TM3'seq: A Tagmentation-Mediated 3' Sequencing Approach for Improving Scalability of RNAseq Experiments.

RNA-seq has become the standard tool for collecting genome-wide expression data in diverse fields, from quantitative genetics and medical genomics to ecology and developmental biology. However, RNA-seq library preparation is still prohibitive for many laboratories. Recently, the field of single-cell transcriptomics has reduced costs and increased throughput by adopting early barcoding and pooling of individual samples -producing a single final library containing all samples. In contrast, RNA-seq protocols where each sample is processed individually are significantly more expensive and lower throughput than single-cell approaches. Yet, many projects depend on individual library generation to preserve important samples or for follow-up re-sequencing experiments. Improving on currently available RNA-seq methods we have developed TM3'seq, a 3'-enriched library preparation protocol that uses Tn5 transposase and preserves sample identity at each step. TM3'seq is designed for high-throughput processing of individual samples (96 samples in 6h, with only 3h hands-on time) at a fraction of the cost of commercial kits ($1.5 per sample). The protocol was tested in a range of human and Drosophila melanogaster RNA samples, recovering transcriptomes of the same quality and reliability than the commercial NEBNext kit. We expect that the cost- and time-efficient features of TM3'seq make large-scale RNA-seq experiments more permissive for the entire scientific community.

New opportunities for designing effective small interfering RNAs.

Small interfering RNAs (siRNAs) that silence genes of infectious diseases are potentially potent drugs. A continuing obstacle for siRNA-based drugs is how to improve their efficacy for adequate dosage. To overcome this obstacle, the interactions of antiviral siRNAs, tested in vivo, were computationally examined within the RNA-induced silencing complex (RISC). Thermodynamics data show that a persistent RISC cofactor is significantly more exothermic for effective antiviral siRNAs than their ineffective counterparts. Detailed inspection of viral RNA secondary structures reveals that effective antiviral siRNAs target hairpin or pseudoknot loops. These structures are critical for initial RISC interactions since they partially lack intramolecular complementary base pairing. Importing two temporary RISC cofactors from magnesium-rich hairpins and/or pseudoknots then kickstarts full RNA hybridization and hydrolysis. Current siRNA design guidelines are based on RNA primary sequence data. Herein, the thermodynamics of RISC cofactors and targeting magnesium-rich RNA secondary structures provide additional guidelines for improving siRNA design.

Low-cost and user-friendly biosensor to test the integrity of mRNA molecules suitable for field applications.

The use of mRNA in biotechnology has expanded with novel applications such as vaccines and therapeutic mRNA delivery recently demonstrated. For mRNA to be used in patients, quality control assays will need to be routinely established. Currently, there is a gap between the highly sophisticated RNA integrity tests available and broader application of mRNA-based products by non-specialist users, e.g. in mass vaccination campaigns. Therefore, the aim of this work was to develop a low-cost biosensor able to test the integrity of a mRNA molecule with low technological requirements and easy end-user application. The biosensor is based on a bi-functional fusion protein, composed by the λN peptide that recognizes its cognate aptamer encoded on the 5' end of the RNA under study and ß-lactamase, which is able to produce a colorimetric response through a simple test. We propose two different mechanisms for signal processing adapted to two levels of technological sophistication, one based on spectrophotometric measurements and other on visual inspection. We show that the proposed λN-ßLac chimeric protein specifically targets its cognate RNA aptamer, boxB, using both gel shift and biolayer interferometry assays. More importantly, the results presented confirm the biosensor performs reliably, with a wide dynamic range and a proportional response at different percentages of full-length RNA, even when gene-sized mRNAs were used. Thus, the features of the proposed biosensor would allow to end-users of products such as mRNA vaccines to test the integrity of the product before its application in a low-cost fashion, enabling a more reliable application of these products.

Structural Bases for the Fitness Cost of the Antibiotic-Resistance and Lethal Mutations at Position 1408 of 16S rRNA.

To understand a structural basis for the fitness cost of the A1408G antibiotic-resistance mutation in the ribosomal A-site RNA, we have determined crystal structures of its A1408C and A1408U lethal mutants, and made comparison with previously solved structures of the wild type and the antibiotic-resistant mutant. The A-site RNA containing an asymmetric internal loop functions as a molecular switch to discriminate a single cognate tRNA from several near-cognate tRNAs by its conformational ON/OFF switching. Overall structures of the "off" states of the A1408C/U lethal mutants are very similar to those of the wild type and the A1408G antibiotic-resistant mutant. However, significant differences are found in local base stacking interactions including the functionally important A1492 and A1493 residues. In the wild type and the A1408G antibiotic-resistant mutant "off" states, both adenines are exposed to the solvent region. On the other hand, one of the corresponding adenines of the lethal A1408C/U mutants stay deeply inside their A-site helices by forming a purine-pyrimidine AoC or A-U base pair and is sandwiched between the upper and lower bases. Therefore, the ON/OFF switching might unfavorably occur in the lethal mutants compared to the wild type and the A1408G antibiotic-resistant mutant. It is probable that bacteria manage to acquire antibiotic resistance without losing the function of the A-site molecular switch by mutating the position 1408 only from A to G, but not to pyrimidine base C or U.

RNA Display Methods for the Discovery of Bioactive Macrocycles.

The past two decades have witnessed the emergence of macrocycles, including macrocyclic peptides, as a promising yet underexploited class of de novo drug candidates. Both rational/computational design and in vitro display systems have contributed tremendously to the development of cyclic peptide binders of either traditional targets such as cell-surface receptors and enzymes or challenging targets such as protein-protein interaction surfaces. mRNA display, a key platform technology for the discovery of cyclic peptide ligands, has become one of the leading strategies that can generate natural-product-like macrocyclic peptide binders with antibody-like affinities. On the basis of the original cell-free transcription/translation system, mRNA display is highly evolvable to realize its full potential by applying genetic reprogramming and chemical/enzymatic modifications. In addition, mRNA display also allows the follow-up hit-to-lead development using high-throughput focused affinity maturation. Finally, mRNA-displayed peptides can be readily engineered to create chemical conjugates based on known small molecules or biologics. This review covers the birth and growth of mRNA display and discusses the above features of mRNA display with success stories and future perspectives and is up to date as of August 2018.

Imbalance learning for the prediction of N6-Methylation sites in mRNAs.

BACKGROUND: N6-methyladenosine (m6A) is an important epigenetic modification which plays various roles in mRNA metabolism and embryogenesis directly related to human diseases. To identify m6A in a large scale, machine learning methods have been developed to make predictions on m6A sites. However, there are two main drawbacks of these methods. The first is the inadequate learning of the imbalanced m6A samples which are much less than the non-m6A samples, by their balanced learning approaches. Second, the features used by these methods are not outstanding to represent m6A sequence characteristics. RESULTS: We propose to use cost-sensitive learning ideas to resolve the imbalance data issues in the human mRNA m6A prediction problem. This cost-sensitive approach applies to the entire imbalanced dataset, without random equal-size selection of negative samples, for an adequate learning. Along with site location and entropy features, top-ranked positions with the highest single nucleotide polymorphism specificity in the window sequences are taken as new features in our imbalance learning. On an independent dataset, our overall prediction performance is much superior to the existing predictors. Our method shows stronger robustness against the imbalance changes in the tests on 9 datasets whose imbalance ratios range from 1:1 to 9:1. Our method also outperforms the existing predictors on 1226 individual transcripts. It is found that the new types of features are indeed of high significance in the m6A prediction. The case studies on gene c-Jun and CBFB demonstrate the detailed prediction capacity to improve the prediction performance. CONCLUSION: The proposed cost-sensitive model and the new features are useful in human mRNA m6A prediction. Our method achieves better correctness and robustness than the existing predictors in independent test and case studies. The results suggest that imbalance learning is promising to improve the performance of m6A prediction.

Allele-specific SHAPE-MaP assessment of the effects of somatic variation and protein binding on mRNA structure.

The impact of inherited and somatic mutations on messenger RNA (mRNA) structure remains poorly understood. Recent technological advances that leverage next-generation sequencing to obtain experimental structure data, such as SHAPE-MaP, can reveal structural effects of mutations, especially when these data are incorporated into structure modeling. Here, we analyze the ability of SHAPE-MaP to detect the relatively subtle structural changes caused by single-nucleotide mutations. We find that allele-specific sorting greatly improved our detection ability. Thus, we used SHAPE-MaP with a novel combination of clone-free robotic mutagenesis and allele-specific sorting to perform a rapid, comprehensive survey of noncoding somatic and inherited riboSNitches in two cancer-associated mRNAs, TPT1 and LCP1 Using rigorous thermodynamic modeling of the Boltzmann suboptimal ensemble, we identified a subset of mutations that change TPT1 and LCP1 RNA structure, with approximately 14% of all variants identified as riboSNitches. To confirm that these in vitro structures were biologically relevant, we tested how dependent TPT1 and LCP1 mRNA structures were on their environments. We performed SHAPE-MaP on TPT1 and LCP1 mRNAs in the presence or absence of cellular proteins and found that both mRNAs have similar overall folds in all conditions. RiboSNitches identified within these mRNAs in vitro likely exist under biological conditions. Overall, these data reveal a robust mRNA structural landscape where differences in environmental conditions and most sequence variants do not significantly alter RNA structural ensembles. Finally, predicting riboSNitches in mRNAs from sequence alone remains particularly challenging; these data will provide the community with benchmarks for further algorithmic development.

Polyvinylsulfonic acid: A Low-cost RNase inhibitor for enhanced RNA preservation and cell-free protein translation.

The effectiveness and economics of polyvinyl sulfonic acid (PVSA) as a ribonuclease inhibitor for in vitro systems is reported. PVSA was shown to inhibit RNA cleavage in the presence of RNase A as well as in the presence of Escherichia coli lysate, suggesting that PVSA can act as a broader ribonuclease inhibitor. In addition, PVSA was shown to improve the integrity of mRNA transcripts by up to 5-fold in vitro as measured by their translational viability. Improved preservation of mRNA transcripts in the presence of PVSA under common RNA storage conditions is also reported. A cost comparison with commercially available RNAse inhibitors indicates the economic practicality of PVSA which is approximately 1,700 times less expensive than commonly used ribonuclease inhibitors. PVSA can also be separated from RNA by alcohol precipitation for applications that may be sensitive to the presence of PVSA.

mRNA/protein sequence complementarity and its determinants: The impact of affinity scales.

It has recently been demonstrated that the nucleobase-density profiles of mRNA coding sequences are related in a complementary manner to the nucleobase-affinity profiles of their cognate protein sequences. Based on this, it has been proposed that cognate mRNA/protein pairs may bind in a co-aligned manner, especially if unstructured. Here, we study the dependence of mRNA/protein sequence complementarity on the properties of the nucleobase/amino-acid affinity scales used. Specifically, we sample the space of randomly generated scales by employing a Monte Carlo strategy with a fitness function that depends directly on the level of complementarity. For model organisms representing all three domains of life, we show that even short searches reproducibly converge upon highly optimized scales, implying that the topology of the underlying fitness landscape is decidedly funnel-like. Furthermore, the optimized scales, generated without any consideration of the physicochemical attributes of nucleobases or amino acids, resemble closely the nucleobase/amino-acid binding affinity scales obtained from experimental structures of RNA-protein complexes. This provides support for the claim that mRNA/protein sequence complementarity may indeed be related to binding between the two. Finally, we characterize suboptimal scales and show that intermediate-to-high complementarity can be reached by substantially diverse scales, but with select amino acids contributing disproportionally. Our results expose the dependence of cognate mRNA/protein sequence complementarity on the properties of the underlying nucleobase/amino-acid affinity scales and provide quantitative constraints that any physical scales need to satisfy for the complementarity to hold.

A comprehensive assessment of RNA-seq protocols for degraded and low-quantity samples.

BACKGROUND: RNA-sequencing (RNA-seq) has emerged as one of the most sensitive tool for gene expression analysis. Among the library preparation methods available, the standard poly(A) + enrichment provides a comprehensive, detailed, and accurate view of polyadenylated RNAs. However, on samples of suboptimal quality ribosomal RNA depletion and exon capture methods have recently been reported as better alternatives. METHODS: We compared for the first time three commercial Illumina library preparation kits (TruSeq Stranded mRNA, TruSeq Ribo-Zero rRNA Removal, and TruSeq RNA Access) as representatives of these three different approaches using well-established human reference RNA samples from the MAQC/SEQC consortium on a wide range of input amounts (from 100 ng down to 1 ng) and degradation levels (intact, degraded, and highly degraded). RESULTS: We assessed the accuracy of the generated expression values by comparison to gold standard TaqMan qPCR measurements and gained unprecedented insight into the limits of applicability in terms of input quantity and sample quality of each protocol. We found that each protocol generates highly reproducible results (R 2 > 0.92) on intact RNA samples down to input amounts of 10 ng. For degraded RNA samples, Ribo-Zero showed clear performance advantages over the other two protocols as it generated more accurate and better reproducible gene expression results even at very low input amounts such as 1 ng and 2 ng. For highly degraded RNA samples, RNA Access performed best generating reliable data down to 5 ng input. CONCLUSIONS: We found that the ribosomal RNA depletion protocol from Illumina works very well at amounts far below recommendation and over a good range of intact and degraded material. We also infer that the exome-capture protocol (RNA Access, Illumina) performs better than other methods on highly degraded and low amount samples.

[Web server for prediction of miRNAs and their precursors and binding sites].

A microRNA (miRNA) is a small noncoding RNA molecule about 22 nucleotides in length. The paper describes a web server for predicting miRNAs and their precursors and binding sites. The predictions are based on either sequence similarity to known miRNAs of 223 organisms or context-structural hidden Markov models. It has been shown that the proposed methods of prediction of human miRNAs and pre-miRNAs outperform the existing ones in accuracy. The average deviation of predicted 5'-ends of human miRNAs from actual positions is 3.13 nt in the case of predicting one pair of complementary miRNAs (miRNA-miRNA* duplex). A useful option for our application is the prediction of an additional miRNA pair. In this mode, the pairs closest to actual miRNA deviate by 1.61 nt on average. The proposed method also shows good performance in predicting mouse miRNAs. Binding sites for miRNAs are predicted by two known approaches based on complementarity and thermodynamic stability of the miRNA-mRNA duplex and on a new approach, which takes into account miRNAs competition for the site. The role of the secondary structure in miRNA processing is considered. The web server is available at http://wwwmgs.bionet.nsc.ru/mgs/programs/rnaanalys/.

Single Molecule Cluster Analysis dissects splicing pathway conformational dynamics.

We report Single Molecule Cluster Analysis (SiMCAn), which utilizes hierarchical clustering of hidden Markov modeling-fitted single-molecule fluorescence resonance energy transfer (smFRET) trajectories to dissect the complex conformational dynamics of biomolecular machines. We used this method to study the conformational dynamics of a precursor mRNA during the splicing cycle as carried out by the spliceosome. By clustering common dynamic behaviors derived from selectively blocked splicing reactions, SiMCAn was able to identify the signature conformations and dynamic behaviors of multiple ATP-dependent intermediates. In addition, it identified an open conformation adopted late in splicing by a 3' splice-site mutant, invoking a mechanism for substrate proofreading. SiMCAn enables rapid interpretation of complex single-molecule behaviors and should prove useful for the comprehensive analysis of a plethora of dynamic cellular machines.

HEPeak: an HMM-based exome peak-finding package for RNA epigenome sequencing data.

BACKGROUND: Methylated RNA Immunoprecipatation combined with RNA sequencing (MeRIP-seq) is revolutionizing the de novo study of RNA epigenomics at a higher resolution. However, this new technology poses unique bioinformatics problems that call for novel and sophisticated statistical computational solutions, aiming at identifying and characterizing transcriptome-wide methyltranscriptome. RESULTS: We developed HEP, a Hidden Markov Model (HMM)-based Exome Peak-finding algorithm for predicting transcriptome methylation sites using MeRIP-seq data. In contrast to exomePeak, our previously developed MeRIP-seq peak calling algorithm, HEPeak models the correlation between continuous bins in an m6A peak region and it is a model-based approach, which admits rigorous statistical inference. HEPeak was evaluated on a simulated MeRIP-seq dataset and achieved higher sensitivity and specificity than exomePeak. HEPeak was also applied to real MeRIP-seq datasets from human HEK293T cell line and mouse midbrain cells and was shown to be able to recapitulate known m6A distribution in transcripts and identify novel m6A sites in long non-coding RNAs. CONCLUSIONS: In this paper, a novel HMM-based peak calling algorithm, HEPeak, was developed for peak calling for MeRIP-seq data. HEPeak is written in R and is publicly available.

RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures.

This paper is a report of a second round of RNA-Puzzles, a collective and blind experiment in three-dimensional (3D) RNA structure prediction. Three puzzles, Puzzles 5, 6, and 10, represented sequences of three large RNA structures with limited or no homology with previously solved RNA molecules. A lariat-capping ribozyme, as well as riboswitches complexed to adenosylcobalamin and tRNA, were predicted by seven groups using RNAComposer, ModeRNA/SimRNA, Vfold, Rosetta, DMD, MC-Fold, 3dRNA, and AMBER refinement. Some groups derived models using data from state-of-the-art chemical-mapping methods (SHAPE, DMS, CMCT, and mutate-and-map). The comparisons between the predictions and the three subsequently released crystallographic structures, solved at diffraction resolutions of 2.5-3.2 Å, were carried out automatically using various sets of quality indicators. The comparisons clearly demonstrate the state of present-day de novo prediction abilities as well as the limitations of these state-of-the-art methods. All of the best prediction models have similar topologies to the native structures, which suggests that computational methods for RNA structure prediction can already provide useful structural information for biological problems. However, the prediction accuracy for non-Watson-Crick interactions, key to proper folding of RNAs, is low and some predicted models had high Clash Scores. These two difficulties point to some of the continuing bottlenecks in RNA structure prediction. All submitted models are available for download at http://ahsoka.u-strasbg.fr/rnapuzzles/.

Hybrid pathwise sensitivity methods for discrete stochastic models of chemical reaction systems.

Stochastic models are often used to help understand the behavior of intracellular biochemical processes. The most common such models are continuous time Markov chains (CTMCs). Parametric sensitivities, which are derivatives of expectations of model output quantities with respect to model parameters, are useful in this setting for a variety of applications. In this paper, we introduce a class of hybrid pathwise differentiation methods for the numerical estimation of parametric sensitivities. The new hybrid methods combine elements from the three main classes of procedures for sensitivity estimation and have a number of desirable qualities. First, the new methods are unbiased for a broad class of problems. Second, the methods are applicable to nearly any physically relevant biochemical CTMC model. Third, and as we demonstrate on several numerical examples, the new methods are quite efficient, particularly if one wishes to estimate the full gradient of parametric sensitivities. The methods are rather intuitive and utilize the multilevel Monte Carlo philosophy of splitting an expectation into separate parts and handling each in an efficient manner.

Structural model of an mRNA in complex with the bacterial chaperone Hfq.

The Sm-like protein Hfq (host factor Q-beta phage) facilitates regulation by bacterial small noncoding RNAs (sRNAs) in response to stress and other environmental signals. Here, we present a low-resolution model of Escherichia coli Hfq bound to the rpoS mRNA, a bacterial stress response gene that is targeted by three different sRNAs. Selective 2'-hydroxyl acylation and primer extension, small-angle X-ray scattering, and Monte Carlo molecular dynamics simulations show that the distal face and lateral rim of Hfq interact with three sites in the rpoS leader, folding the RNA into a compact tertiary structure. These interactions are needed for sRNA regulation of rpoS translation and position the sRNA target adjacent to an sRNA binding region on the proximal face of Hfq. Our results show how Hfq specifically distorts the structure of the rpoS mRNA to enable sRNA base pairing and translational control.

Next-generation in situ hybridization chain reaction: higher gain, lower cost, greater durability.

Hybridization chain reaction (HCR) provides multiplexed, isothermal, enzyme-free, molecular signal amplification in diverse settings. Within intact vertebrate embryos, where signal-to-background is at a premium, HCR in situ amplification enables simultaneous mapping of multiple target mRNAs, addressing a longstanding challenge in the biological sciences. With this approach, RNA probes complementary to mRNA targets trigger chain reactions in which metastable fluorophore-labeled RNA hairpins self-assemble into tethered fluorescent amplification polymers. The properties of HCR lead to straightforward multiplexing, deep sample penetration, high signal-to-background, and sharp subcellular signal localization within fixed whole-mount zebrafish embryos, a standard model system for the study of vertebrate development. However, RNA reagents are expensive and vulnerable to enzymatic degradation. Moreover, the stringent hybridization conditions used to destabilize nonspecific hairpin binding also reduce the energetic driving force for HCR polymerization, creating a trade-off between minimization of background and maximization of signal. Here, we eliminate this trade-off by demonstrating that low background levels can be achieved using permissive in situ amplification conditions (0% formamide, room temperature) and engineer next-generation DNA HCR amplifiers that maximize the free energy benefit per polymerization step while preserving the kinetic trapping property that underlies conditional polymerization, dramatically increasing signal gain, reducing reagent cost, and improving reagent durability.

Assessment of myometrial transcriptome changes associated with spontaneous human labour by high-throughput RNA-seq.

The transition of the human uterus from a quiescent to a contractile state takes place over a number of weeks. On such biological time scales, cellular phenotype is modified by changes in the transcriptome, which in turn is under the control of the underlying endocrine, paracrine, and biophysical processes resulting from the ongoing pregnancy. In this study, we characterize the transition of the human myometrial transcriptome at term from not in labour (NIL) to in labour (LAB) using high throughput RNA sequencing (RNA-seq). RNA was isolated from the myometrium of uterine biopsies from patients at term who were not in labour (n = 5) and at term in spontaneous labour (n = 5) without augmentation. A total of 143.6 million separate reads were sequenced, achieving, on average, ∼13 times coverage of the expressed human transcriptome per sample. Principal component analysis indicated that the NIL and LAB transcriptomes could be distinguished as two distinct clusters. A comparison of the NIL and LAB groups, using three different statistical approaches (baySeq, edgeR, and DESeq), demonstrated an overlap of 764 differentially expressed genes. A comparison with currently available microarray data revealed only a partial overlap in differentially expressed genes. We conclude that the described RNA-seq data sets represent the first fully annotated catalogue of expressed mRNAs in human myometrium. When considered together, the full expression repertoire and the differentially expressed gene sets should provide an excellent resource for formulating new hypotheses of physiological function, as well as the discovery of novel therapeutic targets.

Use of Bru-Seq and BruChase-Seq for genome-wide assessment of the synthesis and stability of RNA.

Gene expression studies commonly examine total cellular RNA, which only provides information about its steady-state pool of RNA. It remains unclear whether differences in the steady-state reflects variable rates of transcription or RNA degradation. To specifically monitor RNA synthesis and degradation genome-wide, we developed Bru-Seq and BruChase-Seq. These assays are based on metabolic pulse-chase labeling of RNA using bromouridine (Bru). In Bru-Seq, recently labeled RNAs are sequenced to reveal spans of nascent transcription in the genome. In BruChase-Seq, cells are chased in uridine for different periods of time following Bru-labeling, allowing for the isolation of RNA populations of specific ages. Here we describe these methodologies in detail and highlight their usefulness in assessing RNA synthesis and stability as well as splicing kinetics with examples of specific genes from different human cell lines.