RNA Framework for Assaying the Structure of RNAs by High-Throughput Sequencing

RNA structure is a key player in regulating a plethora of biological processes. A large part of the functions carried out by RNA is mediated by its structure. To this end, in the last decade big effort has been put in the development of new RNA probing methods based on Next-Generation Sequencing (NGS), aimed at the rapid transcriptome-scale interrogation of RNA structures. In this chapter we describe RNA Framework, the to date most comprehensive toolkit for the analysis of NGS-based RNA structure probing experiments. By using two published datasets, we here illustrate how to use the different components of the RNA Framework and how to choose the analysis parameters according to the experimental setup.</p

Computational approaches for RNA structure ensemble deconvolution from structure probing data

RNA structure probing experiments have emerged over the last decade as a straightforward way to determine the structure of RNA molecules in a number of different contexts. Although powerful, the ability of RNA to dynamically interconvert between, and to simultaneously populate, alternative structural configurations, poses a nontrivial challenge to the interpretation of data derived from these experiments. Recent efforts aimed at developing computational methods for the reconstruction of coexisting alternative RNA conformations from structure probing data are paving the way to the study of RNA structure ensembles, even in the context of living cells. In this review, we critically discuss these methods, their limitations and possible future improvements

Effect of time and of precursor molecule on the deposition of hydrophobic nanolayers on ethyelene tetrafluoroethylene–silicon oxide substrates

A method was developed for generating transparent and hydrophobic nanolayers chemisorbed onto flexible substrates of ethylene tetrafluoroethylene–silicon oxide (ETFE–SiOx). In particular, the effect of the deposition time and of the precursor molecule on the nanocoating process was analyzed with the aim of pursuing an optimization of the above method in an industrial application perspective. It was found that precursor molecule of triethoxysilane allowed to obtain better hydrophobic properties on the SiOx surface in shorter times compared to trichlorosilane, reaching the 92 % of final contact angle (CA) value of 106° after only 1 h of deposition. The optical properties and surface morphology were also assessed in function of time, revealing that an initial transparency reduction is followed by a subsequent transmittance increase during the self assembly of fluoroalkylsilanes on the SiOx surface, coherently with the surface roughness analysis data. Encouraging results were also obtained in terms of oleophobic properties improvement of the nanocoated surfaces

High-throughput single nucleotide variant discovery in E14 mouse embryonic stem cells provides a new reference genome assembly

Mouse E14 embryonic stem cells (ESCs) are a well-characterized and widespread used ESC line, often employed for genome-wide studies involving next generation sequencing analysis. More than 2×10(9) sequences made on Illumina platform derived from the genome of E14 ESCs were used to build a database of about 2.7×10(6) single nucleotide variants (SNVs). The identified variants are enriched in intergenic regions, but several thousands reside in gene exons and regulatory regions, such as promoters, enhancers, splicing sites and untranslated regions of RNA, thus indicating high probability of an important functional impact on the molecular biology of these cells. We created a new E14 genome assembly reference that increases the number of mapped reads of about 5%. We performed a Reduced Representation Bisulfite Sequencing on E14 ESCs and we obtained an increase of about 120,000 called CpGs and avoided about 20,000 wrong CpG calls with respect to the mm9 genome reference

Stiffness constants prediction of nanocomposites using a periodic 3D-FEM model

Predictive models, which enable the prediction of nanocomposite properties from their morphologies and account for polymer orientation, could greatly assist the exploitation of this new class of materials in more diversified and demanding market fields. This article focuses on the prediction of effective elastic properties (Young's moduli) of polymer nanocomposite films (copolyamide-6/nanoclay) using 3D analytical (based on the Mori-Tanaka theory) and 3D finite element (FE) models. The analytical model accounts for the orientation of polymer chains induced by drawing. 3D FE model exploits the representative volume element concept and accounts for the nanocomposite morphology as determined from transmission electron microscopy experiments. Model predictions were compared with experimental results obtained for nanocomposite films produced by means a pilot-scale film blowing equipment and collected at different draw ratios

Effect of short-chain branching on melt fracture behavior of metallocene and conventional poly(ethylene/α-olefin) copolymers

A phenomenon that can represent a great problem in melt processing is extrudate distortion. This effect can range in intensity from a loss of gloss to gross distortion and is the factor that limits the production rate in certain processes such as the blown film extrusion of linear low-density polyethylene (LLDPE). The aim of this work was to investigate the effects that molecular weight distribution and short-chain branch length have on the observed melt fracture phenomena for poly(ethylene/-olefin) resins with similar weight comonomer content and molecular weight. The flow stability analysis conducted in this study has shown that, even increasing of few carbon atoms the short-chain branch length of the resins, the surface melt fracture phenomena are reduced and/or eliminated. Moreover, the comparison between the metallocene (mLLDPE) and conventional LLDPE samples, with the same comonomer (hexene), showed that the metallocene-catalyzed resin exhibits early onset and more severe melt fracture, due to its narrower molecular weight distribution

SHAPE-guided RNA structure homology search and motif discovery

The rapidly growing popularity of RNA structure probing methods is leading to increasingly large amounts of available RNA structure information. This demands the development of efficient tools for the identification of RNAs sharing regions of structural similarity by direct comparison of their reactivity profiles, hence enabling the discovery of conserved structural features. We here introduce SHAPEwarp, a largely sequence-agnostic SHAPE-guided algorithm for the identification of structurally-similar regions in RNA molecules. Analysis of Dengue, Zika and coronavirus genomes recapitulates known regulatory RNA structures and identifies novel highly-conserved structural elements. This work represents a preliminary step towards the model-free search and identification of shared and conserved RNA structural features within transcriptomes.Molecular basis of virus replication, viral pathogenesis and antiviral strategie

MREdictor: a two-step dynamic interaction model that accounts for mRNA accessibility and Pumilio binding accurately predicts microRNA targets.

The prediction of pairing between microRNAs (miRNAs) and the miRNA recognition elements (MREs) on mRNAs is expected to be an important tool for understanding gene regulation. Here, we show that mRNAs that contain Pumilio recognition elements (PRE) in the proximity of predicted miRNA-binding sites are more likely to form stable secondary structures within their 3′-UTR, and we demonstrated using a PUM1 and PUM2 double knockdown that Pumilio proteins are general regulators of miRNA accessibility. On the basis of these findings, we developed a computational method for predicting miRNA targets that accounts for the presence of PRE in the proximity of seed-match sequences within poorly accessible structures. Moreover, we implement the miRNA-MRE duplex pairing as a two-step model, which better fits the available structural data. This algorithm, called MREdictor, allows for the identification of miRNA targets in poorly accessible regions and is not restricted to a perfect seed-match; these features are not present in other computational prediction methods