Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure

Targeting RNA with small molecules, from RNA structures to precision medicines: IUPHAR review: 40

RNA plays important roles in regulating both health and disease biology in all kingdoms of life. Notably, RNA can form intricate three-dimensional structures, and their biological functions are dependent on these structures. Targeting the structured regions of RNA with small molecules has gained increasing attention over the past decade, because it provides both chemical probes to study fundamental biology processes and lead medicines for diseases with unmet medical needs. Recent advances in RNA structure prediction and determination and RNA biology have accelerated the rational design and development of RNA-targeted small molecules to modulate disease pathology. However, challenges remain in advancing RNA-targeted small molecules towards clinical applications. This review summarizes strategies to study RNA structures, to identify small molecules recognizing these structures, and to augment the functionality of RNA-binding small molecules. We focus on recent advances in developing RNA-targeted small molecules as potential therapeutics in a variety of diseases, encompassing different modes of actions and targeting strategies. Furthermore, we present the current gaps between early-stage discovery of RNA-binding small molecules and their clinical applications, as well as a roadmap to overcome these challenges in the near future.

Towards the genomic sequence code of DNA fragility for machine learning

Genomic DNA breakages and the subsequent insertion and deletion mutations are important contributors to genome instability and linked diseases. Unlike the research in point mutations, the relationship between DNA sequence context and the propensity for strand breaks remains elusive. Here, by analyzing the differences and commonalities across myriads of genomic breakage datasets, we extract the sequence-linked rules and patterns behind DNA fragility. We show the overall deconvolution of the sequence influence into short-, mid- and long-range effects, and the stressor-dependent differences in defining the range and compositional effects on DNA fragility. We summarize and release our feature compendium as a library that can be seamlessly incorporated into genomic machine learning procedures, where DNA fragility is of concern, and train a generalized DNA fragility model on cancer-associated breakages. Structural variants (SVs) tend to stabilize regions in which they emerge, with the effect most pronounced for pathogenic SVs. In contrast, the effects of chromothripsis are seen across regions less prone to breakages. We find that viral integration may bring genome fragility, particularly for cancer-associated viruses. Overall, this work offers novel insights into the genomic sequence basis of DNA fragility and presents a powerful machine learning resource to further enhance our understanding of genome (in)stability and evolution.

memerna: Sparse RNA Folding Including Coaxial Stacking

Determining RNA secondary structure is a core problem in computational biology. Fast algorithms for predicting secondary structure are fundamental to this task.Department of Biochemistry & Biophysics, University of Rochester Medical Center, Rochester, NY, USA We describe a modified formulation of the Zuker-Stiegler algorithm with coaxial stacking, a stabilising interaction in which the ends of helices in multi-loops are stacked. In particular, optimal coaxial stacking is computed as part of the dynamic programming state, rather than in an inner loop. We introduce a new notion of sparsity, which we call replaceability. Replaceability is a more general condition and applicable in more places than the triangle inequality that is used by previous sparse folding methods. We also introduce non-monotonic candidate lists as an additional sparsification tool. Existing usages of the triangle inequality for sparsification can be thought of as an application of both replaceability and monotonicity together. The modified recurrences along with replaceability allows sparsification to be applied to coaxial stacking as well, which increases the speed of the algorithm. We implemented this algorithm in software we call memerna, which we show to have the fastest exact (non-heuristic) implementation of RNA folding under the complete Turner 2004 model with coaxial stacking, out of several popular RNA folding tools supporting coaxial stacking. We also introduce a new notation for secondary structure which includes coaxial stacking, terminal mismatches, and dangles (CTDs) information. The memerna package 0.1 release is available at https://github.com/Edgeworth/memerna/tree/release/0.1.

The small noncoding RNA Vaultrc5 is dispensable to mouse development

Vault RNAs (vtRNAs) are evolutionarily conserved small noncoding RNAs transcribed by RNA polymerase III. Vault RNAs were initially described as components of the vault particle, but have since been assigned multiple vault-independent functions, including regulation of PKR activity, apoptosis, autophagy, lysosome biogenesis, and viral particle trafficking. The full-length transcript has also been described as a noncanonical source of miRNAs, which are processed in a DICER-dependent manner. As central molecules in vault-dependent and independent processes, vtRNAs have been attributed numerous biological roles, including regulation of cell proliferation and survival, response to viral infections, drug resistance, and animal development. Yet, their impact to mammalian physiology remains largely unexplored. To study vault RNAs in vivo, we generated a mouse line with a conditional Vaultrc5 loss-of-function allele. Because Vaultrc5 is the sole murine vtRNA, this allele enables the characterization of the physiological requirements of this conserved class of small regulatory RNAs in mammals. Using this strain, we show that mice constitutively null for Vaultrc5 are viable and histologically normal but have a slight reduction in platelet counts, pointing to a potential role for vtRNAs in hematopoiesis. This work paves the way for further in vivo characterizations of this abundant but mysterious RNA molecule. Specifically, it enables the study of the biological consequences of constitutive or lineage-specific Vaultrc5 deletion and of the physiological requirements for an intact Vaultrc5 during normal hematopoiesis or in response to cellular stresses such as oncogene expression, viral infection, or drug treatment.

Metastasis-Associated Lung Adenocarcinoma Transcript 1 (MALAT1) lncRNA Conformational Dynamics in Complex with RNA-Binding Protein with Serine-Rich Domain 1 (RNPS1) in the Pan-cancer Splicing and Gene Expression

Alternative splicing events increase the transcriptomic and proteomic complexity in cancers. Overexpression of metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), a highly conserved lncRNA, is widely known to promote cancer development, one mechanism for which may be through the regulation of alternative splicing and, thereby, gene expression. Its regulatory interactions with proteins have been a subject of much interest, yet little research has been carried out on the mechanisms adopted. It has been observed that MALAT1 binds to RNA-binding protein with serine-rich domain 1 (RNPS1), being colocalized in the nuclear speckles, and together, these two binding partners may regulate alternative splicing. Upregulated RNPS1 is predicted to play a key role in the pan-cancer development. Experimental tertiary structure of full-length MALAT1 is currently lacking despite the availability of the 3D structure of 3' expression and nuclear retention element. We hypothesize that the computationally modeled tertiary structures of the specific binding motifs in the M-region, E-region, and full-length structures of MALAT1 may adopt a modular structure and bind to the RNPS1 loop region of RS/P domain involved in exon skipping, interacting in a manner fully consistent with the biochemical experiments. Extensive observations using the powerful molecular dynamics (MD) simulations of MALAT1 regions bound to RNPS1 suggested that all three regions form interactive, yet stable complexes. The ranking of the MM-GBSA- and MM-PBSA-derived binding free energies between these complexes corroborated well in the MD simulations and experiments. Energy decomposition analyses suggested that arginines in the RNPS1 protein are among the major contributors toward the binding free energies as calculated by MM-GBSA present in the Amber package; while among the nucleotides, the major contributors were nucleotides with G and A nucleobases, with more contributory effect in comparison to arginines, across the bound M-region, E-region, and full-length MALAT1. This suggests that specific purines play a greater role in the complex formation, in a loop-specific manner, and the more proactive approach in complexation tilts toward MALAT1. To the best of our knowledge, our studies are the first studies taking a unique approach, utilizing the binding motifs to deduce a tertiary structure of MALAT1, toward our understanding of the lncRNA-protein interactions, stability, and binding on a structural basis. The therapeutic implications of targeting this complex formation to regulate splicing and hence, oncogenesis, is further envisaged.

DecoyFinder: Identification of Contaminants in Sets of Homologous RNA Sequences

Motivation

RNA structure is essential for the function of many non-coding RNAs. Using multiple homologous sequences, which share structure and function, secondary structure can be predicted with much higher accuracy than with a single sequence. It can be difficult, however, to establish a set of homologous sequences when their structure is not yet known. We developed a method to identify sequences in a set of putative homologs that are in fact non-homologs.

Results

Previously, we developed TurboFold to estimate conserved structure using multiple, unaligned RNA homologs. Here, we report that the positive predictive value of TurboFold is significantly reduced by the presence of contamination by non-homologous sequences, although the reduction is less than 1%. We developed a method called DecoyFinder, which applies machine learning trained with features determined by TurboFold, to detect sequences that are not homologous with the other sequences in the set. This method can identify approximately 45% of non-homologous sequences, at a rate of 5% misidentification of true homologous sequences.

Availability

DecoyFinder and TurboFold are incorporated in RNAstructure, which is provided for free and open source under the GPL V2 license. It can be downloaded at http://rna.urmc.rochester.edu/RNAstructure.html.

Thermodynamics and Kinetics-Directed Regulation of Nucleic Acid-Based Molecular Recognition

Nucleic acid-based molecular recognition plays crucial roles in various fields like biosensing and disease diagnostics. To achieve optimal detection and analysis, it is essential to regulate the response performance of nucleic acid probes or switches to match specific application requirements by regulating thermodynamics and kinetics properties. However, the impacts of thermodynamics and kinetics theories on recognition performance are sometimes obscure and the relative conclusions are not intuitive. To promote the thorough understanding and rational utilization of thermodynamics and kinetics theories, this review focuses on the landmarks and recent advances of nucleic acid thermodynamics and kinetics and summarizes the nucleic acid thermodynamics and kinetics-based strategies for regulation of nucleic acid-based molecular recognition. This work hopes such a review can provide reference and guidance for the development and optimization of nucleic acid probes and switches in the future, as well as for advancements in other nucleic acid-related fields.

Genome-Wide Identification of Stable RNA Secondary Structures Across Multiple Organisms Using Chemical Probing Data: Insights into Short Structural Motifs and RNA-Targeting Therapeutics

Small molecules targeting specific RNA binding sites, including stable and transient RNA structures, are emerging as effective pharmacological approaches for modulating gene expression. However, little is understood about how stable RNA secondary structures are shared across organisms, an important factor in controlling drug selectivity. In this study, I provide an analytical pipeline named RNA Secondary Structure Finder (R2S-Finder) to discover short, stable RNA structural motifs for humans, Escherichia coli ( E. coli ), SARS-CoV-2, and Zika virus by leveraging existing in vivo and in vitro genome-wide chemical RNA-probing datasets. I found several common features across organisms. For example, apart from the well-documented tetraloops, AU-rich tetraloops are widely present in different organisms. I also found that the 5' untranslated region (UTR) contains a higher proportion of stable structures than the coding sequences in humans, SARS-CoV-2, and Zika virus. In general, stable structures predicted from in vitro (protein-free) and in vivo datasets are consistent in humans, E. coli , and SARS-CoV-2, indicating that most stable structure formation were driven by RNA folding alone, while a larger variation was found between in vitro and in vivo data with certain RNA types, such as human long intergenic non-coding RNAs (lincRNAs). Finally, I predicted stable three- and four-way RNA junctions that exist both in vivo and in vitro conditions, which can potentially serve as drug targets. All results of stable sequences, stem-loops, internal loops, bulges, and three- and four-way junctions have been collated in the R2S-Finder database ( https://github.com/JingxinWangLab/R2S-Finder ), which is coded in hyperlinked HTML pages and tabulated in CSV files.

The separation between mRNA-ends is more variable than expected

Effective circularization of mRNA molecules is a key step for the efficient initiation of translation. Research has shown that the intrinsic separation of the ends of mRNA molecules is rather small, suggesting that intramolecular arrangements could provide this effective circularization. Considering that the innate proximity of RNA ends might have important unknown biological implications, we aimed to determine whether the close proximity of the ends of mRNA molecules is a conserved feature across organisms and gain further insights into the functional effects of the proximity of RNA ends. To do so, we studied the secondary structure of 274 full native mRNA molecules from 17 different organisms to calculate the contour length (CL) of the external loop as an index of their end-to-end separation. Our computational predictions show bigger variations (from 0.59 to 31.8 nm) than previously reported and also than those observed in random sequences. Our results suggest that separations larger than 18.5 nm are not favored, whereas short separations could be related to phenotypical stability. Overall, our work implies the existence of a biological mechanism responsible for the increase in the observed variability, suggesting that the CL features of the exterior loop could be relevant for the initiation of translation and that a short CL could contribute to the stability of phenotypes.

NNDB: An Expanded Database of Nearest Neighbor Parameters for Predicting Stability of Nucleic Acid Secondary Structures

Nearest neighbor thermodynamic parameters are widely used for RNA and DNA secondary structure prediction and to model thermodynamic ensembles of secondary structures. The Nearest Neighbor Database (NNDB) is a freely available web resource (https://rna.urmc.rochester.edu/NNDB) that provides the functional forms, parameter values, and example calculations. The NNDB provides the 1999 and 2004 set of RNA folding nearest neighbor parameters. We expanded the database to include a set of DNA parameters and a set of RNA parameters that includes m6A in addition to the canonical RNA nucleobases. The site was redesigned using the Quarto open-source publishing system. A downloadable PDF version of the complete resource and downloadable sets of nearest neighbor parameters are available.

Application of plasmid stabilization systems for heterologous protein expression in Escherichia coli

BACKGROUND

Plasmids are the most commonly used vectors for heterologous protein expression in Escherichia coli. However, the plasmid copy number decreases with the segregational instability, which inevitably leads to a decrease in the yield of heterologous protein.

METHODS AND RESULTS

In this study, plasmid stabilization systems were used to enhance the expression level of heterologous proteins in E. coli. With the investigation of protein expression level, biomass and plasmid retention rate in different plasmid stabilization systems, the hok/sok system had the greatest potential on plasmid stabilization. In order to further investigate the molecular mechanism of hok/sok system, the structure of the binding region of hok mRNA and sok antisense RNA was modified based on the minimum free energy of mRNA, which resulted in the reduction of the binding efficiency of hok mRNA and sok asRNA, and then the toxicity of the Hok protein led to the decreased viability of the host cells. Finally, the hok/sok plasmid stabilization system was testified in 5 L fermenter, and the plasmid retention rate and protein expression level were significantly increased without the addition of antibiotics.

CONCLUSIONS

This study lays a solid foundation for a deeper understanding of the mechanism of the hok/sok plasmid stabilization system and improving the productivity of heterologous protein in E. coli.

Weak-cooperative binding of a long single-stranded DNA chain on a surface

Binding gene-wide single-stranded nucleic acids to surface-immobilized complementary probes is an important but challenging process for biophysical studies and diagnostic applications. The challenge comes from the conformational dynamics of the long chain that affects its accessibility and weakens its hybridization to the probes. We investigated the binding of bacteriophage genome M13mp18 on several different 20-mer probes immobilized on the surface of a multi-spot, label-free biosensor, and observed that only a few of them display strong binding capability with dissociation constant as low as 10 pM. Comparing experimental data and computational analysis of the M13mp18 chain structural features, we found that the capturing performance of a specific probe is directly related to the multiplicity of binding sites on the genomic strand, and poorly connected with the predicted secondary and tertiary structure. We show that a model of weak cooperativity of transient bonds is compatible with the measured binding kinetics and accounts for the enhancement of probe capturing observed when more than 20 partial pairings with binding free energy lower than -10 kcal mol-1 are present. This mechanism provides a specific pattern of response of a genomic strand on a panel of properly selected oligomer probe sequences.

RNA tertiary structure and conformational dynamics revealed by BASH MaP

The functional effects of an RNA can arise from complex three-dimensional folds known as tertiary structures. However, predicting the tertiary structure of an RNA and whether an RNA adopts distinct tertiary conformations remains challenging. To address this, we developed BASH MaP, a single-molecule dimethyl sulfate (DMS) footprinting method and DAGGER, a computational pipeline, to identify alternative tertiary structures adopted by different molecules of RNA. BASH MaP utilizes potassium borohydride to reveal the chemical accessibility of the N7 position of guanosine, a key mediator of tertiary structures. We used BASH MaP to identify diverse conformational states and dynamics of RNA G-quadruplexes, an important RNA tertiary motif, in vitro and in cells. BASH MaP and DAGGER analysis of the fluorogenic aptamer Spinach reveals that it adopts alternative tertiary conformations which determine its fluorescence states. BASH MaP thus provides an approach for structural analysis of RNA by revealing previously undetectable tertiary structures.

RNA 5-methylcytosine marks mitochondrial double-stranded RNAs for degradation and cytosolic release

Mitochondria are essential regulators of innate immunity. They generate long mitochondrial double-stranded RNAs (mt-dsRNAs) and release them into the cytosol to trigger an immune response under pathological stress conditions. Yet the regulation of these self-immunogenic RNAs remains largely unknown. Here, we employ CRISPR screening on mitochondrial RNA (mtRNA)-binding proteins and identify NOP2/Sun RNA methyltransferase 4 (NSUN4) as a key regulator of mt-dsRNA expression in human cells. We find that NSUN4 induces 5-methylcytosine (m5C) modification on mtRNAs, especially on the termini of light-strand long noncoding RNAs. These m5C-modified RNAs are recognized by complement C1q-binding protein (C1QBP), which recruits polyribonucleotide nucleotidyltransferase to facilitate RNA turnover. Suppression of NSUN4 or C1QBP results in increased mt-dsRNA expression, while C1QBP deficiency also leads to increased cytosolic mt-dsRNAs and subsequent immune activation. Collectively, our study unveils the mechanism underlying the selective degradation of light-strand mtRNAs and establishes a molecular mark for mtRNA decay and cytosolic release.

The guide-RNA sequence dictates the slicing kinetics and conformational dynamics of the Argonaute silencing complex

The RNA-induced silencing complex (RISC), which powers RNA interference (RNAi), consists of a guide RNA and an Argonaute protein that slices target RNAs complementary to the guide. We find that, for different guide-RNA sequences, slicing rates of perfectly complementary bound targets can be surprisingly different (>250-fold range), and that faster slicing confers better knockdown in cells. Nucleotide sequence identities at guide-RNA positions 7, 10, and 17 underlie much of this variation in slicing rates. Analysis of one of these determinants implicates a structural distortion at guide nucleotides 6-7 in promoting slicing. Moreover, slicing directed by different guide sequences has an unanticipated, 600-fold range in 3'-mismatch tolerance, attributable to guides with weak (AU-rich) central pairing requiring extensive 3' complementarity (pairing beyond position 16) to more fully populate the slicing-competent conformation. Together, our analyses identify sequence determinants of RISC activity and provide biochemical and conformational rationale for their action.

Discovery and Quantification of Long-Range RNA Base Pairs in Coronavirus Genomes with SEARCH-MaP and SEISMIC-RNA

RNA molecules perform a diversity of essential functions for which their linear sequences must fold into higher-order structures. Techniques including crystallography and cryogenic electron microscopy have revealed 3D structures of ribosomal, transfer, and other well-structured RNAs; while chemical probing with sequencing facilitates secondary structure modeling of any RNAs of interest, even within cells. Ongoing efforts continue increasing the accuracy, resolution, and ability to distinguish coexisting alternative structures. However, no method can discover and quantify alternative structures with base pairs spanning arbitrarily long distances - an obstacle for studying viral, messenger, and long noncoding RNAs, which may form long-range base pairs. Here, we introduce the method of Structure Ensemble Ablation by Reverse Complement Hybridization with Mutational Profiling (SEARCH-MaP) and software for Structure Ensemble Inference by Sequencing, Mutation Identification, and Clustering of RNA (SEISMIC-RNA). We use SEARCH-MaP and SEISMIC-RNA to discover that the frameshift stimulating element of SARS coronavirus 2 base-pairs with another element 1 kilobase downstream in nearly half of RNA molecules, and that this structure competes with a pseudoknot that stimulates ribosomal frameshifting. Moreover, we identify long-range base pairs involving the frameshift stimulating element in other coronaviruses including SARS coronavirus 1 and transmissible gastroenteritis virus, and model the full genomic secondary structure of the latter. These findings suggest that long-range base pairs are common in coronaviruses and may regulate ribosomal frameshifting, which is essential for viral RNA synthesis. We anticipate that SEARCH-MaP will enable solving many RNA structure ensembles that have eluded characterization, thereby enhancing our general understanding of RNA structures and their functions. SEISMIC-RNA, software for analyzing mutational profiling data at any scale, could power future studies on RNA structure and is available on GitHub and the Python Package Index.

Protocell Effects on RNA Folding, Function, and Evolution

ConspectusCreating a living system from nonliving matter is a great challenge in chemistry and biophysics. The early history of life can provide inspiration from the idea of the prebiotic "RNA World" established by ribozymes, in which all genetic and catalytic activities were executed by RNA. Such a system could be much simpler than the interdependent central dogma characterizing life today. At the same time, cooperative systems require a mechanism such as cellular compartmentalization in order to survive and evolve. Minimal cells might therefore consist of simple vesicles enclosing a prebiotic RNA metabolism.The internal volume of a vesicle is a distinctive environment due to its closed boundary, which alters diffusion and available volume for macromolecules and changes effective molecular concentrations, among other considerations. These physical effects are mechanistically distinct from chemical interactions, such as electrostatic repulsion, that might also occur between the membrane boundary and encapsulated contents. Both indirect and direct interactions between the membrane and RNA can give rise to nonintuitive, "emergent" behaviors in the model protocell system. We have been examining how encapsulation inside membrane vesicles would affect the folding and activity of entrapped RNA.Using biophysical techniques such as FRET, we characterized ribozyme folding and activity inside vesicles. Encapsulation inside model protocells generally promoted RNA folding, consistent with an excluded volume effect, independently of chemical interactions. This energetic stabilization translated into increased ribozyme activity in two different systems that were studied (hairpin ribozyme and self-aminoacylating RNAs). A particularly intriguing finding was that encapsulation could rescue the activity of mutant ribozymes, suggesting that encapsulation could affect not only folding and activity but also evolution. To study this further, we developed a high-throughput sequencing assay to measure the aminoacylation kinetics of many thousands of ribozyme variants in parallel. The results revealed an unexpected tendency for encapsulation to improve the better ribozyme variants more than worse variants. During evolution, this effect would create a tilted playing field, so to speak, that would give additional fitness gains to already-high-activity variants. According to Fisher's Fundamental Theorem of Natural Selection, the increased variance in fitness should manifest as faster evolutionary adaptation. This prediction was borne out experimentally during in vitro evolution, where we observed that the initially diverse ribozyme population converged more quickly to the most active sequences when they were encapsulated inside vesicles.The studies in this Account have expanded our understanding of emergent protocell behavior, by showing how simply entrapping an RNA inside a vesicle, which could occur spontaneously during vesicle formation, might profoundly affect the evolutionary landscape of the RNA. Because of the exponential dynamics of replication and selection, even small changes to activity and function could lead to major evolutionary consequences. By closely studying the details of minimal yet surprisingly complex protocells, we might one day trace a pathway from encapsulated RNA to a living system.

Selective and non-selective evolutionary signatures found in the simplest replicative biological entities

Mitoviruses, which are considered evolutionary relics of extinct alpha-proteobacteria RNA phages, represent one of the simplest self-replicating biological systems. This study aims to quantitatively describe genomes and identify potential genomic signatures that support the protein phylogenetic-based classification criterion. Genomic variables, such as mononucleotide and dinucleotide composition, codon usage bias, and minimal free energy derived from optimized predicted RNA secondary structure, were analyzed. From the values obtained, the main evolutionary pressures were discussed, indicating that natural selection plays a significant role in shaping mitovirus genomes. However, neutral evolution also makes a significant contribution. This study reveals a significant discovery of structural divergence in Kvaramitovirus. The energy minimization approach employed to study 2D folding in this study reveals a distinct spatial organization of their genomes, providing evidence for the hypothesis of a single evolutionary event of circularization in the most recent common ancestor of the lineage. This hypothesis was discussed in light of recent discoveries by other researchers that partially support the existence of mitoviruses with circular genomes. Finally, this study represents a significant advancement in the understanding of mitoviruses, as it quantitatively describes the nucleotide sequence at the family and genus taxonomic levels. Additionally, we provide hypotheses that can be experimentally validated to inspire new research and address the gaps in knowledge of this fascinating, basally divergent RNA virus lineage.

Targeting sgRNA N secondary structure as a way of inhibiting SARS-CoV-2 replication

SARS-CoV-2 is a betacoronavirus that causes COVID-19, a global pandemic that has resulted in many infections, deaths, and socio-economic challenges. The virus has a large positive-sense, single-stranded RNA genome of ∼30 kb, which produces subgenomic RNAs (sgRNAs) through discontinuous transcription. The most abundant sgRNA is sgRNA N, which encodes the nucleocapsid (N) protein. In this study, we probed the secondary structure of sgRNA N and a shorter model without a 3' UTR in vitro, using the SHAPE (selective 2'-hydroxyl acylation analyzed by a primer extension) method and chemical mapping with dimethyl sulfate and 1-cyclohexyl-(2-morpholinoethyl) carbodiimide metho-p-toluene sulfonate. We revealed the secondary structure of sgRNA N and its shorter variant for the first time and compared them with the genomic RNA N structure. Based on the structural information, we designed gapmers, siRNAs and antisense oligonucleotides (ASOs) to target the N protein coding region of sgRNA N. We also generated eukaryotic expression vectors containing the complete sequence of sgRNA N and used them to screen for new SARS-CoV-2 gene N expression inhibitors. Our study provides novel insights into the structure and function of sgRNA N and potential therapeutic tools against SARS-CoV-2.

Discovery and Quantification of Long-Range RNA Base Pairs in Coronavirus Genomes with SEARCH-MaP and SEISMIC-RNA

RNA molecules perform a diversity of essential functions for which their linear sequences must fold into higher-order structures. Techniques including crystallography and cryogenic electron microscopy have revealed 3D structures of ribosomal, transfer, and other well-structured RNAs; while chemical probing with sequencing facilitates secondary structure modeling of any RNAs of interest, even within cells. Ongoing efforts continue increasing the accuracy, resolution, and ability to distinguish coexisting alternative structures. However, no method can discover and quantify alternative structures with base pairs spanning arbitrarily long distances - an obstacle for studying viral, messenger, and long noncoding RNAs, which may form long-range base pairs. Here, we introduce the method of Structure Ensemble Ablation by Reverse Complement Hybridization with Mutational Profiling (SEARCH-MaP) and software for Structure Ensemble Inference by Sequencing, Mutation Identification, and Clustering of RNA (SEISMIC-RNA). We use SEARCH-MaP and SEISMIC-RNA to discover that the frameshift stimulating element of SARS coronavirus 2 base-pairs with another element 1 kilobase downstream in nearly half of RNA molecules, and that this structure competes with a pseudoknot that stimulates ribosomal frameshifting. Moreover, we identify long-range base pairs involving the frameshift stimulating element in other coronaviruses including SARS coronavirus 1 and transmissible gastroenteritis virus, and model the full genomic secondary structure of the latter. These findings suggest that long-range base pairs are common in coronaviruses and may regulate ribosomal frameshifting, which is essential for viral RNA synthesis. We anticipate that SEARCH-MaP will enable solving many RNA structure ensembles that have eluded characterization, thereby enhancing our general understanding of RNA structures and their functions. SEISMIC-RNA, software for analyzing mutational profiling data at any scale, could power future studies on RNA structure and is available on GitHub and the Python Package Index.

Bioinformatics-Driven mRNA-Based Vaccine Design for Controlling Tinea Cruris Induced by Trichophyton rubrum

Tinea cruris, a dermatophyte fungal infection predominantly caused by Trichophyton rubrum and Epidermophyton floccosum, primarily affects the groin, pubic region, and adjacent thigh. Its recurrence is frequent, attributable to repeated fungal infections in susceptible individuals, especially those with onychomycosis or tinea pedis, which act as reservoirs for dermatophytes. Given the persistent nature of tinea cruris, vaccination emerges as a promising strategy for fungal infection management, offering targeted, durable protection against various fungal species. Vaccines stimulate both humoral and cell-mediated immunity and are administered prophylactically to prevent infections while minimizing the risk of antifungal resistance development. Developing fungal vaccines is challenging due to the thick fungal cell wall, similarities between fungal and human cells, antigenic variation, and evolutionary resemblance to animals, complicating non-toxic target identification and T-cell response variability. No prior research has shown an mRNA vaccine for T. rubrum. Hence, this study proposes a novel mRNA-based vaccine for tinea cruris, potentially offering long-term immunity and reducing reliance on antifungal medications. This study explores the complete proteome of T. rubrum, identifying potential protein candidates for vaccine development through reverse vaccinology. Immunogenic epitopes from these candidates were mapped and integrated into multitope vaccines and reverse translated to construct mRNA vaccines. Then, the mRNA was translated and computationally assessed for physicochemical, chemical, and immunological attributes. Notably, 1,3-beta-glucanosyltransferase, CFEM domain-containing protein, cell wall galactomannoprotein, and LysM domain-containing protein emerged as promising vaccine targets. Antigenic, immunogenic, non-toxic, and non-allergenic cytotoxic T lymphocyte, helper T lymphocyte, and B lymphocyte epitopes were selected and linked with appropriate linkers and Toll-like receptor (TLR) agonist adjuvants to formulate vaccine candidates targeting T. rubrum. The protein-based vaccines underwent reverse translation to construct the mRNA vaccines, which, after inoculation, were translated again by host ribosomes to work as potential components for triggering the immune response. After that, molecular docking, normal mode analysis, and molecular dynamic simulation confirmed strong binding affinities and stable complexes between vaccines and TLR receptors. Furthermore, immune simulations of vaccines with and without adjuvant demonstrated activation of immune responses, evidenced by elevated levels of IgG1, IgG2, IgM antibodies, cytokines, and interleukins. There was no significant change in antibody production between vaccines with and without adjuvants, but adjuvants are crucial for activating the innate immune response via TLRs. Although mRNA vaccines hold promise against fungal infections, further research is essential to assess their safety and efficacy. Experimental validation is crucial for evaluating their immunogenicity, effectiveness, and safety.

Possible involvement of three-stemmed pseudoknots in regulating translational initiation in human mRNAs

RNA pseudoknots play a crucial role in various cellular functions. Established pseudoknots show significant variation in both size and structural complexity. Specifically, three-stemmed pseudoknots are characterized by an additional stem-loop embedded in their structure. Recent findings highlight these pseudoknots as bacterial riboswitches and potent stimulators for programmed ribosomal frameshifting in RNA viruses like SARS-CoV2. To investigate the possible presence of functional three-stemmed pseudoknots in human mRNAs, we employed in-house developed computational methods to detect such structures within a dataset comprising 21,780 full-length human mRNA sequences. Numerous three-stemmed pseudoknots were identified. A selected set of 14 potential instances are presented, in which the start codon of the mRNA is found in close proximity either upstream, downstream, or within the identified three-stemmed pseudoknot. These pseudoknots likely play a role in translational initiation regulation. The probability of their existence gains support from their ranking as the most stable pseudoknot identified in the entire mRNA sequence, structural conservation across homologous mRNAs, stereochemical feasibility as demonstrated by structural modeling, and classification as members of the CPK-1 pseudoknot family, which includes many well-established pseudoknots. Furthermore, in four of the mRNAs, two or three closely spaced or tandem three-stemmed pseudoknots were identified. These findings suggest the frequent occurrence of three-stemmed pseudoknots in human mRNAs. A stepwise co-transcriptional folding mechanism is proposed for the formation of a three-stemmed pseudoknot structure. Our results not only provide fresh insights into the structures and functions of pseudoknots but also unveil the potential to target pseudoknots for treating human diseases.

The guide RNA sequence dictates the slicing kinetics and conformational dynamics of the Argonaute silencing complex

The RNA-induced silencing complex (RISC), which powers RNA interference (RNAi), consists of a guide RNA and an Argonaute protein that slices target RNAs complementary to the guide. We find that for different guide-RNA sequences, slicing rates of perfectly complementary, bound targets can be surprisingly different (>250-fold range), and that faster slicing confers better knockdown in cells. Nucleotide sequence identities at guide-RNA positions 7, 10, and 17 underlie much of this variation in slicing rates. Analysis of one of these determinants implicates a structural distortion at guide nucleotides 6-7 in promoting slicing. Moreover, slicing directed by different guide sequences has an unanticipated, 600-fold range in 3'-mismatch tolerance, attributable to guides with weak (AU-rich) central pairing requiring extensive 3' complementarity (pairing beyond position 16) to more fully populate the slicing-competent conformation. Together, our analyses identify sequence determinants of RISC activity and provide biochemical and conformational rationale for their action.

The small non-coding RNA Vaultrc5 is dispensable to mouse development

Vault RNAs (vRNAs) are evolutionarily conserved small non-coding RNAs transcribed by RNA polymerase lll. Initially described as components of the vault particle, they have since also been described as noncanonical miRNA precursors and as riboregulators of autophagy. As central molecules in these processes, vRNAs have been attributed numerous biological roles including regulation of cell proliferation and survival, response to viral infections, drug resistance, and animal development. Yet, their impact to mammalian physiology remains largely unexplored. To study vault RNAs in vivo, we generated a mouse line with a conditional Vaultrc5 loss of function allele. Because Vaultrc5 is the sole murine vRNA, this allele enables the characterization of the physiological requirements of this conserved class of small regulatory RNAs in mammals. Using this strain, we show that mice constitutively null for Vaultrc5 are viable and histologically normal but have a slight reduction in platelet counts pointing to a potential role for vRNAs in hematopoiesis. This work paves the way for further in vivo characterizations of this abundant but mysterious RNA molecule. Specifically, it enables the study of the biological consequences of constitutive or lineage-specific Vaultrc5 deletion and of the physiological requirements for an intact Vaultrc5 during normal hematopoiesis or in response to cellular stresses such as oncogene expression, viral infection, or drug treatment.

Designing novel peptides for detecting the Omicron variant, specifying SARS-CoV-2, and simultaneously screening coronavirus infections

In this study in silico a candidate diagnostic peptide-based tool was designed in four stages including diagnosis of coronavirus diseases, simultaneously identifying of COVID-19 and SARS from other members of this family, specific identification of SARS-CoV2, and diagnosis of COVID-19 Omicron. Designed candidate peptides consist of four immunodominant peptides from the proteins of the SARS-CoV-2 spike (S) and membrane (M). The tertiary structure of each peptide was predicted. The stimulation ability of the humoral immunity for each peptide was evaluated. Finally, in silico cloning was performed to develop an expression strategy for each peptide. These four peptides have suitable immunogenicity, appropriate construct, and the ability to be expressed in E.coli. These results must be experimentally validated in vitro and in vivo to ensure the immunogenicity of the kit.Communicated by Ramaswamy H. Sarma.

sincFold: end-to-end learning of short- and long-range interactions in RNA secondary structure

MOTIVATION

Coding and noncoding RNA molecules participate in many important biological processes. Noncoding RNAs fold into well-defined secondary structures to exert their functions. However, the computational prediction of the secondary structure from a raw RNA sequence is a long-standing unsolved problem, which after decades of almost unchanged performance has now re-emerged due to deep learning. Traditional RNA secondary structure prediction algorithms have been mostly based on thermodynamic models and dynamic programming for free energy minimization. More recently deep learning methods have shown competitive performance compared with the classical ones, but there is still a wide margin for improvement.

RESULTS

In this work we present sincFold, an end-to-end deep learning approach, that predicts the nucleotides contact matrix using only the RNA sequence as input. The model is based on 1D and 2D residual neural networks that can learn short- and long-range interaction patterns. We show that structures can be accurately predicted with minimal physical assumptions. Extensive experiments were conducted on several benchmark datasets, considering sequence homology and cross-family validation. sincFold was compared with classical methods and recent deep learning models, showing that it can outperform the state-of-the-art methods.

Knotify_V2.0: Deciphering RNA Secondary Structures with H-Type Pseudoknots and Hairpin Loops

Accurately predicting the pairing order of bases in RNA molecules is essential for anticipating RNA secondary structures. Consequently, this task holds significant importance in unveiling previously unknown biological processes. The urgent need to comprehend RNA structures has been accentuated by the unprecedented impact of the widespread COVID-19 pandemic. This paper presents a framework, Knotify_V2.0, which makes use of syntactic pattern recognition techniques in order to predict RNA structures, with a specific emphasis on tackling the demanding task of predicting H-type pseudoknots that encompass bulges and hairpins. By leveraging the expressive capabilities of a Context-Free Grammar (CFG), the suggested framework integrates the inherent benefits of CFG and makes use of minimum free energy and maximum base pairing criteria. This integration enables the effective management of this inherently ambiguous task. The main contribution of Knotify_V2.0 compared to earlier versions lies in its capacity to identify additional motifs like bulges and hairpins within the internal loops of the pseudoknot. Notably, the proposed methodology, Knotify_V2.0, demonstrates superior accuracy in predicting core stems compared to state-of-the-art frameworks. Knotify_V2.0 exhibited exceptional performance by accurately identifying both core base pairing that form the ground truth pseudoknot in 70% of the examined sequences. Furthermore, Knotify_V2.0 narrowed the performance gap with Knotty, which had demonstrated better performance than Knotify and even surpassed it in Recall and F1-score metrics. Knotify_V2.0 achieved a higher count of true positives (tp) and a significantly lower count of false negatives (fn) compared to Knotify, highlighting improvements in Prediction and Recall metrics, respectively. Consequently, Knotify_V2.0 achieved a higher F1-score than any other platform. The source code and comprehensive implementation details of Knotify_V2.0 are publicly available on GitHub.

Tying the knot: Unraveling the intricacies of the coronavirus frameshift pseudoknot

Understanding and targeting functional RNA structures towards treatment of coronavirus infection can help us to prepare for novel variants of SARS-CoV-2 (the virus causing COVID-19), and any other coronaviruses that could emerge via human-to-human transmission or potential zoonotic (inter-species) events. Leveraging the fact that all coronaviruses use a mechanism known as -1 programmed ribosomal frameshifting (-1 PRF) to replicate, we apply algorithms to predict the most energetically favourable secondary structures (each nucleotide involved in at most one pairing) that may be involved in regulating the -1 PRF event in coronaviruses, especially SARS-CoV-2. We compute previously unknown most stable structure predictions for the frameshift site of coronaviruses via hierarchical folding, a biologically motivated framework where initial non-crossing structure folds first, followed by subsequent, possibly crossing (pseudoknotted), structures. Using mutual information from 181 coronavirus sequences, in conjunction with the algorithm KnotAli, we compute secondary structure predictions for the frameshift site of different coronaviruses. We then utilize the Shapify algorithm to obtain most stable SARS-CoV-2 secondary structure predictions guided by frameshift sequence-specific and genome-wide experimental data. We build on our previous secondary structure investigation of the singular SARS-CoV-2 68 nt frameshift element sequence, by using Shapify to obtain predictions for 132 extended sequences and including covariation information. Previous investigations have not applied hierarchical folding to extended length SARS-CoV-2 frameshift sequences. By doing so, we simulate the effects of ribosome interaction with the frameshift site, providing insight to biological function. We contribute in-depth discussion to contextualize secondary structure dual-graph motifs for SARS-CoV-2, highlighting the energetic stability of the previously identified 3_8 motif alongside the known dominant 3_3 and 3_6 (native-type) -1 PRF structures. Using a combination of thermodynamic methods and sequence covariation, our novel predictions suggest function of the attenuator hairpin via previously unknown pseudoknotted base pairing. While certain initial RNA folding is consistent, other pseudoknotted base pairs form which indicate potential conformational switching between the two structures.

Development and application of an RNA nanostructure to induce transient RNAi in difficult transgenic plants

The development of RNA interference (RNAi) is crucial for studying plant gene function. Its use, is limited to a few plants with well-established transgenic techniques. Spray-induced gene silencing (SIGS) introduces exogenous double-stranded RNA (dsRNA) into plants by spraying, injection, or irrigation, triggering the RNAi pathway to instantly silence target genes. As is a transient RNAi technology that does not rely on transgenic methods, SIGS has significant potential for studying gene function in plants lacking advanced transgenic technology. In this study, to enhance their stability and delivery efficiency, siRNAs were used as structural motifs to construct RNA nanoparticles (NPs) of four shapes: triangle, square, pentagon, and hexagon. These NPs, when synthesized by Escherichia coli, showed that triangular and square shapes accumulated more efficiently than pentagon and hexagon shapes. Bioassays revealed that RNA squares had the highest RNAi efficiency, followed by RNA triangles, with GFP-dsRNA showing the lowest efficiency at 4 and 7 days post-spray. We further explored the use of RNA squares in inducing transient RNAi in plants that are difficult to transform genetically. The results indicated that Panax notoginseng-derived MYB2 (PnMYB2) and Camellia oleifera-derived GUT (CoGUT) were significantly suppressed in P. notoginseng and C. oleifera, respectively, following the application of PnMYB2- and CoGUT-specific RNA squares. These findings suggest that RNA squares are highly effective in SIGS and can be utilized for gene function research in plants.

Heterobifunctional small molecules to modulate RNA function

RNA has diverse cellular functionality, including regulating gene expression, protein translation, and cellular response to stimuli, due to its intricate structures. Over the past decade, small molecules have been discovered that target functional structures within cellular RNAs and modulate their function. Simple binding, however, is often insufficient, resulting in low or even no biological activity. To overcome this challenge, heterobifunctional compounds have been developed that can covalently bind to the RNA target, alter RNA sequence, or induce its cleavage. Herein, we review the recent progress in the field of RNA-targeted heterobifunctional compounds using representative case studies. We identify critical gaps and limitations and propose a strategic pathway for future developments of RNA-targeted molecules with augmented functionalities.

High frequency of transition to transversion ratio in the stem region of RNA secondary structure of untranslated region of SARS-CoV-2

Introduction

The propensity of nucleotide bases to form pairs, causes folding and the formation of secondary structure in the RNA. Therefore, purine (R): pyrimidine (Y) base-pairing is vital to maintain uniform lateral dimension in RNA secondary structure. Transversions or base substitutions between R and Y bases, are more detrimental to the stability of RNA secondary structure, than transitions derived from substitutions between A and G or C and T. The study of transversion and transition base substitutions is important to understand evolutionary mechanisms of RNA secondary structure in the 5'  and 3'  untranslated (UTR) regions of SARS-CoV-2. In this work, we carried out comparative analysis of transition and transversion base substitutions in the stem and loop regions of RNA secondary structure of SARS-CoV-2.

Methods

We have considered the experimentally determined and well documented stem and loop regions of 5' and 3' UTR regions of SARS-CoV-2 for base substitution analysis. The secondary structure comprising of stem and loop regions were visualized using the RNAfold web server. The GISAID repository was used to extract base sequence alignment of the UTR regions. Python scripts were developed for comparative analysis of transversion and transition frequencies in the stem and the loop regions.

Results

The results of base substitution analysis revealed a higher transition (ti) to transversion (tv) ratio (ti/tv) in the stem region of UTR of RNA secondary structure of SARS-CoV-2 reported during the early stage of the pandemic. The higher ti/tv ratio in the stem region suggested the influence of secondary structure in selecting the pattern of base substitutions. This differential pattern of ti/tv values between stem and loop regions was not observed among the Delta and Omicron variants that dominated the later stage of the pandemic. It is noteworthy that the ti/tv values in the stem and loop regions were similar among the later dominant Delta and Omicron variant strains which is to be investigated to understand the rapid evolution and global adaptation of SARS-CoV-2.

Conclusion

Our findings implicate the lower frequency of transversions than the transitions in the stem regions of UTRs of SARS-CoV-2. The RNA secondary structures are associated with replication, translation, and packaging, further investigations are needed to understand these base substitutions across different variants of SARS-CoV-2.

Co-delivery of Cas9 mRNA and guide RNAs for editing of LGMN gene represses breast cancer cell metastasis

Legumain (or asparagine endopeptidase/AEP) is a lysosomal cysteine endopeptidase associated with increased invasive and migratory behavior in a variety of cancers. In this study, co-delivery of Cas9 mRNA and guide RNA (gRNA) by lipid nanoparticles (LNP) for editing of LGMN gene was performed. For in-vitro transcription (IVT) of gRNA, two templates were designed: linearized pUC57-T7-gRNA and T7-gRNA oligos, and the effectiveness of gRNA was verified in multiple ways. Cas9 plasmid was modified and optimized for IVT of Cas9 mRNA. The effects of LGMN gene editing on lysosomal/autophagic function and cancer cell metastasis were investigated. Co-delivery of Cas9 mRNA and gRNA resulted in impaired lysosomal/autophagic degradation, clone formation, migration, and invasion capacity of cancer cells in-vitro. Experimental lung metastasis experiment indicates co-delivery of Cas9 mRNA and gRNA by LNP reduced the migration and invasion capacity of cancer cells in-vivo. These results indicate that co-delivery of Cas9 mRNA and gRNA can enhance the efficiency of CRISPR/Cas9-mediated gene editing in-vitro and in-vivo, and suggest that Cas9 mRNA and gRNA gene editing of LGMN may be a potential treatment for breast tumor metastasis.

Molecular Detection of Hemoplasma in animals in Tamil Nadu, India and Hemoplasma genome analysis

Hemoplasma are small pleomorphic wall-less Gram-positive bacteria that infect erythrocytes of various mammalian hosts. They generally cause asymptomatic or chronic anaemia but occasionally causes overt life-threatening hemolytic anaemia. In the present study, 316 cattle, 115 sheep, 61 goats and 6 buffalo blood samples were collected from various villages or organized farms located in nine districts of Tamil Nadu to detect the hemoplasma by PCR. Overall prevalence of 43.04%, 65.22%, and 44.26% hemoplasma DNA was observed in cattle, sheep and goats, respectively. In total, 21 hemoplasma positive samples were sequenced for 16S rRNA gene which revealed 8 Mycoplasma wenyonii, 11 'Candidatus Mycoplasma haemobos' and one Mycoplasma ovis infection. Sheep blood samples from Chennai district were infected with 'Ca. M. haemobos' whereas sheep sample from Thiruvannamalai district was infected with M. wenyonii. At least 50% genes in the hemoplasma genomes were paralogous genes whose functions were not known. Only 'Ca. M. haemolamae' genome contained one primitive CRISPR system without any cas genes. Antimicrobial resistance genes (ARG) could not be identified in any of the hemoplasma genomes but homologous ARG were identified in all the genomes. Adhesion related gene EF-Tu was detected in all 14 hemoplasma genomes but enolase gene was detected only in 'Ca. M. haemohominis' SWG34-3 genome. This is the first report on the prevalence of hemoplasma infection in cattle, sheep and goat in India.

Recent applications of artificial intelligence in RNA-targeted small molecule drug discovery

INTRODUCTION

Targeting RNAs with small molecules offers an alternative to the conventional protein-targeted drug discovery and can potentially address unmet and emerging medical needs. The recent rise of interest in the strategy has already resulted in large amounts of data on disease associated RNAs, as well as on small molecules that bind to such RNAs. Artificial intelligence (AI) approaches, including machine learning and deep learning, present an opportunity to speed up the discovery of RNA-targeted small molecules by improving decision-making efficiency and quality.

AREAS COVERED

The topics described in this review include the recent applications of AI in the identification of RNA targets, RNA structure determination, screening of chemical compound libraries, and hit-to-lead optimization. The impact and limitations of the recent AI applications are discussed, along with an outlook on the possible applications of next-generation AI tools for the discovery of novel RNA-targeted small molecule drugs.

EXPERT OPINION

Key areas for improvement include developing AI tools for understanding RNA dynamics and RNA - small molecule interactions. High-quality and comprehensive data still need to be generated especially on the biological activity of small molecules that target RNAs.

Accurate prediction of RNA secondary structure including pseudoknots through solving minimum-cost flow with learned potentials

Pseudoknots are key structure motifs of RNA and pseudoknotted RNAs play important roles in a variety of biological processes. Here, we present KnotFold, an accurate approach to the prediction of RNA secondary structure including pseudoknots. The key elements of KnotFold include a learned potential function and a minimum-cost flow algorithm to find the secondary structure with the lowest potential. KnotFold learns the potential from the RNAs with known structures using an attention-based neural network, thus avoiding the inaccuracy of hand-crafted energy functions. The specially designed minimum-cost flow algorithm used by KnotFold considers all possible combinations of base pairs and selects from them the optimal combination. The algorithm breaks the restriction of nested base pairs required by the widely used dynamic programming algorithms, thus enabling the identification of pseudoknots. Using 1,009 pseudoknotted RNAs as representatives, we demonstrate the successful application of KnotFold in predicting RNA secondary structures including pseudoknots with accuracy higher than the state-of-the-art approaches. We anticipate that KnotFold, with its superior accuracy, will greatly facilitate the understanding of RNA structures and functionalities.

SparseRNAfolD: optimized sparse RNA pseudoknot-free folding with dangle consideration

MOTIVATION

Computational RNA secondary structure prediction by free energy minimization is indispensable for analyzing structural RNAs and their interactions. These methods find the structure with the minimum free energy (MFE) among exponentially many possible structures and have a restrictive time and space complexity ( O ( n 3 ) time and O ( n 2 ) space for pseudoknot-free structures) for longer RNA sequences. Furthermore, accurate free energy calculations, including dangle contributions can be difficult and costly to implement, particularly when optimizing for time and space requirements.

RESULTS

Here we introduce a fast and efficient sparsified MFE pseudoknot-free structure prediction algorithm, SparseRNAFolD, that utilizes an accurate energy model that accounts for dangle contributions. While the sparsification technique was previously employed to improve the time and space complexity of a pseudoknot-free structure prediction method with a realistic energy model, SparseMFEFold, it was not extended to include dangle contributions due to the complexity of computation. This may come at the cost of prediction accuracy. In this work, we compare three different sparsified implementations for dangle contributions and provide pros and cons of each method. As well, we compare our algorithm to LinearFold, a linear time and space algorithm, where we find that in practice, SparseRNAFolD has lower memory consumption across all lengths of sequence and a faster time for lengths up to 1000 bases.

CONCLUSION

Our SparseRNAFolD algorithm is an MFE-based algorithm that guarantees optimality of result and employs the most general energy model, including dangle contributions. We provide a basis for applying dangles to sparsified recursion in a pseudoknot-free model that has the potential to be extended to pseudoknots.

Probing the legitimate initiation of RNA synthesis by Qβ replicase with oligonucleotide primers

Oligoribonucleotides complementary to the template 3' terminus were tested for their ability to initiate RNA synthesis on legitimate templates capable of exponential amplification by Qβ replicase. Oligonucleotides shorter than the distance to the nearest predicted template hairpin proved able to serve as primers, with the optimal length varying for different templates, suggesting that during initiation the template retains its native fold incorporating the 3' terminus. The priming activity of an oligonucleotide is greatly enhanced by its 5'-triphosphate group, the effect being strongly dependent on Mg2+ ions. This indicates that, unlike other studied RNA polymerases, Qβ replicase binds the 5'-triphosphate of the initiating nucleotide GTP, and this binding is needed for the replication of legitimate templates.

DERNA Enables Pareto Optimal RNA Design

The design of an RNA sequence v that encodes an input target protein sequence w is a crucial aspect of messenger RNA (mRNA) vaccine development. There are an exponential number of possible RNA sequences for a single target protein due to codon degeneracy. These potential RNA sequences can assume various secondary structure conformations, each with distinct minimum free energy (MFE), impacting thermodynamic stability and mRNA half-life. Furthermore, the presence of species-specific codon usage bias, quantified by the codon adaptation index (CAI), plays a vital role in translation efficiency. While earlier studies focused on optimizing either MFE or CAI, recent research has underscored the advantages of simultaneously optimizing both objectives. However, optimizing one objective comes at the expense of the other. In this work, we present the Pareto Optimal RNA Design problem, aiming to identify the set of Pareto optimal solutions for which no alternative solutions exist that exhibit better MFE and CAI values. Our algorithm DEsign RNA (DERNA) uses the weighted sum method to enumerate the Pareto front by optimizing convex combinations of both objectives. We use dynamic programming to solve each convex combination in O ( | w | 3 ) time and O ( | w | 2 ) space. Compared with a CDSfold, previous approach that only optimizes MFE, we show on a benchmark data set that DERNA obtains solutions with identical MFE but superior CAI. Moreover, we show that DERNA matches the performance in terms of solution quality of LinearDesign, a recent approach that similarly seeks to balance MFE and CAI. We conclude by demonstrating our method's potential for mRNA vaccine design for the SARS-CoV-2 spike protein.

Graph Convolutional Network for predicting secondary structure of RNA

The prediction of RNA secondary structures is essential for understanding its underlying principles and applications in diverse fields, including molecular diagnostics and RNA-based therapeutic strategies. However, the complexity of the search space presents a challenge. This work proposes a Graph Convolutional Network (GCNfold) for predicting the RNA secondary structure. GCNfold considers an RNA sequence as graph-structured data and predicts posterior base-pairing probabilities given the prior base-pairing probabilities, calculated using McCaskill's partition function. The performance of GCNfold surpasses that of the state-of-the-art folding algorithms, as we have incorporated minimum free energy information into the richly parameterized network, enhancing its robustness in predicting non-homologous RNA secondary structures. A Symmetric Argmax Post-processing algorithm ensures that GCNfold formulates valid structures. To validate our algorithm, we applied it to the SARS-CoV-2 E gene and determined the secondary structure of the E-gene across the Betacoronavirus subgenera.

Dissection of protein and RNA regions required for SPEN binding to XIST A-repeat RNA

XIST noncoding RNA promotes the initiation of X chromosome silencing by recruiting the protein SPEN to one X chromosome in female mammals. The SPEN protein is also called SHARP (SMRT and HDAC-associated repressor protein) and MINT (Msx-2 interacting nuclear target) in humans. SPEN recruits N-CoR2 and HDAC3 to initiate histone deacetylation on the X chromosome, leading to the formation of repressive chromatin marks and silencing gene expression. We dissected the contributions of different RNA and protein regions to the formation of a human XIST-SPEN complex in vitro and identified novel sequence and structure determinants that may contribute to X chromosome silencing initiation. Binding of SPEN to XIST RNA requires RRM 4 of the protein, in contrast to the requirement of RRM 3 and RRM 4 for specific binding to SRA RNA. Measurements of SPEN binding to full-length, dimeric, trimeric, or other truncated versions of the A-repeat region revealed that high-affinity binding of XIST to SPEN in vitro requires a minimum of four A-repeat segments. SPEN binding to XIST A-repeat RNA changes the accessibility of the RNA at specific nucleotide sequences, as indicated by changes in RNA reactivity through chemical structure probing. Based on computational modeling, we found that inter-repeat duplexes formed by multiple A-repeats can present an unpaired adenosine in the context of a double-stranded region of RNA. The presence of this specific combination of sequence and structural motifs correlates with high-affinity SPEN binding in vitro. These data provide new information on the molecular basis of the XIST and SPEN interaction.

Differentiable partition function calculation for RNA

Ribonucleic acid (RNA) is an essential molecule in a wide range of biological functions. In 1990, McCaskill introduced a dynamic programming algorithm for computing the partition function of an RNA sequence. McCaskill's algorithm is widely used today for understanding the thermodynamic properties of RNA. In this work, we introduce a generalization of McCaskill's algorithm that is well-defined over continuous inputs. Crucially, this enables us to implement an end-to-end differentiable partition function calculation. The derivative can be computed with respect to the input, or to any other fixed values, such as the parameters of the energy model. This builds a bridge between RNA thermodynamics and the tools of differentiable programming including deep learning as it enables the partition function to be incorporated directly into any end-to-end differentiable pipeline. To demonstrate the effectiveness of our new approach, we tackle the inverse folding problem directly using gradient optimization. We find that using the gradient to optimize the sequence directly is sufficient to arrive at sequences with a high probability of folding into the desired structure. This indicates that the gradients we compute are meaningful.

Molecular modeling and simulation studies of SELEX-derived high-affinity DNA aptamers to the Ebola virus nucleoprotein

Ebola viral disease (EVD) is a highly infectious and potentially fatal illness with a case fatality rate ranging from 25% to 90%. To effectively control its spread, there is a need for rapid, reliable and lowcost point-of-care (P OC) diagnostic tests. While various EVD diagnostic tests exist, few are P OC tests, and many are not cost-effective. The use of antibodies in these tests has limitations, prompting the exploration of aptamers as potential alternatives. Various proteins from the Ebola virus (EBOV) proteome, including EBOV nucleoprotein (NP), are considered viable targets for diagnostic assays. A previous study identified three aptamers (Apt1. Apt2 and Apt3) with high affinity for EBOV NP using systemic evolution of ligands by exponential enrichment (SELEX). This study aimed to employ in silico methods, such as Phyre2, RNAfold, RNAComposer, HADDOCK and GROMACS, to model the structures of EBOV NP and the aptamers, and to investigate their binding. The in silico analysis revealed successful binding of all the three aptamers to EBOV NP, with a suggested ranking of Apt1 > Apt2 > Apt3 based on binding affinity. Microscale thermophoresis (MST) analysis confirmed the binding, providing dissociation constants of 25 ± 2.84, 56 ± 2.76 and 140 ±3.69 nM for Apt1, Apt2 and Apt3, respectively. The study shows that the findings of the in silico analysis was in agreement with the MST analysis. Inclusion of these in silico approaches in diagnostic assay development can expedite the selection of candidate aptamers, potentially overcoming challenges associated with aptamer application in diagnostics.Communicated by Ramaswamy H. Sarma.

Disparate macrophage responses are linked to infection outcome of Hantan virus in humans or rodents

Hantaan virus (HTNV) is asymptomatically carried by rodents, yet causes lethal hemorrhagic fever with renal syndrome in humans, the underlying mechanisms of which remain to be elucidated. Here, we show that differential macrophage responses may determine disparate infection outcomes. In mice, late-phase inactivation of inflammatory macrophage prevents cytokine storm syndrome that usually occurs in HTNV-infected patients. This is attained by elaborate crosstalk between Notch and NF-κB pathways. Mechanistically, Notch receptors activated by HTNV enhance NF-κB signaling by recruiting IKKβ and p65, promoting inflammatory macrophage polarization in both species. However, in mice rather than humans, Notch-mediated inflammation is timely restrained by a series of murine-specific long noncoding RNAs transcribed by the Notch pathway in a negative feedback manner. Among them, the lnc-ip65 detaches p65 from the Notch receptor and inhibits p65 phosphorylation, rewiring macrophages from the pro-inflammation to the pro-resolution phenotype. Genetic ablation of lnc-ip65 leads to destructive HTNV infection in mice. Thus, our findings reveal an immune-braking function of murine noncoding RNAs, offering a special therapeutic strategy for HTNV infection.

Assessing the Quality of Cotranscriptional Folding Simulations

Structural changes in RNAs are an important contributor to controlling gene expression not only at the posttranscriptional stage but also during transcription. A subclass of riboswitches and RNA thermometers located in the 5' region of the primary transcript regulates the downstream functional unit - usually an ORF - through premature termination of transcription. Not only such elements occur naturally, but they are also attractive devices in synthetic biology. The possibility to design such riboswitches or RNA thermometers is thus of considerable practical interest. Since these functional RNA elements act already during transcription, it is important to model and understand the dynamics of folding and, in particular, the formation of intermediate structures concurrently with transcription. Cotranscriptional folding simulations are therefore an important step to verify the functionality of design constructs before conducting expensive and labor-intensive wet lab experiments. For RNAs, full-fledged molecular dynamics simulations are far beyond practical reach because of both the size of the molecules and the timescales of interest. Even at the simplified level of secondary structures, further approximations are necessary. The BarMap approach is based on representing the secondary structure landscape for each individual transcription step by a coarse-grained representation that only retains a small set of low-energy local minima and the energy barriers between them. The folding dynamics between two transcriptional elongation steps is modeled as a Markov process on this representation. Maps between pairs of consecutive coarse-grained landscapes make it possible to follow the folding process as it changes in response to transcription elongation. In its original implementation, the BarMap software provides a general framework to investigate RNA folding dynamics on temporally changing landscapes. It is, however, difficult to use in particular for specific scenarios such as cotranscriptional folding. To overcome this limitation, we developed the user-friendly BarMap-QA pipeline described in detail in this contribution. It is illustrated here by an elaborate example that emphasizes the careful monitoring of several quality measures. Using an iterative workflow, a reliable and complete kinetics simulation of a synthetic, transcription-regulating riboswitch is obtained using minimal computational resources. All programs and scripts used in this contribution are free software and available for download as a source distribution for Linux® or as a platform-independent Docker® image including support for Apple macOS® and Microsoft Windows®.

Estimating RNA Secondary Structure Folding Free Energy Changes with efn2

A number of analyses require estimates of the folding free energy changes of specific RNA secondary structures. These predictions are often based on a set of nearest neighbor parameters that models the folding stability of a RNA secondary structure as the sum of folding stabilities of the structural elements that comprise the secondary structure. In the software suite RNAstructure, the free energy change calculation is implemented in the program efn2. The efn2 program estimates the folding free energy change and the experimental uncertainty in the folding free energy change. It can be run through the graphical user interface for RNAstructure, from the command line, or a web server. This chapter provides detailed protocols for using efn2.

Impact on splicing in Saccharomyces cerevisiae of random 50-base sequences inserted into an intron

Intron splicing is a key regulatory step in gene expression in eukaryotes. Three sequence elements required for splicing-5' and 3' splice sites and a branchpoint-are especially well-characterized in Saccharomyces cerevisiae, but our understanding of additional intron features that impact splicing in this organism is incomplete, due largely to its small number of introns. To overcome this limitation, we constructed a library in S. cerevisiae of random 50-nt (N50) elements individually inserted into the intron of a reporter gene and quantified canonical splicing and the use of cryptic splice sites by sequencing analysis. More than 70% of approximately 140,000 N50 elements reduced splicing by at least 20%. N50 features, including higher GC content, presence of GU repeats, and stronger predicted secondary structure of its pre-mRNA, correlated with reduced splicing efficiency. A likely basis for the reduced splicing of such a large proportion of variants is the formation of RNA structures that pair N50 bases-such as the GU repeats-with other bases specifically within the reporter pre-mRNA analyzed. However, multiple models were unable to explain more than a small fraction of the variance in splicing efficiency across the library, suggesting that complex nonlinear interactions in RNA structures are not accurately captured by RNA structure prediction methods. Our results imply that the specific context of a pre-mRNA may determine the bases allowable in an intron to prevent secondary structures that reduce splicing. This large data set can serve as a resource for further exploration of splicing mechanisms.

Creation of Architecturally Minimal Transcriptionally Activating Riboswitches Responsive to Theophylline Reveals an Unconventional Design Strategy

Riboswitches are noncoding RNA switches that are largely utilized in bacteria and play a significant role in synthetic biology. Nonetheless, their natural counterparts possess lengthy sequences and intricate structures, posing challenges for their modular integration into complex gene circuits. Consequently, it is imperative to develop simplified synthetic riboswitches that can be effortlessly incorporated into gene circuits. The conventional approach to generate synthetic riboswitches entails tedious library construction and extensive screening, which frequently yields suboptimal performance. To overcome this obstacle, alternative methods are urgently needed. In this study, we created a novel approach to designing a diverse set of transcription-activating riboswitches that exhibit high performance and broad compatibility. The strategy involved starting with a synthetic theophylline RNA aptamer and designing an expression platform that forms a transcriptional terminator in its inactive state but switches to an antiterminator when it is activated. Several sequences were designed, constructed, and subjected to virtual screening, resulting in the identification of two transcription-activating riboswitches. These riboswitches were then engineered to reduce the basal leakage and increase the activation level through extending the hairpin region using a screened random sequence. These architecturally minimal synthetic riboswitches were highly adapted to different constitutive promoters in a modular manner, generating a differentially responsive output to theophylline. As a proof-of-principle, the synthetic riboswitches were applied to rewire a synthetic quorum-sensing circuit (QSC). The reprogrammed QSC successfully modulated the temporal responsive profile against the activation. This strategy is expected to expand the variety of high-performance riboswitches that are responsive to different ligands, thereby further facilitating the design of complex genetic circuits.

Iron inhibits glioblastoma cell migration and polarization

Glioblastoma is one of the deadliest malignancies facing modern oncology today. The ability of glioblastoma cells to diffusely spread into neighboring healthy brain makes complete surgical resection nearly impossible and contributes to the recurrent disease faced by most patients. Although research into the impact of iron on glioblastoma has addressed proliferation, there has been little investigation into how cellular iron impacts the ability of glioblastoma cells to migrate-a key question, especially in the context of the diffuse spread observed in these tumors. Herein, we show that increasing cellular iron content results in decreased migratory capacity of human glioblastoma cells. The decrease in migratory capacity was accompanied by a decrease in cellular polarization in the direction of movement. Expression of CDC42, a Rho GTPase that is essential for both cellular migration and establishment of polarity in the direction of cell movement, was reduced upon iron treatment. We then analyzed a single-cell RNA-seq dataset of human glioblastoma samples and found that cells at the tumor periphery had a gene signature that is consistent with having lower levels of cellular iron. Altogether, our results suggest that cellular iron content is impacting glioblastoma cell migratory capacity and that cells with higher iron levels exhibit reduced motility.

RNA tertiary structure prediction using RNAComposer in CASP15

As CASP15 participants, in the new category of 3D RNA structure prediction, we applied expert modeling with the support of our proprietary system RNAComposer. Although RNAComposer is primarily known as an automated web server, its features allow it to be used interactively, for example, for homology-based modeling or assembling models from user-provided structural elements. In the paper, we present various scenarios of applying the system to predict the 3D RNA structures that we employed. Their combination with expert input, comparative analysis of models, and routines to select representative resultant structures form a ready-for-reuse workflow. With selected examples, we demonstrate its application for the in silico modeling of natural and synthetic RNA molecules targeted in CASP15.

ADAR1 regulates vascular remodeling in hypoxic pulmonary hypertension through N1-methyladenosine modification of circCDK17

Pulmonary hypertension (PH) is an extremely malignant pulmonary vascular disease of unknown etiology. ADAR1 is an RNA editing enzyme that converts adenosine in RNA to inosine, thereby affecting RNA expression. However, the role of ADAR1 in PH development remains unclear. In the present study, we investigated the biological role and molecular mechanism of ADAR1 in PH pulmonary vascular remodeling. Overexpression of ADAR1 aggravated PH progression and promoted the proliferation of pulmonary artery smooth muscle cells (PASMCs). Conversely, inhibition of ADAR1 produced opposite effects. High-throughput whole transcriptome sequencing showed that ADAR1 was an important regulator of circRNAs in PH. CircCDK17 level was significantly lowered in the serum of PH patients. The effects of ADAR1 on cell cycle progression and proliferation were mediated by circCDK17. ADAR1 affects the stability of circCDK17 by mediating A-to-I modification at the A5 and A293 sites of circCDK17 to prevent it from m1A modification. We demonstrate for the first time that ADAR1 contributes to the PH development, at least partially, through m1A modification of circCDK17 and the subsequent PASMCs proliferation. Our study provides a novel therapeutic strategy for treatment of PH and the evidence for circCDK17 as a potential novel marker for the diagnosis of this disease.