bioRxivMicrobiologyPreprint
Comprehensive in virio structure probing analysis of the influenza A virus identifies a functional RNA structure involved in replication and segment interactions
N. Takizawa, K. Higashi, R. K. Kawaguchi, Y. Gotoh, Y. Suzuki, T. Hayashi, K. Kurokawa
Abstract
The influenza A virus genome is segmented into eight viral RNAs (vRNA). Secondary structures on vRNA are thought to be involved in the viral proliferation process, such as intersegment interactions that are necessary for segment bundling. However, the functional RNA structure on vRNA is not well known because the secondary structure of vRNA in virion was partially unwound by binding viral non-specific RNA binding proteins in a sequence-independent manner. Here, we establish the global map of the vRNA secondary structure in virion using the combination of dimethyl sulfate (DMS)-seq and selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE)-seq. By integrating DMS-seq and SHAPE-seq analyses with robust statistical analysis, we inferred quite a few bases paired regions including a pseudoknot structure on segment 5. Notably, when cells were infected with the recombinant virus which had mutations in the pseudoknot structure, the impairment of replication and packaging was observed on the other specific segment. Moreover, we analyzed the comprehensive intersegment RNA interactions in virion by ligation of interacting RNA followed by high-throughput sequencing (LIGR-seq). Our LIGR-seq analysis revealed that the intersegment interactions of the specific segment became less frequent and rearranged in the recombinant virus in concordance with the strength of genome packaging impairment. Our data provide evidence that the functional RNA structure motif on the influenza A virus genome can affect the efficiency of replication and segment bundling through the segment interactions.
- organism: virus
- assay: bulk RNA-seq
- research focus: host-pathogen & immune response
- research focus: cell type annotation
- research focus: spatial domain detection
- approach: experimental assay
- approach: statistical model
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