Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure

Thermodynamics of unfolding mechanisms of mouse mammary tumor virus pseudoknot from a coarse-grained loop-entropy model

Pseudoknotted RNA molecules play important biological roles that depend on their folded structure. To understand the underlying principles that determine their thermodynamics and folding/unfolding mechanisms, we carried out a study on a variant of the mouse mammary tumor virus pseudoknotted RNA (VPK), a widely studied model system for RNA pseudoknots. Our method is based on a coarse-grained discrete-state model and the algorithm of PK3D (pseudoknot structure predictor in three-dimensional space), with RNA loops explicitly constructed and their conformational entropic effects incorporated. Our loop entropy calculations are validated by accurately capturing previously measured melting temperatures of RNA hairpins with varying loop lengths. For each of the hairpins that constitutes the VPK, we identified alternative conformations that are more stable than the hairpin structures at low temperatures and predicted their populations at different temperatures. Our predictions were validated by thermodynamic experiments on these hairpins. We further computed the heat capacity profiles of VPK, which are in excellent agreement with available experimental data. Notably, our model provides detailed information on the unfolding mechanisms of pseudoknotted RNA. Analysis of the distribution of base-pairing probability of VPK reveals a cooperative unfolding mechanism instead of a simple sequential unfolding of first one stem and then the other. Specifically, we find a simultaneous "loosening" of both stems as the temperature is raised, whereby both stems become partially melted and co-exist during the unfolding process.

QRNAstruct: a method for extracting secondary structural features of RNA via regression with biological activity

Recent technological advances have enabled the generation of large amounts of data consisting of RNA sequences and their functional activity. Here, we propose a method for extracting secondary structure features that affect the functional activity of RNA from sequence–activity data. Given pairs of RNA sequences and their corresponding bioactivity values, our method calculates position-specific structural features of the input RNA sequences, considering every possible secondary structure of each RNA. A Ridge regression model is trained using the structural features as feature vectors and the bioactivity values as response variables. Optimized model parameters indicate how secondary structure features affect bioactivity. We used our method to extract intramolecular structural features of bacterial translation initiation sites and self-cleaving ribozymes, and the intermolecular features between rRNAs and Shine–Dalgarno sequences and between U1 RNAs and splicing sites. We not only identified known structural features but also revealed more detailed insights into structure–activity relationships than previously reported. Importantly, the datasets we analyzed here were obtained from different experimental systems and differed in size, sequence length and similarity, and number of RNA molecules involved, demonstrating that our method is applicable to various types of data consisting of RNA sequences and bioactivity values.

Programmable siRNA pro-drugs that activate RNAi activity in response to specific cellular RNA biomarkers

Since Paul Ehrlich's introduction of the "magic bullet" concept in 1908, drug developers have been seeking new ways to target drug activity to diseased cells while limiting effects on normal tissues. In recent years, it has been proposed that coupling riboswitches capable of detecting RNA biomarkers to small interfering RNAs (siRNAs) to create siRNA pro-drugs could selectively activate RNA interference (RNAi) activity in specific cells. However, this concept has not been achieved previously. We report here that we have accomplished this goal, validating a simple and programmable new design that functions reliably in mammalian cells. We show that these conditionally activated siRNAs (Cond-siRNAs) can switch RNAi activity against different targets between clearly distinguished OFF and ON states in response to different cellular RNA biomarkers. Notably, in a rat cardiomyocyte cell line (H9C2), one version of our construct demonstrated biologically meaningful inhibition of a heart-disease-related target gene protein phosphatase 3 catalytic subunit alpha (PPP3CA) in response to increased expression of the pathological marker atrial natriuretic peptide (NPPA) messenger RNA (mRNA). Our results demonstrate the ability of synthetic riboswitches to regulate gene expression in mammalian cells, opening a new path for development of programmable siRNA pro-drugs.

Integrative Assessments on Molecular Taxonomy of Acidiferrobacter thiooxydans ZJ and Its Environmental Adaptation Based on Mobile Genetic Elements

Acidiferrobacter spp. are facultatively anaerobic acidophiles that belong to a distinctive Acidiferrobacteraceae family, which are similar to Ectothiorhodospiraceae phylogenetically, and are closely related to Acidithiobacillia class/subdivision physiologically. The limited genome information has kept them from being studied on molecular taxonomy and environmental adaptation in depth. Herein, Af. thiooxydans ZJ was isolated from acid mine drainage (AMD), and the complete genome sequence was reported to scan its genetic constitution for taxonomic and adaptative feature exploration. The genome has a single chromosome of 3,302,271 base pairs (bp), with a GC content of 63.61%. The phylogenetic tree based on OrthoANI highlighted the unique position of Af. thiooxydans ZJ, which harbored more unique genes among the strains from Ectothiorhodospiraceae and Acidithiobacillaceae by pan-genome analysis. The diverse mobile genetic elements (MGEs), such as insertion sequence (IS), clustered regularly interspaced short palindromic repeat (CRISPR), prophage, and genomic island (GI), have been identified and characterized in Af. thiooxydans ZJ. The results showed that Af. thiooxydans ZJ may effectively resist the infection of foreign viruses and gain functional gene fragments or clusters to shape its own genome advantageously. This study will offer more evidence of the genomic plasticity and improve our understanding of evolutionary adaptation mechanisms to extreme AMD environment, which could expand the potential utilization of Af. thiooxydans ZJ as an iron and sulfur oxidizer in industrial bioleaching.

The RNA Secondary Structure Analysis Reveals Potential for Emergence of Pathogenic Flaviviruses

The Flavivirus genus is divided into four groups: Mosquito-borne flaviviruses, Tick-borne flaviviruses, no-known vector flaviviruses, and Insect specific flaviviruses. Millions of people are affected worldwide every year due to the flaviviral infections. The 5' UTR of the RNA genome plays a critical role in the biology of flaviviruses. To explore any correlation between the topology of the 5' UTR and pathogenesis, a global scale study of the RNA secondary structure of different groups of flaviviruses has been conducted. We found that most of the pathogenic flaviviruses, irrespective of their mode of transmission, tend to form a Y shaped topology in the Stem loop A of the 5' UTR. Some of the current non-pathogenic flaviviruses were also observed to form Y shaped structure. Based on this study, it has been proposed that the flaviviruses having the Y shaped topology in their 5' UTR regions may have the potential to become pathogenic.

Retroviral infection of human neurospheres and use of stem Cell EVs to repair cellular damage

HIV-1 remains an incurable infection that is associated with substantial economic and epidemiologic impacts. HIV-associated neurocognitive disorders (HAND) are commonly linked with HIV-1 infection; despite the development of combination antiretroviral therapy (cART), HAND is still reported to affect at least 50% of HIV-1 infected individuals. It is believed that the over-amplification of inflammatory pathways, along with release of toxic viral proteins from infected cells, are primarily responsible for the neurological damage that is observed in HAND; however, the underlying mechanisms are not well-defined. Therefore, there is an unmet need to develop more physiologically relevant and reliable platforms for studying these pathologies. In recent years, neurospheres derived from induced pluripotent stem cells (iPSCs) have been utilized to model the effects of different neurotropic viruses. Here, we report the generation of neurospheres from iPSC-derived neural progenitor cells (NPCs) and we show that these cultures are permissive to retroviral (e.g. HIV-1, HTLV-1) replication. In addition, we also examine the potential effects of stem cell derived extracellular vesicles (EVs) on HIV-1 damaged cells as there is abundant literature supporting the reparative and regenerative properties of stem cell EVs in the context of various CNS pathologies. Consistent with the literature, our data suggests that stem cell EVs may modulate neuroprotective and anti-inflammatory properties in damaged cells. Collectively, this study demonstrates the feasibility of NPC-derived neurospheres for modeling HIV-1 infection and, subsequently, highlights the potential of stem cell EVs for rescuing cellular damage induced by HIV-1 infection.

Knotify: An Efficient Parallel Platform for RNA Pseudoknot Prediction Using Syntactic Pattern Recognition

Obtaining valuable clues for noncoding RNA (ribonucleic acid) subsequences remains a significant challenge, acknowledging that most of the human genome transcribes into noncoding RNA parts related to unknown biological operations. Capturing these clues relies on accurate “base pairing” prediction, also known as “RNA secondary structure prediction”. As COVID-19 is considered a severe global threat, the single-stranded SARS-CoV-2 virus reveals the importance of establishing an efficient RNA analysis toolkit. This work aimed to contribute to that by introducing a novel system committed to predicting RNA secondary structure patterns (i.e., RNA’s pseudoknots) that leverage syntactic pattern-recognition strategies. Having focused on the pseudoknot predictions, we formalized the secondary structure prediction of the RNA to be primarily a parsing and, secondly, an optimization problem. The proposed methodology addresses the problem of predicting pseudoknots of the first order (H-type). We introduce a context-free grammar (CFG) that affords enough expression power to recognize potential pseudoknot pattern. In addition, an alternative methodology of detecting possible pseudoknots is also implemented as well, using a brute-force algorithm. Any input sequence may highlight multiple potential folding patterns requiring a strict methodology to determine the single biologically realistic one. We conscripted a novel heuristic over the widely accepted notion of free-energy minimization to tackle such ambiguity in a performant way by utilizing each pattern’s context to unveil the most prominent pseudoknot pattern. The overall process features polynomial-time complexity, while its parallel implementation enhances the end performance, as proportional to the deployed hardware. The proposed methodology does succeed in predicting the core stems of any RNA pseudoknot of the test dataset by performing a 76.4% recall ratio. The methodology achieved a F1-score equal to 0.774 and MCC equal 0.543 in discovering all the stems of an RNA sequence, outperforming the particular task. Measurements were taken using a dataset of 262 RNA sequences establishing a performance speed of 1.31, 3.45, and 7.76 compared to three well-known platforms. The implementation source code is publicly available under knotify github repo.

Conservation of Differential Animal MicroRNA Processing by Drosha and Dicer

In complex biochemical systems, an enzyme, protein, or RNA, symbolized as E, has hundreds or thousands of substrates or interacting partners. The relative specificity hypothesis proposes that such an E would differentially interact with and influence its many distinct, downstream substrates, thereby regulating the underlying biological process (es). The importance of relative specificity has been underappreciated, and evidence of its physiological consequences particularly lacking. Previously we showed that human Drosha and Dicer ribonucleases (RNases) both discriminate their respective microRNA (miRNA) substrates, and that differential cleavage by Drosha contributes to global differential miRNA expression. If relative specificity is an important biological mechanism, it should be evolutionarily conserved. To test this hypothesis, we hereby examined the cleavage of hundreds of zebrafish and fruitfly miRNA intermediates by Drosha and Dicer and the impact on miRNA biogenesis in these organisms. We showed that Drosha action regulates differential miRNA expression in zebrafish and fruitflies and identified the conserved secondary structure features and sequences in miRNA transcripts that control Drosha activity and miRNA expression. Our results established the conservation of miRNA processing mechanisms and regulatory functions by Drosha and Dicer, greatly strengthened the evidence for the physiological consequences of relative specificity as well as demonstrated its evolutionary significance.

Stem-loop formation drives RNA folding in mechanical unzipping experiments

Accurate knowledge of RNA hybridization is essential for understanding RNA structure and function. Here we mechanically unzip and rezip a 2-kbp RNA hairpin and derive the 10 nearest-neighbor base pair (NNBP) RNA free energies in sodium and magnesium with 0.1 kcal/mol precision using optical tweezers. Notably, force-distance curves (FDCs) exhibit strong irreversible effects with hysteresis and several intermediates, precluding the extraction of the NNBP energies with currently available methods. The combination of a suitable RNA synthesis with a tailored pulling protocol allowed us to obtain the fully reversible FDCs necessary to derive the NNBP energies. We demonstrate the equivalence of sodium and magnesium free-energy salt corrections at the level of individual NNBP. To characterize the irreversibility of the unzipping-rezipping process, we introduce a barrier energy landscape of the stem-loop structures forming along the complementary strands, which compete against the formation of the native hairpin. This landscape correlates with the hysteresis observed along the FDCs. RNA sequence analysis shows that base stacking and base pairing stabilize the stem-loops that kinetically trap the long-lived intermediates observed in the FDC. Stem-loops formation appears as a general mechanism to explain a wide range of behaviors observed in RNA folding.

N 6-methyladenosine enhances post-transcriptional gene regulation by microRNAs

MOTIVATION

N ⁶-methyladenosine (m⁶A) is the most prevalent modification in eukaryotic messenger RNAs. MicroRNAs (miRNAs) are abundant post-transcriptional regulators of gene expression. Correlation between m⁶A and miRNA-targeting sites has been reported to suggest possible involvement of m⁶A in miRNA-mediated gene regulation. However, it is unknown what the regulatory effects might be. In this study, we performed comprehensive analyses of high-throughput data on m⁶A and miRNA target binding and regulation.

RESULTS

We found that the level of miRNA-mediated target suppression is significantly enhanced when m⁶A is present on target mRNAs. The evolutionary conservation for miRNA-binding sites with m⁶A modification is significantly higher than that for miRNA-binding sites without modification. These findings suggest functional significance of m⁶A modification in post-transcriptional gene regulation by miRNAs. We also found that methylated targets have more stable structure than non-methylated targets, as indicated by significantly higher GC content. Furthermore, miRNA-binding sites that can be potentially methylated are significantly less accessible without methylation than those that do not possess potential methylation sites. Since either RNA-binding proteins or m⁶A modification by itself can destabilize RNA structure, we propose a model in which m⁶A alters local target secondary structure to increase accessibility for efficient binding by Argonaute proteins, leading to enhanced miRNA-mediated regulation.

AVAILABILITY AND IMPLEMENTATION

N/A.

Quorum sensing and iron-dependent coordinated control of autoinducer-2 production via small RNA RyhB in Vibrio vulnificus

Roles for the non-coding small RNA RyhB in quorum-sensing and iron-dependent gene modulation in the human pathogen V. vulnificus were assessed in this study. Both the quorum sensing master regulator SmcR and the Fur-iron complex were observed to bind to the region upstream of the non-coding small RNA RyhB gene to repress expression, which suggests that RyhB is associated with both quorum-sensing and iron-dependent signaling in this pathogen. We found that expression of LuxS, which is responsible for the biosynthesis of autoinducer-2 (AI-2), was higher in wild type than in a ryhB-deletion isotype. RyhB binds directly to the 5′-UTR (untranslated region) of the luxS transcript to form a heteroduplex, which not only stabilizes luxS mRNA but also disrupts the secondary structure that normally obscures the translational start codon and thereby allows translation of LuxS to begin. The binding of RyhB to luxS mRNA requires the chaperone protein Hfq, which stabilizes RyhB. These results demonstrate that the small RNA RyhB is a key element associated with feedback control of AI-2 production, and that it inhibits quorum-sensing signaling in an iron-dependent manner. This study, taken together with previous studies, shows that iron availability and cell density signals are funneled to SmcR and RyhB, and that these regulators coordinate cognate signal pathways that result in the proper balance of protein expression in response to environmental conditions.

Screening of Minimalist Noncanonical Sites in Duplex DNA and RNA Reveals Context and Motif-Selective Binding by Fluorogenic Base Probes

We hypothesize that programmable hybridization to noncanonical nucleic acid motifs may be achieved by macromolecular display of binders to individual noncanonical pairs (NCPs). As each recognition element may individually have weak binding to an NCP, we developed a semi-rational approach to detect low affinity interactions between selected nitrogenous bases and noncanonical sites in duplex DNA and RNA. A set of fluorogenic probes was synthesized by coupling abiotic (triazines, pyrimidines) and native RNA bases to thiazole orange (TO) dye. This probe library was screened against duplex nucleic acid substrates bearing single abasic, single NCP, and tandem NCP sites. Probe engagement with NCP sites was reported by 100-1000× fluorescence enhancement over background. Binding is strongly context-dependent, reflective of both molecular recognition and stability: less stable motifs are more likely to bind a synthetic probe. Further, DNA and RNA substrates exhibit entirely different abasic and single NCP binding profiles. While probe binding in the abasic and single NCP screens was monotonous, much richer binding profiles were observed with the screen of tandem NCP sites in RNA, in part due to increased steric accessibility. In addition to known binding interactions between the triazine melamine (M) and T/U sites, the NCP screens identified new targeting elements for pyrimidine-rich motifs in single NCPs and 2×2 internal bulges. We anticipate that semi-rational approaches of this type will lead to programmable noncanonical hybridization strategies at the macromolecular level.

RNA Structure Prediction, Analysis, and Design: An Introduction to Web-Based Tools

Understanding RNA structure has become critical in the study of RNA in their roles as mediators of biological processes. To aid in these studies, computational algorithms that utilize thermodynamics have been developed to predict RNA secondary structure. Due to the importance of intermolecular interactions, the algorithms have been expanded to determine and predict RNA-RNA hybridization. This chapter discusses popular webservers with the tools for RNA secondary structure prediction, RNA-RNA hybridization, and design. We address key features that distinguish common-functioning programs and their purposes for the interests of the user. Ultimately, we hope this review elucidates web-based tools researchers may take advantage of in their investigations of RNA structure and function.

Design of Ribocomputing Devices for Complex Cellular Logic

The ability to control cell function is a critical goal for synthetic biology and motivates the development of ever-improving methods for precise regulation of gene expression. RNA-based systems represent powerful tools for this purpose since they can take full advantage of the predictable and programmable base pairing properties of RNA to control gene expression. This chapter is focused on the computational design of RNA-only biological circuits that can execute complex Boolean logic expressions in living cells. These ribocomputing devices use toehold switches as building blocks for circuit construction, integrating sensing, computation, and signal generation functions within a gate RNA transcript that regulates expression of a gene of interest. The gate RNA in turn assesses the assembly state of networks of interacting input RNAs to execute AND, OR, and NOT operations with high dynamic range in E. coli. Harnessing in silico tools for device design facilitates scaling of the circuits to complex logic expressions, including four-input AND, six-input OR, and disjunctive normal form expressions with up to 12 inputs. This molecular architecture provides an intuitive and modular strategy for devising logic systems that can be readily engineered using RNA sequence design software and applied in vivo and in vitro. In this chapter, we describe the process for designing ribocomputing devices from the generation of orthogonal toehold switch libraries through to their use as building blocks for AND, OR, and NOT circuitry.

Predicting the Structure of a Viroid : Structure, Structure Distribution, Consensus Structure, and Structure Drawing

Viroids are small non-coding RNAs that require a special sequence and structure to be replicated and transported by the host machinery. Many of these features can be predicted and later experimentally verified. Here, we will present workflows to predict viroid structures and draw the predicted structures in a pleasing and descriptive way using recently developed software.

RNA Secondary Structures Regulate Adsorption of Fragments onto Flat Substrates

RNA is a functionally rich molecule with multilevel, hierarchical structures whose role in the adsorption to molecular substrates is only beginning to be elucidated. Here, we introduce a multiscale simulation approach that combines a tractable coarse-grained RNA structural model with an interaction potential of a structureless flat adsorbing substrate. Within this approach, we study the specific role of stem-hairpin and multibranch RNA secondary structure motifs on its adsorption phenomenology. Our findings identify a dual regime of adsorption for short RNA fragments with and without the secondary structure and underline the adsorption efficiency in both cases as a function of the surface interaction strength. The observed behavior results from an interplay between the number of contacts formed at the surface and the conformational entropy of the RNA molecule. The adsorption phenomenology of RNA seems to persist also for much longer RNAs as qualitatively observed by comparing the trends of our simulations with a theoretical approach based on an ideal semiflexible polymer chain.

RNA sequence and structure control assembly and function of RNA condensates

Intracellular condensates formed through liquid-liquid phase separation (LLPS) primarily contain proteins and RNA. Recent evidence points to major contributions of RNA self-assembly in the formation of intracellular condensates. As the majority of previous studies on LLPS have focused on protein biochemistry, effects of biological RNAs on LLPS remain largely unexplored. In this study, we investigate the effects of crowding, metal ions, and RNA structure on formation of RNA condensates lacking proteins. Using bacterial riboswitches as a model system, we first demonstrate that LLPS of RNA is promoted by molecular crowding, as evidenced by formation of RNA droplets in the presence of polyethylene glycol (PEG 8K). Crowders are not essential for LLPS, however. Elevated Mg²⁺ concentrations promote LLPS of specific riboswitches without PEG. Calculations identify key RNA structural and sequence elements that potentiate the formation of PEG-free condensates; these calculations are corroborated by key wet-bench experiments. Based on this, we implement structure-guided design to generate condensates with novel functions including ligand binding. Finally, we show that RNA condensates help protect their RNA components from degradation by nucleases, suggesting potential biological roles for such higher-order RNA assemblies in controlling gene expression through RNA stability. By utilizing both natural and artificial RNAs, our study provides mechanistic insight into the contributions of intrinsic RNA properties and extrinsic environmental conditions to the formation and regulation of condensates comprised of RNAs.

LazySampling and LinearSampling: Fast Stochastic Sampling of RNA Secondary Structure with Applications to SARS-CoV-2

Many RNAs fold into multiple structures at equilibrium. The classical stochastic sampling algorithm can sample secondary structures according to their probabilities in the Boltzmann ensemble, and is widely used. However, this algorithm, consisting of a bottom-up partition function phase followed by a top-down sampling phase, suffers from three limitations: (a) the formulation and implementation of the sampling phase are unnecessarily complicated; (b) the sampling phase repeatedly recalculates many redundant recursions already done during the partition function phase; (c) the partition function runtime scales cubically with the sequence length. These issues prevent stochastic sampling from being used for very long RNAs such as the full genomes of SARS-CoV-2. To address these problems, we first adopt a hypergraph framework under which the sampling algorithm can be greatly simplified. We then present three sampling algorithms under this framework, among which the LazySampling algorithm is the fastest by eliminating redundant work in the sampling phase via on-demand caching. Based on LazySampling, we further replace the cubic-time partition function by a linear-time approximate one, and derive LinearSampling, an end-to-end linear-time sampling algorithm that is orders of magnitude faster than the standard one. For instance, LinearSampling is 176Ã- faster (38.9s vs. 1.9h) than Vienna RNAsubopt on the full genome of Ebola virus (18,959 nt ). More importantly, LinearSampling is the first RNA structure sampling algorithm to scale up to the full-genome of SARS-CoV-2 without local window constraints, taking only 69.2 seconds on its reference sequence (29,903 nt ). The resulting sample correlates well with the experimentally-guided structures. On the SARS-CoV-2 genome, LinearSampling finds 23 regions of 15 nt with high accessibilities, which are potential targets for COVID-19 diagnostics and drug design. See code: https://github.com/LinearFold/LinearSampling.

Thermal adaptation of mRNA secondary structure: stability versus lability

Macromolecular function commonly involves rapidly reversible alterations in three-dimensional structure (conformation). To allow these essential conformational changes, macromolecules must possess higher order structures that are appropriately balanced between rigidity and flexibility. Because of the low stabilization free energies (marginal stabilities) of macromolecule conformations, temperature changes have strong effects on conformation and, thereby, on function. As is well known for proteins, during evolution, temperature-adaptive changes in sequence foster retention of optimal marginal stability at a species' normal physiological temperatures. Here, we extend this type of analysis to messenger RNAs (mRNAs), a class of macromolecules for which the stability-lability balance has not been elucidated. We employ in silico methods to determine secondary structures and estimate changes in free energy of folding (ΔG_fold) for 25 orthologous mRNAs that encode the enzyme cytosolic malate dehydrogenase in marine mollusks with adaptation temperatures spanning an almost 60 °C range. The change in free energy that occurs during formation of the ensemble of mRNA secondary structures is significantly correlated with adaptation temperature: ΔG_fold values are all negative and their absolute values increase with adaptation temperature. A principal mechanism underlying these adaptations is a significant increase in synonymous guanine + cytosine substitutions with increasing temperature. These findings open up an avenue of exploration in molecular evolution and raise interesting questions about the interaction between temperature-adaptive changes in mRNA sequence and in the proteins they encode.

Ribonuclease recruitment using a small molecule reduced c9ALS/FTD r(G4C2) repeat expansion in vitro and in vivo ALS models

[Figure: see text].

Regulation of the ThiM riboswitch is facilitated by the trapped structure formed during transcription of the wild-type sequence

The ThiM riboswitch from Escherichia coli is a typical mRNA device that modulates downstream gene expression by sensing TPP. The helix-based RNA folding theory is used to investigate its detailed regulatory behaviors in cells. This RNA molecule is transcriptionally trapped in a state with the unstructured SD sequence in the absence of TPP, which induces downstream gene expression. As a key step to turn on gene expression, formation of this trapped state (the genetic ON state) highly depends on the co-transcriptional folding of its wild-type sequence. Instead of stabilities of the genetic ON and OFF states, the transcription rate, pause, and ligand levels are combined to affect the ThiM riboswitch-mediated gene regulation, which is consistent with a kinetic control model.

Selectivity of Exportin 5 binding to human precursor microRNAs

Exportin 5 (Exp5, XPO5) is a nuclear export factor that functions in the microRNA (miRNA) biogenesis pathway to transport precursor miRNAs (pre-miRNAs) from the nucleus to the cytoplasm. Most of our current understanding of the Exp5 and pre-miRNA interaction is based on the investigation of how Exp5 binds to human pre-miR-30a (pre-miR-30 for short). As there are hundreds of human miRNA genes, how representative pre-miR-30 is, whether or how Exp5 interacts with distinct cargoes differentially, or whether Exp5 regulates miRNA expression, is unknown. Here we examined and compared the interactions between Exp5 and 157 human pre-miRNAs. We found that Exp5 binds distinct pre-miRNAs with modest variations in efficiencies, with the 3' overhang and the apical loop in pre-miRNAs being the two major discriminating factors. Exp5 binding efficiencies do not significantly correlate with endogenous miRNA expression, suggesting that Exp5 activity does not contribute to differential miRNA expression in vivo. Nonetheless, in human cells with reduced Exp5 levels, preferential Exp5 binding correlated with miRNA expression changes. Thus, our study provides a global picture of how Exp5 interacts with human pre-miRNAs and its role in regulating miRNA expression.

The major evolutionary transitions and codes of life

Major evolutionary transitions as well as the evolution of codes of life are key elements in macroevolution which are characterized by increase in complexity Major evolutionary transitions ensues by a transition in individuality and by the evolution of a novel mode of using, transmitting or storing information. Here is where codes of life enter the picture: they are arbitrary mappings between different (mostly) molecular species. This flexibility allows information to be employed in a variety of ways, which can fuel evolutionary innovation. The collation of the list of major evolutionary transitions and the list of codes of life show a clear pattern: codes evolved prior to a major evolutionary transition and then played roles in the transition and/or in the transformation of the new individual. The evolution of a new code of life is in itself not a major evolutionary transition but allow major evolutionary transitions to happen. This could help us to identify new organic codes.

Context-sensitivity of isosteric substitutions of non-Watson-Crick basepairs in recurrent RNA 3D motifs

Sequence variation in a widespread, recurrent, structured RNA 3D motif, the Sarcin/Ricin (S/R), was studied to address three related questions: First, how do the stabilities of structured RNA 3D motifs, composed of non-Watson–Crick (non-WC) basepairs, compare to WC-paired helices of similar length and sequence? Second, what are the effects on the stabilities of such motifs of isosteric and non-isosteric base substitutions in the non-WC pairs? And third, is there selection for particular base combinations in non-WC basepairs, depending on the temperature regime to which an organism adapts? A survey of large and small subunit rRNAs from organisms adapted to different temperatures revealed the presence of systematic sequence variations at many non-WC paired sites of S/R motifs. UV melting analysis and enzymatic digestion assays of oligonucleotides containing the motif suggest that more stable motifs tend to be more rigid. We further found that the base substitutions at non-Watson–Crick pairing sites can significantly affect the thermodynamic stabilities of S/R motifs and these effects are highly context specific indicating the importance of base-stacking and base-phosphate interactions on motif stability. This study highlights the significance of non-canonical base pairs and their contributions to modulating the stability and flexibility of RNA molecules.

Developing an Updated Strategy for Estimating the Free-Energy Parameters in RNA Duplexes

For the last 20 years, it has been common lore that the free energy of RNA duplexes formed from canonical Watson-Crick base pairs (bps) can be largely approximated with dinucleotide bp parameters and a few simple corrective constants that are duplex independent. Additionally, the standard benchmark set of duplexes used to generate the parameters were GC-rich in the shorter duplexes and AU-rich in the longer duplexes, and the length of the majority of the duplexes ranged between 6 and 8 bps. We were curious if other models would generate similar results and whether adding longer duplexes of 17 bps would affect the conclusions. We developed a gradient-descent fitting program for obtaining free-energy parameters-the changes in Gibbs free energy (ΔG), enthalpy (ΔH), and entropy (ΔS), and the melting temperature (Tm)-directly from the experimental melting curves. Using gradient descent and a genetic algorithm, the duplex melting results were combined with the standard benchmark data to obtain bp parameters. Both the standard (Turner) model and a new model that includes length-dependent terms were tested. Both models could fit the standard benchmark data; however, the new model could handle longer sequences better. We developed an updated strategy for fitting the duplex melting data.

Rapid structure-function insights via hairpin-centric analysis of big RNA structure probing datasets

The functions of RNA are often tied to its structure, hence analyzing structure is of significant interest when studying cellular processes. Recently, large-scale structure probing (SP) studies have enabled assessment of global structure-function relationships via standard data summarizations or local folding. Here, we approach structure quantification from a hairpin-centric perspective where putative hairpins are identified in SP datasets and used as a means to capture local structural effects. This has the advantage of rapid processing of big (e.g. transcriptome-wide) data as RNA folding is circumvented, yet it captures more information than simple data summarizations. We reformulate a statistical learning algorithm we previously developed to significantly improve precision of hairpin detection, then introduce a novel nucleotide-wise measure, termed the hairpin-derived structure level (HDSL), which captures local structuredness by accounting for the presence of likely hairpin elements. Applying HDSL to data from recent studies recapitulates, strengthens and expands on their findings which were obtained by more comprehensive folding algorithms, yet our analyses are orders of magnitude faster. These results demonstrate that hairpin detection is a promising avenue for global and rapid structure-function analysis, furthering our understanding of RNA biology and the principal features which drive biological insights from SP data.

Clusters of hairpins induce intrinsic transcription termination in bacteria

Intrinsic transcription termination (ITT) sites are currently identified by locating single and double-adjacent RNA hairpins downstream of the stop codon. ITTs for a limited number of genes/operons in only a few bacterial genomes are currently known. This lack of coverage is a lacuna in the existing ITT inference methods. We have studied the inter-operon regions of 13 genomes covering all major phyla in bacteria, for which good quality public RNA-seq data exist. We identify ITT sites in 87% of cases by predicting hairpin(s) and validate against 81% of cases for which the RNA-seq derived sites could be calculated. We identify 72% of these sites correctly, with 98% of them located ≤ 80 bases downstream of the stop codon. The predicted hairpins form a cluster (when present < 15 bases) in two-thirds of the cases, the remaining being single hairpins. The largest number of clusters is formed by two hairpins, and the occurrence decreases exponentially with an increasing number of hairpins in the cluster. Our study reveals that hairpins form an effective ITT unit when they act in concert in a cluster. Their pervasiveness along with single hairpin terminators corroborates a wider utilization of ITT mechanisms for transcription control across bacteria.

Single-sequence and profile-based prediction of RNA solvent accessibility using dilated convolutional neural network

MOTIVATION

RNA solvent accessibility, similar to protein solvent accessibility, reflects the structural regions that are accessible to solvents or other functional biomolecules, and plays an important role for structural and functional characterization. Unlike protein solvent accessibility, only a few tools are available for predicting RNA solvent accessibility despite the fact that millions of RNA transcripts have unknown structures and functions. Also, these tools have limited accuracy. Here, we have developed RNAsnap2 that uses a dilated convolutional neural network with a new feature, based on predicted base-pairing probabilities from LinearPartition.

RESULTS

Using the same training set from the recent predictor RNAsol, RNAsnap2 provides an 11% improvement in median Pearson Correlation Coefficient (PCC) and 9% improvement in mean absolute errors for the same test set of 45 RNA chains. A larger improvement (22% in median PCC) is observed for 31 newly deposited RNA chains that are non-redundant and independent from the training and the test sets. A single-sequence version of RNAsnap2 (i.e. without using sequence profiles generated from homology search by Infernal) has achieved comparable performance to the profile-based RNAsol. In addition, RNAsnap2 has achieved comparable performance for protein-bound and protein-free RNAs. Both RNAsnap2 and RNAsnap2 (SingleSeq) are expected to be useful for searching structural signatures and locating functional regions of non-coding RNAs.

AVAILABILITY AND IMPLEMENTATION

Standalone-versions of RNAsnap2 and RNAsnap2 (SingleSeq) are available at https://github.com/jaswindersingh2/RNAsnap2. Direct prediction can also be made at https://sparks-lab.org/server/rnasnap2. The datasets used in this research can also be downloaded from the GITHUB and the webserver mentioned above.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Structural 3D Domain Reconstruction of the RNA Genome from Viruses with Secondary Structure Models

Three-dimensional RNA domain reconstruction is important for the assembly, disassembly and delivery functionalities of a packed proteinaceus capsid. However, to date, the self-association of RNA molecules is still an open problem. Recent chemical probing reports provide, with high reliability, the secondary structure of diverse RNA ensembles, such as those of viral genomes. Here, we present a method for reconstructing the complete 3D structure of RNA genomes, which combines a coarse-grained model with a subdomain composition scheme to obtain the entire genome inside proteinaceus capsids based on secondary structures from experimental techniques. Despite the amount of sampling involved in the folded and also unfolded RNA molecules, advanced microscope techniques can provide points of anchoring, which enhance our model to include interactions between capsid pentamers and RNA subdomains. To test our method, we tackle the satellite tobacco mosaic virus (STMV) genome, which has been widely studied by both experimental and computational communities. We provide not only a methodology to structurally analyze the tertiary conformations of the RNA genome inside capsids, but a flexible platform that allows the easy implementation of features/descriptors coming from both theoretical and experimental approaches.

Review of machine learning methods for RNA secondary structure prediction

Secondary structure plays an important role in determining the function of noncoding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine learning (ML) technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on ML technologies and a tabularized summary of the most important methods in this field. The current pending challenges in the field of RNA secondary structure prediction and future trends are also discussed.

In silico strategies for modeling RNA aptamers and predicting binding sites of their molecular targets

RNA aptamers are single-stranded nucleic acids of 20-100 nucleotides, with high sensitivity and specificity against particular molecular targets. In vitro production and selection of aptamers can be performed using the SELEX method. However, this procedure requires considerable time and cost. In this sense, bioinformatics tools play an important role in reducing the time and cost associated with development and production of aptamers. In this article, we propose bioinformatics strategies for modeling and analysis of the interaction with molecular targets for two RNA aptamers: ATP binding RNA aptamer and iSpinach aptamer. For this purpose, molecular modeling of the tertiary structure of the aptamers was performed with two servers (SimRNA and RNAComposer); and AutoDock Vina and rDock programs were used to dock their respective ligands. The predictions developed with these methods could be used for in silico design of RNA aptamers, through a simple and accessible methodology.Supplemental data for this article is available online at https://doi.org/10.1080/15257770.2021.1951754 .

RNA 3D Structure Prediction Using Coarse-Grained Models

The three-dimensional (3D) structures of Ribonucleic acid (RNA) molecules are essential to understanding their various and important biological functions. However, experimental determination of the atomic structures is laborious and technically difficult. The large gap between the number of sequences and the experimentally determined structures enables the thriving development of computational approaches to modeling RNAs. However, computational methods based on all-atom simulations are intractable for large RNA systems, which demand long time simulations. Facing such a challenge, many coarse-grained (CG) models have been developed. Here, we provide a review of CG models for modeling RNA 3D structures, compare the performance of the different models, and offer insights into potential future developments.

Looking at the Nudibranch Family Myrrhinidae (Gastropoda, Heterobranchia) from a Mitochondrial '2D Folding Structure' Point of View

Integrative taxonomy is an evolving field of multidisciplinary studies often utilised to elucidate phylogenetic reconstructions that were poorly understood in the past. The systematics of many taxa have been resolved by combining data from different research approaches, i.e., molecular, ecological, behavioural, morphological and chemical. Regarding molecular analysis, there is currently a search for new genetic markers that could be diagnostic at different taxonomic levels and that can be added to the canonical ones. In marine Heterobranchia, the most widely used mitochondrial markers, COI and 16S, are usually analysed by comparing the primary sequence. The 16S rRNA molecule can be folded into a 2D secondary structure that has been poorly exploited in the past study of heterobranchs, despite 2D molecular analyses being sources of possible diagnostic characters. Comparison of the results from the phylogenetic analyses of a concatenated (the nuclear H3 and the mitochondrial COI and 16S markers) dataset (including 30 species belonging to eight accepted genera) and from the 2D folding structure analyses of the 16S rRNA from the type species of the genera investigated demonstrated the diagnostic power of this RNA molecule to reveal the systematics of four genera belonging to the family Myrrhinidae (Gastropoda, Heterobranchia). The "molecular morphological" approach to the 16S rRNA revealed to be a powerful tool to delimit at both species and genus taxonomic levels and to be a useful way of recovering information that is usually lost in phylogenetic analyses. While the validity of the genera Godiva, Hermissenda and Phyllodesmium are confirmed, a new genus is necessary and introduced for Dondice banyulensis, Nemesis gen. nov. and the monospecific genus Nanuca is here synonymised with Dondice, with Nanuca sebastiani transferred into Dondice as Dondice sebastiani comb. nov.

A map of the SARS-CoV-2 RNA structurome

SARS-CoV-2 has exploded throughout the human population. To facilitate efforts to gain insights into SARS-CoV-2 biology and to target the virus therapeutically, it is essential to have a roadmap of likely functional regions embedded in its RNA genome. In this report, we used a bioinformatics approach, ScanFold, to deduce the local RNA structural landscape of the SARS-CoV-2 genome with the highest likelihood of being functional. We recapitulate previously-known elements of RNA structure and provide a model for the folding of an essential frameshift signal. Our results find that SARS-CoV-2 is greatly enriched in unusually stable and likely evolutionarily ordered RNA structure, which provides a large reservoir of potential drug targets for RNA-binding small molecules. Results are enhanced via the re-analyses of publicly-available genome-wide biochemical structure probing datasets that are broadly in agreement with our models. Additionally, ScanFold was updated to incorporate experimental data as constraints in the analysis to facilitate comparisons between ScanFold and other RNA modelling approaches. Ultimately, ScanFold was able to identify eight highly structured/conserved motifs in SARS-CoV-2 that agree with experimental data, without explicitly using these data. All results are made available via a public database (the RNAStructuromeDB: https://structurome.bb.iastate.edu/sars-cov-2) and model comparisons are readily viewable at https://structurome.bb.iastate.edu/sars-cov-2-global-model-comparisons.

The energy-spectrum of bicompatible sequences

Background

Genotype-phenotype maps provide a meaningful filtration of sequence space and RNA secondary structures are particular such phenotypes. Compatible sequences, which satisfy the base-pairing constraints of a given RNA structure, play an important role in the context of neutral evolution. Sequences that are simultaneously compatible with two given structures (bicompatible sequences), are beacons in phenotypic transitions, induced by erroneously replicating populations of RNA sequences. RNA riboswitches, which are capable of expressing two distinct secondary structures without changing the underlying sequence, are one example of bicompatible sequences in living organisms.

Results

We present a full loop energy model Boltzmann sampler of bicompatible sequences for pairs of structures. The sequence sampler employs a dynamic programming routine whose time complexity is polynomial when assuming the maximum number of exposed vertices, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\kappa $$\end{document}κ, is a constant. The parameter \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\kappa $$\end{document}κ depends on the two structures and can be very large. We introduce a novel topological framework encapsulating the relations between loops that sheds light on the understanding of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\kappa $$\end{document}κ. Based on this framework, we give an algorithm to sample sequences with minimum \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\kappa $$\end{document}κ on a particular topologically classified case as well as giving hints to the solution in the other cases. As a result, we utilize our sequence sampler to study some established riboswitches.

Conclusion

Our analysis of riboswitch sequences shows that a pair of structures needs to satisfy key properties in order to facilitate phenotypic transitions and that pairs of random structures are unlikely to do so. Our analysis observes a distinct signature of riboswitch sequences, suggesting a new criterion for identifying native sequences and sequences subjected to evolutionary pressure. Our free software is available at: https://github.com/FenixHuang667/Bifold.

Characteristic chemical probing patterns of loop motifs improve prediction accuracy of RNA secondary structures

RNA structures play a fundamental role in nearly every aspect of cellular physiology and pathology. Gaining insights into the functions of RNA molecules requires accurate predictions of RNA secondary structures. However, the existing thermodynamic folding models remain less accurate than desired, even when chemical probing data, such as selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) reactivities, are used as restraints. Unlike most SHAPE-directed algorithms that only consider SHAPE restraints for base pairing, we extract two-dimensional structural features encoded in SHAPE data and establish robust relationships between characteristic SHAPE patterns and loop motifs of various types (hairpin, internal, and bulge) and lengths (2-11 nucleotides). Such characteristic SHAPE patterns are closely related to the sugar pucker conformations of loop residues. Based on these patterns, we propose a computational method, SHAPELoop, which refines the predicted results of the existing methods, thereby further improving their prediction accuracy. In addition, SHAPELoop can provide information about local or global structural rearrangements (including pseudoknots) and help researchers to easily test their hypothesized secondary structures.

From genotypes to organisms: State-of-the-art and perspectives of a cornerstone in evolutionary dynamics

Understanding how genotypes map onto phenotypes, fitness, and eventually organisms is arguably the next major missing piece in a fully predictive theory of evolution. We refer to this generally as the problem of the genotype-phenotype map. Though we are still far from achieving a complete picture of these relationships, our current understanding of simpler questions, such as the structure induced in the space of genotypes by sequences mapped to molecular structures, has revealed important facts that deeply affect the dynamical description of evolutionary processes. Empirical evidence supporting the fundamental relevance of features such as phenotypic bias is mounting as well, while the synthesis of conceptual and experimental progress leads to questioning current assumptions on the nature of evolutionary dynamics-cancer progression models or synthetic biology approaches being notable examples. This work delves with a critical and constructive attitude into our current knowledge of how genotypes map onto molecular phenotypes and organismal functions, and discusses theoretical and empirical avenues to broaden and improve this comprehension. As a final goal, this community should aim at deriving an updated picture of evolutionary processes soundly relying on the structural properties of genotype spaces, as revealed by modern techniques of molecular and functional analysis.

Annotation of the local context of the RNA secondary structure improves the classification and prediction of A-minors

Non-coding RNAs play a crucial role in various cellular processes in living organisms, and RNA functions heavily depend on molecule structures composed of stems, loops, and various tertiary motifs. Among those, the most frequent are A-minor interactions, which are often involved in the formation of more complex motifs such as kink-turns and pseudoknots. We present a novel classification of A-minors in terms of RNA secondary structure where each nucleotide of an A-minor is attributed to the stem or loop, and each pair of nucleotides is attributed to their relative position within the secondary structure. By analyzing classes of A-minors in known RNA structures, we found that the largest classes are mostly homogeneous and preferably localize with known A-minor co-motifs, e.g. tetraloop-tetraloop receptor and coaxial stacking. Detailed analysis of local A-minors within internal loops revealed a novel recurrent RNA tertiary motif, the across-bulged motif. Interestingly, the motif resembles the previously known GAAA/11nt motif but with the local adenines performing the role of the GAAA-tetraloop. By using machine learning, we show that particular classes of local A-minors can be predicted from sequence and secondary structure. The proposed classification is the first step toward automatic annotation of not only A-minors and their co-motifs but various types of RNA tertiary motifs as well.

Competition between bridged dinucleotides and activated mononucleotides determines the error frequency of nonenzymatic RNA primer extension

Nonenzymatic copying of RNA templates with activated nucleotides is a useful model for studying the emergence of heredity at the origin of life. Previous experiments with defined-sequence templates have pointed to the poor fidelity of primer extension as a major problem. Here we examine the origin of mismatches during primer extension on random templates in the simultaneous presence of all four 2-aminoimidazole-activated nucleotides. Using a deep sequencing approach that reports on millions of individual template-product pairs, we are able to examine correct and incorrect polymerization as a function of sequence context. We have previously shown that the predominant pathway for primer extension involves reaction with imidazolium-bridged dinucleotides, which form spontaneously by the reaction of two mononucleotides with each other. We now show that the sequences of correctly paired products reveal patterns that are expected from the bridged dinucleotide mechanism, whereas those associated with mismatches are consistent with direct reaction of the primer with activated mononucleotides. Increasing the ratio of bridged dinucleotides to activated mononucleotides, either by using purified components or by using isocyanide-based activation chemistry, reduces the error frequency. Our results point to testable strategies for the accurate nonenzymatic copying of arbitrary RNA sequences.

RNA Secondary Structure Prediction with Pseudoknots Using Chemical Reaction Optimization Algorithm

RNA molecules play a significant role in cell function especially including pseudoknots. In past decades, several methods have been developed to predict RNA secondary structure with pseudoknots and the most popular one uses minimum free energy. It is a nondeterministic polynomial-time hard (NP-hard) problem. We have proposed an approach based on a metaheuristic algorithm named Chemical Reaction Optimization (CRO) to solve the RNA pseudoknotted structure prediction problem. The reaction operators of CRO algorithm have been redesigned and used on the generated population to find the structure with the minimum free energy. Besides, we have developed an additional operator called Repair operator which has a great influence on our algorithm in increasing accuracy. It helps to increase the true positive base pairs while decreasing the false positive and false negative base pairs. Four energy models have been applied to calculate the energy. To evaluate the performance, we have used four datasets containing RNA pseudoknotted sequences taken from the RNA STRAND and Pseudobase++ database. We have compared the proposed approach with some existing algorithms and shown that our CRO based model is a better prediction method in terms of accuracy and speed.

Cotranscriptional Kinetic Folding of RNA Secondary Structures Including Pseudoknots

Computational prediction of ribonucleic acid (RNA) structures is an important problem in computational structural biology. Studies of RNA structure formation often assume that the process starts from a fully synthesized sequence. Experimental evidence, however, has shown that RNA folds concurrently with its elongation. We investigate RNA secondary structure formation, including pseudoknots, that takes into account the cotranscriptional effects. We propose a single-nucleotide resolution kinetic model of the folding process of RNA molecules, where the polymerase-driven elongation of an RNA strand by a new nucleotide is included as a primitive operation, together with a stochastic simulation method that implements this folding concurrently with the transcriptional synthesis. Numerical case studies show that our cotranscriptional RNA folding model can predict the formation of conformations that are favored in actual biological systems. Our new computational tool can thus provide quantitative predictions and offer useful insights into the kinetics of RNA folding.

Ensemble Switching Unveils a Kinetic Rheostat Mechanism of the Eukaryotic Thiamine Pyrophosphate Riboswitch

Thiamine pyrophosphate (TPP) riboswitches regulate thiamine metabolism by inhibiting the translation of enzymes essential to thiamine synthesis pathways upon binding to thiamine pyrophosphate in cells across all domains of life. Recent work on the Arabidopsis thaliana TPP riboswitch suggests a multi-step TPP binding process involving multiple riboswitch configurational ensembles and that Mg2+ dependence underlies the mechanism of TPP recognition and subsequent transition to the expression-inhibiting state of the aptamer domain followed by changes in the expression platform. However, details of the relationship between TPP riboswitch conformational changes and interactions with TPP and Mg2+ ¬¬in the aptamer domain constituting this mechanism are unknown. Therefore, we integrated single-molecule multiparameter fluorescence and force spectroscopy with atomistic molecular dynamics simulations and found that conformational transitions within the aptamer domain's sensor helices associated with TPP and Mg2+ ligand binding occurred between at least five different ensembles on timescales ranging from µs to ms. These dynamics are orders of magnitude faster than the 10 second-timescale folding kinetics associated with expression-state switching in the switch sequence. Together, our results show that a TPP and Mg2+ dependent mechanism determines dynamic configurational state ensemble switching of the aptamer domain's sensor helices that regulates the stability of the switch helix, which ultimately may lead to the expression-inhibiting state of the riboswitch. Additionally, we propose that two pathways exist for ligand recognition and that this mechanism underlies a kinetic rheostat-like behavior of the Arabidopsis thaliana TPP riboswitch.

Nonequilibrium thermodynamics of the RNA-RNA interaction underlying a genetic transposition program

Thermodynamic descriptions are powerful tools to formally study complex gene expression programs evolved in living cells on the basis of macromolecular interactions. While transcriptional regulations are often modeled in the equilibrium, other interactions that occur in the cell follow a more complex pattern. Here, we adopt a nonequilibrium thermodynamic scheme to explain the RNA-RNA interaction underlying IS10 transposition. We determine the energy landscape associated with such an interaction at the base-pair resolution, and we present an original scaling law for expression prediction that depends on different free energies characterizing that landscape. Then, we show that massive experimental data of the IS10 RNA-controlled expression are better explained by this thermodynamic description in nonequilibrium. Overall, these results contribute to better comprehend the kinetics of post-transcriptional regulations and, more broadly, the functional consequences of processes out of the equilibrium in biology.

Improving RNA Branching Predictions: Advances and Limitations

Minimum free energy prediction of RNA secondary structures is based on the Nearest Neighbor Thermodynamics Model. While such predictions are typically good, the accuracy can vary widely even for short sequences, and the branching thermodynamics are an important factor in this variance. Recently, the simplest model for multiloop energetics—a linear function of the number of branches and unpaired nucleotides—was found to be the best. Subsequently, a parametric analysis demonstrated that per family accuracy can be improved by changing the weightings in this linear function. However, the extent of improvement was not known due to the ad hoc method used to find the new parameters. Here we develop a branch-and-bound algorithm that finds the set of optimal parameters with the highest average accuracy for a given set of sequences. Our analysis shows that the previous ad hoc parameters are nearly optimal for tRNA and 5S rRNA sequences on both training and testing sets. Moreover, cross-family improvement is possible but more difficult because competing parameter regions favor different families. The results also indicate that restricting the unpaired nucleotide penalty to small values is warranted. This reduction makes analyzing longer sequences using the present techniques more feasible.

Deep Learning Method for RNA Secondary Structure Prediction with Pseudoknots Based on Large-Scale Data

Traditional machine learning methods are widely used in the field of RNA secondary structure prediction and have achieved good results. However, with the emergence of large-scale data, deep learning methods have more advantages than traditional machine learning methods. As the number of network layers increases in deep learning, there will often be problems such as increased parameters and overfitting. We used two deep learning models, GoogLeNet and TCN, to predict RNA secondary results. And from the perspective of the depth and width of the network, improvements are made based on the neural network model, which can effectively improve the computational efficiency while extracting more feature information. We process the existing real RNA data through experiments, use deep learning models to extract useful features from a large amount of RNA sequence data and structure data, and then predict the extracted features to obtain each base’s pairing probability. The characteristics of RNA secondary structure and dynamic programming methods are used to process the base prediction results, and the structure with the largest sum of the probability of each base pairing is obtained, and this structure will be used as the optimal RNA secondary structure. We, respectively, evaluated GoogLeNet and TCN models based on 5sRNA, tRNA data, and tmRNA data, and compared them with other standard prediction algorithms. The sensitivity and specificity of the GoogLeNet model on the 5sRNA and tRNA data sets are about 16% higher than the best prediction results in other algorithms. The sensitivity and specificity of the GoogLeNet model on the tmRNA dataset are about 9% higher than the best prediction results in other algorithms. As deep learning algorithms’ performance is related to the size of the data set, as the scale of RNA data continues to expand, the prediction accuracy of deep learning methods for RNA secondary structure will continue to improve.

Accurate prediction of genome-wide RNA secondary structure profile based on extreme gradient boosting

MOTIVATION

RNA secondary structure plays a vital role in fundamental cellular processes, and identification of RNA secondary structure is a key step to understand RNA functions. Recently, a few experimental methods were developed to profile genome-wide RNA secondary structure, i.e. the pairing probability of each nucleotide, through high-throughput sequencing techniques. However, these high-throughput methods have low precision and cannot cover all nucleotides due to limited sequencing coverage.

RESULTS

Here, we have developed a new method for the prediction of genome-wide RNA secondary structure profile from RNA sequence based on the extreme gradient boosting technique. The method achieves predictions with areas under the receiver operating characteristic curve (AUC) >0.9 on three different datasets, and AUC of 0.888 by another independent test on the recently released Zika virus data. These AUCs are consistently >5% greater than those by the CROSS method recently developed based on a shallow neural network. Further analysis on the 1000 Genome Project data showed that our predicted unpaired probabilities are highly correlated (>0.8) with the minor allele frequencies at synonymous, non-synonymous mutations, and mutations in untranslated regions, which were higher than those generated by RNAplfold. Moreover, the prediction over all human mRNA indicated a consistent result with previous observation that there is a periodic distribution of unpaired probability on codons. The accurate predictions by our method indicate that such model trained on genome-wide experimental data might be an alternative for analytical methods.

AVAILABILITY AND IMPLEMENTATION

The GRASP is available for academic use at https://github.com/sysu-yanglab/GRASP.

SUPPLEMENTARY INFORMATION

Supplementary data are available online.

Characterization of a green Stentor with symbiotic algae growing in an extremely oligotrophic environment and storing large amounts of starch granules in its cytoplasm

The genus Stentor is a relatively well-known ciliate owing to its lucid trumpet shape. Stentor pyriformis represents a green, short, and fat Stentor, but it is a little-known species. We investigated 124 ponds and wetlands in Japan and confirmed the presence of S. pyriformis at 23 locations. All these ponds were noticeably oligotrophic. With the improvement of oligotrophic culture conditions, we succeeded in long-term cultivation of three strains of S. pyriformis. The cytoplasm of S. piriformis contains a large number of 1-3 μm refractive granules that turn brown by Lugol's staining. The granules also show a typical Maltese-cross pattern by polarization microscopy, strongly suggesting that the granules are made of amylopectin-rich starch. By analyzing the algal rDNA, it was found that all S. pyriformis symbionts investigated in this study were Chlorella variabilis. This species is known as the symbiont of Paramecium bursaria and is physiologically specialized for endosymbiosis. Genetic discrepancies between C. variabilis of S. pyriformis and P. bursaria may indicate that algal sharing was an old incident. Having symbiotic algae and storing carbohydrate granules in the cytoplasm is considered a powerful strategy for this ciliate to withstand oligotrophic and cold winter environments in highland bogs.