In vivo secondary structural analysis of Influenza A virus genomic RNA.

Influenza A virus (IAV) is a respiratory virus that causes epidemics and pandemics. Knowledge of IAV RNA secondary structure in vivo is crucial for a better understanding of virus biology. Moreover, it is a fundament for the development of new RNA-targeting antivirals. Chemical RNA mapping using selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) coupled with Mutational Profiling (MaP) allows for the thorough examination of secondary structures in low-abundance RNAs in their biological context. So far, the method has been used for analyzing the RNA secondary structures of several viruses including SARS-CoV-2 in virio and in cellulo. Here, we used SHAPE-MaP and dimethyl sulfate mutational profiling with sequencing (DMS-MaPseq) for genome-wide secondary structure analysis of viral RNA (vRNA) of the pandemic influenza A/California/04/2009 (H1N1) strain in both in virio and in cellulo environments. Experimental data allowed the prediction of the secondary structures of all eight vRNA segments in virio and, for the first time, the structures of vRNA5, 7, and 8 in cellulo. We conducted a comprehensive structural analysis of the proposed vRNA structures to reveal the motifs predicted with the highest accuracy. We also performed a base-pairs conservation analysis of the predicted vRNA structures and revealed many highly conserved vRNA motifs among the IAVs. The structural motifs presented herein are potential candidates for new IAV antiviral strategies.

Generation and Functional Analysis of Defective Viral Genomes during SARS-CoV-2 Infection.

Defective viral genomes (DVGs) have been identified in many RNA viruses as a major factor influencing antiviral immune response and viral pathogenesis. However, the generation and function of DVGs in SARS-CoV-2 infection are less known. In this study, we elucidated DVG generation in SARS-CoV-2 and its relationship with host antiviral immune response. We observed DVGs ubiquitously from transcriptome sequencing (RNA-seq) data sets of in vitro infections and autopsy lung tissues of COVID-19 patients. Four genomic hot spots were identified for DVG recombination, and RNA secondary structures were suggested to mediate DVG formation. Functionally, bulk and single-cell RNA-seq analysis indicated the interferon (IFN) stimulation of SARS-CoV-2 DVGs. We further applied our criteria to the next-generation sequencing (NGS) data set from a published cohort study and observed a significantly higher amount and frequency of DVG in symptomatic patients than those in asymptomatic patients. Finally, we observed exceptionally diverse DVG populations in one immunosuppressive patient up to 140 days after the first positive test of COVID-19, suggesting for the first time an association between DVGs and persistent viral infections in SARS-CoV-2. Together, our findings strongly suggest a critical role of DVGs in modulating host IFN responses and symptom development, calling for further inquiry into the mechanisms of DVG generation and into how DVGs modulate host responses and infection outcome during SARS-CoV-2 infection. IMPORTANCE Defective viral genomes (DVGs) are generated ubiquitously in many RNA viruses, including SARS-CoV-2. Their interference activity to full-length viruses and IFN stimulation provide the potential for them to be used in novel antiviral therapies and vaccine development. SARS-CoV-2 DVGs are generated through the recombination of two discontinuous genomic fragments by viral polymerase complex, and this recombination is also one of the major mechanisms for the emergence of new coronaviruses. Focusing on the generation and function of SARS-CoV-2 DVGs, these studies identify new hot spots for nonhomologous recombination and strongly suggest that the secondary structures within viral genomes mediate the recombination. Furthermore, these studies provide the first evidence for IFN stimulation activity of de novo DVGs during natural SARS-CoV-2 infection. These findings set up the foundation for further mechanism studies of SARS-CoV-2 recombination and provide evidence to harness the immunostimulatory potential of DVGs in the development of a vaccine and antivirals for SARS-CoV-2.

Structural Landscape of nsp Coding Genomic Regions of SARS-CoV-2-ssRNA Genome: A Structural Genomics Approach Toward Identification of Druggable Genome, Ligand-Binding Pockets, and Structure-Based Druggability.

SARS-CoV-2 has a single-stranded RNA genome (+ssRNA), and synthesizes structural and non-structural proteins (nsps). All 16 nsp are synthesized from the ORF1a, and ORF1b regions associated with different life cycle preprocesses, including replication. The regions of ORF1a synthesizes nsp1 to 11, and ORF1b synthesizes nsp12 to 16. In this paper, we have predicted the secondary structure conformations, entropy & mountain plots, RNA secondary structure in a linear fashion, and 3D structure of nsp coding genes of the SARS-CoV-2 genome. We have also analyzed the A, T, G, C, A+T, and G+C contents, GC-profiling of these genes, showing the range of the GC content from 34.23 to 48.52%. We have observed that the GC-profile value of the nsp coding genomic regions was less (about 0.375) compared to the whole genome (about 0.38). Additionally, druggable pockets were identified from the secondary structure-guided 3D structural conformations. For secondary structure generation of all the nsp coding genes (nsp 1-16), we used a recent algorithm-based tool (deep learning-based) along with the conventional algorithms (centroid and MFE-based) to develop secondary structural conformations, and we found stem-loop, multi-branch loop, pseudoknot, and the bulge structural components, etc. The 3D model shows bound and unbound forms, branched structures, duplex structures, three-way junctions, four-way junctions, etc. Finally, we identified binding pockets of nsp coding genes which will help as a fundamental resource for future researchers to develop RNA-targeted therapeutics using the druggable genome.

SARS-CoV-2 Variants of Concern and Variations within Their Genome Architecture: Does Nucleotide Distribution and Mutation Rate Alter the Functionality and Evolution of the Virus?

SARS-CoV-2 virus pathogenicity and transmissibility are correlated with the mutations acquired over time, giving rise to variants of concern (VOCs). Mutations can significantly influence the genetic make-up of the virus. Herein, we analyzed the SARS-CoV-2 genomes and sub-genomic nucleotide composition in relation to the mutation rate. Nucleotide percentage distributions of 1397 in-house-sequenced SARS-CoV-2 genomes were enumerated, and comparative analyses (i) within the VOCs and of (ii) recovered and mortality patients were performed. Fisher's test was carried out to highlight the significant mutations, followed by RNA secondary structure prediction and protein modeling for their functional impacts. Subsequently, a uniform dinucleotide composition of AT and GC was found across study cohorts. Notably, the N gene was observed to have a high GC percentage coupled with a relatively higher mutation rate. Functional analysis demonstrated the N gene mutations, C29144T and G29332T, to induce structural changes at the RNA level. Protein secondary structure prediction with N gene missense mutations revealed a differential composition of alpha helices, beta sheets, and coils, whereas the tertiary structure displayed no significant changes. Additionally, the N gene CTD region displayed no mutations. The analysis highlighted the importance of N protein in viral evolution with CTD as a possible target for antiviral drugs.

Recent advances in RNA structurome.

RNA structures are essential to support RNA functions and regulation in various biological processes. Recently, a range of novel technologies have been developed to decode genome-wide RNA structures and novel modes of functionality across a wide range of species. In this review, we summarize key strategies for probing the RNA structurome and discuss the pros and cons of representative technologies. In particular, these new technologies have been applied to dissect the structural landscape of the SARS-CoV-2 RNA genome. We also summarize the functionalities of RNA structures discovered in different regulatory layers-including RNA processing, transport, localization, and mRNA translation-across viruses, bacteria, animals, and plants. We review many versatile RNA structural elements in the context of different physiological and pathological processes (e.g., cell differentiation, stress response, and viral replication). Finally, we discuss future prospects for RNA structural studies to map the RNA structurome at higher resolution and at the single-molecule and single-cell level, and to decipher novel modes of RNA structures and functions for innovative applications.

RNA Secondary Structure Database, Analysis Tool-Set, and Case-Study Results on SARS-CoV-2

COVID-19 pandemic has brought immense attention to SARS-CoV-2 and related microbiology studies. To defeat this deadly virus, its RNA is being studied by many researchers around the globe. This study primarily aims to compile a large RNA dataset to analyze RNA secondary structure of SARS-CoV-2 efficiently. We propose improvements on database creation and maintenance, and structure analysis tools. As a continuation of our previous works, we automate the creation of RNA secondary structures database in a new format by converting data collected from publicly available online resources. We present new secondary structure analysis algorithms that improve performance of existing tools. Results of GPU-based implementation are also presented for RNA search operations. We also introduce tools with new objectives, which answer fundamental RNA secondary structure queries. Our tools on the current database have been tested with SARS-CoV-2 related RNA secondary structures. A novel RNA secondary structure search-based multiple RNA comparison is introduced and tested too. Structural-only and structure-with-nucleotide search results particularly related to SARS-CoV-2 are presented in details. As a successful case study, the framework presented here offers some unique capabilities and is shown as a useful exploratory tool for future RNA analysis studies. © 2021 IEEE.

Secondary Structure of Influenza A Virus Genomic Segment 8 RNA Folded in a Cellular Environment.

Influenza A virus (IAV) is a member of the single-stranded RNA (ssRNA) family of viruses. The most recent global pandemic caused by the SARS-CoV-2 virus has shown the major threat that RNA viruses can pose to humanity. In comparison, influenza has an even higher pandemic potential as a result of its high rate of mutations within its relatively short (<13 kbp) genome, as well as its capability to undergo genetic reassortment. In light of this threat, and the fact that RNA structure is connected to a broad range of known biological functions, deeper investigation of viral RNA (vRNA) structures is of high interest. Here, for the first time, we propose a secondary structure for segment 8 vRNA (vRNA8) of A/California/04/2009 (H1N1) formed in the presence of cellular and viral components. This structure shows similarities with prior in vitro experiments. Additionally, we determined the location of several well-defined, conserved structural motifs of vRNA8 within IAV strains with possible functionality. These RNA motifs appear to fold independently of regional nucleoprotein (NP)-binding affinity, but a low or uneven distribution of NP in each motif region is noted. This research also highlights several accessible sites for oligonucleotide tools and small molecules in vRNA8 in a cellular environment that might be a target for influenza A virus inhibition on the RNA level.

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.

LinearTurboFold: Linear-time global prediction of conserved structures for RNA homologs with applications to SARS-CoV-2.

The constant emergence of COVID-19 variants reduces the effectiveness of existing vaccines and test kits. Therefore, it is critical to identify conserved structures in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes as potential targets for variant-proof diagnostics and therapeutics. However, the algorithms to predict these conserved structures, which simultaneously fold and align multiple RNA homologs, scale at best cubically with sequence length and are thus infeasible for coronaviruses, which possess the longest genomes (∼30,000 nt) among RNA viruses. As a result, existing efforts on modeling SARS-CoV-2 structures resort to single-sequence folding as well as local folding methods with short window sizes, which inevitably neglect long-range interactions that are crucial in RNA functions. Here we present LinearTurboFold, an efficient algorithm for folding RNA homologs that scales linearly with sequence length, enabling unprecedented global structural analysis on SARS-CoV-2. Surprisingly, on a group of SARS-CoV-2 and SARS-related genomes, LinearTurboFold's purely in silico prediction not only is close to experimentally guided models for local structures, but also goes far beyond them by capturing the end-to-end pairs between 5' and 3' untranslated regions (UTRs) (∼29,800 nt apart) that match perfectly with a purely experimental work. Furthermore, LinearTurboFold identifies undiscovered conserved structures and conserved accessible regions as potential targets for designing efficient and mutation-insensitive small-molecule drugs, antisense oligonucleotides, small interfering RNAs (siRNAs), CRISPR-Cas13 guide RNAs, and RT-PCR primers. LinearTurboFold is a general technique that can also be applied to other RNA viruses and full-length genome studies and will be a useful tool in fighting the current and future pandemics.

Detailed Dissection and Critical Evaluation of the Pfizer/BioNTech and Moderna mRNA Vaccines.

The design of Pfizer/BioNTech and Moderna mRNA vaccines involves many different types of optimizations. Proper optimization of vaccine mRNA can reduce dosage required for each injection leading to more efficient immunization programs. The mRNA components of the vaccine need to have a 5'-UTR to load ribosomes efficiently onto the mRNA for translation initiation, optimized codon usage for efficient translation elongation, and optimal stop codon for efficient translation termination. Both 5'-UTR and the downstream 3'-UTR should be optimized for mRNA stability. The replacement of uridine by N1-methylpseudourinine (Ψ) complicates some of these optimization processes because Ψ is more versatile in wobbling than U. Different optimizations can conflict with each other, and compromises would need to be made. I highlight the similarities and differences between Pfizer/BioNTech and Moderna mRNA vaccines and discuss the advantage and disadvantage of each to facilitate future vaccine improvement. In particular, I point out a few optimizations in the design of the two mRNA vaccines that have not been performed properly.

Implications of SARS-CoV-2 Mutations for Genomic RNA Structure and Host microRNA Targeting.

The SARS-CoV-2 virus is a recently-emerged zoonotic pathogen already well adapted to transmission and replication in humans. Although the mutation rate is limited, recently introduced mutations in SARS-CoV-2 have the potential to alter viral fitness. In addition to amino acid changes, mutations could affect RNA secondary structure critical to viral life cycle, or interfere with sequences targeted by host miRNAs. We have analysed subsets of genomes from SARS-CoV-2 isolates from around the globe and show that several mutations introduce changes in Watson-Crick pairing, with resultant changes in predicted secondary structure. Filtering to targets matching miRNAs expressed in SARS-CoV-2-permissive host cells, we identified ten separate target sequences in the SARS-CoV-2 genome; three of these targets have been lost through conserved mutations. A genomic site targeted by the highly abundant miR-197-5p, overexpressed in patients with cardiovascular disease, is lost by a conserved mutation. Our results are compatible with a model that SARS-CoV-2 replication within the human host is constrained by host miRNA defences. The impact of these and further mutations on secondary structures, miRNA targets or potential splice sites offers a new context in which to view future SARS-CoV-2 evolution, and a potential platform for engineering conditional attenuation to vaccine development, as well as providing a better understanding of viral tropism and pathogenesis.

Developing effective siRNAs to reduce the expression of key viral genes of COVID-19.

The COVID-19 pandemic has been raging worldwide for more than a year. Many efforts have been made to create vaccines and develop new antiviral drugs to cope with the disease. Here, we propose the application of short interfering RNAs (siRNAs) to degrade the viral genome, thus reducing viral infection. By introducing the concept of the probability of binding efficiency (PBE) and combining the secondary structures of RNA molecules, we designed 11 siRNAs that target the consensus regions of three key viral genes: the spike (S), nucleocapsid (N) and membrane (M) genes of SARS-CoV-2. The silencing efficiencies of the siRNAs were determined in human lung and endothelial cells overexpressing these viral genes. The results suggested that most of the siRNAs could significantly reduce the expression of the viral genes with inhibition rates above 50% in 24 hours. This work not only provides a strategy for designing potentially effective siRNAs against target genes but also validates several potent siRNAs that can be used in the clinical development of preventative medication for COVID-19 in the future.

In vivo structural characterization of the SARS-CoV-2 RNA genome identifies host proteins vulnerable to repurposed drugs.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the ongoing coronavirus disease 2019 (COVID-19) pandemic. Understanding of the RNA virus and its interactions with host proteins could improve therapeutic interventions for COVID-19. By using icSHAPE, we determined the structural landscape of SARS-CoV-2 RNA in infected human cells and from refolded RNAs, as well as the regulatory untranslated regions of SARS-CoV-2 and six other coronaviruses. We validated several structural elements predicted in silico and discovered structural features that affect the translation and abundance of subgenomic viral RNAs in cells. The structural data informed a deep-learning tool to predict 42 host proteins that bind to SARS-CoV-2 RNA. Strikingly, antisense oligonucleotides targeting the structural elements and FDA-approved drugs inhibiting the SARS-CoV-2 RNA binding proteins dramatically reduced SARS-CoV-2 infection in cells derived from human liver and lung tumors. Our findings thus shed light on coronavirus and reveal multiple candidate therapeutics for COVID-19 treatment.

Comprehensive in vivo secondary structure of the SARS-CoV-2 genome reveals novel regulatory motifs and mechanisms.

Severe-acute-respiratory-syndrome-related coronavirus 2 (SARS-CoV-2) is the positive-sense RNA virus that causes coronavirus disease 2019 (COVID-19). The genome of SARS-CoV-2 is unique among viral RNAs in its vast potential to form RNA structures, yet as much as 97% of its 30 kilobases have not been structurally explored. Here, we apply a novel long amplicon strategy to determine the secondary structure of the SARS-CoV-2 RNA genome at single-nucleotide resolution in infected cells. Our in-depth structural analysis reveals networks of well-folded RNA structures throughout Orf1ab and reveals aspects of SARS-CoV-2 genome architecture that distinguish it from other RNA viruses. Evolutionary analysis shows that several features of the SARS-CoV-2 genomic structure are conserved across ß-coronaviruses, and we pinpoint regions of well-folded RNA structure that merit downstream functional analysis. The native, secondary structure of SARS-CoV-2 presented here is a roadmap that will facilitate focused studies on the viral life cycle, facilitate primer design, and guide the identification of RNA drug targets against COVID-19.

Pervasive RNA Secondary Structure in the Genomes of SARS-CoV-2 and Other Coronaviruses.

The ultimate outcome of the coronavirus disease 2019 (COVID-19) pandemic is unknown and is dependent on a complex interplay of its pathogenicity, transmissibility, and population immunity. In the current study, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was investigated for the presence of large-scale internal RNA base pairing in its genome. This property, termed genome-scale ordered RNA structure (GORS) has been previously associated with host persistence in other positive-strand RNA viruses, potentially through its shielding effect on viral RNA recognition in the cell. Genomes of SARS-CoV-2 were remarkably structured, with minimum folding energy differences (MFEDs) of 15%, substantially greater than previously examined viruses such as hepatitis C virus (HCV) (MFED of 7 to 9%). High MFED values were shared with all coronavirus genomes analyzed and created by several hundred consecutive energetically favored stem-loops throughout the genome. In contrast to replication-associated RNA structure, GORS was poorly conserved in the positions and identities of base pairing with other sarbecoviruses-even similarly positioned stem-loops in SARS-CoV-2 and SARS-CoV rarely shared homologous pairings, indicative of more rapid evolutionary change in RNA structure than in the underlying coding sequences. Sites predicted to be base paired in SARS-CoV-2 showed less sequence diversity than unpaired sites, suggesting that disruption of RNA structure by mutation imposes a fitness cost on the virus that is potentially restrictive to its longer evolution. Although functionally uncharacterized, GORS in SARS-CoV-2 and other coronaviruses represents important elements in their cellular interactions that may contribute to their persistence and transmissibility.IMPORTANCE The detection and characterization of large-scale RNA secondary structure in the genome of SARS-CoV-2 indicate an extraordinary and unsuspected degree of genome structural organization; this could be effectively visualized through a newly developed contour plotting method that displays positions, structural features, and conservation of RNA secondary structure between related viruses. Such RNA structure imposes a substantial evolutionary cost; paired sites showed greater restriction in diversity and represent a substantial additional constraint in reconstructing its molecular epidemiology. Its biological relevance arises from previously documented associations between possession of structured genomes and persistence, as documented for HCV and several other RNA viruses infecting humans and mammals. Shared properties potentially conferred by large-scale structure in SARS-CoV-2 include increasing evidence for prolonged infections and induced immune dysfunction that prevents development of protective immunity. The findings provide an additional element to cellular interactions that potentially influences the natural history of SARS-CoV-2, its pathogenicity, and its transmission.