A modularized MapReduce framework to support RNA secondary structure prediction and analysis workflows

Ribonucleic acid (RNA) is a polymeric nucleic acid that is crucial for cellular function, regulating gene expression and encoding/decoding protein/DNA molecules. Recent discoveries of diverse functionality in non-coding RNAs have led to unprecedented demand for RNA 3D structure determination. With current technology, general, accurate prediction of 3D structures for large RNAs from the sequence remains computationally intractable. One of the principal challenges arises from the conformational flexibility of RNA, especially in loop/junction regions, which results in a rugged energy landscape. Several strategies exist to overcome this challenge, including incorporation of efficient experimental information and coarse-grained (CG) modeling to improve computational sampling of the structural ensemble. A second challenge is the inclusion of naturally modified derivatives of canonical RNA nucleotides in structure analysis. Most RNA prediction strategies rely upon the canonical nucleotides (adenine (A), uracil (U), guanine (G), and cytosine (C)), ignoring the effects of modified nucleotides on the structure and system dynamics. In general, RNA molecules contain rigid and flexible structural elements, which can be probed using efficient selective 2'-hydroxyl analyzed by primer extension (SHAPE) experiments. SHAPE experiments selectively modify flexible RNA nucleotides and can be processed to produce a characteristic reactivity profile for an RNA molecule that contains structural information. Incorporation of efficient experimental information, such as SHAPE, in predicting RNA 3D structure is highly desirable for overcoming the current knowledge gap between RNA sequence and 3D structure. In the first project, we introduce a physics-based model, the 3D structure-SHAPE relationship (3DSSR) model, to predict the SHAPE reactivity from the structure and show how this model may be used to sieve SHAPE-compatible structures from a pool of low-energy decoys and refine our predictions. In the second project, we compare 3DSSR performance to that of a convolutional neural network (CNN) trained on the SHAPE data and RNA structures, showing that 3DSSR outperforms the CNN given the limited data available. In the third project, we further improve the 3DSSR model, gaining deeper insights into the SHAPE reaction and biases. In the fourth project, we explore the theory underpinning the iterative simulated CG RNA folding model (IsRNA). In establishing the underlying mechanics driving the success of the model, we were able to clarify and improve the parameterization method while expanding the model interpretation, which should broaden application of the method to other biopolymers, such as protein. We found that the parameterization method follows statistical mechanics principles but also has a Bayesian interpretation. Further, we found that the parameterization process can benefit from application of the principle of maximum entropy, which improves simulation and parameterization efficiency. In the fifth project, we investigate the impact of nucleotide modification on the structure and configurational ensemble of RNA molecules using free energy calculations. By applying modifications to a common RNA hairpin, we estimate the impact on the stability of the structural ensemble, identifying specific interactions that drive changes to the potential of mean force (PMF) and showing the context and modification-dependence of the variable alterations to the structure stability.

Some properties of viral ribonucleic acid-dependent amino acid incorporation

RNA-Seq, named as an abbreviation of "RNA sequencing" and sometimes spelled RNA-seq, RNAseq, or RNASeq, uses next-generation sequencing (NGS) to reveal the presence and quantity of ribonucleic acid (RNA) in a biological sample at a given moment.[1][2] RNA-Seq is used to analyze the continuously changing cellular transcriptome (Figure 1). Specifically, RNA-Seq facilitates the ability to look at alternative gene spliced transcripts, post-transcriptional modifications, gene fusion, mutations/single nucleotide polymorphisms (SNPs) and changes in gene expression over time, or differences in gene expression in different groups or treatments.[3] In addition to messenger RNA (mRNA) transcripts, RNA-Seq can look at different populations of RNA to include total RNA, small RNA, such as microRNA (miRNA), transfer RNA (tRNA), and ribosomal profiling.[4] RNA-Seq can also be used to determine exon/intron boundaries and verify or amend previously annotated 5' and 3' gene boundaries. Recent advances in RNA-Seq include single cell sequencing, in situ sequencing of fixed tissue, and native RNA molecule sequencing with single-molecule real-time sequencing.[5] Prior to RNA-Seq, gene expression studies were done with hybridization-based microarrays. Issues with microarrays include cross-hybridization artifacts, poor quantification of lowly and highly expressed genes, and needing to know the sequence a priori.[6] Because of these technical issues, transcriptomics transitioned to sequencing-based methods. These progressed from Sanger sequencing of Expressed Sequence Tag libraries, to chemical tag-based methods (e.g., serial analysis of gene expression), and finally to the current technology, next-gen sequencing of complementary DNA ( cDNA), notably RNA-Seq.

Use of linear regression model to compare RNA secondary structures

12] Purification of DNA or RNA sequences complementary to mercurated recombinant DNA by sulfhydryl sepharose chromatography

We present the results of large-scale molecular simulations, run over several tens of nanoseconds, of 25-mer sequences of single-stranded ribonucleic acid (RNA) in bulk water and at the surface of three hydrated positively charged MgAl layered double hydroxide (LDH) minerals. The three LDHs differ in surface charge density, through varying the number of isomorphic Al substitutions. Over the course of the simulations, RNA adsorbs tightly to the LDH surface through electrostatic interactions between the charged RNA phosphate groups and the alumina charge sites present in the LDH sheet. The RNA strands arrange parallel to the surface with the base groups aligning normal to the surface and exposed to the bulk aqueous region. This templating effect makes LDH a candidate for amplifying the population of a known RNA sequence from a small number of RNAs. The structure and interactions of RNA at a positively charged, hydroxylated LDH surface were compared with those of RNA at a positively charged calcium montmorillonite surface, allowing us to establish the comparative effect of complexation and water structure at hydroxide and silicate surfaces. The systems were studied by computing radial distribution functions, atom density plots, and radii of gyration, as well as visualization. An observation pertinent to the role of these minerals in prebiotic chemistry is that, for a given charge density on the mineral surface, different genetic sequences of RNA adopt different configurations.

The ribonucleic acid (RNA) of paramyxoviruses has been characterized by biochemical and physiochemical methods. However, paramyxovirus RNA molecules have not been studied by electron microscopy. The molecular weights of these single-stranded viral RNA molecules are not known as yet. Since electron microscopy has been found to be useful for the characterization of single-stranded RNA, this investigation was initiated to examine the morphology and length measurements of paramyxovirus RNA's.Sendai virus Z strain and Newcastle disease virus (NDV), Milano strain, were used. For these studies it was necessary to develop a method of extracting RNA molecules from purified virus particles. Highly purified Sendai virus was treated with pronase (300 μg/ml) at 37°C for 30 minutes and the RNA extracted by the sodium dodecyl sulfate (SDS)-phenol procedure.

Atomic Model of the Thermus thermophilus 70S Ribosome Developed in Silico

Objective: Esophageal squamous cell carcinoma (ESCC), a commonly encountered malignant tumor in the gastrointestinal tract, poses a significant health burden. Bufalin, a pharmacologically active molecule, has been shown to exhibit antitumor activity against various types of cancers. This study investigates the molecular mechanism underpinning the effects of bufalin on ESCCs. Materials and Methods: The impact of bufalin on the proliferation and migration of ESCC cells was evaluated through the utilization of the Cell Counting Kit-8 (CCK-8), scratch assay, and transwell assay. In addition, ribonucleic acid (RNA) sequencing was performed to identify genes that were abnormally expressed in response to bufalin treatment. Western blotting was utilized to ascertain the expression levels of protein arginine methyltransferase-6 (PRMT6), phosphorylated AKT (p-AKT), and phosphorylated mammalian target of rapamycin (p-mTOR). Cell transfection was then performed to observe the rescue effect of PRMT6 on bufalin. Results: Bufalin displayed a significant time-dependent inhibition of the proliferation, migration, and invasion of ECA109 cells. An RNA sequencing (RNA-seq) analysis revealed that PRMT6 expression was downregulated in the cells treated with bufalin. PRMT6 promoted the proliferation, migration, and invasive potential of ECA109 cells. The overexpression of PRMT6 boosted p-AKT and p-mTOR levels in ECA109 cells and reversed bufalin inhibition. Conclusions: These findings indicate that bufalin exerts its inhibitory effects on ESCCs through the PRMT6/AKT/mTOR signaling pathway. These findings lay the groundwork for bufalin as a promising therapeutic candidate for the treatment of ESCC.

Background Understanding the genetic basis for the molecular classification of sinonasal undifferentiated carcinoma (SNUC) based on SMARCB1 may improve our understating regarding the nature of the disease. The objective of the study was to compare the genetic profile of SMARCB1-retained (SR-SNUC) and SMARCB1-deficient SNUC (SD-SNUC). Methods Formalin-fixed, paraffin-embedded tissue from treatment-naive patients with SNUC were selected. Three cases of SR-SNUC, four cases of SD-SNUC, and four samples of nontumor tissue (control samples) were selected. Ribonucleic acid (RNA) sequencing was performed. Results SR-SNUC had a higher number of variants (1 variant for every 15,000 bases) compared with SD-SNUC (1 variant every 29,000 bases). The ratio of missense to silent mutation ratio was higher for SR-SNUC (0.8) as compared with SD-SNUC (0.7). Approximately 1,500 genes were differentially expressed between SR-SNUC and SD-SNUC. The genes that had a higher expression in SR-SNUC included TPD52L1, B3GNT3, GFY, TJP3, ELL3, CYP4F3, ALDH3B2, CKMT1B, VIPR1, SLC7A5, PPP2R2C, UPK3B, MUC1, ELF5, STY7, and H2AC14. The gene that had a higher expression in SD-SNUC was ZFHX4. Most of these genes were related to either protein translation or immune regulation. The most common (n = 3, 75%) mechanisms of loss of SMARCB1 gene in SD-SNUC was loss of heterozygosity. Conclusion RNA sequencing is a viable and informative approach for genomic profiling of archival SNUC samples. Both SR-SNUC and SD-SNUC were noted to have distinct genetic profiles underlying the molecular classification of these diseases.

Amoebicidal action of isoliquiritigenin and glabridin from Glycyrrhiza species: Mechanisms and effects against Acanthamoeba castellanii.

Over the last few years, many optimization algorithms have been developed to predict the optimal secondary structure of ribonucleic acid (RNA) with “minimum free energy” (MFE). These algorithms are either inspired by dynamic programming or by meta-heuristic techniques. RNA participates in several biological activities in the organism. These activities involve protein synthesis, understanding the functional behavior of RNA molecules, coding, decoding and gene expression, carrier of transferring genetic information, formation of protein, catalyst in biomedical reactions and structural molecule in cellular organelles, transcription, etc. Beside the said activities, the major role of RNA is in developing new drugs and understanding several diseases occurred due to genetic disorder and viruses. For the above said activities, it is required to predict the correct RNA secondary structure having minimum free energy with desired prediction accuracy. This paper presents a meta-heuristic optimization algorithm to obtain the optimal secondary structure of RNA with required functionality and requires less time than the others in the literature. The performance of the proposed algorithm is checked with different existing state-of-the-art techniques. It is found that the proposed algorithm gives better results against the other techniques.

An experiment was conducted to test the hypothesis that inclusion of a conventional torula yeast or a torula yeast produced from forestry byproducts (i.e., woody torula yeast) in diets for weanling pigs instead of fish meal and plasma protein improves growth performance and intestinal health of pigs. A total of 120 weanling pigs (6.53 ± 0.78 kg) were allotted to three treatments with ten replicate pens per diet. Pigs were fed one of three diets from days 1 to 14 post-weaning (phase 1), whereas all pigs were fed a common diet in phase 2 (days 15 to 28). The three treatments in phase 1 included a control diet with 5% fish meal, 3.5% plasma protein, and no torula yeast. The second diet contained 1.5% fish meal, 14% woody torula yeast, and no plasma protein, whereas the third diet contained 1.5% fish meal, 14% conventional torula yeast, and no plasma protein. Fecal scores were assessed every other day. On day 7, one pig per pen was euthanized to collect ileal tissue and mucosa for determination of morphology and for ribonucleic acid (RNA) sequencing analysis. At the end of phases 1 and 2, blood samples were collected and concentrations of cytokines, plasma urea nitrogen (PUN), peptide YY, immunoglobulin G, total protein, and albumin were analyzed. Results indicated that both torula yeast sources could replace fish meal and plasma protein without affecting growth performance, intestinal morphology, or blood characteristics of pigs. Pigs fed a diet containing torula yeast had improved (P < 0.05) fecal scores during phase 1. Pigs fed the conventional torula yeast diet had greater (P < 0.05) concentration of interleukin-2 compared with pigs fed the control diet. On day 14, greater (P < 0.05) concentrations of interleukin-4 and interleukin-10 were observed in pigs fed the diet containing the woody torula yeast or conventional torula yeast compared with pigs fed the control diet. Results from the RNA sequencing indicated that 19 of 24 analyzed genes involved in digestion and absorption of protein and vitamins were downregulated in pigs fed the diet containing woody torula yeast compared with pigs fed the control diet. However, only two genes (i.e., ANKS4B and FAM54A) were downregulated in pigs fed the woody torula yeast diet compared with the conventional torula yeast diet. In conclusion, using woody or conventional torula yeast instead of fish meal and plasma protein in the phase 1 diet for weanling pigs may improve intestinal health without influencing growth performance of pigs.

Use of ribonucleic acid (RNA) interference to regulate protein expression has become an important research topic and gene therapy tool, and therefore, finding suitable vehicles for delivery of small RNAs into cells is of crucial importance. Layered double metal hydroxides such as hydrotalcite (HT) have shown great promise as nonviral vectors for transport of deoxyribose nucleic acid (DNA), proteins, and drugs into cells, but the adsorption of RNAs to these materials has been little explored. In this study, the binding of small RNAs with different lengths and levels of secondary structure to HT nanoparticles has been analyzed and compared to results obtained with small DNAs in concurrent experiments. Initial experiments established the spectrophotometric properties of HT in aqueous solutions and determined that HT particles could be readily sedimented with near 100% efficiencies. Use of RNA+HT cosedimentation experiments as well as electrophoretic mobility shift assays demonstrated strong adsorption of RNA 25mers to HT, with twofold greater binding of single-stranded RNAs relative to double-stranded molecules. Strong affinities were also observed with ssRNA and dsRNA 54mers and with more complex transfer RNA molecules. Competition binding and RNA displacement experiments indicated that RNA-HT associations were strong and were only modestly affected by the presence of high concentrations of inorganic anions.

N4-acetylcytidine (ac4C) is a modification found in ribonucleic acid (RNA) related to diseases. Expensive and labor-intensive methods hindered the exploration of ac4C mechanisms and the development of specific anti-ac4C drugs. Therefore, an advanced prediction model for ac4C in RNA is urgently needed. Despite the construction of various prediction models, several limitations exist: (1) insufficient resolution at base level for ac4C sites; (2) lack of information on species other than Homo sapiens; (3) lack of information on RNA other than mRNA; and (4) lack of interpretation for each prediction. In light of these limitations, we have reconstructed the previous benchmark dataset and introduced a new dataset including balanced RNA sequences from multiple species and RNA types, while also providing base-level resolution for ac4C sites. Additionally, we have proposed a novel transformer-based architecture and pipeline for predicting ac4C sites, allowing for highly accurate predictions, visually interpretable results and no restrictions on the length of input RNA sequences. Statistically, our work has improved the accuracy of predicting specific ac4C sites in multiple species from less than 40% to around 85%, achieving a high AUC > 0.9. These results significantly surpass the performance of all existing models.