Structural landscape of the complete genomes of dengue virus serotypes and other viral hemorrhagic fevers

Characterization of SLA RNA promoter from dengue virus and its interaction with the viral non-structural NS5 protein

The Dengue virus (DENV) is the most significant arthropod-borne viral pathogen in humans with 400 million infections annually. DENV comprises four distinct serotypes (DENV-1 to -4) which complicates vaccine development. Any of the four serotypes can cause clinical illness but with distinctive infection dynamics. Variations in sequences identified within the four genomes induce structural differences in crucial RNA motifs that were suggested to be correlated to the degree of pathogenicity among DENV-1 to -4. In particular, the RNA Stem-loop A (SLA) at the 5'-end of the genome, acts as a key regulator of the viral replication cycle by interacting with the viral NS5 polymerase to initiate the minus-strand viral RNA synthesis and later to methylate and cap the synthesized RNA. The molecular details of this interaction remain not fully described. Here, we report the solution secondary structures of SLA from DENV-1 to -4. Our results highlight that the four SLA exhibit structural and dynamic differences. Secondly, to determine whether SLA RNA contains serotype-specific determinants for the recognition by the viral NS5 protein, we investigated interactions between SLA from DENV -1 to -4 and DENV2 NS5 using combined biophysical approaches. Our results show that NS5 from DENV2 is able to bind SLA from other serotypes, but that other viral or host factors may be necessary to stabilize the complex and promote the catalytically active state of the NS5. By contrast, we show that a serotype-specific binding is driven by specific interactions involving conformational changes within the SLA RNA.

A high-throughput approach to predict A-to-I effects on RNA structure indicates a change of double-stranded content in non-coding RNAs

RNA molecules undergo a number of chemical modifications whose effects can alter their structure and molecular interactions. Previous studies have shown that RNA editing can impact the formation of ribonucleoprotein complexes and influence the assembly of membrane-less organelles such as stress-granules. For instance, N6-methyladenosine (m6A) enhances SG formation and N1-methyladenosine (m1A) prevents their transition to solid-like aggregates. Yet, very little is known about adenosine to inosine (A-to-I) modification that is very abundant in human cells and not only impacts mRNAs but also non-coding RNAs. Here, we built the CROSSalive predictor of A-to-I effects on RNA structure based on high-throughput in-cell experiments. Our method shows an accuracy of 90% in predicting the single and double-stranded content of transcripts and identifies a general enrichment of double-stranded regions caused by A-to-I in long intergenic non-coding RNAs (lincRNAs). For the individual cases of NEAT1, NORAD and XIST, we investigated the relationship between A-to-I editing and interactions with RNA-binding proteins using available CLIP data and catRAPID predictions. We found that A-to-I editing is linked to alteration of interaction sites with proteins involved in phase-separation, which suggests that RNP assembly can be influenced by A-to-I. CROSSalive is available at http://service.tartaglialab.com/new_submission/crossalive. This article is protected by copyright. All rights reserved.

Computational and Experimental Approaches to Study the RNA Secondary Structures of RNA Viruses

Most pandemics of recent decades can be traced to RNA viruses, including HIV, SARS, influenza, dengue, Zika, and SARS-CoV-2. These RNA viruses impose considerable social and economic burdens on our society, resulting in a high number of deaths and high treatment costs. As these RNA viruses utilize an RNA genome, which is important for different stages of the viral life cycle, including replication, translation, and packaging, studying how the genome folds is important to understand virus function. In this review, we summarize recent advances in computational and high-throughput RNA structure-mapping approaches and their use in understanding structures within RNA virus genomes. In particular, we focus on the genome structures of the dengue, Zika, and SARS-CoV-2 viruses due to recent significant outbreaks of these viruses around the world.

RNAvigator: A Pipeline to Identify Candidates for Functional RNA Structure Elements

Identifying structural elements in long and complex RNAs, such as long non-coding and RNA viruses, can shed light on the functionality and mechanisms of such RNAs. Here we present RNAvigator, a tool able to identify elements of structural importance by using experimental SHAPE data or SHAPE-like predictions in conjunction with stability and entropy assessments. RNAvigator recognizes regions that are the most stable, unambiguous, and structured on RNA molecules, and thus potentially functional. When relying on predictions, RNAvigator uses the CROSS algorithm, a neural network trained on experimental data that achieved an AUC of 0.74 on hepatitis C virus SHAPE-MaP data and which was able to improve the predictive power of Superfold. By using RNAvigator, we can identify known functional regions on the complete hepatitis C virus genome, including the regulatory regions CRE and IRES, and the 3’ UTR of dengue virus, a region known for the presence of structural elements essential for its replication, and functional regions of long non-coding RNAs such as XIST and HOTAIR. We envision that RNAvigator will be a useful tool for studying long and complex RNA molecules using known chemical probing data or, if they are not available, by employing predicted profiles.