microRNA-guided immunity against respiratory virus infection in human and mouse lung cells.

Viral infectivity depends on multiple factors. Recent studies showed that the interaction between viral RNAs and endogenous microRNAs (miRNAs) regulates viral infectivity; viral RNAs function as a sponge of endogenous miRNAs and result in upregulation of its original target genes, while endogenous miRNAs target viral RNAs directly and result in repression of viral gene expression. In this study, we analyzed the possible interaction between parainfluenza virus RNA and endogenous miRNAs in human and mouse lungs. We showed that the parainfluenza virus can form base pairs with human miRNAs abundantly than mouse miRNAs. Furthermore, we analyzed that the sponge effect of endogenous miRNAs on viral RNAs may induce the upregulation of transcription regulatory factors. Then, we performed RNA-sequence analysis and observed the upregulation of transcription regulatory factors in the early stages of parainfluenza virus infection. Our studies showed how the differential expression of endogenous miRNAs in lungs could contribute to respiratory virus infection and species- or tissue-specific mechanisms and common mechanisms could be conserved in humans and mice and regulated by miRNAs during viral infection.

How to do RNA-RNA Interaction Prediction? A Use-Case Driven Handbook Using IntaRNA.

Computational prediction of RNA-RNA interactions (RRI) is a central methodology for the specific investigation of inter-molecular RNA interactions and regulatory effects of non-coding RNAs like eukaryotic microRNAs or prokaryotic small RNAs. Available methods can be classified according to their underlying prediction strategies, each implicating specific capabilities and restrictions often not transparent to the non-expert user. Within this work, we review seven classes of RRI prediction strategies and discuss the advantages and limitations of respective tools, since such knowledge is essential for selecting the right tool in the first place.Among the RRI prediction strategies, accessibility-based approaches have been shown to provide the most reliable predictions. Here, we describe how IntaRNA, as one of the state-of-the-art accessibility-based tools, can be applied in various use cases for the task of computational RRI prediction. Detailed hands-on examples for individual RRI predictions as well as large-scale target prediction scenarios are provided. We illustrate the flexibility and capabilities of IntaRNA through the examples. Each example is designed using real-life data from the literature and is accompanied by instructions on interpreting the respective results from IntaRNA output. Our use-case driven instructions enable non-expert users to comprehensively understand and utilize IntaRNA's features for effective RRI predictions.

Stoichiometry of long noncoding RNA interactions with other RNAs: Insights from OIP5-AS1.

Long noncoding (lnc)RNAs modulate gene expression programs in a range of developmental processes in different organs. In skeletal muscle, lncRNAs have been implicated in myogenesis, the process whereby muscle precursor cells form muscle fibers during embryonic development and regenerate muscle fibers in the adult. Here, we discuss OIP5-AS1, a lncRNA that is highly expressed in skeletal muscle and is capable of coordinating protein expression programs during myogenesis. Given that several myogenic functions of OIP5-AS1 involve interactions with MEF2C mRNA and with the microRNA miR-7, it was critical to carefully evaluate the precise levels of OIP5-AS1 during myogenesis. We discuss the approaches used to examine lncRNA copy number using OIP5-AS1 as an example, focusing on quantification by quantitative PCR analysis with reference to nucleic acids of known abundance, by droplet digital (dd)PCR measurement, and by microscopic visualization of individual lncRNAs in cells. We discuss considerations of RNA stoichiometry in light of developmental processes in which lncRNAs are implicated. This article is categorized under: Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs.

Long non-coding RNA Loc105611671 promotes the proliferation of ovarian granulosa cells and steroid hormone production upregulation of CDC42.

Granulosa cells (GCs) are essential for follicular development, and long non-coding RNAs (LncRNAs) are known to support the maintenance of this process and hormone synthesis in mammals. Nevertheless, the regulatory roles of these lncRNAs within sheep follicular GCs remain largely unexplored. This study delved into the influence of a Loc105611671, on the proliferation and steroid hormone synthesis of sheep ovarian GCs and the associated target genes in vitro. Cell Counting Kit-8 (CCK-8) gain-of-function experiments indicated that overexpression of Loc105611671 significantly boosted GCs proliferation, along with estrogen (E2) and progesterone (P4) levels. Further mechanistic scrutiny revealed that Loc105611671 is primarily localized within the cytoplasm of ovarian granulosa cells and engages in molecular interplay with CDC42. This interaction results in the upregulation of CDC42 protein expression. Moreover, it was discerned that increased CDC42 levels contribute to augmented proliferation of follicular granulosa cells and the secretion of E2 and P4. Experiments involving co-transfection elucidated that the concurrent overexpression of CDC42 and Loc105611671 acted synergistically to potentiate these effects. These findings provide insights into the molecular underpinnings of fecundity in ovine species and may inform future strategies for enhancing reproductive outcomes.

Bipartite viral RNA genome heterodimerization influences genome packaging and virion thermostability.

Multi-segmented viruses often multimerize their genomic segments to ensure efficient and stoichiometric packaging of the correct genetic cargo. In the bipartite Nodaviridae family, genome heterodimerization is also observed and conserved among different species. However, the nucleotide composition and biological function for this heterodimer remain unclear. Using Flock House virus as a model system, we developed a next-generation sequencing approach ("XL-ClickSeq") to probe heterodimer site sequences. We identified an intermolecular base-pairing site which contributed to heterodimerization in both wild-type and defective virus particles. Mutagenic disruption of this heterodimer site exhibited significant deficiencies in genome packaging and encapsidation specificity to viral genomic RNAs. Furthermore, the disruption of this intermolecular interaction directly impacts the thermostability of the mature virions. These results demonstrate that the intermolecular RNA-RNA interactions within the encapsidated genome of an RNA virus have an important role on virus particle integrity and thus may impact its transmission to a new host.IMPORTANCEFlock House virus is a member of Nodaviridae family of viruses, which provides a well-studied model virus for non-enveloped RNA virus assembly, cell entry, and replication. The Flock House virus genome consists of two separate RNA molecules, which can form a heterodimer upon heating of virus particles. Although similar RNA dimerization is utilized by other viruses (such as retroviruses) as a packaging mechanism and is conserved among Nodaviruses, the role of heterodimerization in the Nodavirus replication cycle is unclear. In this research, we identified the RNA sequences contributing to Flock House virus genome heterodimerization and discovered that such RNA-RNA interaction plays an essential role in virus packaging efficiency and particle integrity. This provides significant insight into how the interaction of packaged viral RNA may have a broader impact on the structural and functional properties of virus particles.

Defining Bacterial RNA-RNA Interactomes Using CLASH.

Methicillin-resistant Staphylococcus aureus (MRSA) is a bacterial pathogen accounting for high mortality rates among infected patients. Transcriptomic regulation by small RNAs (sRNAs) has been shown to regulate networks promoting antibiotic resistance and virulence in S. aureus. Yet, the biological role of most sRNAs during MRSA host infection remains unknown. To fill this gap, in collaboration with the lab of Jai Tree, we performed comprehensive RNA-RNA interactome analyses in MRSA using CLASH under conditions that mimic the host environment. Here we present a detailed version of this optimized CLASH (cross-linking, ligation, and sequencing of hybrids) protocol we recently developed, which has been tailored to explore the RNA interactome in S. aureus as well as other Gram-positive bacteria. Alongside, we introduce a compilation of helpful Python functions for analyzing folding energies of putative RNA-RNA interactions and streamlining sRNA and mRNA seed discovery in CLASH data. In the accompanying computational demonstration, we aim to establish a standardized strategy to evaluate the likelihood that observed chimeras arise from true RNA-RNA interactions.

3D feasibility of 2D RNA-RNA interaction paths by stepwise folding simulations.

The structure of an RNA, and even more so its interactions with other RNAs, provide valuable information about its function. Secondary structure-based tools for RNA-RNA interaction predictions provide a quick way to identify possible interaction targets and structures. However, these tools ignore the effect of steric hindrance on the tertiary (3D) structure level, and do not consider whether a suitable folding pathway exists to form the interaction. As a consequence, these tools often predict interactions that are unrealistically long and could be formed (in three dimensions) only by going through highly entangled intermediates. Here, we present a computational pipeline to assess whether a proposed secondary (2D) structure interaction is sterically feasible and reachable along a plausible folding pathway. To this end, we simulate the folding of a series of 3D structures along a given 2D folding path. To avoid the complexity of large-scale atomic resolution simulations, our pipeline uses coarse-grained 3D modeling and breaks up the folding path into small steps, each corresponding to the extension of the interaction by 1 or 2 bp. We apply our pipeline to analyze RNA-RNA interaction formation for three selected RNA-RNA complexes. We find that kissing hairpins, in contrast to interactions in the exterior loop, are difficult to extend and tend to get stuck at an interaction length of 6 bp. Our tool, including source code, documentation, and sample data, is available at www.github.com/irenekb/RRI-3D.

SARS-CoV-2 RNA stabilizes host mRNAs to elicit immunopathogenesis.

SARS-CoV-2 RNA interacts with host factors to suppress interferon responses and simultaneously induces cytokine release to drive the development of severe coronavirus disease 2019 (COVID-19). However, how SARS-CoV-2 hijacks host RNAs to elicit such imbalanced immune responses remains elusive. Here, we analyzed SARS-CoV-2 RNA in situ structures and interactions in infected cells and patient lung samples using RIC-seq. We discovered that SARS-CoV-2 RNA forms 2,095 potential duplexes with the 3' UTRs of 205 host mRNAs to increase their stability by recruiting RNA-binding protein YBX3 in A549 cells. Disrupting the SARS-CoV-2-to-host RNA duplex or knocking down YBX3 decreased host mRNA stability and reduced viral replication. Among SARS-CoV-2-stabilized host targets, NFKBIZ was crucial for promoting cytokine production and reducing interferon responses, probably contributing to cytokine storm induction. Our study uncovers the crucial roles of RNA-RNA interactions in the immunopathogenesis of RNA viruses such as SARS-CoV-2 and provides valuable host targets for drug development.

A Hitchhiker's guide to RNA-RNA structure and interaction prediction tools.

RNA biology has risen to prominence after a remarkable discovery of diverse functions of noncoding RNA (ncRNA). Most untranslated transcripts often exert their regulatory functions into RNA-RNA complexes via base pairing with complementary sequences in other RNAs. An interplay between RNAs is essential, as it possesses various functional roles in human cells, including genetic translation, RNA splicing, editing, ribosomal RNA maturation, RNA degradation and the regulation of metabolic pathways/riboswitches. Moreover, the pervasive transcription of the human genome allows for the discovery of novel genomic functions via RNA interactome investigation. The advancement of experimental procedures has resulted in an explosion of documented data, necessitating the development of efficient and precise computational tools and algorithms. This review provides an extensive update on RNA-RNA interaction (RRI) analysis via thermodynamic- and comparative-based RNA secondary structure prediction (RSP) and RNA-RNA interaction prediction (RIP) tools and their general functions. We also highlighted the current knowledge of RRIs and the limitations of RNA interactome mapping via experimental data. Then, the gap between RSP and RIP, the importance of RNA homologues, the relationship between pseudoknots, and RNA folding thermodynamics are discussed. It is hoped that these emerging prediction tools will deepen the understanding of RNA-associated interactions in human diseases and hasten treatment processes.

The novel RNA-RNA activation of H19 on MyoD transcripts promoting myogenic differentiation of goat muscle satellite cells.

The elaborate interplay of coding and noncoding factors governs muscle growth and development. Here, we reported a mutual activation between long noncoding RNA (lncRNA) H19 and MyoD (myogenic determination gene number 1) in the muscle process. We successfully cloned the two isoforms of goat H19, which were significantly enriched and positively correlated with MyoD transcripts in skeletal muscles or differentiating muscle satellite cells (MuSCs). To systematically screen genes altered by H19, we performed RNA-seq using cDNA libraries of differentiating H19-deficiency MuSCs and consequently anchored MyoD as the critical genes in mediating H19 function. Intriguingly, some transcripts of MyoD and H19 overlapped in the cytoplasm, which was dramatically damaged when the core complementary nucleotides were mutated. Meanwhile, MyoD RNA successfully pulled down H19 in MS2-RIP experiments. Furthermore, HuR could bind both H19 and MyoD transcripts, while H19 or its truncated mutants successfully stabilized MyoD mRNA, with or without HuR deficiency. In turn, novel functional MyoD protein-binding sites were identified in the promoter and exons of the H19 gene. Our results suggest that MyoD activates H19 transcriptionally, and RNA-RNA hybridization is critical for H19-promoted MyoD expression, which extends our knowledge of the hierarchy of regulatory networks in muscle growth.

Computational Prediction of RNA-RNA Interactions between Small RNA Tracks from Betacoronavirus Nonstructural Protein 3 and Neurotrophin Genes during Infection of an Epithelial Lung Cancer Cell Line: Potential Role of Novel Small Regulatory RNA.

Whether RNA-RNA interactions of cytoplasmic RNA viruses, such as Betacoronavirus, might end in the biogenesis of putative virus-derived small RNAs as miRNA-like molecules has been controversial. Even more, whether RNA-RNA interactions of wild animal viruses may act as virus-derived small RNAs is unknown. Here, we address these issues in four ways. First, we use conserved RNA structures undergoing negative selection in the genomes of SARS-CoV, MERS-CoV, and SARS-CoV-2 circulating in different bat species, intermediate animals, and human hosts. Second, a systematic literature review was conducted to identify Betacoronavirus-targeting hsa-miRNAs involved in lung cell infection. Third, we employed sophisticated long-range RNA-RNA interactions to refine the seed sequence homology of hsa-miRNAs with conserved RNA structures. Fourth, we used high-throughput RNA sequencing of a Betacoronavirus-infected epithelial lung cancer cell line (Calu-3) to validate the results. We proposed nine potential virus-derived small RNAs: two vsRNAs in SARS-CoV (Bats: SB-vsRNA-ORF1a-3p; SB-vsRNA-S-5p), one vsRNA in MERS-CoV (Bats: MB-vsRNA-ORF1b-3p), and six vsRNAs in SARS-CoV-2 (Bats: S2B-vsRNA-ORF1a-5p; intermediate animals: S2I-vsRNA-ORF1a-5p; and humans: S2H-vsRNA-ORF1a-5p, S2H-vsRNA-ORF1a-3p, S2H-vsRNA-ORF1b-3p, S2H-vsRNA-ORF3a-3p), mainly encoded by nonstructural protein 3. Notably, Betacoronavirus-derived small RNAs targeted 74 differentially expressed genes in infected human cells, of which 55 upregulate the molecular mechanisms underlying acute respiratory distress syndrome (ARDS), and the 19 downregulated genes might be implicated in neurotrophin signaling impairment. These results reveal a novel small RNA-based regulatory mechanism involved in neuropathogenesis that must be further studied to validate its therapeutic use.

The human long non-coding RNA LINC00941 and its modes of action in health and disease.

Long non-coding RNAs have gained attention in recent years as they were shown to play crucial roles in the regulation of cellular processes, but the understanding of the exact mechanisms is still incomplete in most cases. This is also true for long non-coding RNA LINC00941, which was recently found to be highly upregulated in various types of cancer influencing cell proliferation and metastasis. Initial studies could not elucidate the mode of action to understand the role and real impact of LINC00941 in tissue homeostasis and cancer development. However, recent analyses have demonstrated multiple potential modes of action of LINC00941 influencing the functionality of various cancer cell types. Correspondingly, LINC00941 was proposed to be involved in regulation of mRNA transcription and modulation of protein stability, respectively. In addition, several experimental approaches suggest a function of LINC00941 as competitive endogenous RNA, thus acting in a post-transcriptional regulatory fashion. This review summarizes our recent knowledge about the mechanisms of action of LINC00941 elucidated so far and discusses its putative role in miRNA sequestering processes. In addition, the functional role of LINC00941 in regulating human keratinocytes is discussed to also highlight its role in normal tissue homeostasis tissue aside from its involvement in cancer.

Double-stemmed and split structural variants of fluorescent RNA Mango aptamers.

Aptamers with fluorogenic ligands are emerging as useful tools to quantify and track RNA molecules. The RNA Mango family of aptamers have a useful combination of tight ligand binding, bright fluorescence, and small size. However, the simple structure of these aptamers, with a single base-paired stem capped by a G-quadruplex, can limit the sequence and structural modifications needed for many use-inspired designs. Here we report new structural variants of RNA Mango that have two base-paired stems attached to the quadruplex. Fluorescence saturation analysis of one of the double-stemmed constructs showed a maximum fluorescence that is ∼75% brighter than the original single-stemmed Mango I. A small number of mutations to nucleotides in the tetraloop-like linker of the second stem were subsequently analyzed. The effect of these mutations on the affinity and fluorescence suggested that the nucleobases of the second linker do not directly interact with the fluorogenic ligand (TO1-biotin), but may instead induce higher fluorescence by indirectly altering the ligand properties in the bound state. The effects of the mutations in this second tetraloop-like linker indicate the potential of this second stem for rational design and reselection experiments. Additionally, we demonstrated that a bimolecular mango designed by splitting the double-stemmed Mango can function when two RNA molecules are cotranscribed from different DNA templates in a single in vitro transcription. This bimolecular Mango has potential application in detecting RNA-RNA interactions. Together, these constructs expand the designability of the Mango aptamers to facilitate future applications of RNA imaging.

Fast RNA-RNA Interaction Prediction Methods for Interaction Analysis of Transcriptome-Scale Large Datasets.

The computational prediction of RNA-RNA interactions has long been studied in RNA informatics. Most of the existing approaches focused on the interaction prediction of short RNAs in small datasets. However, in recent years, two fast prediction methods, RIsearch2 and RIblast, have been developed to predict transcriptome-scale interactions or long RNA interactions. The key idea of the software acceleration of these tools was the integration of a seed-and-extend method, which is used in fast sequence alignment tools, into RNA-RNA interaction prediction. As a result, the two software programs were ten to a thousand times faster than the existing tools; because of this acceleration, detection of genome-wide microRNA target sites or interaction partners of function-unknown long noncoding RNAs has become possible. In this review, we describe the basic concept of the algorithm, its applications, and the future perspectives of the fast RNA-RNA interaction prediction tools.

Web Services for RNA-RNA Interaction Prediction.

Non-coding RNAs have various biological functions such as translational regulation, and RNA-RNA interactions play essential roles in the mechanisms of action of these RNAs. Therefore, RNA-RNA interaction prediction is an important problem in bioinformatics, and many tools have been developed for the computational prediction of RNA-RNA interactions. In addition to the development of novel algorithms with high accuracy, the development and maintenance of web services is essential for enhancing usability by experimental biologists. In this review, we survey web services for RNA-RNA interaction predictions and introduce how to use primary web services. We present various prediction tools, including general interaction prediction tools, prediction tools for specific RNA classes, and RNA-RNA interaction-based RNA design tools. Additionally, we discuss the future perspectives of the development of RNA-RNA interaction prediction tools and the sustainability of web services.

Dynamic landscape of long noncoding RNAs during leaf aging in Arabidopsis.

Leaf senescence, the last stage of leaf development, is essential for whole-plant fitness as it marks the relocation of nutrients from senescing leaves to reproductive or other developing organs. Temporally coordinated physiological and functional changes along leaf aging are fine-tuned by a highly regulated genetic program involving multi-layered regulatory mechanisms. Long noncoding RNAs (lncRNAs) are newly emerging as hidden players in many biological processes; however, their contribution to leaf senescence has been largely unknown. Here, we performed comprehensive analyses of RNA-seq data representing all developmental stages of leaves to determine the genome-wide lncRNA landscape along leaf aging. A total of 771 lncRNAs, including 232 unannotated lncRNAs, were identified. Time-course analysis revealed 446 among 771 developmental age-related lncRNAs (AR-lncRNAs). Intriguingly, the expression of AR-lncRNAs was regulated more dynamically in senescing leaves than in growing leaves, revealing the relevant contribution of these lncRNAs to leaf senescence. Further analyses enabled us to infer the function of lncRNAs, based on their interacting miRNA or mRNA partners. We considered functionally diverse lncRNAs including antisense lncRNAs (which regulate overlapping protein-coding genes), competitive endogenous RNAs (ceRNAs; which regulate paired mRNAs using miRNAs as anchors), and mRNA-interacting lncRNAs (which affect the stability of mRNAs). Furthermore, we experimentally validated the senescence regulatory function of three novel AR-lncRNAs including one antisense lncRNA and two mRNA-interacting lncRNAs through molecular and phenotypic analyses. Our study provides a valuable resource of AR-lncRNAs and potential regulatory networks that link the function of coding mRNA and AR-lncRNAs. Together, our results reveal AR-lncRNAs as important elements in the leaf senescence process.

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.

Unique role for lncRNA HOTAIR in defining depot-specific gene expression patterns in human adipose-derived stem cells.

Accumulation of fat above the waist is an important risk factor in developing obesity-related comorbidities independently of BMI or total fat mass. Deciphering the gene regulatory programs of the adipose tissue precursor cells within upper body or abdominal (ABD) and lower body or gluteofemoral (GF) depots is important to understand their differential capacity for lipid accumulation, maturation, and disease risk. Previous studies identified the HOX transcript antisense intergenic RNA (HOTAIR) as a GF-specific lncRNA; however, its role in adipose tissue biology is still unclear. Using three different approaches (silencing of HOTAIR in GF human adipose-derived stem cells [GF hASCs], overexpression of HOTAIR in ABD hASCs, and ChIRP-seq) to localize HOTAIR binding in GF hASC chromatin, we found that HOTAIR binds and modulates expression, both positively and negatively, of genes involved in adipose tissue-specific pathways, including adipogenesis. We further demonstrate a direct interaction between HOTAIR and genes with high RNAPII binding in their gene bodies, especially at their 3' ends or transcription end sites. Computational analysis suggests HOTAIR binds preferentially to the 3' ends of genes containing predicted strong RNA-RNA interactions with HOTAIR. Together, these results reveal a unique function for HOTAIR in hASC depot-specific regulation of gene expression.

CircZNF609 as a prototype to elucidate the biological function of circRNA-mRNA interactions.

Circular RNAs (circRNAs) are expressed and are regulated in many biological processes but little is known about their ability to directly control mRNA homeostasis. We show that circRNA zinc finger protein 609 (circZNF609) interacts with several mRNAs increasing the final protein levels, which in the case of the cytoskeleton-associated protein 5 (CKAP5) leads to a stabilized microtubule cytoskeleton and an enhanced tumor cell proliferation.

Cellular Computational Logic Using Toehold Switches.

The development of computational logic that carries programmable and predictable features is one of the key requirements for next-generation synthetic biological devices. Despite considerable progress, the construction of synthetic biological arithmetic logic units presents numerous challenges. In this paper, utilizing the unique advantages of RNA molecules in building complex logic circuits in the cellular environment, we demonstrate the RNA-only bitwise logical operation of XOR gates and basic arithmetic operations, including a half adder, a half subtractor, and a Feynman gate, in Escherichia coli. Specifically, de-novo-designed riboregulators, known as toehold switches, were concatenated to enhance the functionality of an OR gate, and a previously utilized antisense RNA strategy was further optimized to construct orthogonal NIMPLY gates. These optimized synthetic logic gates were able to be seamlessly integrated to achieve final arithmetic operations on small molecule inputs in cells. Toehold-switch-based ribocomputing devices may provide a fundamental basis for synthetic RNA-based arithmetic logic units or higher-order systems in cells.