Computational and molecular analysis of conserved influenza A virus RNA secondary structures involved in infectious virion production

Kobayashi, Yuki; Dadonaite, Bernadeta; Doremalen, Neeltje van; Suzuki, Yoshiyuki; Barclay, William; Pybus, Oliver G.

doi:10.1080/15476286.2016.1208331

Cited by 38 publications

(48 citation statements)

References 54 publications

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“…Regions of high correlation between ex virio and in virio SHAPE profiles reveal regions of vRNA which are capable of local secondary structure formation even when NP is bound. Our data also recapitulate the RNA structures that have been identified previously using computational methods 13,14 ( Fig. 1d-e; Extended Data Fig.…”

Section: Figuresupporting

confidence: 87%

The structure of the influenza A virus genome

Dadonaite

Barilaite

Fodor

et al. 2017

Preprint

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Influenza A viruses (IAVs) are segmented single-stranded negative sense RNA 12 viruses that constitute a major threat to human health. The IAV genome 13 consists of eight RNA segments contained in separate viral ribonucleoprotein 14 complexes (vRNPs) that are packaged together into a single virus particle 1,2 . 15While IAVs are generally considered to have an unstructured single-stranded 16 genome, it has also been suggested that secondary RNA structures are 17 required for selective packaging of the eight vRNPs into each virus particle 3,4 . 18Here, we employ high-throughput sequencing approaches to map both the . Although 43 not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/236620 doi: bioRxiv preprint first posted online Dec. 21, 2017; 3 cryo-EM studies revealed the overall architecture and organisation of vRNPs, the 44 resolution of currently available structures is not sufficiently high to provide 45 information about the conformation of the vRNA 8,9 . It is thought that, through 46 specific RNA-RNA interactions, exposed regions of vRNA in the vRNP mediate 47 segment-specific interactions during virion assembly, ensuring that the correct set 48 of eight vRNPs is selected 3,4 . However, the identities of these interacting regions as 49 well as the overall structure of vRNA in vRNPs are currently unknown. 50 51To better understand the vRNA structure in the context of vRNPs, we employed 52 SHAPE-MaP (Selective 2'-Hydroxyl Acylation Analysed by Primer Extension and 53Mutational Profiling), which probes the conformational flexibility of each vRNA 54 nucleotide both ex virio and in virio (Extended Data Fig. 1) 10,11 . For the ex virio 55 experiments, the eight vRNA segments were individually transcribed from plasmid 56 DNA using T7 RNA polymerase (ivtRNA) or "naked" vRNA was purified from 57 deproteinated influenza A/WSN/1933 (H1N1) (WSN) particles (nkvRNA). For the in 58 virio experiments, vRNA was probed in the context of vRNPs directly inside purified 59 virions (Extended Data Fig. 1a). 60 61Three biological replicates of the in virio SHAPE-MaP experiments resulted in highly 62 reproducible SHAPE reactivity profiles suggesting that there is an underlying 63 structure of vRNA in the context of vRNP (Extended Data Fig. 2a). We found that 64 the eight different vRNPs in virio have unique vRNA conformations, as 65 demonstrated by the different SHAPE reactivity profiles (Fig. 1a). Regions of 66 extensive low SHAPE reactivities in virio (where the median-SHAPE value is below 67 zero, Fig. 1a) indicate that the vRNA in the context of vRNPs is capable of 68 accommodating secondary RNA structures with extensive base-pairing. However, 69 comparison of in virio and ex virio SHAPE profiles shows a significant shift in the 70 distribution of SHAPE reactivities in virio, consistent with an overall more open (less 71 structured) RNA conformation (Fig. 1b). In addition, the vRNA forms...

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Section: Figuresupporting

confidence: 87%

The structure of the influenza A virus genome

Dadonaite

Barilaite

Fodor

et al. 2017

Preprint

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“…Near identical aligned sequences may lack complexity that allows accurate RNA structure prediction and are not usually included in the prediction phase (see the success story on “RNA Structures in Coding Regions of Influenza A Virus”). However, the phenotype of a viable virus with a mutation in the structure may be informative (Kobayashi et al, 2016 ).…”

Section: Know Your Enemymentioning

confidence: 99%

“…To improve the power of detecting putative RNA structural elements, subsequent studies focused on specific genes/genome segments, namely HA (surface glycoprotein hemagglutinin; Gultyaev et al, 2016 ), M (Kobayashi et al, 2016 ), and NP (nucleoprotein; Gultyaev et al, 2014 ; Soszynska-Jozwiak et al, 2015 ) using deep multiple sequence alignment. Indeed, new structured RNA elements have been continuously discovered.…”

Section: Viral Success Storiesmentioning

confidence: 99%

Know Your Enemy: Successful Bioinformatic Approaches to Predict Functional RNA Structures in Viral RNAs

Lim

Brown

2018

Front. Microbiol.

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Structured RNA elements may control virus replication, transcription and translation, and their distinct features are being exploited by novel antiviral strategies. Viral RNA elements continue to be discovered using combinations of experimental and computational analyses. However, the wealth of sequence data, notably from deep viral RNA sequencing, viromes, and metagenomes, necessitates computational approaches being used as an essential discovery tool. In this review, we describe practical approaches being used to discover functional RNA elements in viral genomes. In addition to success stories in new and emerging viruses, these approaches have revealed some surprising new features of well-studied viruses e.g., human immunodeficiency virus, hepatitis C virus, influenza, and dengue viruses. Some notable discoveries were facilitated by new comparative analyses of diverse viral genome alignments. Importantly, comparative approaches for finding RNA elements embedded in coding and non-coding regions differ. With the exponential growth of computer power we have progressed from stem-loop prediction on single sequences to cutting edge 3D prediction, and from command line to user friendly web interfaces. Despite these advances, many powerful, user friendly prediction tools and resources are underutilized by the virology community.

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“…3a,b ) in all H3N2 samples in this study may also be due to its importance in packaging full-length or truncated genome into viral-like particles, i.e. viable standard viral particles and/or DIPs 27 28 29 .…”

Section: Discussionmentioning

confidence: 86%

Contamination-controlled high-throughput whole genome sequencing for influenza A viruses using the MiSeq sequencer

Lee

Tang

et al. 2016

Sci Rep

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Accurate full-length genomic sequences are important for viral phylogenetic studies. We developed a targeted high-throughput whole genome sequencing (HT-WGS) method for influenza A viruses, which utilized an enzymatic cleavage-based approach, the Nextera XT DNA library preparation kit, for library preparation. The entire library preparation workflow was adapted for the Sentosa SX101, a liquid handling platform, to automate this labor-intensive step. As the enzymatic cleavage-based approach generates low coverage reads at both ends of the cleaved products, we corrected this loss of sequencing coverage at the termini by introducing modified primers during the targeted amplification step to generate full-length influenza A sequences with even coverage across the whole genome. Another challenge of targeted HTS is the risk of specimen-to-specimen cross-contamination during the library preparation step that results in the calling of false-positive minority variants. We included an in-run, negative system control to capture contamination reads that may be generated during the liquid handling procedures. The upper limits of 99.99% prediction intervals of the contamination rate were adopted as cut-off values of contamination reads. Here, 148 influenza A/H3N2 samples were sequenced using the HTS protocol and were compared against a Sanger-based sequencing method. Our data showed that the rate of specimen-to-specimen cross-contamination was highly significant in HTS.

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Computational and molecular analysis of conserved influenza A virus RNA secondary structures involved in infectious virion production

Cited by 38 publications

References 54 publications

The structure of the influenza A virus genome

The structure of the influenza A virus genome

Know Your Enemy: Successful Bioinformatic Approaches to Predict Functional RNA Structures in Viral RNAs

Contamination-controlled high-throughput whole genome sequencing for influenza A viruses using the MiSeq sequencer

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