Determining the Mutation Bias of Favipiravir in Influenza Virus Using Next-Generation Sequencing

ABSTRACT Favipiravir is a broad-spectrum antiviral drug that may be used to treat influenza. Previous research has identified that favipiravir likely acts as a mutagen, but the precise mutation bias that favipiravir induces in influenza virus RNAs has not been described. Here, we use next-generation sequencing (NGS) with barcoding of individual RNA molecules to accurately and quantitatively detect favipiravir-induced mutations and to sample orders of magnitude more mutations than would be possible through Sanger sequencing. We demonstrate that favipiravir causes mutations and show that favipiravir primarily acts as a guanine analogue and secondarily as an adenine analogue resulting in the accumulation of transition mutations. We also use a standard NGS pipeline to show that the mutagenic effect of favipiravir can be measured by whole-genome sequencing of virus. IMPORTANCE New antiviral drugs are needed as a first line of defense in the event of a novel influenza pandemic. Favipiravir is a broad-spectrum antiviral which is effective against influenza. The exact mechanism of how favipiravir works to inhibit influenza is still unclear. We used next-generation sequencing (NGS) to demonstrate that favipiravir causes mutations in influenza RNA. The greater depth of NGS sequence information over traditional sequencing methods allowed us to precisely determine the bias of particular mutations caused by favipiravir. NGS can also be used in a standard diagnostic pipeline to show that favipiravir is acting on the virus by revealing the mutation bias pattern typical to the drug. Our work will aid in testing whether viruses are resistant to favipiravir and may help demonstrate the effect of favipiravir on viruses in a clinical setting. This will be important if favipiravir is used during a future influenza pandemic.

Download Full-text

Determining the Mutation Bias of Favipiravir in Influenza Using Next-generation Sequencing

10.1101/375378 ◽

2018 ◽

Cited By ~ 1

Author(s):

Daniel H. Goldhill ◽

Pinky Langat ◽

Hongyao Xie ◽

Monica Galiano ◽

Shahjahan Miah ◽

...

Keyword(s):

Next Generation Sequencing ◽

Broad Spectrum ◽

Influenza Pandemic ◽

Antiviral Drug ◽

Sequence Information ◽

Next Generation ◽

Induced Mutations ◽

Mutation Bias ◽

Next Generation Sequencing Ngs ◽

Generation Sequencing

AbstractFavipiravir is a broad spectrum antiviral drug that may be used to treat influenza. Previous research has identified that favipiravir likely acts as a mutagen but the precise mutation bias that favipiravir induces in influenza virus RNAs has not been described. Here, we use next-generation sequencing (NGS) with barcoding of individual RNA molecules to accurately and quantitatively detect favipiravir-induced mutations and to sample orders of magnitude more mutations than would be possible through Sanger sequencing. We demonstrate that favipiravir causes mutations and show that favipiravir primarily acts as a guanine analogue and secondarily as an adenine analogue resulting in the accumulation of transition mutations. We also use a standard NGS pipeline to show that the mutagenic effect of favipiravir can be measured by whole genome sequencing of virus.ImportanceNew antiviral drugs are needed as a first line of defence in the event of a novel influenza pandemic. Favipiravir is a broad-spectrum antiviral which is effective against influenza. The exact mechanism of how favipiravir works to inhibit influenza is still unclear. We used next-generation sequencing (NGS) to demonstrate that favipiravir causes mutations in influenza RNA. The greater depth of NGS sequence information over traditional sequencing methods allowed us to precisely determine the bias of particular mutations caused by favipiravir. NGS can also be used in a standard diagnostic pipeline to show that favipiravir is acting on the virus by revealing the mutation bias pattern typical to the drug. Our work will aid in testing whether viruses are resistant to favipiravir and may help demonstrate the effect of favipiravir on viruses in a clinical setting. This will be important if favipiravir is used during a future influenza pandemic.

Download Full-text

Comparison of Next-Generation Sequencing Systems

Journal of Biomedicine and Biotechnology ◽

10.1155/2012/251364 ◽

2012 ◽

Vol 2012 ◽

pp. 1-11 ◽

Cited By ~ 491

Author(s):

Lin Liu ◽

Yinhu Li ◽

Siliang Li ◽

Ni Hu ◽

Yimin He ◽

...

Keyword(s):

Next Generation Sequencing ◽

Genomic Sequence ◽

Sequence Information ◽

Ion Torrent ◽

Next Generation ◽

Extensive Experience ◽

Ion Torrent Pgm ◽

Fast Development ◽

Next Generation Sequencing Ngs ◽

Generation Sequencing

With fast development and wide applications of next-generation sequencing (NGS) technologies, genomic sequence information is within reach to aid the achievement of goals to decode life mysteries, make better crops, detect pathogens, and improve life qualities. NGS systems are typically represented by SOLiD/Ion Torrent PGM from Life Sciences, Genome Analyzer/HiSeq 2000/MiSeq from Illumina, and GS FLX Titanium/GS Junior from Roche. Beijing Genomics Institute (BGI), which possesses the world’s biggest sequencing capacity, has multiple NGS systems including 137 HiSeq 2000, 27 SOLiD, one Ion Torrent PGM, one MiSeq, and one 454 sequencer. We have accumulated extensive experience in sample handling, sequencing, and bioinformatics analysis. In this paper, technologies of these systems are reviewed, and first-hand data from extensive experience is summarized and analyzed to discuss the advantages and specifics associated with each sequencing system. At last, applications of NGS are summarized.

Download Full-text

Next-generation sequencing and bioinformatic approaches to detect and analyze influenza virus in ferrets

The Journal of Infection in Developing Countries ◽

10.3855/jidc.3749 ◽

2014 ◽

Vol 8 (04) ◽

pp. 498-509 ◽

Cited By ~ 9

Author(s):

Zhen Lin ◽

Amber Farooqui ◽

Guishuang Li ◽

Gane KS Wong ◽

Andrew L Mason ◽

...

Keyword(s):

Influenza Virus ◽

Next Generation Sequencing ◽

Viral Quasispecies ◽

Next Generation ◽

Sequencing Strategy ◽

Bioinformatic Approaches ◽

Next Generation Sequencing Ngs ◽

Viral Sequences ◽

Generation Sequencing

Introduction: Conventional methods used to detect and characterize influenza viruses in biological samples face multiple challenges due to the diversity of subtypes and high dissimilarity of emerging strains. Next-generation sequencing (NGS) is a powerful technique that can facilitate the detection and characterization of influenza, however, the sequencing strategy and the procedures of data analysis possess different aspects that require careful consideration. Methodology: The RNA from the lungs of ferrets infected with influenza A/California/07/2009 was analyzed by next-generation sequencing (NGS) without using specific PCR amplification of the viral sequences. Several bioinformatic approaches were used to resolve the viral genes and detect viral quasispecies. Results: The genomic sequences of influenza virus were characterized to a high level of detail when analyzing the short-reads with either the fast aligner Bowtie2, the general purpose aligner BLASTn or de novo assembly with Abyss. Moreover, when using distant viral sequences as reference, these methods were still able to resolve the viral sequences of a biological sample. Finally, direct sequencing of RNA samples did not provide sufficient coverage of the viral genome to study viral quasispecies, and, therefore, prior amplification of the viral segments by PCR would be required to perform this type of analysis. Conclusions: the introduction of NGS for virus research allows routine full characterization of viral isolates; however, careful design of the sequencing strategy and the procedures for data analysis are still of critical importance.

Download Full-text

Sequencing Framework for the Sensitive Detection and Precise Mapping of Defective Interfering Particle-Associated Deletions across Influenza A and B Viruses

Journal of Virology ◽

10.1128/jvi.00354-19 ◽

2019 ◽

Vol 93 (11) ◽

Cited By ~ 16

Author(s):

Fadi G. Alnaji ◽

Jessica R. Holmes ◽

Gloria Rendon ◽

J. Cristobal Vera ◽

Christopher J. Fields ◽

...

Keyword(s):

Influenza Virus ◽

Next Generation Sequencing ◽

Influenza A ◽

Influenza Viruses ◽

Next Generation ◽

Bioinformatics Pipeline ◽

Associated Sequences ◽

Next Generation Sequencing Ngs ◽

Generation Sequencing ◽

Defective Interfering Particle

ABSTRACT The mechanisms and consequences of defective interfering particle (DIP) formation during influenza virus infection remain poorly understood. The development of next-generation sequencing (NGS) technologies has made it possible to identify large numbers of DIP-associated sequences, providing a powerful tool to better understand their biological relevance. However, NGS approaches pose numerous technical challenges, including the precise identification and mapping of deletion junctions in the presence of frequent mutation and base-calling errors, and the potential for numerous experimental and computational artifacts. Here, we detail an Illumina-based sequencing framework and bioinformatics pipeline capable of generating highly accurate and reproducible profiles of DIP-associated junction sequences. We use a combination of simulated and experimental control data sets to optimize pipeline performance and demonstrate the absence of significant artifacts. Finally, we use this optimized pipeline to reveal how the patterns of DIP-associated junction formation differ between different strains and subtypes of influenza A and B viruses and to demonstrate how these data can provide insight into mechanisms of DIP formation. Overall, this work provides a detailed roadmap for high-resolution profiling and analysis of DIP-associated sequences within influenza virus populations. IMPORTANCE Influenza virus defective interfering particles (DIPs) that harbor internal deletions within their genomes occur naturally during infection in humans and during cell culture. They have been hypothesized to influence the pathogenicity of the virus; however, their specific function remains elusive. The accurate detection of DIP-associated deletion junctions is crucial for understanding DIP biology but is complicated by an array of technical issues that can bias or confound results. Here, we demonstrate a combined experimental and computational framework for detecting DIP-associated deletion junctions using next-generation sequencing (NGS). We detail how to validate pipeline performance and provide the bioinformatics pipeline for groups interested in using it. Using this optimized pipeline, we detect hundreds of distinct deletion junctions generated during infection with a diverse panel of influenza viruses and use these data to test a long-standing hypothesis concerning the molecular details of DIP formation.

Download Full-text