associated with a series of cancers. Precise identification of D sites on RNA is vital for understanding its biological function. A number of computational approaches have been developed for predicting D sites on tRNAs; however, none have considered mRNAs. We present here DPred, the first computational tool for predicting D on mRNAs in yeast from the primary RNA sequences. Built on a local self-attention layer and a convolutional neural network (CNN) layer, the proposed deep learning model outperformed classic machine learning approaches (random forest, support vector machines, etc.) and achieved reasonable accuracy and reliability with areas under the curve of 0.9166 and 0.9027 in jackknife cross-validation and on an independent testing dataset, respectively. Importantly, we showed that distinct sequence signatures are associated with the D sites on mRNAs and tRNAs, implying potentially different formation mechanisms and putative divergent functionality of this modification on the two types of RNA. DPred is available as a user-friendly Web server.

Evolutionary Diversity of Dus2 Enzymes Reveals Novel Structural and Functional Features among Members of the RNA Dihydrouridine Synthases Family.

Dihydrouridine (D) is an abundant modified base found in the tRNAs of most living organisms and was recently detected in eukaryotic mRNAs. This base confers significant conformational plasticity to RNA molecules. The dihydrouridine biosynthetic reaction is catalyzed by a large family of flavoenzymes, the dihydrouridine synthases (Dus). So far, only bacterial Dus enzymes and their complexes with tRNAs have been structurally characterized. Understanding the structure-function relationships of eukaryotic Dus proteins has been hampered by the paucity of structural data. Here, we combined extensive phylogenetic analysis with high-precision 3D molecular modeling of more than 30 Dus2 enzymes selected along the tree of life to determine the evolutionary molecular basis of D biosynthesis by these enzymes. Dus2 is the eukaryotic enzyme responsible for the synthesis of D20 in tRNAs and is involved in some human cancers and in the detoxification of ß-amyloid peptides in Alzheimer's disease. In addition to the domains forming the canonical structure of all Dus, i.e., the catalytic TIM-barrel domain and the helical domain, both participating in RNA recognition in the bacterial Dus, a majority of Dus2 proteins harbor extensions at both ends. While these are mainly unstructured extensions on the N-terminal side, the C-terminal side extensions can adopt well-defined structures such as helices and beta-sheets or even form additional domains such as zinc finger domains. 3D models of Dus2/tRNA complexes were also generated. This study suggests that eukaryotic Dus2 proteins may have an advantage in tRNA recognition over their bacterial counterparts due to their modularity.

DHU-Pred: accurate prediction of dihydrouridine sites using position and composition variant features on diverse classifiers.

Background: Dihydrouridine (D) is a modified transfer RNA post-transcriptional modification (PTM) that occurs abundantly in bacteria, eukaryotes, and archaea. The D modification assists in the stability and conformational flexibility of tRNA. The D modification is also responsible for pulmonary carcinogenesis in humans. Objective: For the detection of D sites, mass spectrometry and site-directed mutagenesis have been developed. However, both are labor-intensive and time-consuming methods. The availability of sequence data has provided the opportunity to build computational models for enhancing the identification of D sites. Based on the sequence data, the DHU-Pred model was proposed in this study to find possible D sites. Methodology: The model was built by employing comprehensive machine learning and feature extraction approaches. It was then validated using in-demand evaluation metrics and rigorous experimentation and testing approaches. Results: The DHU-Pred revealed an accuracy score of 96.9%, which was considerably higher compared to the existing D site predictors. Availability and Implementation: A user-friendly web server for the proposed model was also developed and is freely available for the researchers.