RNA structure prediction using positive and negative evolutionary information

Viroid-like colonists of human microbiomes

Here, we describe "obelisks," a class of heritable RNA elements sharing several properties: (1) apparently circular RNA ∼1 kb genome assemblies, (2) predicted rod-like genome-wide secondary structures, and (3) open reading frames encoding a novel "Oblin" protein superfamily. A subset of obelisks includes a variant hammerhead self-cleaving ribozyme. Obelisks form their own phylogenetic group without detectable similarity to known biological agents. Surveying globally, we identified 29,959 distinct obelisks (clustered at 90% sequence identity) from diverse ecological niches. Obelisks are prevalent in human microbiomes, with detection in ∼7% (29/440) and ∼50% (17/32) of queried stool and oral metatranscriptomes, respectively. We establish Streptococcus sanguinis as a cellular host of a specific obelisk and find that this obelisk's maintenance is not essential for bacterial growth. Our observations identify obelisks as a class of diverse RNAs of yet-to-be-determined impact that have colonized and gone unnoticed in human and global microbiomes.

Rfam 15: RNA families database in 2025

The Rfam database, a widely used repository of non-coding RNA families, has undergone significant updates in release 15.0. This paper introduces major improvements, including the expansion of Rfamseq to 26 106 genomes, a 76% increase, incorporating the latest UniProt reference proteomes and additional viral genomes. Sixty-five RNA families were enhanced using experimentally determined 3D structures, improving the accuracy of consensus secondary structures and annotations. R-scape covariation analysis was used to refine structural predictions in 26 families. Gene Ontology (GO) and Sequence Ontology annotations were comprehensively updated, increasing GO term coverage to 75% of families. The release adds 14 new Hepatitis C Virus RNA families and completes microRNA family synchronization with miRBase, resulting in 1603 microRNA families. New data types, including FULL alignments, have been implemented. Integration with APICURON for improved curator attribution and multiple website enhancements further improve user experience. These updates significantly expand Rfam's coverage and improve annotation quality, reinforcing its critical role in RNA research, genome annotation and the development of machine learning models. Rfam is freely available at https://rfam.org.

De novo gene synthesis by an antiviral reverse transcriptase

Defense-associated reverse transcriptase (DRT) systems perform DNA synthesis to protect bacteria against viral infection, but the identities and functions of their DNA products remain largely unknown. We show that DRT2 systems encode an unprecedented immune pathway that involves de novo gene synthesis through rolling circle reverse transcription of a noncoding RNA (ncRNA). Programmed template jumping on the ncRNA generates a concatemeric cDNA, which becomes double-stranded upon viral infection. This DNA product constitutes a protein-coding, nearly endless open reading frame (neo) gene whose expression leads to potent cell growth arrest, restricting the viral infection. Our work highlights an elegant expansion of genome coding potential through RNA-templated gene creation and challenges conventional paradigms of genetic information encoded along the one-dimensional axis of genomic DNA.

Rfam 15: RNA families database in 2025

The Rfam database, a widely-used repository of non-coding RNA (ncRNA) families, has undergone significant updates in release 15.0. This paper introduces major improvements, including the expansion of Rfamseq to 26,106 genomes, a 76% increase, incorporating the latest UniProt reference proteomes and additional viral genomes. Sixty-five RNA families were enhanced using experimentally determined 3D structures, improving the accuracy of consensus secondary structures and annotations. R-scape covariation analysis was used to refine structural predictions in 26 families. Gene Ontology and Sequence Ontology annotations were comprehensively updated, increasing GO term coverage to 75% of families. The release adds 14 new Hepatitis C Virus RNA families and completes microRNA family synchronisation with miRBase, resulting in 1,603 microRNA families. New data types, including FULL alignments, have been implemented. Integration with APICURON for improved curator attribution and multiple website enhancements further improve user experience. These updates significantly expand Rfam's coverage and improve annotation quality, reinforcing its critical role in RNA research, genome annotation, and the development of machine learning models. Rfam is freely available at https://rfam.org.

Intronic RNA secondary structural information captured for the human MYC pre-mRNA

To address the lack of intronic reads in secondary structure probing data for the human MYC pre-mRNA, we developed a method that combines spliceosomal inhibition with RNA probing and sequencing. Here, the SIRP-seq method was applied to study the secondary structure of human MYC RNAs by chemically probing HeLa cells with dimethyl sulfate in the presence of the small molecule spliceosome inhibitor pladienolide B. Pladienolide B binds to the SF3B complex of the spliceosome to inhibit intron removal during splicing, resulting in retained intronic sequences. This method was used to increase the read coverage over intronic regions of MYC. The purpose for increasing coverage across introns was to generate complete reactivity profiles for intronic sequences via the DMS-MaPseq approach. Notably, depth was sufficient for analysis by the program DRACO, which was able to deduce distinct reactivity profiles and predict multiple secondary structural conformations as well as their suggested stoichiometric abundances. The results presented here provide a new method for intronic RNA secondary structural analyses, as well as specific structural insights relevant to MYC RNA splicing regulation and therapeutic targeting.

Antagonistic conflict between transposon-encoded introns and guide RNAs

TnpB nucleases represent the evolutionary precursors to CRISPR-Cas12 and are widespread in all domains of life. IS605-family TnpB homologs function as programmable RNA-guided homing endonucleases in bacteria, driving transposon maintenance through DNA double-strand break-stimulated homologous recombination. In this work, we uncovered molecular mechanisms of the transposition life cycle of IS607-family elements that, notably, also encode group I introns. We identified specific features for a candidate "IStron" from Clostridium botulinum that allow the element to carefully control the relative levels of spliced products versus functional guide RNAs. Our results suggest that IStron transcripts evolved an ability to balance competing and mutually exclusive activities that promote selfish transposon spread while limiting adverse fitness costs on the host. Collectively, this work highlights molecular innovation in the multifunctional utility of transposon-encoded noncoding RNAs.

Comprehensive survey of conserved RNA secondary structures in full-genome alignment of Hepatitis C virus

Hepatitis C virus (HCV) is a plus-stranded RNA virus that often chronically infects liver hepatocytes and causes liver cirrhosis and cancer. These viruses replicate their genomes employing error-prone replicases. Thereby, they routinely generate a large 'cloud' of RNA genomes (quasispecies) which-by trial and error-comprehensively explore the sequence space available for functional RNA genomes that maintain the ability for efficient replication and immune escape. In this context, it is important to identify which RNA secondary structures in the sequence space of the HCV genome are conserved, likely due to functional requirements. Here, we provide the first genome-wide multiple sequence alignment (MSA) with the prediction of RNA secondary structures throughout all representative full-length HCV genomes. We selected 57 representative genomes by clustering all complete HCV genomes from the BV-BRC database based on k-mer distributions and dimension reduction and adding RefSeq sequences. We include annotations of previously recognized features for easy comparison to other studies. Our results indicate that mainly the core coding region, the C-terminal NS5A region, and the NS5B region contain secondary structure elements that are conserved beyond coding sequence requirements, indicating functionality on the RNA level. In contrast, the genome regions in between contain less highly conserved structures. The results provide a complete description of all conserved RNA secondary structures and make clear that functionally important RNA secondary structures are present in certain HCV genome regions but are largely absent from other regions. Full-genome alignments of all branches of Hepacivirus C are provided in the supplement.

De novo gene synthesis by an antiviral reverse transcriptase

Bacteria defend themselves from viral infection using diverse immune systems, many of which sense and target foreign nucleic acids. Defense-associated reverse transcriptase (DRT) systems provide an intriguing counterpoint to this immune strategy by instead leveraging DNA synthesis, but the identities and functions of their DNA products remain largely unknown. Here we show that DRT2 systems execute an unprecedented immunity mechanism that involves de novo gene synthesis via rolling-circle reverse transcription of a non-coding RNA (ncRNA). Unbiased profiling of RT-associated RNA and DNA ligands in DRT2-expressing cells revealed that reverse transcription generates concatenated cDNA repeats through programmed template jumping on the ncRNA. The presence of phage then triggers second-strand cDNA synthesis, leading to the production of long double-stranded DNA. Remarkably, this DNA product is efficiently transcribed, generating messenger RNAs that encode a stop codon-less, never-ending ORF (neo) whose translation causes potent growth arrest. Phylogenetic analyses and screening of diverse DRT2 homologs further revealed broad conservation of rolling-circle reverse transcription and Neo protein function. Our work highlights an elegant expansion of genome coding potential through RNA-templated gene creation, and challenges conventional paradigms of genetic information encoded along the one-dimensional axis of genomic DNA.

Pseudouridine and N1-methylpseudouridine as potent nucleotide analogues for RNA therapy and vaccine development

Modified nucleosides are integral to modern drug development, serving as crucial building blocks for creating safer, more potent, and more precisely targeted therapeutic interventions. Nucleobase modifications often confer antiviral and anti-cancer activity as monomers. When incorporated into nucleic acid oligomers, they increase stability against degradation by enzymes, enhancing the drugs' lifespan within the body. Moreover, modification strategies can mitigate potential toxic effects and reduce immunogenicity, making drugs safer and better tolerated. Particularly, N1-methylpseudouridine modification improved the efficacy of the mRNA coding for spike protein of COVID-19. This became a crucial step for developing COVID-19 vaccine applied during the 2020 pandemic. This makes N1-methylpseudouridine, and its "parent" analogue pseudouridine, potent nucleotide analogues for future RNA therapy and vaccine development. This review focuses on the structure and properties of pseudouridine and N1-methylpseudouridine. RNA has a greater structural versatility, different conformation, and chemical reactivity than DNA. Watson-Crick pairing is not strictly followed by RNA that has more unusual base pairs and base-triplets. This requires detailed structural studies and structure-activity relationship analyses for RNA, also when modifications are incorporated. Recent successes in this direction are revised in this review. We describe recent successes with using pseudouridine and N1-methylpseudouridine in mRNA drug candidates. We also highlight remaining challenges that need to be solved to develop new mRNA vaccines and therapies.

Unveiling hidden structural patterns in the SARS-CoV-2 genome: Computational insights and comparative analysis

SARS-CoV-2, the causative agent of COVID-19, is known to exhibit secondary structures in its 5' and 3' untranslated regions, along with the frameshifting stimulatory element situated between ORF1a and 1b. To identify additional regions containing conserved structures, we utilized a multiple sequence alignment with related coronaviruses as a starting point. We applied a computational pipeline developed for identifying non-coding RNA elements. Our pipeline employed three different RNA structural prediction approaches. We identified forty genomic regions likely to harbor structures, with ten of them showing three-way consensus substructure predictions among our predictive utilities. We conducted intracomparisons of the predictive utilities within the pipeline and intercomparisons with four previously published SARS-CoV-2 structural datasets. While there was limited agreement on the precise structure, different approaches seemed to converge on regions likely to contain structures in the viral genome. By comparing and combining various computational approaches, we can predict regions most likely to form structures, as well as a probable structure or ensemble of structures. These predictions can be used to guide surveillance, prophylactic measures, or therapeutic efforts. Data and scripts employed in this study may be found at https://doi.org/10.5281/zenodo.8298680.

Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation

Non-coding RNAs (ncRNAs), generated from nonprotein coding DNA sequences, constitute 98-99% of the human genome. Non-coding RNAs encompass diverse functional classes, including microRNAs, small interfering RNAs, PIWI-interacting RNAs, small nuclear RNAs, small nucleolar RNAs, and long non-coding RNAs. With critical involvement in gene expression and regulation across various biological and physiopathological contexts, such as neuronal disorders, immune responses, cardiovascular diseases, and cancer, non-coding RNAs are emerging as disease biomarkers and therapeutic targets. In this review, after providing an overview of non-coding RNAs' role in cell homeostasis, we illustrate the potential and the challenges of state-of-the-art computational methods exploited to study non-coding RNAs biogenesis, function, and modulation. This can be done by directly targeting them with small molecules or by altering their expression by targeting the cellular engines underlying their biosynthesis. Drawing from applications, also taken from our work, we showcase the significance and role of computer simulations in uncovering fundamental facets of ncRNA mechanisms and modulation. This information may set the basis to advance gene modulation tools and therapeutic strategies to address unmet medical needs.

Viroid-like colonists of human microbiomes

Here, we describe the "Obelisks," a previously unrecognised class of viroid-like elements that we first identified in human gut metatranscriptomic data. "Obelisks" share several properties: (i) apparently circular RNA ~1kb genome assemblies, (ii) predicted rod-like secondary structures encompassing the entire genome, and (iii) open reading frames coding for a novel protein superfamily, which we call the "Oblins". We find that Obelisks form their own distinct phylogenetic group with no detectable sequence or structural similarity to known biological agents. Further, Obelisks are prevalent in tested human microbiome metatranscriptomes with representatives detected in ~7% of analysed stool metatranscriptomes (29/440) and in ~50% of analysed oral metatranscriptomes (17/32). Obelisk compositions appear to differ between the anatomic sites and are capable of persisting in individuals, with continued presence over >300 days observed in one case. Large scale searches identified 29,959 Obelisks (clustered at 90% nucleotide identity), with examples from all seven continents and in diverse ecological niches. From this search, a subset of Obelisks are identified to code for Obelisk-specific variants of the hammerhead type-III self-cleaving ribozyme. Lastly, we identified one case of a bacterial species (Streptococcus sanguinis) in which a subset of defined laboratory strains harboured a specific Obelisk RNA population. As such, Obelisks comprise a class of diverse RNAs that have colonised, and gone unnoticed in, human, and global microbiomes.

Antagonistic conflict between transposon-encoded introns and guide RNAs

TnpB nucleases represent the evolutionary precursors to CRISPR-Cas12 and are widespread in all domains of life, presumably due to the critical roles they play in transposon proliferation. IS605family TnpB homologs function in bacteria as programmable homing endonucleases by exploiting transposon-encoded guide RNAs to cleave vacant genomic sites, thereby driving transposon maintenance through DSB-stimulated homologous recombination. Whether this pathway is conserved in other genetic contexts, and in association with other transposases, is unknown. Here we uncover molecular mechanisms of transposition and RNA-guided DNA cleavage by IS607-family elements that, remarkably, also encode catalytic, self-splicing group I introns. After reconstituting and systematically investigating each of these biochemical activities for a candidate 'IStron' derived from Clostridium botulinum, we discovered sequence and structural features of the transposon-encoded RNA that satisfy molecular requirements of a group I intron and TnpB guide RNA, while still retaining the ability to be faithfully mobilized at the DNA level by the TnpA transposase. Strikingly, intron splicing was strongly repressed not only by TnpB, but also by the secondary structure of ωRNA alone, allowing the element to carefully control the relative levels of spliced products versus functional guide RNAs. Our results suggest that IStron transcripts have evolved a sensitive equilibrium to balance competing and mutually exclusive activities that promote transposon maintenance while limiting adverse fitness costs on the host. Collectively, this work explains how diverse enzymatic activities emerged during the selfish spread of IS607-family elements and highlights molecular innovation in the multi-functional utility of transposon-encoded noncoding RNAs.

A snoRNA-tRNA modification network governs codon-biased cellular states

The dynamic balance between tRNA supply and codon usage demand is a fundamental principle in the cellular translation economy. However, the regulation and functional consequences of this balance remain unclear. Here, we use PARIS2 interactome capture, structure modeling, conservation analysis, RNA-protein interaction analysis, and modification mapping to reveal the targets of hundreds of snoRNAs, many of which were previously considered orphans. We identify a snoRNA-tRNA interaction network that is required for global tRNA modifications, including 2'-O-methylation and others. Loss of Fibrillarin, the snoRNA-guided 2'-O-methyltransferase, induces global upregulation of tRNA fragments, a large group of regulatory RNAs. In particular, the snoRNAs D97/D133 guide the 2'-O-methylation of multiple tRNAs, especially for the amino acid methionine (Met), a protein-intrinsic antioxidant. Loss of D97/D133 snoRNAs in human HEK293 cells reduced target tRNA levels and induced codon adaptation of the transcriptome and translatome. Both single and double knockouts of D97 and D133 in HEK293 cells suppress Met-enriched proliferation-related gene expression programs, including, translation, splicing, and mitochondrial energy metabolism, and promote Met-depleted programs related to development, differentiation, and morphogenesis. In a mouse embryonic stem cell model of development, knockdown and knockout of D97/D133 promote differentiation to mesoderm and endoderm fates, such as cardiomyocytes, without compromising pluripotency, consistent with the enhanced development-related gene expression programs in human cells. This work solves a decades-old mystery about orphan snoRNAs and reveals a function of snoRNAs in controlling the codon-biased dichotomous cellular states of proliferation and development.

Transposon-encoded nucleases use guide RNAs to promote their selfish spread

Insertion sequences are compact and pervasive transposable elements found in bacteria, which encode only the genes necessary for their mobilization and maintenance1. IS200- and IS605-family transposons undergo 'peel-and-paste' transposition catalysed by a TnpA transposase2, but they also encode diverse, TnpB- and IscB-family proteins that are evolutionarily related to the CRISPR-associated effectors Cas12 and Cas9, respectively3,4. Recent studies have demonstrated that TnpB and IscB function as RNA-guided DNA endonucleases5,6, but the broader biological role of this activity has remained enigmatic. Here we show that TnpB and IscB are essential to prevent permanent transposon loss as a consequence of the TnpA transposition mechanism. We selected a family of related insertion sequences from Geobacillus stearothermophilus that encode several TnpB and IscB orthologues, and showed that a single TnpA transposase was broadly active for transposon mobilization. The donor joints formed upon religation of transposon-flanking sequences were efficiently targeted for cleavage by RNA-guided TnpB and IscB nucleases, and co-expression of TnpB and TnpA led to substantially greater transposon retention relative to conditions in which TnpA was expressed alone. Notably, TnpA and TnpB also stimulated recombination frequencies, surpassing rates observed with TnpB alone. Collectively, this study reveals that RNA-guided DNA cleavage arose as a primal biochemical activity to bias the selfish inheritance and spread of transposable elements, which was later co-opted during the evolution of CRISPR-Cas adaptive immunity for antiviral defence.

D-ORB: A Web Server to Extract Structural Features of Related But Unaligned RNA Sequences

Identifying the common structural elements of functionally related RNA sequences (family) is usually based on an alignment of the sequences, which is often subject to human bias and may not be accurate. The resulting covariance model (CM) provides probabilities for each base to covary with another, which allows to support evolutionarily the formation of double helical regions and possibly pseudoknots. The coexistence of alternative folds in RNA, resulting from its dynamic nature, may lead to the potential omission of motifs by CM. To overcome this limitation, we present D-ORB, a system of algorithms that identifies overrepresented motifs in the secondary conformational landscapes of a family when compared to those of unrelated sequences. The algorithms are bundled into an easy-to-use website allowing users to submit a family, and optionally provide unrelated sequences. D-ORB produces a non-pseudoknotted secondary structure based on the overrepresented motifs, a deep neural network classifier and two decision trees. When used to model an Rfam family, D-ORB fits overrepresented motifs in the corresponding Rfam structure; more than a hundred Rfam families have been modeled. The statistical approach behind D-ORB derives the structural composition of an RNA family, making it a valuable tool for analyzing and modeling it. Its easy-to-use interface and advanced algorithms make it an essential resource for researchers studying RNA structure. D-ORB is available at https://d-orb.major.iric.ca/.

RNA covariation at helix-level resolution for the identification of evolutionarily conserved RNA structure

Many biologically important RNAs fold into specific 3D structures conserved through evolution. Knowing when an RNA sequence includes a conserved RNA structure that could lead to new biology is not trivial and depends on clues left behind by conservation in the form of covariation and variation. For that purpose, the R-scape statistical test was created to identify from alignments of RNA sequences, the base pairs that significantly covary above phylogenetic expectation. R-scape treats base pairs as independent units. However, RNA base pairs do not occur in isolation. The Watson-Crick (WC) base pairs stack together forming helices that constitute the scaffold that facilitates the formation of the non-WC base pairs, and ultimately the complete 3D structure. The helix-forming WC base pairs carry most of the covariation signal in an RNA structure. Here, I introduce a new measure of statistically significant covariation at helix-level by aggregation of the covariation significance and covariation power calculated at base-pair-level resolution. Performance benchmarks show that helix-level aggregated covariation increases sensitivity in the detection of evolutionarily conserved RNA structure without sacrificing specificity. This additional helix-level sensitivity reveals an artifact that results from using covariation to build an alignment for a hypothetical structure and then testing the alignment for whether its covariation significantly supports the structure. Helix-level reanalysis of the evolutionary evidence for a selection of long non-coding RNAs (lncRNAs) reinforces the evidence against these lncRNAs having a conserved secondary structure.

Thirteen dubious ways to detect conserved structural RNAs

Covariation induced by compensatory base substitutions in RNA alignments is a great way to deduce conserved RNA structure, in principle. In practice, success depends on many factors, importantly the quality and depth of the alignment and the choice of covariation statistic. Measuring covariation between pairs of aligned positions is easy. However, using covariation to infer evolutionarily conserved RNA structure is complicated by other extraneous sources of covariation such as that resulting from homologous sequences having evolved from a common ancestor. In order to provide evidence of evolutionarily conserved RNA structure, a method to distinguish covariation due to sources other than RNA structure is necessary. Moreover, there are several sorts of artifactually generated covariation signals that can further confound the analysis. Additionally, some covariation signal is difficult to detect due to incomplete comparative data. Here, we investigate and critically discuss the practice of inferring conserved RNA structure by comparative sequence analysis. We provide new methods on how to approach and decide which of the numerous long non-coding RNAs (lncRNAs) have biologically relevant structures.

How does precursor RNA structure influence RNA processing and gene expression?

RNA is a fundamental biomolecule that has many purposes within cells. Due to its single-stranded and flexible nature, RNA naturally folds into complex and dynamic structures. Recent technological and computational advances have produced an explosion of RNA structural data. Many RNA structures have regulatory and functional properties. Studying the structure of nascent RNAs is particularly challenging due to their low abundance and long length, but their structures are important because they can influence RNA processing. Precursor RNA processing is a nexus of pathways that determines mature isoform composition and that controls gene expression. In this review, we examine what is known about human nascent RNA structure and the influence of RNA structure on processing of precursor RNAs. These known structures provide examples of how other nascent RNAs may be structured and show how novel RNA structures may influence RNA processing including splicing and polyadenylation. RNA structures can be targeted therapeutically to treat disease.

Transposon-encoded nucleases use guide RNAs to selfishly bias their inheritance

Insertion sequences (IS) are compact and pervasive transposable elements found in bacteria, which encode only the genes necessary for their mobilization and maintenance. IS 200 /IS 605 elements undergo 'peel-and-paste' transposition catalyzed by a TnpA transposase, but intriguingly, they also encode diverse, TnpB- and IscB-family proteins that are evolutionarily related to the CRISPR-associated effectors Cas12 and Cas9, respectively. Recent studies demonstrated that TnpB-family enzymes function as RNA-guided DNA endonucleases, but the broader biological role of this activity has remained enigmatic. Here we show that TnpB/IscB are essential to prevent permanent transposon loss as a consequence of the TnpA transposition mechanism. We selected a family of related IS elements from Geobacillus stearothermophilus that encode diverse TnpB/IscB orthologs, and showed that a single TnpA transposase was active for transposon excision. The donor joints formed upon religation of IS-flanking sequences were efficiently targeted for cleavage by RNA-guided TnpB/IscB nucleases, and co-expression of TnpB together with TnpA led to significantly greater transposon retention, relative to conditions in which TnpA was expressed alone. Remarkably, TnpA and TnpB/IscB recognize the same AT-rich transposon-adjacent motif (TAM) during transposon excision and RNA-guided DNA cleavage, respectively, revealing a striking convergence in the evolution of DNA sequence specificity between collaborating transposase and nuclease proteins. Collectively, our study reveals that RNA-guided DNA cleavage is a primal biochemical activity that arose to bias the selfish inheritance and spread of transposable elements, which was later co-opted during the evolution of CRISPR-Cas adaptive immunity for antiviral defense.

Pseudoknots in RNA Structure Prediction

RNA molecules play active roles in the cell and are important for numerous applications in biotechnology and medicine. The function of an RNA molecule stems from its structure. RNA structure determination is time consuming, challenging, and expensive using experimental methods. Thus, much research has been directed at RNA structure prediction through computational means. Many of these methods focus primarily on the secondary structure of the molecule, ignoring the possibility of pseudoknotted structures. However, pseudoknots are known to play functional roles in many RNA molecules or in their method of interaction with other molecules. Improving the accuracy and efficiency of computational methods that predict pseudoknots is an ongoing challenge for single RNA molecules, RNA-RNA interactions, and RNA-protein interactions. To improve the accuracy of prediction, many methods focus on specific applications while restricting the length and the class of the pseudoknotted structures they can identify. In recent years, computational methods for structure prediction have begun to catch up with the impressive developments seen in biotechnology. Here, we provide a non-comprehensive overview of available pseudoknot prediction methods and their best-use cases. © 2023 Wiley Periodicals LLC.

rRNA expansion segment 7 in eukaryotes: from Signature Fold to tentacles

The ribosomal core is universally conserved across the tree of life. However, eukaryotic ribosomes contain diverse rRNA expansion segments (ESs) on their surfaces. Sites of ES insertions are predicted from sites of insertion of micro-ESs in archaea. Expansion segment 7 (ES7) is one of the most diverse regions of the ribosome, emanating from a short stem loop and ranging to over 750 nucleotides in mammals. We present secondary and full-atom 3D structures of ES7 from species spanning eukaryotic diversity. Our results are based on experimental 3D structures, the accretion model of ribosomal evolution, phylogenetic relationships, multiple sequence alignments, RNA folding algorithms and 3D modeling by RNAComposer. ES7 contains a distinct motif, the 'ES7 Signature Fold', which is generally invariant in 2D topology and 3D structure in all eukaryotic ribosomes. We establish a model in which ES7 developed over evolution through a series of elementary and recursive growth events. The data are sufficient to support an atomic-level accretion path for rRNA growth. The non-monophyletic distribution of some ES7 features across the phylogeny suggests acquisition via convergent processes. And finally, illustrating the power of our approach, we constructed the 2D and 3D structure of the entire LSU rRNA of Mus musculus.

Advances and opportunities in RNA structure experimental determination and computational modeling

Beyond transferring genetic information, RNAs are molecules with diverse functions that include catalyzing biochemical reactions and regulating gene expression. Most of these activities depend on RNAs' specific structures. Therefore, accurately determining RNA structure is integral to advancing our understanding of RNA functions. Here, we summarize the state-of-the-art experimental and computational technologies developed to evaluate RNA secondary and tertiary structures. We also highlight how the rapid increase of experimental data facilitates the integrative modeling approaches for better resolving RNA structures. Finally, we provide our thoughts on the latest advances and challenges in RNA structure determination methods, as well as on future directions for both experimental approaches and artificial intelligence-based computational tools to model RNA structure. Ultimately, we hope the technological advances will deepen our understanding of RNA biology and facilitate RNA structure-based biomedical research such as designing specific RNA structures for therapeutics and deploying RNA-targeting small-molecule drugs.

Variant-Specific Analysis Reveals a Novel Long-Range RNA-RNA Interaction in SARS-CoV-2 Orf1a

Since the start of the COVID-19 pandemic, understanding the pathology of the SARS-CoV-2 RNA virus and its life cycle has been the priority of many researchers. Currently, new variants of the virus have emerged with various levels of pathogenicity and abundance within the human-host population. Although much of viral pathogenicity is attributed to the viral Spike protein’s binding affinity to human lung cells’ ACE2 receptor, comprehensive knowledge on the distinctive features of viral variants that might affect their life cycle and pathogenicity is yet to be attained. Recent in vivo studies into the RNA structure of the SARS-CoV-2 genome have revealed certain long-range RNA-RNA interactions. Using in silico predictions and a large population of SARS-CoV-2 sequences, we observed variant-specific evolutionary changes for certain long-range RRIs. We also found statistical evidence for the existence of one of the thermodynamic-based RRI predictions, namely Comp1, in the Beta variant sequences. A similar test that disregarded sequence variant information did not, however, lead to significant results. When performing population-based analyses, aggregate tests may fail to identify novel interactions due to variant-specific changes. Variant-specific analyses can result in de novo RRI identification.

A Putative long-range RNA-RNA interaction between ORF8 and Spike of SARS-CoV-2

SARS-CoV-2 has affected people worldwide as the causative agent of COVID-19. The virus is related to the highly lethal SARS-CoV-1 responsible for the 2002-2003 SARS outbreak in Asia. Research is ongoing to understand why both viruses have different spreading capacities and mortality rates. Like other beta coronaviruses, RNA-RNA interactions occur between different parts of the viral genomic RNA, resulting in discontinuous transcription and production of various sub-genomic RNAs. These sub-genomic RNAs are then translated into other viral proteins. In this work, we performed a comparative analysis for novel long-range RNA-RNA interactions that may involve the Spike region. Comparing in-silico fragment-based predictions between reference sequences of SARS-CoV-1 and SARS-CoV-2 revealed several predictions amongst which a thermodynamically stable long-range RNA-RNA interaction between (23660-23703 Spike) and (28025-28060 ORF8) unique to SARS-CoV-2 was observed. The patterns of sequence variation using data gathered worldwide further supported the predicted stability of the sub-interacting region (23679-23690 Spike) and (28031-28042 ORF8). Such RNA-RNA interactions can potentially impact viral life cycle including sub-genomic RNA production rates.

KnotAli: informed energy minimization through the use of evolutionary information

BACKGROUND

Improving the prediction of structures, especially those containing pseudoknots (structures with crossing base pairs) is an ongoing challenge. Homology-based methods utilize structural similarities within a family to predict the structure. However, their prediction is limited to the consensus structure, and by the quality of the alignment. Minimum free energy (MFE) based methods, on the other hand, do not rely on familial information and can predict structures of novel RNA molecules. Their prediction normally suffers from inaccuracies due to their underlying energy parameters.

RESULTS

We present a new method for prediction of RNA pseudoknotted secondary structures that combines the strengths of MFE prediction and alignment-based methods. KnotAli takes a multiple RNA sequence alignment as input and uses covariation and thermodynamic energy minimization to predict possibly pseudoknotted secondary structures for each individual sequence in the alignment. We compared KnotAli's performance to that of three other alignment-based programs, two that can handle pseudoknotted structures and one control, on a large data set of 3034 RNA sequences with varying lengths and levels of sequence conservation from 10 families with pseudoknotted and pseudoknot-free reference structures. We produced sequence alignments for each family using two well-known sequence aligners (MUSCLE and MAFFT).

CONCLUSIONS

We found KnotAli's performance to be superior in 6 of the 10 families for MUSCLE and 7 of the 10 for MAFFT. While both KnotAli and Cacofold use background noise correction strategies, we found KnotAli's predictions to be less dependent on the alignment quality. KnotAli can be found online at the Zenodo image: https://doi.org/10.5281/zenodo.5794719.

Identifying candidate structured RNAs in CRISPR operons

Noncoding RNAs with secondary structures play important roles in CRISPR-Cas systems. Many of these structures likely remain undiscovered. We used a large-scale comparative genomics approach to predict 156 novel candidate structured RNAs from 36,111 CRISPR-Cas systems. A number of these were found to overlap with coding genes, including palindromic candidates that overlapped with a variety of Cas genes in type I and III systems. Among these 156 candidates, we identified 46 new models of CRISPR direct repeats and 1 tracrRNA. This tracrRNA model occasionally overlapped with predicted cas9 coding regions, emphasizing the importance of expanding our search windows for novel structure RNAs in coding regions. We also demonstrated that the antirepeat sequence in this tracrRNA model can be used to accurately assign thousands of predicted CRISPR arrays to type II-C systems. This study highlights the importance of unbiased identification of candidate structured RNAs across CRISPR-Cas systems.

Messenger RNA vaccines for cancer immunotherapy: progress promotes promise

The COVID-19 pandemic has elevated mRNA vaccines to global recognition due to their unprecedented success rate in protecting against a deadly virus. This international success is underscored by the remarkable versatility, favorable immunogenicity, and overall safety of the mRNA platform in diverse populations. Although mRNA vaccines have been studied in preclinical models and patients with cancer for almost three decades, development has been slow. The recent technological advances responsible for the COVID-19 vaccines have potential implications for successfully adapting this vaccine platform for cancer therapeutics. Here we discuss the lessons learned along with the chemical, biologic, and immunologic adaptations needed to optimize mRNA technology to successfully treat cancers.

Analyses of human cancer driver genes uncovers evolutionarily conserved RNA structural elements involved in posttranscriptional control

Experimental breakthroughs have provided unprecedented insights into the genes involved in cancer. The identification of such cancer driver genes is a major step in gaining a fuller understanding of oncogenesis and provides novel lists of potential therapeutic targets. A key area that requires additional study is the posttranscriptional control mechanisms at work in cancer driver genes. This is important not only for basic insights into the biology of cancer, but also to advance new therapeutic modalities that target RNA—an emerging field with great promise toward the treatment of various cancers. In the current study we performed an in silico analysis on the transcripts associated with 800 cancer driver genes (10,390 unique transcripts) that identified 179,190 secondary structural motifs with evidence of evolutionarily ordered structures with unusual thermodynamic stability. Narrowing to one transcript per gene, 35,426 predicted structures were subjected to phylogenetic comparisons of sequence and structural conservation. This identified 7,001 RNA secondary structures embedded in transcripts with evidence of covariation between paired sites, supporting structure models and suggesting functional significance. A select set of seven structures were tested in vitro for their ability to regulate gene expression; all were found to have significant effects. These results indicate potentially widespread roles for RNA structure in posttranscriptional control of human cancer driver genes.

OUP accepted manuscript

Accelerated evolution of any portion of the genome is of significant interest, potentially signaling positive selection of phenotypic traits and adaptation. Accelerated evolution remains understudied for structured RNAs, despite the fact that an RNA’s structure is often key to its function. RNA structures are typically characterized by compensatory (structure-preserving) basepair changes that are unexpected given the underlying sequence variation, i.e., they have evolved through negative selection on structure. We address the question of how fast the primary sequence of an RNA can change through evolution while conserving its structure. Specifically, we consider predicted and known structures in vertebrate genomes. After careful control of false discovery rates, we obtain 13 de novo structures (and three known Rfam structures) that we predict to have rapidly evolving sequences—defined as structures where the primary sequences of human and mouse have diverged at least twice as fast (1.5 times for Rfam) as nearby neutrally evolving sequences. Two of the three known structures function in translation inhibition related to infection and immune response. We conclude that rapid sequence divergence does not preclude RNA structure conservation in vertebrates, although these events are relatively rare.

Shared properties and singularities of exoribonuclease-resistant RNAs in viruses

What viral RNA genomes lack in size, they make up for in intricacy. Elaborate RNA structures embedded in viral genomes can hijack essential cellular mechanisms aiding virus propagation. Exoribonuclease-resistant RNAs (xrRNAs) are an emerging class of viral elements, which resist degradation by host cellular exoribonucleases to produce viral RNAs with diverse roles during infection. Detailed three-dimensional structural studies of xrRNAs from flaviviruses and a subset of plant viruses led to a mechanistic model in which xrRNAs block enzymatic digestion using a ring-like structure that encircles the 5' end of the resistant structure. In this mini-review, we describe the state of our understanding of the phylogenetic distribution of xrRNAs, their structures, and their conformational dynamics. Because xrRNAs have now been found in several major superfamilies of RNA viruses, they may represent a more widely used strategy than currently appreciated. Could xrRNAs represent a 'molecular clock' that would help us understand virus evolution and pathogenicity? The more we study xrRNAs in viruses, the closer we get to finding xrRNAs within cellular RNAs.

Discovery of 17 conserved structural RNAs in fungi

Many non-coding RNAs with known functions are structurally conserved: their intramolecular secondary and tertiary interactions are maintained across evolutionary time. Consequently, the presence of conserved structure in multiple sequence alignments can be used to identify candidate functional non-coding RNAs. Here, we present a bioinformatics method that couples iterative homology search with covariation analysis to assess whether a genomic region has evidence of conserved RNA structure. We used this method to examine all unannotated regions of five well-studied fungal genomes (Saccharomyces cerevisiae, Candida albicans, Neurospora crassa, Aspergillus fumigatus, and Schizosaccharomyces pombe). We identified 17 novel structurally conserved non-coding RNA candidates, which include four H/ACA box small nucleolar RNAs, four intergenic RNAs and nine RNA structures located within the introns and untranslated regions (UTRs) of mRNAs. For the two structures in the 3' UTRs of the metabolic genes GLY1 and MET13, we performed experiments that provide evidence against them being eukaryotic riboswitches.

Structural phylogenetic analysis reveals lineage-specific RNA repetitive structural motifs in all coronaviruses and associated variations in SARS-CoV-2

In many single-stranded (ss) RNA viruses, the cis-acting packaging signal that confers selectivity genome packaging usually encompasses short structured RNA repeats. These structural units, termed repetitive structural motifs (RSMs), potentially mediate capsid assembly by specific RNA–protein interactions. However, general knowledge of the conservation and/or the diversity of RSMs in the positive-sense ssRNA coronaviruses (CoVs) is limited. By performing structural phylogenetic analysis, we identified a variety of RSMs in nearly all CoV genomic RNAs, which are exclusively located in the 5′-untranslated regions (UTRs) and/or in the inter-domain regions of poly-protein 1ab coding sequences in a lineage-specific manner. In all alpha- and beta-CoVs, except for Embecovirus spp, two to four copies of 5′-gUUYCGUc-3′ RSMs displaying conserved hexa-loop sequences were generally identified in Stem-loop 5 (SL5) located in the 5′-UTRs of genomic RNAs. In Embecovirus spp., however, two to eight copies of 5′-agc-3′/guAAu RSMs were found in the coding regions of non-structural protein (NSP) 3 and/or NSP15 in open reading frame (ORF) 1ab. In gamma- and delta-CoVs, other types of RSMs were found in several clustered structural elements in 5′-UTRs and/or ORF1ab. The identification of RSM-encompassing structural elements in all CoVs suggests that these RNA elements play fundamental roles in the life cycle of CoVs. In the recently emerged SARS-CoV-2, beta-CoV-specific RSMs are also found in its SL5, displaying two copies of 5′-gUUUCGUc-3′ motifs. However, multiple sequence alignment reveals that the majority of SARS-CoV-2 possesses a variant RSM harboring SL5b C241U, and intriguingly, several variations in the coding sequences of viral proteins, such as Nsp12 P323L, S protein D614G, and N protein R203K-G204R, are concurrently found with such variant RSM. In conclusion, the comprehensive exploration for RSMs reveals phylogenetic insights into the RNA structural elements in CoVs as a whole and provides a new perspective on variations currently found in SARS-CoV-2.

A map of the SARS-CoV-2 RNA structurome

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

Evolutionary conservation of RNA sequence and structure

An RNA structure prediction from a single‐sequence RNA folding program is not evidence for an RNA whose structure is important for function. Random sequences have plausible and complex predicted structures not easily distinguishable from those of structural RNAs. How to tell when an RNA has a conserved structure is a question that requires looking at the evolutionary signature left by the conserved RNA. This question is important not just for long noncoding RNAs which usually lack an identified function, but also for RNA binding protein motifs which can be single stranded RNAs or structures. Here we review recent advances using sequence and structural analysis to determine when RNA structure is conserved or not. Although covariation measures assess structural RNA conservation, one must distinguish covariation due to RNA structure from covariation due to independent phylogenetic substitutions. We review a statistical test to measure false positives expected under the null hypothesis of phylogenetic covariation alone (specificity). We also review a complementary test that measures power, that is, expected covariation derived from sequence variation alone (sensitivity). Power in the absence of covariation signals the absence of a conserved RNA structure. We analyze artifacts that falsely identify conserved RNA structure such as the misuse of programs that do not assess significance, the use of inappropriate statistics confounded by signals other than covariation, or misalignments that induce spurious covariation. Among artifacts that obscure the signal of a conserved RNA structure, we discuss the inclusion of pseudogenes in alignments which increase power but destroy covariation.

This article is categorized under:

RNA Structure and Dynamics > RNA Structure, Dynamics and Chemistry

RNA Evolution and Genomics > Computational Analyses of RNA

RNA Evolution and Genomics > RNA and Ribonucleoprotein Evolution

Improved RNA Secondary Structure and Tertiary Base-pairing Prediction Using Evolutionary Profile, Mutational Coupling and Two-dimensional Transfer Learning

MOTIVATION

The recent discovery of numerous non-coding RNAs (long non-coding RNAs, in particular) has transformed our perception about the roles of RNAs in living organisms. Our ability to understand them, however, is hampered by our inability to solve their secondary and tertiary structures in high resolution efficiently by existing experimental techniques. Computational prediction of RNA secondary structure, on the other hand, has received much-needed improvement, recently, through deep learning of a large approximate data, followed by transfer learning with gold-standard base-pairing structures from high-resolution 3-D structures. Here, we expand this single-sequence-based learning to the use of evolutionary profiles and mutational coupling.

RESULTS

The new method allows large improvement not only in canonical base-pairs (RNA secondary structures) but more so in base-pairing associated with tertiary interactions such as pseudoknots, noncanonical and lone base-pairs. In particular, it is highly accurate for those RNAs of more than 1000 homologous sequences by achieving >0.8 F1-score (harmonic mean of sensitivity and precision) for 14/16 RNAs tested. The method can also significantly improve base-pairing prediction by incorporating artificial but functional homologous sequences generated from deep mutational scanning without any modification. The fully automatic method (publicly available as server and standalone software) should provide the scientific community a new powerful tool to capture not only the secondary structure but also tertiary base-pairing information for building three-dimensional models. It also highlights the future of accurately solving the base-pairing structure by using a large number of natural and/or artificial homologous sequences.

AVAILABILITY

Standalone-version of SPOT-RNA2 is available at https://github.com/jaswindersingh2/SPOT-RNA2. Direct prediction can also be made at https://sparks-lab.org/server/spot-rna2/. The datasets used in this research can also be downloaded from the GITHUB and the webserver mentioned above.

Rfam 14: expanded coverage of metagenomic, viral and microRNA families

Rfam is a database of RNA families where each of the 3444 families is represented by a multiple sequence alignment of known RNA sequences and a covariance model that can be used to search for additional members of the family. Recent developments have involved expert collaborations to improve the quality and coverage of Rfam data, focusing on microRNAs, viral and bacterial RNAs. We have completed the first phase of synchronising microRNA families in Rfam and miRBase, creating 356 new Rfam families and updating 40. We established a procedure for comprehensive annotation of viral RNA families starting with Flavivirus and Coronaviridae RNAs. We have also increased the coverage of bacterial and metagenome-based RNA families from the ZWD database. These developments have enabled a significant growth of the database, with the addition of 759 new families in Rfam 14. To facilitate further community contribution to Rfam, expert users are now able to build and submit new families using the newly developed Rfam Cloud family curation system. New Rfam website features include a new sequence similarity search powered by RNAcentral, as well as search and visualisation of families with pseudoknots. Rfam is freely available at https://rfam.org.

Systematic prediction of genes functionally associated with bacterial retrons and classification of the encoded tripartite systems

Bacterial retrons consist of a reverse transcriptase (RT) and a contiguous non-coding RNA (ncRNA) gene. One third of annotated retrons carry additional open reading frames (ORFs), the contribution and significance of which in retron biology remains to be determined. In this study we developed a computational pipeline for the systematic prediction of genes specifically associated with retron RTs based on a previously reported large dataset representative of the diversity of prokaryotic RTs. We found that retrons generally comprise a tripartite system composed of the ncRNA, the RT and an additional protein or RT-fused domain with diverse enzymatic functions. These retron systems are highly modular, and their components have coevolved to different extents. Based on the additional module, we classified retrons into 13 types, some of which include additional variants. Our findings provide a basis for future studies on the biological function of retrons and for expanding their biotechnological applications.

Remote homology search with hidden Potts models

Most methods for biological sequence homology search and alignment work with primary sequence alone, neglecting higher-order correlations. Recently, statistical physics models called Potts models have been used to infer all-by-all pairwise correlations between sites in deep multiple sequence alignments, and these pairwise couplings have improved 3D structure predictions. Here we extend the use of Potts models from structure prediction to sequence alignment and homology search by developing what we call a hidden Potts model (HPM) that merges a Potts emission process to a generative probability model of insertion and deletion. Because an HPM is incompatible with efficient dynamic programming alignment algorithms, we develop an approximate algorithm based on importance sampling, using simpler probabilistic models as proposal distributions. We test an HPM implementation on RNA structure homology search benchmarks, where we can compare directly to exact alignment methods that capture nested RNA base-pairing correlations (stochastic context-free grammars). HPMs perform promisingly in these proof of principle experiments.