Predicting RNA structure: advances and limitations

Nanopore Direct RNA Sequencing Reveals Virus-Induced Changes in the Transcriptional Landscape in Human Bronchial Epithelial Cells

Direct RNA nanopore sequencing reveals changes in gene expression, polyadenylation, splicing, m6A methylation, and pseudouridylation in response to influenza virus exposure in primary human bronchial epithelial cells. This study focuses on the epitranscriptomic profile of genes in the host immune response. In addition to polyadenylated noncoding RNA, we purified and sequenced nonpolyadenylated noncoding RNA and observed changes in expression, N6-methyl-adenosine (m6A), and pseudouridylation (Ψ) in these novel RNA. Two recently discovered lincRNA with roles in immune response, Chaserr and LEADR , became highly methylated in response to influenza exposure. Several H/ACA type snoRNAs that guide pseudouridylation are decreased in expression in response to influenza, and there is a corresponding decrease in the pseudouridylation of two novel lncRNA. Thus, novel epitranscriptomic changes revealed by direct RNA sequencing with nanopore technology provides unique insights into the host epitranscriptomic changes in epithelial gene networks that respond to influenza virus infection.

Linear-Time Algorithms for RNA Structure Prediction

RNA secondary structure prediction is widely used to understand RNA function. Existing dynamic programming-based algorithms, both the classical minimum free energy (MFE) methods and partition function methods, suffer from a major limitation: their runtimes scale cubically with the RNA length, and this slowness limits their use in genome-wide applications. Inspired by incremental parsing for context-free grammars in computational linguistics, we designed linear-time heuristic algorithms, LinearFold and LinearPartition, to approximate the MFE structure, partition function and base pairing probabilities. These programs are orders of magnitude faster than Vienna RNAfold and CONTRAfold on long sequences. More interestingly, LinearFold and LinearPartition lead to more accurate predictions on the longest sequence families for which the structures are well established (16S and 23S Ribosomal RNAs), as well as improved accuracies for long-range base pairs (500 +  nucleotides apart). This chapter provides protocols for using LinearFold and LinearPartition for secondary structure prediction.

An apta-aggregation based machine learning assay for rapid quantification of lysozyme through texture parameters

A novel assay technique that involves quantification of lysozyme (Lys) through machine learning is put forward here. This article reports the tendency of the well- documented Ellington group anti-Lys aptamer, to produce aggregates when exposed to Lys. This property of apta-aggregation has been exploited here to develop an assay that quantifies the Lys using texture and area parameters from a photograph of the elliptical aggregate mass through machine learning. Two assay sets were made for the experimental procedure: one with high Lys concentration between 25–100 mM and another with low concentration between 1–20 mM. The high concentration set had a sample volume of 10 μl while the low concentration set had a higher sample volume of 100 μl, in order to obtain the statistical texture values reliably from the aggregate mass. The platform exhibited an experimental limit of detection of 1 mM and a response time of less than 10 seconds. Further, two potential operating modes for the aptamer were hypothesized for this aggregation property and the more accurate mode among the two was ascertained through bioinformatics studies.

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.

An NMR-based approach reveals the core structure of the functional domain of SINEUP lncRNAs

Long non-coding RNAs (lncRNAs) are attracting widespread attention for their emerging regulatory, transcriptional, epigenetic, structural and various other functions. Comprehensive transcriptome analysis has revealed that retrotransposon elements (REs) are transcribed and enriched in lncRNA sequences. However, the functions of lncRNAs and the molecular roles of the embedded REs are largely unknown. The secondary and tertiary structures of lncRNAs and their embedded REs are likely to have essential functional roles, but experimental determination and reliable computational prediction of large RNA structures have been extremely challenging. We report here the nuclear magnetic resonance (NMR)-based secondary structure determination of the 167-nt inverted short interspersed nuclear element (SINE) B2, which is embedded in antisense Uchl1 lncRNA and upregulates the translation of sense Uchl1 mRNAs. By using NMR 'fingerprints' as a sensitive probe in the domain survey, we successfully divided the full-length inverted SINE B2 into minimal units made of two discrete structured domains and one dynamic domain without altering their original structures after careful boundary adjustments. This approach allowed us to identify a structured domain in nucleotides 31-119 of the inverted SINE B2. This approach will be applicable to determining the structures of other regulatory lncRNAs.

FARFAR2: Improved De Novo Rosetta Prediction of Complex Global RNA Folds

Predicting RNA three-dimensional structures from sequence could accelerate understanding of the growing number of RNA molecules being discovered across biology. Rosetta's Fragment Assembly of RNA with Full-Atom Refinement (FARFAR) has shown promise in community-wide blind RNA-Puzzle trials, but lack of a systematic and automated benchmark has left unclear what limits FARFAR performance. Here, we benchmark FARFAR2, an algorithm integrating RNA-Puzzle-inspired innovations with updated fragment libraries and helix modeling. In 16 of 21 RNA-Puzzles revisited without experimental data or expert intervention, FARFAR2 recovers native-like structures more accurate than models submitted during the RNA-Puzzles trials. Remaining bottlenecks include conformational sampling for >80-nucleotide problems and scoring function limitations more generally. Supporting these conclusions, preregistered blind models for adenovirus VA-I RNA and five riboswitch complexes predicted native-like folds with 3- to 14 Å root-mean-square deviation accuracies. We present a FARFAR2 webserver and three large model archives (FARFAR2-Classics, FARFAR2-Motifs, and FARFAR2-Puzzles) to guide future applications and advances.

An Aptamer-Based Biosensor for Direct, Label-Free Detection of Melamine in Raw Milk

Melamine, a nitrogen-rich compound, has been used as a food and milk additive to falsely increase the protein content. However, melamine is toxic, and high melamine levels in food or in milk can cause kidney and urinary problems, or even death. Hence, the detection of melamine in food and milk is desirable, for which numerous detection methods have been developed. Several methods have successfully detected melamine in raw milk; however, they require a sample preparation before the analyses. This study aimed to develop an aptamer-DNAzyme conjugated biosensor for label-free detection of melamine, in raw milk, without any sample preparation. An aptamer-DNAzyme conjugated biosensor was developed via screening using microarray analysis to identify the candidate aptamers followed by an optimization, to reduce the background noise and improve the aptamer properties, thereby, enhancing the signal-to-noise (S/N) ratio of the screened biosensor. The developed biosensor was evaluated via colorimetric detection and tested with raw milk without any sample preparation, using N-methylmesoporphyrin IX for fluorescence detection. The biosensor displayed significantly higher signal intensity at 2 mM melamine (S/N ratio, 20.2), which was sufficient to detect melamine at high concentrations, in raw milk.

Differences in Integron Cassette Excision Dynamics Shape a Trade-Off between Evolvability and Genetic Capacitance

Integrons ensure a rapid and "on demand" response to environmental stresses driving bacterial adaptation. They are able to capture, store, and reorder functional gene cassettes due to site-specific recombination catalyzed by their integrase. Integrons can be either sedentary and chromosomally located or mobile when they are associated with transposons and plasmids. They are respectively called sedentary chromosomal integrons (SCIs) and mobile integrons (MIs). MIs are key players in the dissemination of antibiotic resistance genes. Here, we used in silico and in vivo approaches to study cassette excision dynamics in MIs and SCIs. We show that the orientation of cassette arrays relative to replication influences attC site folding and cassette excision by placing the recombinogenic strands of attC sites on either the leading or lagging strand template. We also demonstrate that stability of attC sites and their propensity to form recombinogenic structures also regulate cassette excision. We observe that cassette excision dynamics driven by these factors differ between MIs and SCIs. Cassettes with high excision rates are more commonly found on MIs, which favors their dissemination relative to SCIs. This is especially true for SCIs carried in the Vibrio genus, where maintenance of large cassette arrays and vertical transmission are crucial to serve as a reservoir of adaptive functions. These results expand the repertoire of known processes regulating integron recombination that were previously established and demonstrate that, in terms of cassette dynamics, a subtle trade-off between evolvability and genetic capacitance has been established in bacteria.IMPORTANCE The integron system confers upon bacteria a rapid adaptation capability in changing environments. Specifically, integrons are involved in the continuous emergence of bacteria resistant to almost all antibiotic treatments. The international situation is critical, and in 2050, the annual number of deaths caused by multiresistant bacteria could reach 10 million, exceeding the incidence of deaths related to cancer. It is crucial to increase our understanding of antibiotic resistance dissemination and therefore integron recombination dynamics to find new approaches to cope with the worldwide problem of multiresistance. Here, we studied the dynamics of recombination and dissemination of gene encoding cassettes carried on integrons. By combining in silico and in vivo analyses, we show that cassette excision is highly regulated by replication and by the intrinsic properties of cassette recombination sites. We also demonstrated differences in the dynamics of cassette recombination between mobile and sedentary chromosomal integrons (MIs and SCIs). For MIs, a high cassette recombination rate is favored and timed to conditions when generating diversity (upon which selection can act) allows for a rapid response to environmental conditions and stresses. In contrast, for SCIs, cassette excisions are less frequent, limiting cassette loss and ensuring a large pool of cassettes. We therefore confirm a role of SCIs as reservoirs of adaptive functions and demonstrate that the remarkable adaptive success of integron recombination system is due to its intricate regulation.

The UEA Small RNA Workbench: A Suite of Computational Tools for Small RNA Analysis

RNA silencing (RNA interference, RNAi) is a complex, highly conserved mechanism mediated by short, typically 20-24 nt in length, noncoding RNAs known as small RNAs (sRNAs). They act as guides for the sequence-specific transcriptional and posttranscriptional regulation of target mRNAs and play a key role in the fine-tuning of biological processes such as growth, response to stresses, or defense mechanism.High-throughput sequencing (HTS) technologies are employed to capture the expression levels of sRNA populations. The processing of the resulting big data sets facilitated the computational analysis of the sRNA patterns of variation within biological samples such as time point experiments, tissue series or various treatments. Rapid technological advances enable larger experiments, often with biological replicates leading to a vast amount of raw data. As a result, in this fast-evolving field, the existing methods for sequence characterization and prediction of interaction (regulatory) networks periodically require adapting or in extreme cases, a complete redesign to cope with the data deluge. In addition, the presence of numerous tools focused only on particular steps of HTS analysis hinders the systematic parsing of the results and their interpretation.The UEA small RNA Workbench (v1-4), described in this chapter, provides a user-friendly, modular, interactive analysis in the form of a suite of computational tools designed to process and mine sRNA datasets for interesting characteristics that can be linked back to the observed phenotypes. First, we show how to preprocess the raw sequencing output and prepare it for downstream analysis. Then we review some quality checks that can be used as a first indication of sources of variability between samples. Next we show how the Workbench can provide a comparison of the effects of different normalization approaches on the distributions of expression, enhanced methods for the identification of differentially expressed transcripts and a summary of their corresponding patterns. Finally we describe individual analysis tools such as PAREsnip, for the analysis of PARE (degradome) data or CoLIde for the identification of sRNA loci based on their expression patterns and the visualization of the results using the software. We illustrate the features of the UEA sRNA Workbench on Arabidopsis thaliana and Homo sapiens datasets.

3D RNA and Functional Interactions from Evolutionary Couplings

Non-coding RNAs are ubiquitous, but the discovery of new RNA gene sequences far outpaces the research on the structure and functional interactions of these RNA gene sequences. We mine the evolutionary sequence record to derive precise information about the function and structure of RNAs and RNA-protein complexes. As in protein structure prediction, we use maximum entropy global probability models of sequence co-variation to infer evolutionarily constrained nucleotide-nucleotide interactions within RNA molecules and nucleotide-amino acid interactions in RNA-protein complexes. The predicted contacts allow all-atom blinded 3D structure prediction at good accuracy for several known RNA structures and RNA-protein complexes. For unknown structures, we predict contacts in 160 non-coding RNA families. Beyond 3D structure prediction, evolutionary couplings help identify important functional interactions-e.g., at switch points in riboswitches and at a complex nucleation site in HIV. Aided by increasing sequence accumulation, evolutionary coupling analysis can accelerate the discovery of functional interactions and 3D structures involving RNA.

Model-Free RNA Sequence and Structure Alignment Informed by SHAPE Probing Reveals a Conserved Alternate Secondary Structure for 16S rRNA

Discovery and characterization of functional RNA structures remains challenging due to deficiencies in de novo secondary structure modeling. Here we describe a dynamic programming approach for model-free sequence comparison that incorporates high-throughput chemical probing data. Based on SHAPE probing data alone, ribosomal RNAs (rRNAs) from three diverse organisms--the eubacteria E. coli and C. difficile and the archeon H. volcanii--could be aligned with accuracies comparable to alignments based on actual sequence identity. When both base sequence identity and chemical probing reactivities were considered together, accuracies improved further. Derived sequence alignments and chemical probing data from protein-free RNAs were then used as pseudo-free energy constraints to model consensus secondary structures for the 16S and 23S rRNAs. There are critical differences between these experimentally-informed models and currently accepted models, including in the functionally important neck and decoding regions of the 16S rRNA. We infer that the 16S rRNA has evolved to undergo large-scale changes in base pairing as part of ribosome function. As high-quality RNA probing data become widely available, structurally-informed sequence alignment will become broadly useful for de novo motif and function discovery.

The ViennaRNA web services

The ViennaRNA package is a widely used collection of programs for thermodynamic RNA secondary structure prediction. Over the years, many additional tools have been developed building on the core programs of the package to also address issues related to noncoding RNA detection, RNA folding kinetics, or efficient sequence design considering RNA-RNA hybridizations. The ViennaRNA web services provide easy and user-friendly web access to these tools. This chapter describes how to use this online platform to perform tasks such as prediction of minimum free energy structures, prediction of RNA-RNA hybrids, or noncoding RNA detection. The ViennaRNA web services can be used free of charge and can be accessed via http://rna.tbi.univie.ac.at.

Analysis of RNA secondary structure

RNA has different levels of structural organization. The primary structure is the linear order of the nucleotide monomers, the RNA sequence. During transcription process, the partially synthesized RNA is folded by base-pairing and thermodynamic intramolecular or intermolecular interactions. This results in a dynamic spreading of a secondary structure along the length of the transcribed section of the RNA. The analysis of both primary or secondary structures requires the RNA end-labeling either at its 5' end using a kinase reaction with [gamma-32P]ATP, or at its 3' end using an RNA ligation reaction with [32P]pCp. End-labeled RNAs are then gradually breakdown using hydrolysing chemicals or a variety of enzymes targeting specific RNA sequences and secondary structure. The most commonly used enzymes are RNase A, T1, and V1. The partial digestion of the RNA reveals a mix of truncated RNA fragments of different lengths, called RNA ladder. The products are then separates through a high resolution gel system and subjected to autoradiographic analysis. Each visible fragment is labeled at one end, but comprises an enzyme specific sequence at the other end. Final comparison of the detected RNA ladders reveals a hypothetical model of the secondary RNA structure under assay conditions.