RNA folding with hard and soft constraints

Ageing-associated long non-coding RNA extends lifespan and reduces translation in non-dividing cells

Genomes produce widespread long non-coding RNAs (lncRNAs) of largely unknown functions. We characterize aal1 (ageing-associated lncRNA), which is induced in quiescent fission yeast cells. Deletion of aal1 shortens the chronological lifespan of non-dividing cells, while ectopic overexpression prolongs their lifespan, indicating that aal1 acts in trans. Overexpression of aal1 represses ribosomal-protein gene expression and inhibits cell growth, and aal1 genetically interacts with coding genes functioning in protein translation. The aal1 lncRNA localizes to the cytoplasm and associates with ribosomes. Notably, aal1 overexpression decreases the cellular ribosome content and inhibits protein translation. The aal1 lncRNA binds to the rpl1901 mRNA, encoding a ribosomal protein. The rpl1901 levels are reduced ~2-fold by aal1, which is sufficient to extend lifespan. Remarkably, the expression of the aal1 lncRNA in Drosophila boosts fly lifespan. We propose that aal1 reduces the ribosome content by decreasing Rpl1901 levels, thus attenuating the translational capacity and promoting longevity. Although aal1 is not conserved, its effect in flies suggests that animals feature related mechanisms that modulate ageing, based on the conserved translational machinery.

Application of plasmid stabilization systems for heterologous protein expression in Escherichia coli

BACKGROUND

Plasmids are the most commonly used vectors for heterologous protein expression in Escherichia coli. However, the plasmid copy number decreases with the segregational instability, which inevitably leads to a decrease in the yield of heterologous protein.

METHODS AND RESULTS

In this study, plasmid stabilization systems were used to enhance the expression level of heterologous proteins in E. coli. With the investigation of protein expression level, biomass and plasmid retention rate in different plasmid stabilization systems, the hok/sok system had the greatest potential on plasmid stabilization. In order to further investigate the molecular mechanism of hok/sok system, the structure of the binding region of hok mRNA and sok antisense RNA was modified based on the minimum free energy of mRNA, which resulted in the reduction of the binding efficiency of hok mRNA and sok asRNA, and then the toxicity of the Hok protein led to the decreased viability of the host cells. Finally, the hok/sok plasmid stabilization system was testified in 5 L fermenter, and the plasmid retention rate and protein expression level were significantly increased without the addition of antibiotics.

CONCLUSIONS

This study lays a solid foundation for a deeper understanding of the mechanism of the hok/sok plasmid stabilization system and improving the productivity of heterologous protein in E. coli.

Phylogenetic and Chemical Probing Information as Soft Constraints in RNA Secondary Structure Prediction

Extrinsic, experimental information can be incorporated into thermodynamics-based RNA folding algorithms in the form of pseudo-energies. Evolutionary conservation of RNA secondary structure elements is detectable in alignments of phylogenetically related sequences and provides evidence for the presence of certain base pairs that can also be converted into pseudo-energy contributions. We show that the centroid base pairs computed from a consensus folding model such as RNAalifold result in a substantial improvement of the prediction accuracy for single sequences. Evidence for specific base pairs turns out to be more informative than a position-wise profile for the conservation of the pairing status. A comparison with chemical probing data, furthermore, strongly suggests that phylogenetic base pairing data are more informative than position-specific data on (un)pairedness as obtained from chemical probing experiments. In this context we demonstrate, in addition, that the conversion of signal from probing data into pseudo-energies is possible using thermodynamic structure predictions as a reference instead of known RNA structures.

DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning

It is now known that RNAs play more active roles in cellular pathways beyond simply serving as transcription templates. These biological mechanisms might be mediated by higher RNA stereo conformations, triggering the need to understand RNA secondary structures first. However, experimental protocols for solving RNA structures are unavailable for large-scale investigation due to their high costs and time-consuming nature. Various computational tools were thus developed to predict the RNA secondary structures from sequences. Recently, deep networks have been investigated to help predict RNA structures directly from their sequences. However, existing deep-learning-based tools are more or less suffering from model overfitting due to their complicated problem formulation and defective model training processes, limiting their applications across sequences from different structural families. In this research, we designed a two-stage RNA structure prediction strategy called DEBFold (deep ensemble boosting and folding) based on convolution encoding/decoding and self-attention mechanisms to enhance the existing thermodynamic structure models. Moreover, the model training process followed rigorous steps to achieve an acceptable prediction generalization. On the family-wise reserved test sets and the PDB-derived test set, DEBFold achieves better structure prediction performance over traditional tools and existing deep-learning methods. In summary, we obtained a cutting-edge deep-learning-based structure prediction tool with supreme across-family generalization performance. The DEBFold tool can be accessed at https://cobis.bme.ncku.edu.tw/DEBFold/.

Accumulation Dynamics of Defective Genomes during Experimental Evolution of Two Betacoronaviruses

Virus-encoded replicases often generate aberrant RNA genomes, known as defective viral genomes (DVGs). When co-infected with a helper virus providing necessary proteins, DVGs can multiply and spread. While DVGs depend on the helper virus for propagation, they can in some cases disrupt infectious virus replication, impact immune responses, and affect viral persistence or evolution. Understanding the dynamics of DVGs alongside standard viral genomes during infection remains unclear. To address this, we conducted a long-term experimental evolution of two betacoronaviruses, the human coronavirus OC43 (HCoV-OC43) and the murine hepatitis virus (MHV), in cell culture at both high and low multiplicities of infection (MOI). We then performed RNA-seq at regular time intervals, reconstructed DVGs, and analyzed their accumulation dynamics. Our findings indicate that DVGs evolved to exhibit greater diversity and abundance, with deletions and insertions being the most common types. Notably, some high MOI deletions showed very limited temporary existence, while others became prevalent over time. We observed differences in DVG abundance between high and low MOI conditions in HCoV-OC43 samples. The size distribution of HCoV-OC43 genomes with deletions differed between high and low MOI passages. In low MOI lineages, short and long DVGs were the most common, with an additional cluster in high MOI lineages which became more prevalent along evolutionary time. MHV also showed variations in DVG size distribution at different MOI conditions, though they were less pronounced compared to HCoV-OC43, suggesting a more random distribution of DVG sizes. We identified hotspot regions for deletions that evolved at a high MOI, primarily within cistrons encoding structural and accessory proteins. In conclusion, our study illustrates the widespread formation of DVGs during betacoronavirus evolution, influenced by MOI and cell- and virus-specific factors.

SparseRNAfolD: optimized sparse RNA pseudoknot-free folding with dangle consideration

MOTIVATION

Computational RNA secondary structure prediction by free energy minimization is indispensable for analyzing structural RNAs and their interactions. These methods find the structure with the minimum free energy (MFE) among exponentially many possible structures and have a restrictive time and space complexity ( O ( n 3 ) time and O ( n 2 ) space for pseudoknot-free structures) for longer RNA sequences. Furthermore, accurate free energy calculations, including dangle contributions can be difficult and costly to implement, particularly when optimizing for time and space requirements.

RESULTS

Here we introduce a fast and efficient sparsified MFE pseudoknot-free structure prediction algorithm, SparseRNAFolD, that utilizes an accurate energy model that accounts for dangle contributions. While the sparsification technique was previously employed to improve the time and space complexity of a pseudoknot-free structure prediction method with a realistic energy model, SparseMFEFold, it was not extended to include dangle contributions due to the complexity of computation. This may come at the cost of prediction accuracy. In this work, we compare three different sparsified implementations for dangle contributions and provide pros and cons of each method. As well, we compare our algorithm to LinearFold, a linear time and space algorithm, where we find that in practice, SparseRNAFolD has lower memory consumption across all lengths of sequence and a faster time for lengths up to 1000 bases.

CONCLUSION

Our SparseRNAFolD algorithm is an MFE-based algorithm that guarantees optimality of result and employs the most general energy model, including dangle contributions. We provide a basis for applying dangles to sparsified recursion in a pseudoknot-free model that has the potential to be extended to pseudoknots.

Multiple sequence alignment-based RNA language model and its application to structural inference

Compared with proteins, DNA and RNA are more difficult languages to interpret because four-letter coded DNA/RNA sequences have less information content than 20-letter coded protein sequences. While BERT (Bidirectional Encoder Representations from Transformers)-like language models have been developed for RNA, they are ineffective at capturing the evolutionary information from homologous sequences because unlike proteins, RNA sequences are less conserved. Here, we have developed an unsupervised multiple sequence alignment-based RNA language model (RNA-MSM) by utilizing homologous sequences from an automatic pipeline, RNAcmap, as it can provide significantly more homologous sequences than manually annotated Rfam. We demonstrate that the resulting unsupervised, two-dimensional attention maps and one-dimensional embeddings from RNA-MSM contain structural information. In fact, they can be directly mapped with high accuracy to 2D base pairing probabilities and 1D solvent accessibilities, respectively. Further fine-tuning led to significantly improved performance on these two downstream tasks compared with existing state-of-the-art techniques including SPOT-RNA2 and RNAsnap2. By comparison, RNA-FM, a BERT-based RNA language model, performs worse than one-hot encoding with its embedding in base pair and solvent-accessible surface area prediction. We anticipate that the pre-trained RNA-MSM model can be fine-tuned on many other tasks related to RNA structure and function.

The Multiscale Ernwin/SPQR RNA Structure Prediction Pipeline

Aside from the well-known role in protein synthesis, RNA can perform catalytic, regulatory, and other essential biological functions which are determined by its three-dimensional structure. In this regard, a great effort has been made during the past decade to develop computational tools for the prediction of the structure of RNAs from the knowledge of their sequence, incorporating experimental data to refine or guide the modeling process. Nevertheless, this task can become exceptionally challenging when dealing with long noncoding RNAs, constituted by more than 200 nucleotides, due to their large size and the specific interactions involved. In this chapter, we describe a multiscale approach to predict such structures, incorporating SAXS experimental data into a hierarchical procedure which couples two coarse-grained representations: Ernwin, a helix-based approach, which deals with the global arrangement of secondary structure elements, and SPQR, a nucleotide-centered coarse-grained model, which corrects and refines the structures predicted at the coarser level.We describe the methodology through its application on the Braveheart long noncoding RNA, starting from the SAXS and secondary structure data to propose a refined, all-atom structure.

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).

RNAi-Mediated Silencing in the Insect Cell-Baculovirus Expression System

RNA interference (RNAi) serves as an indispensable tool for gene function studies and has been substantiated through extensive research for its practical applications in the baculovirus expression vector system (BEVS). This chapter expands the RNAi toolkit in insect cell culture by including small interfering RNA (siRNA) in the protocol, in addition to the conventional use of double-stranded RNA (dsRNA). This chapter also brings attention to key design and reporting considerations, based on Minimum Information About an RNAi Experiment (MIARE) guidelines. Recommendations regarding online tools for dsRNA and siRNA design are provided, along with guidance on choosing suitable methods for measuring silencing outcomes.

RNA Secondary Structure Thermodynamics

Several different ways to predict RNA secondary structures have been suggested in the literature. Statistical methods, such as those that utilize stochastic context-free grammars (SCFGs), or approaches based on machine learning aim to predict the best representative structure for the underlying ensemble of possible conformations. Their parameters have therefore been trained on larger subsets of well-curated, known secondary structures. Physics-based methods, on the other hand, usually refrain from using optimized parameters. They model secondary structures from loops as individual building blocks which have been assigned a physical property instead: the free energy of the respective loop. Such free energies are either derived from experiments or from mathematical modeling. This rigorous use of physical properties then allows for the application of statistical mechanics to describe the entire state space of RNA secondary structures in terms of equilibrium probabilities. On that basis, and by using efficient algorithms, many more descriptors of the conformational state space of RNA molecules can be derived to investigate and explain the many functions of RNA molecules. Moreover, compared to other methods, physics-based models allow for a much easier extension with other properties that can be measured experimentally. For instance, small molecules or proteins can bind to an RNA and their binding affinity can be assessed experimentally. Under certain conditions, existing RNA secondary structure prediction tools can be used to model this RNA-ligand binding and to eventually shed light on its impact on structure formation and function.

A Hitchhiker's guide to RNA-RNA structure and interaction prediction tools

RNA biology has risen to prominence after a remarkable discovery of diverse functions of noncoding RNA (ncRNA). Most untranslated transcripts often exert their regulatory functions into RNA-RNA complexes via base pairing with complementary sequences in other RNAs. An interplay between RNAs is essential, as it possesses various functional roles in human cells, including genetic translation, RNA splicing, editing, ribosomal RNA maturation, RNA degradation and the regulation of metabolic pathways/riboswitches. Moreover, the pervasive transcription of the human genome allows for the discovery of novel genomic functions via RNA interactome investigation. The advancement of experimental procedures has resulted in an explosion of documented data, necessitating the development of efficient and precise computational tools and algorithms. This review provides an extensive update on RNA-RNA interaction (RRI) analysis via thermodynamic- and comparative-based RNA secondary structure prediction (RSP) and RNA-RNA interaction prediction (RIP) tools and their general functions. We also highlighted the current knowledge of RRIs and the limitations of RNA interactome mapping via experimental data. Then, the gap between RSP and RIP, the importance of RNA homologues, the relationship between pseudoknots, and RNA folding thermodynamics are discussed. It is hoped that these emerging prediction tools will deepen the understanding of RNA-associated interactions in human diseases and hasten treatment processes.

Modified RNAs and predictions with the ViennaRNA Package

MOTIVATION

In living organisms, many RNA molecules are modified post-transcriptionally. This turns the widely known four-letter RNA alphabet ACGU into a much larger one with currently more than 300 known distinct modified bases. The roles for the majority of modified bases remain uncertain, but many are already well-known for their ability to influence the preferred structures that an RNA may adopt. In fact, tRNAs sometimes require certain modifications to fold into their cloverleaf shaped structure. However, predicting the structure of RNAs with base modifications is still difficult due to the lack of efficient algorithms that can deal with the extended sequence alphabet, as well as missing parameter sets that account for the changes in stability induced by the modified bases.

RESULTS

We present an approach to include sparse energy parameter data for modified bases into the ViennaRNA Package. Our method does not require any changes to the underlying efficient algorithms but instead uses a set of plug-in constraints that adapt the predictions in terms of loop evaluation at runtime. These adaptations are efficient in the sense that they are only performed for loops where additional parameters are actually available for. In addition, our approach also facilitates the inclusion of more modified bases as soon as further parameters become available.

AVAILABILITY AND IMPLEMENTATION

Source code and documentation are available at https://www.tbi.univie.ac.at/RNA.

GCNfold: A novel lightweight model with valid extractors for RNA secondary structure prediction

RNA secondary structure is essential for predicting the tertiary structure and understanding RNA function. Recent research tends to stack numerous modules to design large deep-learning models. This can increase the accuracy to more than 70%, as well as significant training costs and prediction efficiency. We proposed a model with three feature extractors called GCNfold. Structure Extractor utilizes a three-layer Graph Convolutional Network (GCN) to mine the structural information of RNA, such as stems, hairpin, and internal loops. Structure and Sequence Fusion embeds structural information into sequences with Transformer Encoders. Long-distance Dependency Extractor captures long-range pairwise relationships by UNet. The experiments indicate that GCNfold has a small number of parameters, a fast inference speed, and a high accuracy among all models with over 80% accuracy. Additionally, GCNfold-Small takes only 90ms to infer an RNA secondary structure and can achieve close to 90% accuracy on average. The GCNfold code is available on Github https://github.com/EnbinYang/GCNfold.

MicroRNA-Mediated Obstruction of Stem-loop Alternative Splicing (MIMOSAS): a global mechanism for the regulation of alternative splicing

While RNA secondary structures are critical to regulate alternative splicing of long-range pre-mRNA, the factors that modulate RNA structure and interfere with the recognition of the splice sites are largely unknown. Previously, we identified a small, non-coding microRNA that sufficiently affects stable stem structure formation of Nmnat pre-mRNA to regulate the outcomes of alternative splicing. However, the fundamental question remains whether such microRNA-mediated interference with RNA secondary structures is a global molecular mechanism for regulating mRNA splicing. We designed and refined a bioinformatic pipeline to predict candidate microRNAs that potentially interfere with pre-mRNA stem-loop structures, and experimentally verified splicing predictions of three different long-range pre-mRNAs in the Drosophila model system. Specifically, we observed that microRNAs can either disrupt or stabilize stem-loop structures to influence splicing outcomes. Our study suggests that MicroRNA-Mediated Obstruction of Stem-loop Alternative Splicing (MIMOSAS) is a novel regulatory mechanism for the transcriptome-wide regulation of alternative splicing, increases the repertoire of microRNA function and further indicates cellular complexity of post-transcriptional regulation.

MicroRNA-Mediated Obstruction of Stem-loop Alternative Splicing (MIMOSAS): a global mechanism for the regulation of alternative splicing

While RNA secondary structures are critical to regulate alternative splicing of long-range pre-mRNA, the factors that modulate RNA structure and interfere with the recognition of the splice sites are largely unknown. Previously, we identified a small, non-coding microRNA that sufficiently affects stable stem structure formation of Nmnat pre-mRNA to regulate the outcomes of alternative splicing. However, the fundamental question remains whether such microRNA-mediated interference with RNA secondary structures is a global molecular mechanism for regulating mRNA splicing. We designed and refined a bioinformatic pipeline to predict candidate microRNAs that potentially interfere with pre-mRNA stem-loop structures, and experimentally verified splicing predictions of three different long-range pre-mRNAs in the Drosophila model system. Specifically, we observed that microRNAs can either disrupt or stabilize stem-loop structures to influence splicing outcomes. Our study suggests that MicroRNA-Mediated Obstruction of Stem-loop Alternative Splicing (MIMOSAS) is a novel regulatory mechanism for the transcriptome-wide regulation of alternative splicing, increases the repertoire of microRNA function and further indicates cellular complexity of post-transcriptional regulation.

One-Sentence Summary

MicroRNA-Mediated Obstruction of Stem-loop Alternative Splicing (MIMOSAS) is a novel regulatory mechanism for the transcriptome-wide regulation of alternative splicing.

Position-dependent sequence motif preferences of SpCas9 are largely determined by scaffold-complementary spacer motifs

Streptococcus pyogenes Cas9 (SpCas9) nuclease exhibits considerable position-dependent sequence preferences. The reason behind these preferences is not well understood and is difficult to rationalise, since the protein establishes interactions with the target-spacer duplex in a sequence-independent manner. We revealed here that intramolecular interactions within the single guide RNA (sgRNA), between the spacer and the scaffold, cause most of these preferences. By using in cellulo and in vitro SpCas9 activity assays with systematically designed spacer and scaffold sequences and by analysing activity data from a large SpCas9 sequence library, we show that some long (>8 nucleotides) spacer motifs, that are complementary to the RAR unit of the scaffold, interfere with sgRNA loading, and that some motifs of more than 4 nucleotides, that are complementary to the SL1 unit, inhibit DNA binding and cleavage. Furthermore, we show that intramolecular interactions are present in the majority of the inactive sgRNA sequences of the library, suggesting that they are the most important intrinsic determinants of the activity of the SpCas9 ribonucleoprotein complex. We also found that in pegRNAs, sequences at the 3' extension of the sgRNA that are complementary to the SL2 unit are also inhibitory to prime editing, but not to the nuclease activity of SpCas9.

Modeling the 3D structure and conformational dynamics of very large RNAs using coarse-grained molecular simulations

We describe a computational approach to building and simulating realistic 3D models of very large RNA molecules (>1000 nucleotides) at a resolution of one "bead" per nucleotide. The method starts with a predicted secondary structure and uses several stages of energy minimization and Brownian dynamics (BD) simulation to build 3D models. A key step in the protocol is the temporary addition of a 4 ^th spatial dimension that allows all predicted helical elements to become disentangled from each other in an effectively automated way. We then use the resulting 3D models as input to Brownian dynamics simulations that include hydrodynamic interactions (HIs) that allow the diffusive properties of the RNA to be modelled as well as enabling its conformational dynamics to be simulated. To validate the dynamics part of the method, we first show that when applied to small RNAs with known 3D structures the BD-HI simulation models accurately reproduce their experimental hydrodynamic radii (Rh). We then apply the modelling and simulation protocol to a variety of RNAs for which experimental Rh values have been reported ranging in size from 85 to 3569 nucleotides. We show that the 3D models, when used in BD-HI simulations, produce hydrodynamic radii that are usually in good agreement with experimental estimates for RNAs that do not contain tertiary contacts that persist even under very low salt conditions. Finally, we show that sampling of the conformational dynamics of large RNAs on timescales of 100 µs is computationally feasible with BD-HI simulations.

DrTransformer: heuristic cotranscriptional RNA folding using the nearest neighbor energy model

MOTIVATION

Folding during transcription can have an important influence on the structure and function of RNA molecules, as regions closer to the 5' end can fold into metastable structures before potentially stronger interactions with the 3' end become available. Thermodynamic RNA folding models are not suitable to predict structures that result from cotranscriptional folding, as they can only calculate properties of the equilibrium distribution. Other software packages that simulate the kinetic process of RNA folding during transcription exist, but they are mostly applicable for short sequences.

RESULTS

We present a new algorithm that tracks changes to the RNA secondary structure ensemble during transcription. At every transcription step, new representative local minima are identified, a neighborhood relation is defined and transition rates are estimated for kinetic simulations. After every simulation, a part of the ensemble is removed and the remainder is used to search for new representative structures. The presented algorithm is deterministic (up to numeric instabilities of simulations), fast (in comparison with existing methods), and it is capable of folding RNAs much longer than 200 nucleotides.

AVAILABILITY AND IMPLEMENTATION

This software is open-source and available at https://github.com/ViennaRNA/drtransformer.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Employing in silico investigations to determine the cross-kingdom approach for Curcuma longa miRNAs and their human targets

Background

Plant elements and extracts have been used for centuries to treat a wide range of diseases, from cancer to modern lifestyle ailments like viral infections. These plant-based miRNAs have the capacity to control physiological and pathological conditions in both humans and animals, and they might be helpful in the detection and treatment of a variety of diseases. The present study investigates the miRNA of the well-known spice Curcuma Longa and its prospective targets using a variety of bioinformatics techniques.

Results

Using the integrative database of animal, plant, and viral microRNAs known as miRNEST 2.0, nine C. longa miRNAs were predicted. psRNA target service foretells the presence of 23 human target genes linked to a variety of disorders. By interacting with a variety of cellular and metabolic processes, miRNAs 167, 1525, and 756 have been found to be critical regulators of tumour microenvironment. SARS-cov2 and influenza A virus regulation have been connected to ZFP36L1 from miRNA 1525 and ETV5 from miRNA 756, respectively.

Conclusions

The current cross-kingdom study offers fresh knowledge about how to increase the effectiveness of plant-based therapies for disease prevention and serves as a platform for in vitro and in vivo research development.

Graphical abstract

Reekeekee- and roodoodooviruses, two different Microviridae clades constituted by the smallest DNA phages

Small circular single-stranded DNA viruses of the Microviridae family are both prevalent and diverse in all ecosystems. They usually harbor a genome between 4.3 and 6.3 kb, with a microvirus recently isolated from a marine Alphaproteobacteria being the smallest known genome of a DNA phage (4.248 kb). A subfamily, Amoyvirinae, has been proposed to classify this virus and other related small Alphaproteobacteria-infecting phages. Here, we report the discovery, in meta-omics data sets from various aquatic ecosystems, of sixteen complete microvirus genomes significantly smaller (2.991-3.692 kb) than known ones. Phylogenetic analysis reveals that these sixteen genomes represent two related, yet distinct and diverse, novel groups of microviruses-amoyviruses being their closest known relatives. We propose that these small microviruses are members of two tentatively named subfamilies Reekeekeevirinae and Roodoodoovirinae. As known microvirus genomes encode many overlapping and overprinted genes that are not identified by gene prediction software, we developed a new methodology to identify all genes based on protein conservation, amino acid composition, and selection pressure estimations. Surprisingly, only four to five genes could be identified per genome, with the number of overprinted genes lower than that in phiX174. These small genomes thus tend to have both a lower number of genes and a shorter length for each gene, leaving no place for variable gene regions that could harbor overprinted genes. Even more surprisingly, these two Microviridae groups had specific and different gene content, and major differences in their conserved protein sequences, highlighting that these two related groups of small genome microviruses use very different strategies to fulfill their lifecycle with such a small number of genes. The discovery of these genomes and the detailed prediction and annotation of their genome content expand our understanding of ssDNA phages in nature and are further evidence that these viruses have explored a wide range of possibilities during their long evolution.

PUMILIO competes with AUF1 to control DICER1 RNA levels and miRNA processing

DICER1 syndrome is a cancer pre-disposition disorder caused by mutations that disrupt the function of DICER1 in miRNA processing. Studying the molecular, cellular and oncogenic effects of these mutations can reveal novel mechanisms that control cell homeostasis and tumor biology. Here, we conduct the first analysis of pathogenic DICER1 syndrome allele from the DICER1 3'UTR. We find that the DICER1 syndrome allele, rs1252940486, abolishes interaction with the PUMILIO RNA binding protein with the DICER1 3'UTR, resulting in the degradation of the DICER1 mRNA by AUF1. This single mutational event leads to diminished DICER1 mRNA and protein levels, and widespread reprogramming of miRNA networks. The in-depth characterization of the rs1252940486 DICER1 allele, reveals important post-transcriptional regulatory events that control DICER1 levels.

Getting to the bottom of lncRNA mechanism: structure-function relationships

While long non-coding RNAs are known to play key roles in disease and development, relatively few structural studies have been performed for this important class of RNAs. Here, we review functional studies of long non-coding RNAs and expose the need for high-resolution 3-D structural studies, discussing the roles of long non-coding RNAs in the cell and how structure-function relationships might be used to elucidate further understanding. We then describe structural studies of other classes of RNAs using chemical probing, nuclear magnetic resonance, small-angle X-ray scattering, X-ray crystallography, and cryogenic electron microscopy (cryo-EM). Next, we review early structural studies of long non-coding RNAs to date and describe the way forward for the structural biology of long non-coding RNAs in terms of cryo-EM.

Synthetic riboswitches for the analysis of tRNA processing by eukaryotic RNase P enzymes

Removal of the 5'-leader region is an essential step in the maturation of tRNA molecules in all domains of life. This reaction is catalyzed by various RNase P activities, ranging from ribonucleoproteins with ribozyme activity to protein-only forms. In Escherichia coli, the efficiency of RNase P-mediated cleavage can be controlled by computationally designed riboswitch elements in a ligand-dependent way, where the 5'-leader sequence of a tRNA precursor is either sequestered in a hairpin structure or presented as a single-stranded region accessible for maturation. In the presented work, the regulatory potential of such artificial constructs is tested on different forms of eukaryotic RNase P enzymes-two protein-only RNase P enzymes (PRORP1 and PRORP2) from Arabidopsis thaliana and the ribonucleoprotein of Homo sapiens The PRORP enzymes were analyzed in vitro as well as in vivo in a bacterial RNase P complementation system. We also tested in HEK293T cells whether the riboswitches remain functional with human nuclear RNase P. While the regulatory principle of the synthetic riboswitches applies for all tested RNase P enzymes, the results also show differences in the substrate requirements of the individual enzyme versions. Hence, such designed RNase P riboswitches represent a novel tool to investigate the impact of the structural composition of the 5'-leader on substrate recognition by different types of RNase P enzymes.

Evaluation of Early Biomarkers of Atherosclerosis Associated with Polychlorinated Biphenyl Exposure: An in Vitro and in Vivo Study

BACKGROUND

Miscellaneous cardiovascular risk factors have been defined, but the contribution of environmental pollutants exposure on cardiovascular disease (CVD) remains underappreciated.

OBJECTIVE

We investigated the potential impact of typical environmental pollutant exposure on atherogenesis and its underlying mechanisms.

METHODS

We used human umbilical vein endothelial cells (HUVECs) and apolipoprotein E knockout (ApoE-/-) mice to investigate how 2,3,5-trichloro-6-phenyl-[1,4]-benzoquinone (PCB29-pQ, a toxic polychlorinated biphenyl metabolite) affects atherogenesis and identified early biomarkers of CVD associated with PCB29-pQ exposures. Then, we used long noncoding RNAs (lncRNAs) HDAC7-AS1-overexpressing ApoE-/- mice and apolipoprotein E/caveolin 1 double-knockout (ApoE-/-/CAV1-/-) mice to address the role of these early biomarkers in PCB29-pQ-induced atherogenesis. Plasma samples from patients with coronary heart disease (CHD) were also used to confirm our findings.

RESULTS

Our data indicate that lncRNA HDAC7-AS1 bound to MIR-7-5p via argonaute 2 in PCB29-pQ-challenged HUVECs. Our mRNA sequencing assay identified transforming growth factor-β2 (TGF-β2) as a possible target gene of MIR-7-5p; HDAC7-AS1 sponged MIR-7-5p and inhibited the binding of TGF-β2 to MIR-7-5p. The effect of PCB29-pQ-induced endothelial injury, vascular inflammation, development of plaques, and atherogenesis in ApoE-/- mice was greater with MIR-7-5p-mediated TGF-β2 inhibition, whereas HDAC7-AS1-overexpressing ApoE-/- mice and ApoE-/-/CAV1-/- mice showed the opposite effect. Consistently, plasma levels of HDAC7-AS1 and MIR-7-5p were found to be significantly associated individuals diagnosed with CHD.

DISCUSSIONS

These findings demonstrated that a mechanism-based, integrated-omics approach enabled the identification of potentially clinically relevant diagnostic indicators and therapeutic targets of CHD mediated by environmental contaminants using in vitro and in vivo models of HUVECs and ApoE-/- and ApoE-/-/CAV1-/- mice. https://doi.org/10.1289/EHP9833.

dsRBPBind: modeling the effect of RNA secondary structure on double-stranded RNA-protein binding

MOTIVATION

RNA-binding proteins are fundamental to many cellular processes. Double-stranded RNA-binding proteins (dsRBPs) in particular are crucial for RNA interference, mRNA elongation, A-to-I editing, host defense, splicing and a multitude of other important mechanisms. Since dsRBPs require double-stranded RNA to bind, their binding affinity depends on the competition among all possible secondary structures of the target RNA molecule. Here, we introduce a quantitative model that allows calculation of the effective affinity of dsRBPs to any RNA given a principal affinity and the sequence of the RNA, while fully taking into account the entire secondary structure ensemble of the RNA.

RESULTS

We implement our model within the ViennaRNA folding package while maintaining its O(N3) time complexity. We validate our quantitative model by comparing with experimentally determined binding affinities and stoichiometries for transactivation response element RNA-binding protein (TRBP). We also find that the change in dsRBP binding affinity purely due to the presence of alternative RNA structures can be many orders of magnitude and that the predicted affinity of TRBP for pre-miRNA-like constructs correlates with experimentally measured processing rates.

AVAILABILITY AND IMPLEMENTATION

Our modified version of the ViennaRNA package is available for download at http://bioserv.mps.ohio-state.edu/dsRBPBind, is free to use for research and educational purposes, and utilizes simple get/set methods for footprint size, concentration, cooperativity, principal dissociation constant and overlap.

RNA Design Principles for Riboswitches that Regulate RNase P-Mediated tRNA Processing

Riboswitches are an attractive target for the directed design of RNA-based regulators by in silico prediction. These noncoding RNA elements consist of an aptamer platform for the highly selective ligand recognition and an expression platform which controls gene activity typically at the level of transcription or translation. In previous work, we could successfully apply RNA folding prediction to implement a new riboswitch mechanism regulating processing of a tRNA by RNase P. In this contribution, we present detailed information about our pipeline consisting of in silico design combined with the biochemical analysis for the verification of the implemented mechanism. Furthermore, we discuss the applicability of the presented biochemical in vivo and in vitro methods for the characterization of other artificial riboswitches.

Predicting the Structure of a Viroid : Structure, Structure Distribution, Consensus Structure, and Structure Drawing

Viroids are small non-coding RNAs that require a special sequence and structure to be replicated and transported by the host machinery. Many of these features can be predicted and later experimentally verified. Here, we will present workflows to predict viroid structures and draw the predicted structures in a pleasing and descriptive way using recently developed software.

DNA barcoding and coalescent-based delimitation of endosymbiotic clevelandellid ciliates (Ciliophora: Clevelandellida): a shift to molecular taxonomy in the inventory of ciliate diversity in panesthiine cockroaches

Phylogenetically distinct lineages may be hidden behind identical or highly similar morphologies. The phenomenon of morphological crypticity has been recently detected in symbiotic ciliates of the family Clevelandellidae, as multivariate and Fourier shape analyses failed to distinguish genetically distinct taxa. To address the question of species boundaries, the phylogenetic information contained in the rDNA cistron of clevelandellid ciliates, which had been isolated from the digestive tract of blaberid cockroaches, was studied using a multifaceted statistical approach. Multigene phylogenies revealed that the genus Clevelandella is paraphyletic containing members of the genus Paraclevelandia. To resolve the paraphyly of Clevelandella, two new genera, Anteclevelandella gen. nov. and Rhynchoclevelandella gen. nov., are proposed based on morphological synapomorphies and shared molecular characters. Multigene analyses and Bayesian species delimitation supported the existence of 13 distinct species within the family Clevelandellidae, eight of which represent new taxa. Moreover, two new Nyctotherus species were recognized within the clade that is sister to the Clevelandellidae. According to the present distance and network analyses, the first two domains of the 28S rRNA gene showed much higher power for species discrimination than the 18S rRNA gene and ITS region. Therefore, the former molecular marker was proposed to be a suitable group-specific barcode for the family Clevelandellidae.

Analysis of 11,430 recombinant protein production experiments reveals that protein yield is tunable by synonymous codon changes of translation initiation sites

Recombinant protein production is a key process in generating proteins of interest in the pharmaceutical industry and biomedical research. However, about 50% of recombinant proteins fail to be expressed in a variety of host cells. Here we show that the accessibility of translation initiation sites modelled using the mRNA base-unpairing across the Boltzmann's ensemble significantly outperforms alternative features. This approach accurately predicts the successes or failures of expression experiments, which utilised Escherichia coli cells to express 11,430 recombinant proteins from over 189 diverse species. On this basis, we develop TIsigner that uses simulated annealing to modify up to the first nine codons of mRNAs with synonymous substitutions. We show that accessibility captures the key propensity beyond the target region (initiation sites in this case), as a modest number of synonymous changes is sufficient to tune the recombinant protein expression levels. We build a stochastic simulation model and show that higher accessibility leads to higher protein production and slower cell growth, supporting the idea of protein cost, where cell growth is constrained by protein circuits during overexpression.

Systemic CLIP-seq analysis and game theory approach to model microRNA mode of binding

microRNAs (miRNAs) associate with Ago proteins to post-transcriptionally silence gene expression by targeting mRNAs. To characterize the modes of miRNA-binding, we developed a novel computational framework, called optiCLIP, which considers the reproducibility of the identified peaks among replicates based on the peak overlap. We identified 98 999 binding sites for mouse and human miRNAs, from eleven Ago2 CLIP-seq datasets. Clustering the binding preferences, we found heterogeneity of the mode of binding for different miRNAs. Finally, we set up a quantitative model, named miRgame, based on an adaptation of the game theory. We have developed a new algorithm to translate the miRgame into a score that corresponds to a miRNA degree of occupancy for each Ago2 peak. The degree of occupancy summarizes the number of miRNA-binding sites and miRNAs targeting each binding site, and binding energy of each miRNA::RNA heteroduplex in each peak. Ago peaks were stratified accordingly to the degree of occupancy. Target repression correlates with higher score of degree of occupancy and number of miRNA-binding sites within each Ago peak. We validated the biological performance of our new method on miR-155-5p. In conclusion, our data demonstrate that miRNA-binding sites within each Ago2 CLIP-seq peak synergistically interplay to enhance target repression.

Viral MicroRNAs Encoded by Nucleocapsid Gene of SARS-CoV-2 Are Detected during Infection, and Targeting Metabolic Pathways in Host Cells

MicroRNAs (miRNAs) are critical regulators of gene expression that may be used to identify the pathological pathways influenced by disease and cellular interactions. Viral miRNAs (v-miRNAs) encoded by both DNA and RNA viruses induce immune dysregulation, virus production, and disease pathogenesis. Given the absence of effective treatment and the prevalence of highly infective SARS-CoV-2 strains, improved understanding of viral-associated miRNAs could provide novel mechanistic insights into the pathogenesis of COVID-19. In this study, SARS-CoV-2 v-miRNAs were identified by deep sequencing in infected Calu-3 and Vero E6 cell lines. Among the ~0.1% small RNA sequences mapped to the SARS-CoV-2 genome, the top ten SARS-CoV-2 v-miRNAs (including three encoded by the N gene; v-miRNA-N) were selected. After initial screening of conserved v-miRNA-N-28612, which was identified in both SARS-CoV and SARS-CoV-2, its expression was shown to be positively associated with viral load in COVID-19 patients. Further in silico analysis and synthetic-mimic transfection of validated SARS-CoV-2 v-miRNAs revealed novel functional targets and associations with mechanisms of cellular metabolism and biosynthesis. Our findings support the development of v-miRNA-based biomarkers and therapeutic strategies based on improved understanding of the pathophysiology of COVID-19.

DDX3 depletion represses translation of mRNAs with complex 5' UTRs

DDX3 is an RNA chaperone of the DEAD-box family that regulates translation. Ded1, the yeast ortholog of DDX3, is a global regulator of translation, whereas DDX3 is thought to preferentially affect a subset of mRNAs. However, the set of mRNAs that are regulated by DDX3 are unknown, along with the relationship between DDX3 binding and activity. Here, we use ribosome profiling, RNA-seq, and PAR-CLIP to define the set of mRNAs that are regulated by DDX3 in human cells. We find that while DDX3 binds highly expressed mRNAs, depletion of DDX3 particularly affects the translation of a small subset of the transcriptome. We further find that DDX3 binds a site on helix 16 of the human ribosomal rRNA, placing it immediately adjacent to the mRNA entry channel. Translation changes caused by depleting DDX3 levels or expressing an inactive point mutation are different, consistent with different association of these genetic variant types with disease. Taken together, this work defines the subset of the transcriptome that is responsive to DDX3 inhibition, with relevance for basic biology and disease states where DDX3 is altered.

Beyond Plug and Pray: Context Sensitivity and in silico Design of Artificial Neomycin Riboswitches

Gene regulation in prokaryotes often depends on RNA elements such as riboswitches or RNA thermometers located in the 5' untranslated region of mRNA. Rearrangements of the RNA structure in response, e.g., to the binding of small molecules or ions control translational initiation or premature termination of transcription and thus mRNA expression. Such structural responses are amenable to computational modelling, making it possible to rationally design synthetic riboswitches for a given aptamer. Starting from an artificial aptamer, we construct the first synthetic transcriptional riboswitches that respond to the antibiotic neomycin. We show that the switching behaviour in vivo critically depends not only on the sequence of the riboswitch itself, but also on its sequence context. We therefore developed in silico methods to predict the impact of the context, making it possible to adapt the design and to rescue non-functional riboswitches. We furthermore analyse the influence of 5' hairpins with varying stability on neomycin riboswitch activity. Our data highlight the limitations of a simple plug-and-play approach in the design of complex genetic circuits and demonstrate that detailed computational models significantly simplify, improve, and automate the design of transcriptional circuits. Our design software is available under a free licence on GitHub (https://github.com/xileF1337/riboswitch_design).

Ligand-dependent tRNA processing by a rationally designed RNase P riboswitch

We describe a synthetic riboswitch element that implements a regulatory principle which directly addresses an essential tRNA maturation step. Constructed using a rational in silico design approach, this riboswitch regulates RNase P-catalyzed tRNA 5′-processing by either sequestering or exposing the single-stranded 5′-leader region of the tRNA precursor in response to a ligand. A single base pair in the 5′-leader defines the regulatory potential of the riboswitch both in vitro and in vivo. Our data provide proof for prior postulates on the importance of the structure of the leader region for tRNA maturation. We demonstrate that computational predictions of ligand-dependent structural rearrangements can address individual maturation steps of stable non-coding RNAs, thus making them amenable as promising target for regulatory devices that can be used as functional building blocks in synthetic biology.

Full-length annotation with multistrategy RNA-seq uncovers transcriptional regulation of lncRNAs in cotton

Long noncoding RNAs (lncRNAs) are crucial factors during plant development and environmental responses. To build an accurate atlas of lncRNAs in the diploid cotton Gossypium arboreum, we combined Isoform-sequencing, strand-specific RNA-seq (ssRNA-seq), and cap analysis gene expression (CAGE-seq) with PolyA-seq and compiled a pipeline named plant full-length lncRNA to integrate multi-strategy RNA-seq data. In total, 9,240 lncRNAs from 21 tissue samples were identified. 4,405 and 4,805 lncRNA transcripts were supported by CAGE-seq and PolyA-seq, respectively, among which 6.7% and 7.2% had multiple transcription start sites (TSSs) and transcription termination sites (TTSs). We revealed that alternative usage of TSS and TTS of lncRNAs occurs pervasively during plant growth. Besides, we uncovered that many lncRNAs act in cis to regulate adjacent protein-coding genes (PCGs). It was especially interesting to observe 64 cases wherein the lncRNAs were involved in the TSS alternative usage of PCGs. We identified lncRNAs that are coexpressed with ovule- and fiber development-associated PCGs, or linked to GWAS single-nucleotide polymorphisms. We mapped the genome-wide binding sites of two lncRNAs with chromatin isolation by RNA purification sequencing. We also validated the transcriptional regulatory role of lnc-Ga13g0352 via virus-induced gene suppression assay, indicating that this lncRNA might act as a dual-functional regulator that either activates or inhibits the transcription of target genes.

Relative Information Gain: Shannon entropy-based measure of the relative structural conservation in RNA alignments

Structural characterization of RNAs is a dynamic field, offering many modelling possibilities. RNA secondary structure models are usually characterized by an encoding that depicts structural information of the molecule through string representations or graphs. In this work, we provide a generalization of the BEAR encoding (a context-aware structural encoding we previously developed) by expanding the set of alignments used for the construction of substitution matrices and then applying it to secondary structure encodings ranging from fine-grained to more coarse-grained representations. We also introduce a re-interpretation of the Shannon Information applied on RNA alignments, proposing a new scoring metric, the Relative Information Gain (RIG). The RIG score is available for any position in an alignment, showing how different levels of detail encoded in the RNA representation can contribute differently to convey structural information. The approaches presented in this study can be used alongside state-of-the-art tools to synergistically gain insights into the structural elements that RNAs and RNA families are composed of. This additional information could potentially contribute to their improvement or increase the degree of confidence in the secondary structure of families and any set of aligned RNAs.

RNAxplorer: Harnessing the Power of Guiding Potentials to Sample RNA Landscapes

Motivation

Predicting the folding dynamics of RNAs is a computationally difficult problem, first and foremost due to the combinatorial explosion of alternative structures in the folding space. Abstractions are therefore needed to simplify downstream analyses, and thus make them computationally tractable. This can be achieved by various structure sampling algorithms. However, current sampling methods are still time consuming and frequently fail to represent key elements of the folding space.

Method

We introduce RNAxplorer, a novel adaptive sampling method to efficiently explore the structure space of RNAs. RNAxplorer uses dynamic programming to perform an efficient Boltzmann sampling in the presence of guiding potentials, which are accumulated into pseudo-energy terms and reflect similarity to already well-sampled structures. This way, we effectively steer sampling toward underrepresented or unexplored regions of the structure space.

Results

We developed and applied different measures to benchmark our sampling methods against its competitors. Most of the measures show that RNAxplorer produces more diverse structure samples, yields rare conformations that may be inaccessible to other sampling methods and is better at finding the most relevant kinetic traps in the landscape. Thus, it produces a more representative coarse graining of the landscape, which is well suited to subsequently compute better approximations of RNA folding kinetics.

Availabilityand implementation

https://github.com/ViennaRNA/RNAxplorer/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Identification, genetic variation, and structural analysis of 18S rRNA of Theileria orientalis and Theileria velifera-like isolates from Myanmar

Ribosomal RNA genes have been widely used for the identification and phylogenetic analysis of various organisms, including parasitic protozoa. Here, we report nine near full-length Theileria orientalis 18S rRNA gene sequences from cattle from different areas of Myanmar. Phylogenetic analysis of the 18S rRNA genes revealed a considerably close genetic relationship among T. orientalis isolates from Australia, China, Japan, Korea, Myanmar, and Pakistan. We also obtained four Theileria velifera-like (Theileria cf. velifera) 18S rRNA gene sequences from two cattle and two water buffaloes from the northernmost area of Myanmar. The phylogenetic analysis of T. cf. velifera isolates from Myanmar along with T. velifera and T. cf. velifera isolates from African countries suggested an evolutionary lineage of greater complexity in T. velifera-related parasites. DNA alignment analysis indicated the presence of 51 and 55 nucleotide variation positions within the 18S rRNA genes from 15 T. orientalis and 11 T. velifera-related isolates, respectively. Alignment entropy analysis of the 18S rRNA sequences indicated that both T. orientalis and T. velifera-related isolates had three hyper variable regions, corresponding to V2, V4, and V7 regions in eukaryotes. The degree of variation was prominent in the V2 in T. orientalis and V4 in T. velifera-related isolates. The secondary structure analysis of the 18S rRNA predicted using minimum free energy algorism revealed that the structure of V4 region differed most significantly between T. orientalis and T. velifera. These results provide novel insights into common structures, variations and functions of small subunit rRNA in Theileria species.

Expansion segments in bacterial and archaeal 5S ribosomal RNAs

The large ribosomal RNAs of eukaryotes frequently contain expansion sequences that add to the size of the rRNAs but do not affect their overall structural layout and are compatible with major ribosomal function as an mRNA translation machine. The expansion of prokaryotic ribosomal RNAs is much less explored. In order to obtain more insight into the structural variability of these conserved molecules, we herein report the results of a comprehensive search for the expansion sequences in prokaryotic 5S rRNAs. Overall, 89 expanded 5S rRNAs of 15 structural types were identified in 15 archaeal and 36 bacterial genomes. Expansion segments ranging in length from 13 to 109 residues were found to be distributed among 17 insertion sites. The strains harboring the expanded 5S rRNAs belong to the bacterial orders Clostridiales, Halanaerobiales, Thermoanaerobacterales, and Alteromonadales as well as the archael order Halobacterales When several copies of a 5S rRNA gene are present in a genome, the expanded versions may coexist with normal 5S rRNA genes. The insertion sequences are typically capable of forming extended helices, which do not seemingly interfere with folding of the conserved core. The expanded 5S rRNAs have largely been overlooked in 5S rRNA databases.

Diversity and Eco-Evolutionary Associations of Endosymbiotic Astome Ciliates With Their Lumbricid Earthworm Hosts

Coevolution of endosymbionts with their hosts plays an important role in the processes of speciation and is among the most fascinating topics in evolutionary biology. Astome ciliates represent an interesting model for coevolutionary studies because they are so tightly associated with their host organisms that they completely lost the cell oral apparatus. In the present study, we used five nuclear markers (18S rRNA gene, ITS1-5.8S-ITS2 region, and 28S rRNA gene) and two mitochondrial genes (16S rRNA gene and cytochrome c oxidase subunit I) to explore the diversity of astomes inhabiting the digestive tract of lumbricid earthworms at temperate latitudes in Central Europe and to cast more light on their host specificity and coevolution events that shaped their diversification. The present coevolutionary and phylogenetic interaction-adjusted similarity analyses suggested that almost every host switch leads to speciation and firm association with the new host. Nevertheless, the suggested high structural host specificity of astomes needs to be tested with increased earthworm sampling, as only 52 out of 735 lumbricid earthworms (7.07%) were inhabited by ciliates. On the other hand, the diversification of astomes associated with megascolecid and glossoscolecid earthworms might have been driven by duplication events without host switching.

RNA Secondary Structures with Limited Base Pair Span: Exact Backtracking and an Application

The accuracy of RNA secondary structure prediction decreases with the span of a base pair, i.e., the number of nucleotides that it encloses. The dynamic programming algorithms for RNA folding can be easily specialized in order to consider only base pairs with a limited span L, reducing the memory requirements to O(nL), and further to O(n) by interleaving backtracking. However, the latter is an approximation that precludes the retrieval of the globally optimal structure. So far, the ViennaRNA package therefore does not provide a tool for computing optimal, span-restricted minimum energy structure. Here, we report on an efficient backtracking algorithm that reconstructs the globally optimal structure from the locally optimal fragments that are produced by the interleaved backtracking implemented in RNALfold. An implementation is integrated into the ViennaRNA package. The forward and the backtracking recursions of RNALfold are both easily constrained to structural components with a sufficiently negative z-scores. This provides a convenient method in order to identify hyper-stable structural elements. A screen of the C. elegans genome shows that such features are more abundant in real genomic sequences when compared to a di-nucleotide shuffled background model.

ATTfold: RNA Secondary Structure Prediction With Pseudoknots Based on Attention Mechanism

Accurate RNA secondary structure information is the cornerstone of gene function research and RNA tertiary structure prediction. However, most traditional RNA secondary structure prediction algorithms are based on the dynamic programming (DP) algorithm, according to the minimum free energy theory, with both hard and soft constraints. The accuracy is particularly dependent on the accuracy of soft constraints (from experimental data like chemical and enzyme detection). With the elongation of the RNA sequence, the time complexity of DP-based algorithms will increase geometrically, as a result, they are not good at coping with relatively long sequences. Furthermore, due to the complexity of the pseudoknots structure, the secondary structure prediction method, based on traditional algorithms, has great defects which cannot predict the secondary structure with pseudoknots well. Therefore, few algorithms have been available for pseudoknots prediction in the past. The ATTfold algorithm proposed in this article is a deep learning algorithm based on an attention mechanism. It analyzes the global information of the RNA sequence via the characteristics of the attention mechanism, focuses on the correlation between paired bases, and solves the problem of long sequence prediction. Moreover, this algorithm also extracts the effective multi-dimensional features from a great number of RNA sequences and structure information, by combining the exclusive hard constraints of RNA secondary structure. Hence, it accurately determines the pairing position of each base, and obtains the real and effective RNA secondary structure, including pseudoknots. Finally, after training the ATTfold algorithm model through tens of thousands of RNA sequences and their real secondary structures, this algorithm was compared with four classic RNA secondary structure prediction algorithms. The results show that our algorithm significantly outperforms others and more accurately showed the secondary structure of RNA. As the data in RNA sequence databases increase, our deep learning-based algorithm will have superior performance. In the future, this kind of algorithm will be more indispensable.

Predicting MicroRNA Sequence Using CNN and LSTM Stacked in Seq2Seq Architecture

CNN and LSTM have proven their ability in feature extraction and natural language processing, respectively. So, we tried to use their ability to process the language of RNAs, i.e., predicting sequence of microRNAs using the sequence of mRNA. The idea is to extract the features from sequence of mRNA using CNN and use LSTM network for prediction of miRNA. The model has learned the basic features such as seed match at first 2-8 nucleotides starting at the 5' end and counting toward the 3' end. Also, it was able to predict G-U wobble base pair in seed region. While validating on experimentally validated data, the model was able to predict on average 72 percent of miRNAs for specific mRNA and shows highest positive expression fold change of predicted targets on a microarray data generated using anti 25 miRNAs compare to other predicted tools. Codes are available at https://github.com/rajkumar1501/sequence-prediction-using-CNN-and-LSTMs.

Differential expression of miRNAs in skeletal muscles of Indian sheep with diverse carcass and muscle traits

The study presents the miRNA profiles of two Indian sheep populations with divergent carcass and muscle traits. The RNA sequencing of longissimus thoracis muscles from the two populations revealed a total of 400 known miRNAs. Myomirs or miRNAs specific to skeletal muscles identified in our data included oar-miR-1, oar-miR-133b, oar-miR-206 and oar-miR-486. Comparison of the two populations led to identification of 100 differentially expressed miRNAs (p < 0.05). A total of 45 miRNAs exhibited a log₂ fold change of ≥ ( ±) 3.0. Gene Ontology analysis revealed cell proliferation, epithelial to mesenchymal transition, apoptosis, immune response and cell differentiation as the most significant functions of the differentially expressed miRNAs. The differential expression of some miRNAs was validated by qRT-PCR analysis. Enriched pathways included metabolism of proteins and lipids, PI3K-Akt, EGFR and cellular response to stress. The microRNA-gene interaction network revealed miR-21, miR-155, miR-143, miR-221 and miR-23a as the nodal miRNAs, with multiple targets. MicroRNA-21 formed the focal point of the network with 42 interactions. The hub miRNAs identified in our study form putative regulatory candidates for future research on meat quality traits in Indian sheep. Our results provide insight into the biological pathways and regulatory molecules implicated in muscling traits of sheep.

microRNA strand selection: Unwinding the rules

microRNAs (miRNAs) play a central role in the regulation of gene expression by targeting specific mRNAs for degradation or translational repression. Each miRNA is post‐transcriptionally processed into a duplex comprising two strands. One of the two miRNA strands is selectively loaded into an Argonaute protein to form the miRNA‐Induced Silencing Complex (miRISC) in a process referred to as miRNA strand selection. The other strand is ejected from the complex and is subject to degradation. The target gene specificity of miRISC is determined by sequence complementarity between the Argonaute‐loaded miRNA strand and target mRNA. Each strand of the miRNA duplex has the capacity to be loaded into miRISC and possesses a unique seed sequence. Therefore, miRNA strand selection plays a defining role in dictating the specificity of miRISC toward its targets and provides a mechanism to alter gene expression in a switch‐like fashion. Aberrant strand selection can lead to altered gene regulation by miRISC and is observed in several human diseases including cancer. Previous and emerging data shape the rules governing miRNA strand selection and shed light on how these rules can be circumvented in various physiological and pathological contexts.

This article is categorized under:

RNA Processing > Processing of Small RNAs

Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs

Regulatory RNAs/RNAi/Riboswitches > Biogenesis of Effector Small RNAs

Intact RNA structurome reveals mRNA structure-mediated regulation of miRNA cleavage in vivo

MicroRNA (miRNA)-mediated cleavage is involved in numerous essential cellular pathways. miRNAs recognize target RNAs via sequence complementarity. In addition to complementarity, in vitro and in silico studies have suggested that RNA structure may influence the accessibility of mRNAs to miRNA-induced silencing complexes (miRISCs), thereby affecting RNA silencing. However, the regulatory mechanism of mRNA structure in miRNA cleavage remains elusive. We investigated the role of in vivo RNA secondary structure in miRNA cleavage by developing the new CAP-STRUCTURE-seq method to capture the intact mRNA structurome in Arabidopsis thaliana. This approach revealed that miRNA target sites were not structurally accessible for miRISC binding prior to cleavage in vivo. Instead, we found that the unfolding of the target site structure plays a key role in miRISC activity in vivo. We found that the single-strandedness of the two nucleotides immediately downstream of the target site, named Target Adjacent nucleotide Motif, can promote miRNA cleavage but not miRNA binding, thus decoupling target site binding from cleavage. Our findings demonstrate that mRNA structure in vivo can modulate miRNA cleavage, providing evidence of mRNA structure-dependent regulation of biological processes.