Genomic screening of fish-specific genes in gnathostomes and their functions in fin development.

In this study, we comprehensively searched for fish-specific genes in gnathostomes that contribute to development of the fin, a fish-specific trait. Many previous reports suggested that animal group-specific genes are often important for group-specific traits. Clarifying the roles of fish-specific genes in fin development of gnathostomes, for example, can help elucidate the mechanisms underlying the formation of this trait. We first identified 91 fish-specific genes in gnathostomes by comparing the gene repertoire in 16 fish and 35 tetrapod species. RNA-seq analysis narrowed down the 91 candidates to 33 genes that were expressed in the developing pectoral fin. We analyzed the functions of approximately half of the candidate genes by loss-of-function analysis in zebrafish. We found that some of the fish-specific and fin development-related genes, including fgf24 and and1/and2, play roles in fin development. In particular, the newly identified fish-specific gene qkia is expressed in the developing fin muscle and contributes to muscle morphogenesis in the pectoral fin as well as body trunk. These results indicate that the strategy of identifying animal group-specific genes is functional and useful. The methods applied here could be used in future studies to identify trait-associated genes in other animal groups.

Web Services for RNA-RNA Interaction Prediction.

Non-coding RNAs have various biological functions such as translational regulation, and RNA-RNA interactions play essential roles in the mechanisms of action of these RNAs. Therefore, RNA-RNA interaction prediction is an important problem in bioinformatics, and many tools have been developed for the computational prediction of RNA-RNA interactions. In addition to the development of novel algorithms with high accuracy, the development and maintenance of web services is essential for enhancing usability by experimental biologists. In this review, we survey web services for RNA-RNA interaction predictions and introduce how to use primary web services. We present various prediction tools, including general interaction prediction tools, prediction tools for specific RNA classes, and RNA-RNA interaction-based RNA design tools. Additionally, we discuss the future perspectives of the development of RNA-RNA interaction prediction tools and the sustainability of web services.

Remarkable improvement in detection of readthrough downstream-of-gene transcripts by semi-extractable RNA-sequencing.

The mammalian cell nucleus contains dozens of membrane-less nuclear bodies that play significant roles in various aspects of gene expression. Several nuclear bodies are nucleated by specific architectural noncoding RNAs (arcRNAs) acting as structural scaffolds. We have reported that a minor population of cellular RNAs exhibits an unusual semi-extractable feature upon using the conventional procedure of RNA preparation and that needle shearing or heating of cell lysates remarkably improves extraction of dozens of RNAs. Because semi-extractable RNAs, including known arcRNAs, commonly localize in nuclear bodies, this feature may be a hallmark of arcRNAs. Using the semi-extractability of RNA, we performed genome-wide screening of semi-extractable long noncoding RNAs to identify new candidate arcRNAs for arcRNA under hyperosmotic and heat stress conditions. After screening stress-inducible and semi-extractable RNAs, hundreds of readthrough downstream-of-gene (DoG) transcripts over several hundreds of kilobases, many of which were not detected among RNAs prepared by the conventional extraction procedure, were found to be stress-inducible and semi-extractable. We further characterized some of the abundant DoGs and found that stress-inducible transient extension of the 3'-UTR made DoGs semi-extractable. Furthermore, they were localized in distinct nuclear foci that were sensitive to 1,6-hexanediol. These data suggest that semi-extractable DoGs exhibit arcRNA-like features and our semi-extractable RNA-seq is a powerful tool to extensively monitor DoGs that are induced under specific physiological conditions.

DDX41 coordinates RNA splicing and transcriptional elongation to prevent DNA replication stress in hematopoietic cells.

Myeloid malignancies with DDX41 mutations are often associated with bone marrow failure and cytopenia before overt disease manifestation. However, the mechanisms underlying these specific conditions remain elusive. Here, we demonstrate that loss of DDX41 function impairs efficient RNA splicing, resulting in DNA replication stress with excess R-loop formation. Mechanistically, DDX41 binds to the 5' splice site (5'SS) of coding RNA and coordinates RNA splicing and transcriptional elongation; loss of DDX41 prevents splicing-coupled transient pausing of RNA polymerase II at 5'SS, causing aberrant R-loop formation and transcription-replication collisions. Although the degree of DNA replication stress acquired in S phase is small, cells undergo mitosis with under-replicated DNA being remained, resulting in micronuclei formation and significant DNA damage, thus leading to impaired cell proliferation and genomic instability. These processes may be responsible for disease phenotypes associated with DDX41 mutations.

Extended ensemble simulations of a SARS-CoV-2 nsp1-5'-UTR complex.

Nonstructural protein 1 (nsp1) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a 180-residue protein that blocks translation of host mRNAs in SARS-CoV-2-infected cells. Although it is known that SARS-CoV-2's own RNA evades nsp1's host translation shutoff, the molecular mechanism underlying the evasion was poorly understood. We performed an extended ensemble molecular dynamics simulation to investigate the mechanism of the viral RNA evasion. Simulation results suggested that the stem loop structure of the SARS-CoV-2 RNA 5'-untranslated region (SL1) binds to both nsp1's N-terminal globular region and intrinsically disordered region. The consistency of the results was assessed by modeling nsp1-40S ribosome structure based on reported nsp1 experiments, including the X-ray crystallographic structure analysis, the cryo-EM electron density map, and cross-linking experiments. The SL1 binding region predicted from the simulation was open to the solvent, yet the ribosome could interact with SL1. Cluster analysis of the binding mode and detailed analysis of the binding poses suggest residues Arg124, Lys47, Arg43, and Asn126 may be involved in the SL1 recognition mechanism, consistent with the existing mutational analysis.

m6 A modification of HSATIII lncRNAs regulates temperature-dependent splicing.

Nuclear stress bodies (nSBs) are nuclear membraneless organelles formed around stress-inducible HSATIII architectural long noncoding RNAs (lncRNAs). nSBs repress splicing of hundreds of introns during thermal stress recovery, which are partly regulated by CLK1 kinase phosphorylation of temperature-dependent Ser/Arg-rich splicing factors (SRSFs). Here, we report a distinct mechanism for this splicing repression through protein sequestration by nSBs. Comprehensive identification of RNA-binding proteins revealed HSATIII association with proteins related to N6 -methyladenosine (m6 A) RNA modification. 11% of the first adenosine in the repetitive HSATIII sequence were m6 A-modified. nSBs sequester the m6 A writer complex to methylate HSATIII, leading to subsequent sequestration of the nuclear m6 A reader, YTHDC1. Sequestration of these factors from the nucleoplasm represses m6 A modification of pre-mRNAs, leading to repression of m6 A-dependent splicing during stress recovery phase. Thus, nSBs serve as a common platform for regulation of temperature-dependent splicing through dual mechanisms employing two distinct ribonucleoprotein modules with partially m6 A-modified architectural lncRNAs.

MeCP2 Levels Regulate the 3D Structure of Heterochromatic Foci in Mouse Neurons.

Methyl-CpG binding protein 2 (MeCP2) is a nuclear protein critical for normal brain function, and both depletion and overexpression of MeCP2 lead to severe neurodevelopmental disease, Rett syndrome (RTT) and MECP2 multiplication disorder, respectively. However, the molecular mechanism by which abnormal MeCP2 dosage causes neuronal dysfunction remains unclear. As MeCP2 expression is nearly equivalent to that of core histones and because it binds DNA throughout the genome, one possible function of MeCP2 is to regulate the 3D structure of chromatin. Here, to examine whether and how MeCP2 levels impact chromatin structure, we used high-resolution confocal and electron microscopy and examined heterochromatic foci of neurons in mice. Using models of RTT and MECP2 triplication syndrome, we found that the heterochromatin structure was significantly affected by the alteration in MeCP2 levels. Analysis of mice expressing either MeCP2-R270X or MeCP2-G273X, which have nonsense mutations in the upstream and downstream regions of the AT-hook 2 domain, respectively, showed that the magnitude of heterochromatin changes was tightly correlated with the phenotypic severity. Postnatal alteration in MeCP2 levels also induced significant changes in the heterochromatin structure, which underscored importance of correct MeCP2 dosage in mature neurons. Finally, functional analysis of MeCP2-overexpressing mice showed that the behavioral and transcriptomic alterations in these mice correlated significantly with the MeCP2 levels and occurred in parallel with the heterochromatin changes. Taken together, our findings demonstrate the essential role of MeCP2 in regulating the 3D structure of neuronal chromatin, which may serve as a potential mechanism that drives pathogenesis of MeCP2-related disorders.SIGNIFICANCE STATEMENT Neuronal function is critically dependent on methyl-CpG binding protein 2 (MeCP2), a nuclear protein abundantly expressed in neurons. The importance of MeCP2 is underscored by the severe childhood neurologic disorders, Rett syndrome (RTT) and MECP2 multiplication disorders, which are caused by depletion and overabundance of MeCP2, respectively. To clarify the molecular function of MeCP2 and to understand the pathogenesis of MECP2-related disorders, we performed detailed structural analyses of neuronal nuclei by using mouse models and high-resolution microscopy. We show that the level of MeCP2 critically regulates 3D structure of heterochromatic foci, and this is mediated in part by the AT-hook 2 domain of MeCP2. Our results demonstrate that one primary function of MeCP2 is to regulate chromatin structure.

Free-Energy Calculation of Ribonucleic Inosines and Its Application to Nearest-Neighbor Parameters.

Can current simulations quantitatively predict the stability of ribonucleic acids (RNAs)? In this research, we apply a free-energy perturbation simulation of RNAs containing inosine, a modified ribonucleic base, to the derivation of RNA nearest-neighbor parameters. A parameter set derived solely from 30 simulations was used to predict the free-energy difference of the RNA duplex with a mean unbiased error of 0.70 kcal/mol, which is a level of accuracy comparable to that obtained with parameters derived from 25 experiments. We further show that the error can be lowered to 0.60 kcal/mol by combining the simulation-derived free-energy differences with experimentally measured differences. This protocol can be used as a versatile method for deriving nearest-neighbor parameters of RNAs with various modified bases.

Finding the direct optimal RNA barrier energy and improving pathways with an arbitrary energy model.

MOTIVATION: RNA folding kinetics plays an important role in the biological functions of RNA molecules. An important goal in the investigation of the kinetic behavior of RNAs is to find the folding pathway with the lowest energy barrier. For this purpose, most of the existing methods use heuristics because the number of possible pathways is huge even if only the shortest (direct) folding pathways are considered. RESULTS: In this study, we propose a new method using a best-first search strategy to efficiently compute the exact solution of the minimum barrier energy of direct pathways. Using our method, we can find the exact direct pathways within a Hamming distance of 20, whereas the previous methods even miss the exact short pathways. Moreover, our method can be used to improve the pathways found by existing methods for exploring indirect pathways. AVAILABILITY AND IMPLEMENTATION: The source code and datasets created and used in this research are available at https://github.com/eukaryo/czno. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

RintC: fast and accuracy-aware decomposition of distributions of RNA secondary structures with extended logsumexp.

BACKGROUND: Analysis of secondary structures is essential for understanding the functions of RNAs. Because RNA molecules thermally fluctuate, it is necessary to analyze the probability distributions of their secondary structures. Existing methods, however, are not applicable to long RNAs owing to their high computational complexity. Additionally, previous research has suffered from two numerical difficulties: overflow and significant numerical errors. RESULT: In this research, we reduced the computational complexity of calculating the landscape of the probability distribution of secondary structures by introducing a maximum-span constraint. In addition, we resolved numerical computation problems through two techniques: extended logsumexp and accuracy-guaranteed numerical computation. We analyzed the stability of the secondary structures of 16S ribosomal RNAs at various temperatures without overflow. The results obtained are consistent with previous research on thermophilic bacteria, suggesting that our method is applicable in thermal stability analysis. Furthermore, we quantitatively assessed numerical stability using our method.. CONCLUSION: These results demonstrate that the proposed method is applicable to long RNAs..

LncRNA-dependent nuclear stress bodies promote intron retention through SR protein phosphorylation.

A number of long noncoding RNAs (lncRNAs) are induced in response to specific stresses to construct membrane-less nuclear bodies; however, their function remains poorly understood. Here, we report the role of nuclear stress bodies (nSBs) formed on highly repetitive satellite III (HSATIII) lncRNAs derived from primate-specific satellite III repeats upon thermal stress exposure. A transcriptomic analysis revealed that depletion of HSATIII lncRNAs, resulting in elimination of nSBs, promoted splicing of 533 retained introns during thermal stress recovery. A HSATIII-Comprehensive identification of RNA-binding proteins by mass spectrometry (ChIRP-MS) analysis identified multiple splicing factors in nSBs, including serine and arginine-rich pre-mRNA splicing factors (SRSFs), the phosphorylation states of which affect splicing patterns. SRSFs are rapidly de-phosphorylated upon thermal stress exposure. During stress recovery, CDC like kinase 1 (CLK1) was recruited to nSBs and accelerated the re-phosphorylation of SRSF9, thereby promoting target intron retention. Our findings suggest that HSATIII-dependent nSBs serve as a conditional platform for phosphorylation of SRSFs by CLK1 to promote the rapid adaptation of gene expression through intron retention following thermal stress exposure.

LncRRIsearch: A Web Server for lncRNA-RNA Interaction Prediction Integrated With Tissue-Specific Expression and Subcellular Localization Data.

Long non-coding RNAs (lncRNAs) play critical roles in various biological processes, but the function of the majority of lncRNAs is still unclear. One approach for estimating a function of a lncRNA is the identification of its interaction target because functions of lncRNAs are expressed through interaction with other biomolecules in quite a few cases. In this paper, we developed "LncRRIsearch," which is a web server for comprehensive prediction of human and mouse lncRNA-lncRNA and lncRNA-mRNA interaction. The prediction was conducted using RIblast, which is a fast and accurate RNA-RNA interaction prediction tool. Users can investigate interaction target RNAs of a particular lncRNA through a web interface. In addition, we integrated tissue-specific expression and subcellular localization data for the lncRNAs with the web server. These data enable users to examine tissue-specific or subcellular localized lncRNA interactions. LncRRIsearch is publicly accessible at http://rtools.cbrc.jp/LncRRIsearch/.

reactIDR: evaluation of the statistical reproducibility of high-throughput structural analyses towards a robust RNA structure prediction.

BACKGROUND: Recently, next-generation sequencing techniques have been applied for the detection of RNA secondary structures, which is referred to as high-throughput RNA structural (HTS) analyses, and many different protocols have been used to detect comprehensive RNA structures at single-nucleotide resolution. However, the existing computational analyses heavily depend on the experimental methodology to generate data, which results in difficulties associated with statistically sound comparisons or combining the results obtained using different HTS methods. RESULTS: Here, we introduced a statistical framework, reactIDR, which can be applied to the experimental data obtained using multiple HTS methodologies. Using this approach, nucleotides are classified into three structural categories, loop, stem/background, and unmapped. reactIDR uses the irreproducible discovery rate (IDR) with a hidden Markov model to discriminate between the true and spurious signals obtained in the replicated HTS experiments accurately, and it is able to incorporate an expectation-maximization algorithm and supervised learning for efficient parameter optimization. The results of our analyses of the real-life HTS data showed that reactIDR had the highest accuracy in the classification of ribosomal RNA stem/loop structures when using both individual and integrated HTS datasets, and its results corresponded the best to the three-dimensional structures. CONCLUSIONS: We have developed a novel software, reactIDR, for the prediction of stem/loop regions from the HTS analysis datasets. For the rRNA structure analyses, reactIDR was shown to have robust accuracy across different datasets by using the reproducibility criterion, suggesting its potential for increasing the value of existing HTS datasets. reactIDR is publicly available at https://github.com/carushi/reactIDR .

Capturing alternative secondary structures of RNA by decomposition of base-pairing probabilities.

BACKGROUND: It is known that functional RNAs often switch their functions by forming different secondary structures. Popular tools for RNA secondary structures prediction, however, predict the single 'best' structures, and do not produce alternative structures. There are bioinformatics tools to predict suboptimal structures, but it is difficult to detect which alternative secondary structures are essential. RESULTS: We proposed a new computational method to detect essential alternative secondary structures from RNA sequences by decomposing the base-pairing probability matrix. The decomposition is calculated by a newly implemented software tool, RintW, which efficiently computes the base-pairing probability distributions over the Hamming distance from arbitrary reference secondary structures. The proposed approach has been demonstrated on ROSE element RNA thermometer sequence and Lysine RNA ribo-switch, showing that the proposed approach captures conformational changes in secondary structures. CONCLUSIONS: We have shown that alternative secondary structures are captured by decomposing base-paring probabilities over Hamming distance. Source code is available from http://www.ncRNA.org/RintW .

Identification of Transposable Elements Contributing to Tissue-Specific Expression of Long Non-Coding RNAs.

It has been recently suggested that transposable elements (TEs) are re-used as functional elements of long non-coding RNAs (lncRNAs). This is supported by some examples such as the human endogenous retrovirus subfamily H (HERVH) elements contained within lncRNAs and expressed specifically in human embryonic stem cells (hESCs), as required to maintain hESC identity. There are at least two unanswered questions about all lncRNAs. How many TEs are re-used within lncRNAs? Are there any other TEs that affect tissue specificity of lncRNA expression? To answer these questions, we comprehensively identify TEs that are significantly related to tissue-specific expression levels of lncRNAs. We downloaded lncRNA expression data corresponding to normal human tissue from the Expression Atlas and transformed the data into tissue specificity estimates. Then, Fisher's exact tests were performed to verify whether the presence or absence of TE-derived sequences influences the tissue specificity of lncRNA expression. Many TE-tissue pairs associated with tissue-specific expression of lncRNAs were detected, indicating that multiple TE families can be re-used as functional domains or regulatory sequences of lncRNAs. In particular, we found that the antisense promoter region of L1PA2, a LINE-1 subfamily, appears to act as a promoter for lncRNAs with placenta-specific expression.

Computational prediction of lncRNA-mRNA interactionsby integrating tissue specificity in human transcriptome.

Long noncoding RNAs (lncRNAs) play a key role in normal tissue differentiation and cancer development through their tissue-specific expression in the human transcriptome. Recent investigations of macromolecular interactions have shown that tissue-specific lncRNAs form base-pairing interactions with various mRNAs associated with tissue-differentiation, suggesting that tissue specificity is an important factor controlling human lncRNA-mRNA interactions.Here, we report investigations of the tissue specificities of lncRNAs and mRNAs by using RNA-seq data across various human tissues as well as computational predictions of tissue-specific lncRNA-mRNA interactions inferred by integrating the tissue specificity of lncRNAs and mRNAs into our comprehensive prediction of human lncRNA-RNA interactions. Our predicted lncRNA-mRNA interactions were evaluated by comparisons with experimentally validated lncRNA-mRNA interactions (between the TINCR lncRNA and mRNAs), showing the improvement of prediction accuracy over previous prediction methods that did not account for tissue specificities of lncRNAs and mRNAs. In addition, our predictions suggest that the potential functions of TINCR lncRNA not only for epidermal differentiation but also for esophageal development through lncRNA-mRNA interactions. REVIEWERS: This article was reviewed by Dr. Weixiong Zhang and Dr. Bojan Zagrovic.

Improved Accuracy in RNA-Protein Rigid Body Docking by Incorporating Force Field for Molecular Dynamics Simulation into the Scoring Function.

RNA-protein interactions play fundamental roles in many biological processes. To understand these interactions, it is necessary to know the three-dimensional structures of RNA-protein complexes. However, determining the tertiary structure of these complexes is often difficult, suggesting that an accurate rigid body docking for RNA-protein complexes is needed. In general, the rigid body docking process is divided into two steps: generating candidate structures from the individual RNA and protein structures and then narrowing down the candidates. In this study, we focus on the former problem to improve the prediction accuracy in RNA-protein docking. Our method is based on the integration of physicochemical information about RNA into ZDOCK, which is known as one of the most successful computer programs for protein-protein docking. Because recent studies showed the current force field for molecular dynamics simulation of protein and nucleic acids is quite accurate, we modeled the physicochemical information about RNA by force fields such as AMBER and CHARMM. A comprehensive benchmark of RNA-protein docking, using three recently developed data sets, reveals the remarkable prediction accuracy of the proposed method compared with existing programs for docking: the highest success rate is 34.7% for the predicted structure of the RNA-protein complex with the best score and 79.2% for 3,600 predicted ones. Three full atomistic force fields for RNA (AMBER94, AMBER99, and CHARMM22) produced almost the same accurate result, which showed current force fields for nucleic acids are quite accurate. In addition, we found that the electrostatic interaction and the representation of shape complementary between protein and RNA plays the important roles for accurate prediction of the native structures of RNA-protein complexes.

Comprehensive prediction of lncRNA-RNA interactions in human transcriptome.

MOTIVATION: Recent studies have revealed that large numbers of non-coding RNAs are transcribed in humans, but only a few of them have been identified with their functions. Identification of the interaction target RNAs of the non-coding RNAs is an important step in predicting their functions. The current experimental methods to identify RNA-RNA interactions, however, are not fast enough to apply to a whole human transcriptome. Therefore, computational predictions of RNA-RNA interactions are desirable, but this is a challenging task due to the huge computational costs involved. RESULTS: Here, we report comprehensive predictions of the interaction targets of lncRNAs in a whole human transcriptome for the first time. To achieve this, we developed an integrated pipeline for predicting RNA-RNA interactions on the K computer, which is one of the fastest super-computers in the world. Comparisons with experimentally-validated lncRNA-RNA interactions support the quality of the predictions. Additionally, we have developed a database that catalogs the predicted lncRNA-RNA interactions to provide fundamental information about the targets of lncRNAs.

Bioinformatics tools for lncRNA research.

Current experimental methods to identify the functions of a large number of the candidates of long non-coding RNAs (lncRNAs) are limited in their throughput. Therefore, it is essential to know which tools are effective for understanding lncRNAs so that reasonable speed and accuracy can be achieved. In this paper, we review the currently available bioinformatics tools and databases that are useful for finding non-coding RNAs and analyzing their structures, conservation, interactions, co-expressions and localization. This article is part of a Special Issue entitled: Clues to long noncoding RNA taxonomy1, edited by Dr. Tetsuro Hirose and Dr. Shinichi Nakagawa.

Analysis of base-pairing probabilities of RNA molecules involved in protein-RNA interactions.

MOTIVATION: Understanding the details of protein-RNA interactions is important to reveal the functions of both the RNAs and the proteins. In these interactions, the secondary structures of the RNAs play an important role. Because RNA secondary structures in protein-RNA complexes are variable, considering the ensemble of RNA secondary structures is a useful approach. In particular, recent studies have supported the idea that, in the analysis of RNA secondary structures, the base-pairing probabilities (BPPs) of RNAs (i.e. the probabilities of forming a base pair in the ensemble of RNA secondary structures) provide richer and more robust information about the structures than a single RNA secondary structure, for example, the minimum free energy structure or a snapshot of structures in the Protein Data Bank. However, there has been no investigation of the BPPs in protein-RNA interactions. RESULTS: In this study, we analyzed BPPs of RNA molecules involved in known protein-RNA complexes in the Protein Data Bank. Our analysis suggests that, in the tertiary structures, the BPPs (which are computed using only sequence information) for unpaired nucleotides with intermolecular hydrogen bonds (hbonds) to amino acids were significantly lower than those for unpaired nucleotides without hbonds. On the other hand, no difference was found between the BPPs for paired nucleotides with and without intermolecular hbonds. Those findings were commonly supported by three probabilistic models, which provide the ensemble of RNA secondary structures, including the McCaskill model based on Turner's free energy of secondary structures.