RNA Secondary Structure Analysis Research Articles

Genetic polymorphism in untranslated regions of PRKCZ influences mRNA structure, stability and binding sites

BackgroundVariations in untranslated regions (UTR) alter regulatory pathways impacting phenotype, disease onset, and course of disease. Protein kinase C Zeta (PRKCZ), a serine-threonine kinase, is implicated in cardiovascular, neurological and oncological disorders. Due to limited research on PRKCZ, this study aimed to investigate the impact of UTR genetic variants’ on binding sites for transcription factors and miRNA. RNA secondary structure, eQTLs, and variation tolerance analysis were also part of the study.MethodsThe data related to PRKCZ gene variants was downloaded from the Ensembl genome browser, COSMIC and gnomAD. The RegulomeDB database was used to assess the functional impact of 5’ UTR and 3’UTR variants. The analysis of the transcription binding sites (TFBS) was done through the Alibaba tool, and the Kyoto Encyclopaedia of Genes and Genomes (KEGG) was employed to identify pathways associated with PRKCZ. To predict the effect of variants on microRNA binding sites, PolymiRTS was utilized for 3’ UTR variants, and the SNPinfo tool was used for 5’ UTR variants.ResultsThe results obtained indicated that a total of 24 variants present in the 3’ UTR and 25 variants present in the 5’ UTR were most detrimental. TFBS analysis revealed that 5’ UTR variants added YY1, repressor, and Oct1, whereas 3’ UTR variants added AP-2alpha, AhR, Da, GR, and USF binding sites. The study predicted TFs that influenced PRKCZ expression. RNA secondary structure analysis showed that eight 5’ UTR and six 3’ UTR altered the RNA structure by either removal or addition of the stem-loop. The microRNA binding site analysis highlighted that seven 3’ UTR and one 5’ UTR variant altered the conserved site and also created new binding sites. eQTLs analysis showed that one variant was associated with PRKCZ expression in the lung and thyroid. The variation tolerance analysis revealed that PRKCZ was an intolerant gene.ConclusionThis study laid the groundwork for future studies aimed at targeting PRKCZ as a therapeutic target.

Comprehensive Analysis of CRISPR-Cas Systems and Their Influence on Antibiotic Resistance in Salmonella enterica Strains

Salmonella enterica is a gram-negative bacterium that demonstrates a remarkable ability to acquire antibiotic resistance genes (ARGs). The role of the CRISPR-Cas system in influencing antibiotic resistance in S. enterica is still under investigation. This study explores the distribution and impact of CRISPR-Cas systems on antibiotic resistance by analyzing 316 S. enterica genomes. We conducted sequence alignments, phylogenetic analyses, and conservation studies on Cas genes, direct repeats (DRs), and leader sequences. Promoter predictions and RNA secondary structure analyses were also performed. ARGs were identified, and their correlation with Cas gene clusters was evaluated. Our findings revealed that 82.33% of strains possess complete CRISPR-Cas systems, while 17.66% have orphan CRISPRs. We identified 290 distinct DRs, most of which formed stable stem-loop structures, although no promoter regions were detected within the leader sequences. Most spacers were chromosome-targeting, with a smaller proportion homologous to phages and plasmids. Importantly, strains with complete CRISPR-Cas systems showed a higher incidence of ARGs compared with those with orphan or no CRISPR systems. Specifically, the incidence of ARGs was 54.3% higher in strains with complete CRISPR-Cas systems than in strains without CRISPR-Cas systems, and 15.1% higher than in strains with orphan CRISPRs. Spearman’s correlation analysis confirmed a statistically significant but weak correlation between the presence of Cas genes and the frequency of ARGs ( P-value = 3.892e–06). These results suggest that CRISPR-Cas systems may play a role in the acquisition of ARGs, potentially through mutations under antibiotic pressure. Future studies should investigate mutations, particularly in Cas3—the signature protein of type I CRISPR-Cas systems. In addition, experimental validation, such as culturing S. enterica strains with complete CRISPR-Cas systems under different antibiotic conditions, followed by sequencing to assess the uptake or absence of newly acquired ARGs, would help clarify the potential role of CRISPR-Cas systems in bacterial adaptation to antimicrobial pressures.

RNA Secondary Structure Analysis Using RNAstructure.

RNAstructure is a user-friendly program for the prediction and analysis of RNA secondary structure. It is available as a web server, a program with a graphical user interface, or a set of command line tools. The programs are available for Microsoft Windows, macOS, or Linux. This article provides protocols for prediction of RNA secondary structure (using the web server, the graphical user interface, or the command line) and high-affinity oligonucleotide binding sites to a structured RNA target (using the graphical user interface). © 2023 Wiley Periodicals LLC. Basic Protocol 1: Predicting RNA secondary structure using the RNAstructure web server Alternate Protocol 1: Predicting secondary structure and base pair probabilities using the RNAstructure graphical user interface Alternate Protocol 2: Predicting secondary structure and base pair probabilities using the RNAstructure command line interface Basic Protocol 2: Predicting binding affinities of oligonucleotides complementary to an RNA target using OligoWalk.

Genome-wide identification of A-to-I RNA editing events provides the functional implications in PDAC.

RNA editing, a wide-acknowledged post-transcriptional mechanism, has been reported to be involved in the occurrence and development of cancer, especially the abnormal alteration of adenosine to inosine. However, fewer studies focus on pancreaticcancer. Therefore, we aimed to explore the possible linkages between altered RNA editing events and the development of PDAC. We characterized the global A-to-I RNA editing spectrum from RNA and matched whole-genome sequencing data of 41 primary PDAC and adjacent normal tissues. The following analyses were performed: different editing level and RNA expression analysis,pathway analysis, motif analysis, RNA secondary structure analysis, alternative splicing events analysis, and survival analysis.The RNA editing of single-cell RNA public sequencing data was also characterized. A large number of adaptive RNA editing events with significant differences in editing levels were identified, which are mainly regulated by ADAR1. Moreover, RNA editing in tumors has a higher editing level and more abundant editing sites in general. 140genes were screened out since they were identified with significantly different RNA editing events and were significantly different in expression level between tumor and matched normal samples. Further analysis showed a preference that in the tumor-specific group, they are mainly enriched in cancer-related signal pathways, while in the normal tissue-specific group, they are mainly enriched in pancreatic secretion. At the same time, we also found positively selected differentially edited sites in a series of cancer immune genes, including EGF, IGF1R, and PIK3CD. RNA editing might participate in pathogenisis of PDAC through regulating the alternative splicing and RNA secondary structure of important genesto further regulate gene expression and protein synthesis, including RAB27B and CERS4. Furthermore, single cell sequencing results showed that type2 ductal cells contributed the most to RNA editing events in tumors. RNA editing is an epigenetic mechanism involved in the occurrence and development of pancreatic cancer, which has the potential to diagnose of PDAC and is closely related to the prognosis.

Analysis of Hop Stunt Viroid Diversity in Grapevine (Vitis vinifera L.) in Slovakia: Coexistence of Two Particular Genetic Groups

The hop stunt viroid (HSVd) is a widespread subviral pathogen infecting a broad spectrum of plant hosts including grapevine (Vitis vinifera L.). Despite its omnipresence in virtually all grapevine growing areas around the world, molecular data characterizing HSVd populations are missing from Slovakia. Analysis of the complete nucleotide sequences of 19 grapevine variants revealed the existence of two genetic HSVd groups in Slovakia (internally named the "6A" and "7A" groups based on the particular stretch of adenines at nucleotide positions 39-44/45, respectively). Despite their sampling at different times in various unrelated vineyards, the 6A and 7A groups are characterized by low intra-group divergence (~0.3 and 0.2%, respectively). On the other hand, inter-group divergence reached 2.2% due to several mutations, seven of which were found to be group-specific and mainly (except for one) located in the region of the pathogenic domain. Interestingly, in addition to their frequent co-existence within the same geographical location, the mixed infection of the 6A and 7A type sequence variants was also unequivocally and repeatedly proven within single grapevine plants. The RNA secondary structure analysis of representative isolates from each of these two genetic groups indicated a potential compensatory explanation of such mutations. These group-specific sites could be pointing towards the evolutionary selection linked to the necessity of the viroid to retain its structural conformational integrity, crucial for its functional biochemical ability to interact with specific grapevine cellular host factors required for HSVd propagation.

RNA Secondary Structure Alteration Caused by Single Nucleotide Variants.

A point mutation in coding RNA can cause not only an amino acid substitution but also a dynamic change of RNA secondary structure, leading to a dysfunctional RNA. Although in silico structure prediction has been used to detect structure-disrupting point mutations known as riboSNitches, exhaustive simulation of long RNAs is needed to detect a significant enrichment or depletion of riboSNitches in functional RNAs. Here, we have developed a novel algorithm Radiam (RNA secondary structure Analysis with Deletion, Insertion, And substitution Mutations) for a comprehensive riboSNitch analysis of long RNAs. Radiam is based on the ParasoR framework, which efficiently computes local RNA secondary structures for long RNAs. ParasoR can compute a variety of structure scores over globally consistent structures with maximal span constraints for the base pair distance. Using the reusable structure database made by ParasoR, Radiam performs an efficient recomputation of the secondary structures for mutated sequences. An exhaustive simulation of Radiam is expected to find reliable riboSNitch candidates on long RNAs by evaluating their statistical significance in terms of the change of local structure stability.

Differential analysis of RNA structure probing experiments at nucleotide resolution: uncovering regulatory functions of RNA structure

RNAs perform their function by forming specific structures, which can change across cellular conditions. Structure probing experiments combined with next generation sequencing technology have enabled transcriptome-wide analysis of RNA secondary structure in various cellular conditions. Differential analysis of structure probing data in different conditions can reveal the RNA structurally variable regions (SVRs), which is important for understanding RNA functions. Here, we propose DiffScan, a computational framework for normalization and differential analysis of structure probing data in high resolution. DiffScan preprocesses structure probing datasets to remove systematic bias, and then scans the transcripts to identify SVRs and adaptively determines their lengths and locations. The proposed approach is compatible with most structure probing platforms (e.g., icSHAPE, DMS-seq). When evaluated with simulated and benchmark datasets, DiffScan identifies structurally variable regions at nucleotide resolution, with substantial improvement in accuracy compared with existing SVR detection methods. Moreover, the improvement is robust when tested in multiple structure probing platforms. Application of DiffScan in a dataset of multi-subcellular RNA structurome and a subsequent motif enrichment analysis suggest potential links of RNA structural variation and mRNA abundance, possibly mediated by RNA binding proteins such as the serine/arginine rich splicing factors. This work provides an effective tool for differential analysis of RNA secondary structure, reinforcing the power of structure probing experiments in deciphering the dynamic RNA structurome.

Classification and clustering of RNA crosslink-ligation data reveal complex structures and homodimers.

The recent development and application of methods based on the general principle of "crosslinking and proximity ligation" (crosslink-ligation) are revolutionizing RNA structure studies in living cells. However, extracting structure information from such data presents unique challenges. Here, we introduce a set of computational tools for the systematic analysis of data from a wide variety of crosslink-ligation methods, specifically focusing on read mapping, alignment classification, and clustering. We design a new strategy to map short reads with irregular gaps at high sensitivity and specificity. Analysis of previously published data reveals distinct properties and bias caused by the crosslinking reactions. We perform rigorous and exhaustive classification of alignments and discover eight types of arrangements that provide distinct information on RNA structures and interactions. To deconvolve the dense and intertwined gapped alignments, we develop a network/graph-based tool Crosslinked RNA Secondary Structure Analysis using Network Techniques (CRSSANT), which enables clustering of gapped alignments and discovery of new alternative and dynamic conformations. We discover that multiple crosslinking and ligation events can occur on the same RNA, generating multisegment alignments to report complex high-level RNA structures and multi-RNA interactions. We find that alignments with overlapped segments are produced from potential homodimers and develop a new method for their de novo identification. Analysis of overlapping alignments revealed potential new homodimers in cellular noncoding RNAs and RNA virus genomes in the Picornaviridae family. Together, this suite of computational tools enables rapid and efficient analysis of RNA structure and interaction data in living cells.

Mapping the Conformational Landscape of the Neutral Network of RNA Sequences That Connect Two Functional Distinctly Different Ribozymes.

During evolution of an RNA world, the development of enzymatic function was essential. Such enzymatic function was linked to RNA sequences capable of adopting specific RNA folds that possess catalytic pockets to promote catalysis. Within this primordial RNA world, initially evolved self‐replicating ribozymes presumably mutated to ribozymes with new functions. Schultes and Bartel (Science 2000, 289, 448–452) investigated such conversion from one ribozyme to a new ribozyme with distinctly different catalytic functions. Within a neutral network that linked these two prototype ribozymes, a single RNA chain could be identified that exhibited both enzymatic functions. As commented by Schultes and Bartel, this system possessing one sequence with two enzymatic functions serves as a paradigm for an evolutionary system that allows neutral drifts by stepwise mutation from one ribozyme into a different ribozyme without loss of intermittent function. Here, we investigated this complex functional diversification of ancestral ribozymes by analyzing several RNA sequences within this neutral network between two ribozymes with class III ligase activity and with self‐cleavage reactivity. We utilized rapid RNA sample preparation for NMR spectroscopic studies together with SHAPE analysis and in‐line probing to characterize secondary structure changes within the neutral network. Our investigations allowed delineation of the secondary structure space and by comparison with the previously determined catalytic function allowed correlation of the structure‐function relation of ribozyme function in this neutral network.

In silico analysis of local RNA secondary structure in influenza virus A, B and C finds evidence of widespread ordered stability but little evidence of significant covariation

Influenza virus is a persistent threat to human health; indeed, the deadliest modern pandemic was in 1918 when an H1N1 virus killed an estimated 50 million people globally. The intent of this work is to better understand influenza from an RNA-centric perspective to provide local, structural motifs with likely significance to the influenza infectious cycle for therapeutic targeting. To accomplish this, we analyzed over four hundred thousand RNA sequences spanning three major clades: influenza A, B and C. We scanned influenza segments for local secondary structure, identified/modeled motifs of likely functionality, and coupled the results to an analysis of evolutionary conservation. We discovered 185 significant regions of predicted ordered stability, yet evidence of sequence covariation was limited to 7 motifs, where 3—found in influenza C—had higher than expected amounts of sequence covariation.

Digenic heterozygous mutations of KCNH2 and SCN5A induced young and early-onset long QT syndrome and sinoatrial node dysfunction.

IntroductionLong QT syndrome (LQTS) is a life‐threatening inherited channelopathy, and prolonged QT intervals easily trigger malignant arrhythmias, especially torsades de pointes and ventricular fibrillation.Materials and methodsThe proband with overlapped phenotypes of LQTS and sinoatrial node dysfunction underwent some necessary examinations, including echocardiography, electrocardiogram (ECG), and Holter monitoring. Next, whole‐exome sequencing was performed, and candidate genes were validated by Sanger sequencing. RNA secondary structure and protein physical‐chemical parameter analyses were used to predict the possible structural change of the proteins induced by the mutations.ResultsWe identified the digenic heterozygous mutations of KCNH2 p.307_308del (NM_001204798, c.921_923del) and SCN5A p.R1865H (NM_001160160, c.G5594A) in the female and young proband (II: 1) of LQTS and ventricular fibrillation with repeat syncope at rest. Subsequently, she occurred with obvious sinus arrest with persistent ventricular pacing of implantable cardioverter‐defibrillator. The heterozygous SCN5Ap.R1865H was carried by her father and sister but not carried by I:2. II:1 carried with KCNH2 p.307_308del as a de novo mutation, but not existed in other family members. RNA secondary structure of KCNH2 p.307_308del showed a false regional double helix, and its amino acids' hydrophobicity was significantly weakened. For the Nav1.5 protein property, SCN5A p.R1865H slightly increased the molecular weight and aliphatic index but reduced the instability index.ConclusionsThe digenic heterozygous KCNH2 and SCN5A mutations were associated with young early‐onset long QT syndrome and sinoatrial node dysfunction.

The RNA Secondary Structure Analysis Reveals Potential for Emergence of Pathogenic Flaviviruses.

The Flavivirus genus is divided into four groups: Mosquito-borne flaviviruses, Tick-borne flaviviruses, no-known vector flaviviruses, and Insect specific flaviviruses. Millions of people are affected worldwide every year due to the flaviviral infections. The 5' UTR of the RNA genome plays a critical role in the biology of flaviviruses. To explore any correlation between the topology of the 5' UTR and pathogenesis, a global scale study of the RNA secondary structure of different groups of flaviviruses has been conducted. We found that most of the pathogenic flaviviruses, irrespective of their mode of transmission, tend to form a Y shaped topology in the Stem loop A of the 5' UTR. Some of the current non-pathogenic flaviviruses were also observed to form Y shaped structure. Based on this study, it has been proposed that the flaviviruses having the Y shaped topology in their 5' UTR regions may have the potential to become pathogenic.

Visualization of RNA secondary structure with pseudoknots

The function of pseudoknots cannot be ignored in the RNA secondary structure. Existing methods for analyzing RNA secondary structures with pseudoknots exhibit many shortcomings. This paper presents a novel RNA secondary structure visualization method in the case of a joint analysis of RNA primary structures and secondary structures. The way is based on the page number representation of the RNA secondary structure. It innovatively uses five vectors to represent bases, which are sequentially connected to outline the characteristics of the RNA secondary structure. The method covers almost all the constituent elements of the RNA secondary structure and extracts features completely. Experiments are based on the available techniques for large-scale annotation of RNA secondary structures, using a combination method of discrete wavelet transform and fractal dimension. The classification effect is compared with the previous RNA secondary structure representation methods. Experimental results show that the RNA secondary structure visualization method proposed in this paper has good application prospects in RNA secondary structure classification.

A novel epigenetic mechanism unravels hsa-miR-148a-3p-mediated CYP2B6 downregulation in alcoholic hepatitis disease

Cytochrome P450 (CYP) enzymes play critical roles in drug transformation, and the total CYPs are markedly decreased in alcoholic hepatitis (AH), a fatal alcoholic liver disease. miRNAs are endogenous small noncoding RNAs that regulate many essential biological processes. Knowledge concerning miRNA regulation of CYPs in AH disease is limited. Here we presented the changes of key CYPs in liver samples of AH patients retrieved from GEO database, performed in silico prediction of miRNAs potentially targeting the dysregulated CYP transcripts, and deciphered a novel mechanism underlying miRNA mediated CYPs expression in liver cells. Nine miRNAs were predicted to regulate CYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C19, CYP2J2, and CYP3A4, among which hsa-miR-148a-3p was selected as a case study. Biochemical and molecular evidences demonstrated that miR-148a promoted CYP2B6 expression by increasing mRNA stability via directly binding to the 3′UTR sequence, and that this positive posttranscriptional regulation was AGO1/2-dependent. Further, luciferase reporter gene assay and RNA secondary structure analysis illustrated that the seedless target site, not the seed target site, controlled miR-148a-mediated CYP2B6 upregulation. Moreover, we identified HNF4A as a liver-specific transcription factor of MIR-148A through EMSA and chromatin immunoprecipitation experiments. In conclusion, ethanol downregulated miR-148a in hepatocytes through HNF4A regulation, which eventually decreased CYP2B6 expression. Our finding will benefit the understanding of dysregulated drug metabolism in AH patients and highlight an unconventional mechanism for epigenetic regulation of CYP gene expression.

RNAStructViz: graphical base pairing analysis.

We present a new graphical tool for RNA secondary structure analysis. The central feature is the ability to visually compare/contrast up to three base pairing configurations for a given sequence in a compact, standardized circular arc diagram layout. This is complemented by a built-in CT-style file viewer and radial layout substructure viewer which are directly linked to the arc diagram window via the zoom selection tool. Additional functionality includes the computation of some numerical information, and the ability to export images and data for later use. This tool should be of use to researchers seeking to better understand similarities and differences between structural alternatives for an RNA sequence. https://github.com/gtDMMB/RNAStructViz/wiki.

Minimal Secondary Structure Formation on mRNAs with a Shine-Dalgarno Sequence for Chromosomal Genes in <i>Rhodobacter sphaeroides</i>

The Shine-Dalgarno (SD) sequence, when present, is known to promote translation initiation in a bacterial cell. However, the thermodynamic stability of the messenger RNA (mRNA) through its secondary structures has an inhibitory effect on the efficiency of translation. This poses the question of whether bacterial mRNAs with SD have low secondary structure formation or not. About 3500 protein-coding genes in Rhodobacter sphaeroides were analyzed and a sliding window analysis of the last 100 nucleotides of the 5’ UTR and the first 100 nucleotides of ORFs was performed using RNAfold, a software for RNA secondary structure analysis. It was shown that mRNAs with SD are less stable than those without SD for genes located on the primary chromosome, but not for the plasmid encoded genes. Furthermore, mRNA stability is similar for genes within each chromosome except those encoded by the accessory chromosome (second chromosome). Results highlight the possible contribution of other factors like replicon-specific nucleotide composition (GC content), codon bias, and protein stability in determining the efficiency of translation initiation in both SD-dependent and SD-independent translation systems.

Prediction and differential analysis of RNA secondary structure

BackgroundRNA structure is the crucial basis for RNA function in various cellular processes. Over the last decade, high throughput structure profiling (SP) experiments have brought enormous insight into RNA secondary structure.ResultsIn this review, we first provide an overview of approaches for RNA secondary structure prediction, including free energy‐based algorithms and comparative sequence analysis. Then we introduce SP technologies, databases to document SP data, and pipelines/algorithms to normalize and interpret SP data. Computational frameworks that incorporate SP data in RNA secondary structure prediction are also presented.ConclusionsWe finally discuss potential directions for improvement in the prediction and differential analysis of RNA secondary structure.

‌RNA secondary structure and qRT-PCR analyses pertained to expressed anti-CD25 CAR in NK-92 cell line

‌RNA secondary structure and qRT-PCR analyses pertained to expressed anti-CD25 CAR in NK-92 cell line

Comparison of Pseudoknotted RNA Secondary Structures by Topological Centroid Identification and Tree Edit Distance

Comparison of RNA structures is one of the most crucial analysis for elucidating their individual functions and promoting medical applications. Because it is widely accepted that their functions and structures are strongly correlated, various methods for RNA secondary structure analysis have been proposed owing to the difficulty in predicting RNA three-dimensional structure directly from its sequence. However, there are few methods dealing with RNA secondary structures with a specific and complex partial structure called pseudoknot despite its significance to biological process, which is a big obstacle for analyzing their functions. In this study, we propose a novel tree representation of pseudoknotted RNA secondary structures by topological centroid identification and their comparison methods based on the tree edit distance. In the proposed method, a given graph representing an RNA secondary structure is transformed to a tree rooted at one of the vertices constituting the topological centroid that is identified by removing cycles with peeling processing for the graph. When comparing tree-represented RNA secondary structures collected from a public database using the tree edit distance and functional gene groups defined by Gene Ontology (GO), the proposed method showed better clustering results according to their GOs than canonical RNA sequence-based comparison. In addition, we also report a case that the combination of the tree edit distance and the sequence edit distance shows a better classification of the pseudoknotted RNA secondary structures.

Predicting RNA secondary structure via adaptive deep recurrent neural networks with energy-based filter

BackgroundRNA secondary structure prediction is an important issue in structural bioinformatics, and RNA pseudoknotted secondary structure prediction represents an NP-hard problem. Recently, many different machine-learning methods, Markov models, and neural networks have been employed for this problem, with encouraging results regarding their predictive accuracy; however, their performances are usually limited by the requirements of the learning model and over-fitting, which requires use of a fixed number of training features. Because most natural biological sequences have variable lengths, the sequences have to be truncated before the features are employed by the learning model, which not only leads to the loss of information but also destroys biological-sequence integrity.ResultsTo address this problem, we propose an adaptive sequence length based on deep-learning model and integrate an energy-based filter to remove the over-fitting base pairs.ConclusionsComparative experiments conducted on an authoritative dataset RNA STRAND (RNA secondary STRucture and statistical Analysis Database) revealed a 12% higher accuracy relative to three currently used methods.