Mass spectrometry in the biology of RNA and its modifications

Many powerful analytical techniques for investigation of nucleic acids exist in the average modern molecular biology lab. The current review will focus on questions in RNA biology that have been answered by the use of mass spectrometry, which means that new biological information is the purpose and outcome of most of the studies we refer to. The review begins with a brief account of the subject "MS in the biology of RNA" and an overview of the prevalent RNA modifications identified to date. Fundamental considerations about mass spectrometric analysis of RNA are presented with the aim of detailing the analytical possibilities and challenges relating to the unique chemical nature of nucleic acids. The main biological topics covered are RNA modifications and the enzymes that perform the modifications. Modifications of RNA are essential in biology, and it is a field where mass spectrometry clearly adds knowledge of biological importance compared to traditional methods used in nucleic acid research. The biological applications are divided into analyses exclusively performed at the building block (mainly nucleoside) level and investigations involving mass spectrometry at the oligonucleotide level. We conclude the review discussing aspects of RNA identification and quantifications, which are upcoming fields for MS in RNA research. This article is part of a Special Section entitled: Understanding genome regulation and genetic diversity by mass spectrometry.     

ProbKnot: Fast prediction of RNA secondary structure including pseudoknots

It is a significant challenge to predict RNA secondary structures including pseudoknots. Here, a new algorithm capable of predicting pseudoknots of any topology, ProbKnot, is reported. ProbKnot assembles maximum expected accuracy structures from computed base-pairing probabilities in O(N²) time, where N is the length of the sequence. The performance of ProbKnot was measured by comparing predicted structures with known structures for a large database of RNA sequences with fewer than 700 nucleotides. The percentage of known pairs correctly predicted was 69.3%. Additionally, the percentage of predicted pairs in the known structure was 61.3%. This performance is the highest of four tested algorithms that are capable of pseudoknot prediction. The program is available for download at: http://rna.urmc.rochester.edu/RNAstructure.html.     

Secondary structure prediction of interacting RNA molecules

Computational tools for prediction of the secondary structure of two or more interacting nucleic acid molecules are useful for understanding mechanisms for ribozyme function, determining the affinity of an oligonucleotide primer to its target, and designing good antisense oligonucleotides, novel ribozymes, DNA code words, or nanostructures. Here, we introduce new algorithms for prediction of the minimum free energy pseudoknot-free secondary structure of two or more nucleic acid molecules, and for prediction of alternative low-energy (sub-optimal) secondary structures for two nucleic acid molecules. We provide a comprehensive analysis of our predictions against secondary structures of interacting RNA molecules drawn from the literature. Analysis of our tools on 17 sequences of up to 200 nucleotides that do not form pseudoknots shows that they have 79% accuracy, on average, for the minimum free energy predictions. When the best of 100 sub-optimal foldings is taken, the average accuracy increases to 91%. The accuracy decreases as the sequences increase in length and as the number of pseudoknots and tertiary interactions increases. Our algorithms extend the free energy minimization algorithm of Zuker and Stiegler for secondary structure prediction, and the sub-optimal folding algorithm by Wuchty et al. Implementations of our algorithms are freely available in the package MultiRNAFold.     

Improved free energy parameters for RNA pseudoknotted secondary structure prediction

Accurate prediction of RNA pseudoknotted secondary structures from the base sequence is a challenging computational problem. Since prediction algorithms rely on thermodynamic energy models to identify low-energy structures, prediction accuracy relies in large part on the quality of free energy change parameters. In this work, we use our earlier constraint generation and Boltzmann likelihood parameter estimation methods to obtain new energy parameters for two energy models for secondary structures with pseudoknots, namely, the Dirks–Pierce (DP) and the Cao–Chen (CC) models. To train our parameters, and also to test their accuracy, we create a large data set of both pseudoknotted and pseudoknot-free secondary structures. In addition to structural data our training data set also includes thermodynamic data, for which experimentally determined free energy changes are available for sequences and their reference structures. When incorporated into the HotKnots prediction algorithm, our new parameters result in significantly improved secondary structure prediction on our test data set. Specifically, the prediction accuracy when using our new parameters improves from 68% to 79% for the DP model, and from 70% to 77% for the CC model.     

Secondary structure prediction for aligned RNA sequences

Most functional RNA molecules have characteristic secondary structures that are highly conserved in evolution. Here we present a method for computing the consensus structure of a set aligned RNA sequences taking into account both thermodynamic stability and sequence covariation. Comparison with phylogenetic structures of rRNAs shows that a reliability of prediction of more than 80% is achieved for only five related sequences. As an application we show that the Early Noduline mRNA contains significant secondary structure that is supported by sequence covariation.     

Atomic force microscope-based single-molecule force spectroscopy of RNA unfolding

Single-molecule force spectroscopy (SMFS) using the atomic force microscope (AFM) has emerged as an important tool for probing biomolecular interaction and exploring the forces, dynamics, and energy landscapes that underlie function and specificity of molecular interaction. These studies require attaching biomolecules on solid supports and AFM tips to measure unbinding forces between individual binding partners. Herein we describe efficient and robust protocols for probing RNA interaction by AFM and show their value on two well-known RNA regulators, the Rev-responsive element (RRE) from the HIV-1 genome and an adenine-sensing riboswitch. The results show the great potential of AFM–SMFS in the investigation of RNA molecular interactions, which will contribute to the development of bionanodevices sensing single RNA molecules.     

In vitro analysis of RNA interference in Drosophila melanogaster

Double-stranded RNA (dsRNA) triggers the destruction of mRNA sharing sequence with the dsRNA, a phenomenon termed RNA interference (RNAi). The dsRNA is converted by endonucleolytic cleavage into 21- to 23-nt small interfering RNAs (siRNAs), which direct a multiprotein complex, the RNA-induced silencing complex to cleave RNA complementary to the siRNA. RNAi can be recapitulated in vitro in lysates of syncytial blastoderm Drosophila embryos. These lysates reproduce all of the known steps in the RNAi pathway in flies and mammals. Here we explain how to prepare and use Drosophila embryo lysates to dissect the mechanism of RNAi.     

Synthesis of spin-labeled riboswitch RNAs using convertible nucleosides and DNA-catalyzed RNA ligation

Chemically stable nitroxide radicals that can be monitored by electron paramagnetic resonance (EPR) spectroscopy can provide information on structural and dynamic properties of functional RNA such as riboswitches. The convertible nucleoside approach is used to install 2,2,6,6-tetramethylpiperidin-1-oxyl (TEMPO) and 2,2,5,5-tetramethylpyrrolidin-1-oxyl (proxyl) labels at the exocyclic N⁴-amino group of cytidine and 2′-O-methylcytidine nucleotides in RNA. To obtain site-specifically labeled long riboswitch RNAs beyond the limit of solid-phase synthesis, we report the ligation of spin-labeled RNA using an in vitro selected deoxyribozyme as catalyst, and demonstrate the synthesis of TEMPO-labeled 53 nt SAM-III and 118 nt SAM-I riboswitch domains (SAM = S-adenosylmethionine).     

Therapeutic potential of RNA interference for neurological disorders

During the past decade, numerous molecular mediators of neurodegenerative diseases and neurological disorders have been identified and validated, yet few novel therapies have emerged and the unmet medical needs remain high. These molecular mediators belong to target classes such as ion channels, neurotransmitters and neurotransmitter receptors, cytokines, growth factors, enzymes and other proteins. In some cases, substantial pre-clinical validation exists, but the molecular target has not been readily druggable with small molecules, proteins or antibodies. RNA interference represents a therapeutic approach applicable to such non-druggable targets. Both non-viral and viral delivery strategies are being undertaken for in vivo silencing of molecular targets by RNA interference, which has resulted in robust efficacy in animal models of Alzheimer's disease, ALS, Huntington's disease, spinocerebellar ataxia, anxiety, depression, neuropathic pain, encephalitis and glioblastoma. These proof-of-concept data in animal models, together with the commencement of clinical trials using RNA interference for macular degeneration and respiratory syncytial virus infection, point to the potential of direct RNA interference for neurological disorders and neurodegenerative diseases.     

Fully differentiable coarse-grained and all-atom knowledge-based potentials for RNA structure evaluation

RNA molecules play integral roles in gene regulation, and understanding their structures gives us important insights into their biological functions. Despite recent developments in template-based and parameterized energy functions, the structure of RNA--in particular the nonhelical regions--is still difficult to predict. Knowledge-based potentials have proven efficient in protein structure prediction. In this work, we describe two differentiable knowledge-based potentials derived from a curated data set of RNA structures, with all-atom or coarse-grained representation, respectively. We focus on one aspect of the prediction problem: the identification of native-like RNA conformations from a set of near-native models. Using a variety of near-native RNA models generated from three independent methods, we show that our potential is able to distinguish the native structure and identify native-like conformations, even at the coarse-grained level. The all-atom version of our knowledge-based potential performs better and appears to be more effective at discriminating near-native RNA conformations than one of the most highly regarded parameterized potential. The fully differentiable form of our potentials will additionally likely be useful for structure refinement and/or molecular dynamics simulations.     

Parameters affecting the activity of antisense RNA sequences in tobacco protoplasts

Summary Plasmids containing various fragments of the -glucuronidase (GUS) gene were placed in antisense orientation downstream of the cauliflower mosaic virus 35S promoter and cotransfected with a 35S-gus construct into tobacco mesophyll protoplasts. None of the partial-length sequences were as effective as the full-length sequence in reducing GUS activity. The presence of a polyadenylation sequence downstream of the antisense sequence had an enhancing effect. The activity of the antisense sequence was largely affected by the incubation temperature of the transfected protoplasts. The chloramphenicol acetyltransferase (CAT) gene was fused to the gus coding sequence. When this construct was cotransfected with an antisense sequence directed against CAT, GUS activity was reduced. The implications of these results for the design and uses of antisense sequences are discussed.     

Genome-wide application of RNAi to the discovery of potential drug targets

Progress is being made in the development of RNA interference-based (RNAi-based) strategies for the control of gene expression. It has been demonstrated that small interfering RNAs (siRNAs) can silence the expression of target genes in a sequence-specific manner in mammalian cells. Various groups, including our own, have developed systems for vector-mediated specific RNAi. Vector-based siRNA- (or shRNA) expression libraries directed against the entire human genome and siRNA libraries based on chemically synthesized oligonucleotides now allow the rapid identification of functional genes and potential drug targets. Use of such libraries will enhance our understanding of numerous biological phenomena and contribute to the rational design of drugs against heritable, infectious and malignant diseases.     

Cardioviral RNA structure logo analysis: entropy, correlations, and prediction

In recent years, there has been an increased number of sequenced RNAs leading to the development of new RNA databases. Thus, predicting RNA structure from multiple alignments is an important issue to understand its function. Since RNA secondary structures are often conserved in evolution, developing methods to identify covariate sites in an alignment can be essential for discovering structural elements. Structure Logo is a technique established on the basis of entropy and mutual information measured to analyze RNA sequences from an alignment. We proposed an efficient Structure Logo approach to analyze conservations and correlations in a set of Cardioviral RNA sequences. The entropy and mutual information content were measured to examine the conservations and correlations, respectively. The conserved secondary structure motifs were predicted on the basis of the conservation and correlation analyses. Our predictive motifs were similar to the ones observed in the viral RNA structure database, and the correlations between bases also corresponded to the secondary structure in the database.     

Revolutions in RNA secondary structure prediction

RNA structure formation is hierarchical and, therefore, secondary structure, the sum of canonical base-pairs, can generally be predicted without knowledge of the three-dimensional structure. Secondary structure prediction algorithms evolved from predicting a single, lowest free energy structure to their current state where statistics can be determined from the thermodynamic ensemble. This article reviews the free energy minimization technique and the salient revolutions in the dynamic programming algorithm methods for secondary structure prediction. Emphasis is placed on highlighting the recently developed method, which statistically samples structures from the complete Boltzmann ensemble.