Dataset Information

Influenza Viruses in Mice: Deep Sequencing Analysis of Serial Passage and Effects of Sialic Acid Structural Variation.

ABSTRACT: Influenza A viruses have regularly jumped to new host species to cause epidemics or pandemics, an evolutionary process that involves variation in the viral traits necessary to overcome host barriers and facilitate transmission. Mice are not a natural host for influenza virus but are frequently used as models in studies of pathogenesis, often after multiple passages to achieve higher viral titers that result in clinical disease such as weight loss or death. Here, we examine the processes of influenza A virus infection and evolution in mice by comparing single nucleotide variations of a human H1N1 pandemic virus, a seasonal H3N2 virus, and an H3N2 canine influenza virus during experimental passage. We also compared replication and sequence variation in wild-type mice expressing N-glycolylneuraminic acid (Neu5Gc) with those seen in mice expressing only N-acetylneuraminic acid (Neu5Ac). Viruses derived from plasmids were propagated in MDCK cells and then passaged in mice up to four times. Full-genome deep sequencing of the plasmids, cultured viruses, and viruses from mice at various passages revealed only small numbers of mutational changes. The H3N2 canine influenza virus showed increases in frequency of sporadic mutations in the PB2, PA, and NA segments. The H1N1 pandemic virus grew well in mice, and while it exhibited the maintenance of some minority mutations, there was no clear evidence for adaptive evolution. The H3N2 seasonal virus did not establish in the mice. Finally, there were no clear sequence differences associated with the presence or absence of Neu5Gc.IMPORTANCE Mice are commonly used as a model to study the growth and virulence of influenza A viruses in mammals but are not a natural host and have distinct sialic acid receptor profiles compared to humans. Using experimental infections with different subtypes of influenza A virus derived from different hosts, we found that evolution of influenza A virus in mice did not necessarily proceed through the linear accumulation of host-adaptive mutations, that there was variation in the patterns of mutations detected in each repetition, and that the mutation dynamics depended on the virus examined. In addition, variation in the viral receptor, sialic acid, did not affect influenza virus evolution in this model. Overall, our results show that while mice provide a useful animal model for influenza virus pathology, host passage evolution will vary depending on the specific virus tested.

SUBMITTER: Wasik BR

PROVIDER: S-EPMC6854484 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Influenza Viruses in Mice: Deep Sequencing Analysis of Serial Passage and Effects of Sialic Acid Structural Variation.

Wasik Brian R BR Voorhees Ian E H IEH Barnard Karen N KN Alford-Lawrence Brynn K BK Weichert Wendy S WS Hood Grace G Nogales Aitor A Martínez-Sobrido Luis L Holmes Edward C EC Parrish Colin R CR

Journal of virology 20191113 23

Influenza A viruses have regularly jumped to new host species to cause epidemics or pandemics, an evolutionary process that involves variation in the viral traits necessary to overcome host barriers and facilitate transmission. Mice are not a natural host for influenza virus but are frequently used as models in studies of pathogenesis, often after multiple passages to achieve higher viral titers that result in clinical disease such as weight loss or death. Here, we examine the processes of influ ...[more]

PMID: 31511393

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Project description:Influenza A virus (IAV) binds its host cell using the major viral surface protein hemagglutinin (HA). HA recognizes sialic acid, a plasma membrane glycan that functions as the specific primary attachment factor (AF). Since sialic acid alone cannot fulfill a signaling function, the virus needs to activate downstream factors to trigger endocytic uptake. Recently, the epidermal growth factor receptor (EGFR), a member of the receptor-tyrosine kinase family, was shown to be activated by IAV and transmit cell entry signals. However, how IAV's binding to sialic acid leads to engagement and activation of EGFR remains largely unclear. We used multicolor super-resolution microscopy to study the lateral organization of both IAV's AFs and its functional receptor EGFR at the scale of the IAV particle. Intriguingly, quantitative cluster analysis revealed that AFs and EGFR are organized in partially overlapping submicrometer clusters in the plasma membrane of A549 cells. Within AF domains, the local AF concentration reaches on average 10-fold the background concentration and tends to increase towards the cluster center, thereby representing a multivalent virus-binding platform. Using our experimentally measured cluster characteristics, we simulated virus diffusion on a flat membrane. The results predict that the local AF concentration strongly influences the distinct mobility pattern of IAVs, in a manner consistent with live-cell single-virus tracking data. In contrast to AFs, EGFR resides in smaller clusters. Virus binding activates EGFR, but interestingly, this process occurs without a major lateral EGFR redistribution, indicating the activation of pre-formed clusters, which we show are long-lived. Taken together, our results provide a quantitative understanding of the initial steps of influenza virus infection. Co-clustering of AF and EGFR permit a cooperative effect of binding and signaling at specific platforms, thus linking their spatial organization to their functional role during virus-cell binding and receptor activation.

Project description:BackgroundMassively parallel DNA sequencing technologies have enabled the sequencing of several individual human genomes. These technologies are also being used in novel ways for mRNA expression profiling, genome-wide discovery of transcription-factor binding sites, small RNA discovery, etc. The multitude of sequencing platforms, each with their unique characteristics, pose a number of design challenges, regarding the technology to be used and the depth of sequencing required for a particular sequencing application. Here we describe a number of analytical and empirical results to address design questions for two applications: detection of structural variations from paired-end sequencing and estimating mRNA transcript abundance.ResultsFor structural variation, our results provide explicit trade-offs between the detection and resolution of rearrangement breakpoints, and the optimal mix of paired-read insert lengths. Specifically, we prove that optimal detection and resolution of breakpoints is achieved using a mix of exactly two insert library lengths. Furthermore, we derive explicit formulae to determine these insert length combinations, enabling a 15% improvement in breakpoint detection at the same experimental cost. On empirical short read data, these predictions show good concordance with Illumina 200 bp and 2 Kbp insert length libraries. For transcriptome sequencing, we determine the sequencing depth needed to detect rare transcripts from a small pilot study. With only 1 Million reads, we derive corrections that enable almost perfect prediction of the underlying expression probability distribution, and use this to predict the sequencing depth required to detect low expressed genes with greater than 95% probability.ConclusionsTogether, our results form a generic framework for many design considerations related to high-throughput sequencing. We provide software tools http://bix.ucsd.edu/projects/NGS-DesignTools to derive platform independent guidelines for designing sequencing experiments (amount of sequencing, choice of insert length, mix of libraries) for novel applications of next generation sequencing.

Dataset Information

Influenza Viruses in Mice: Deep Sequencing Analysis of Serial Passage and Effects of Sialic Acid Structural Variation.

Publications

Influenza Viruses in Mice: Deep Sequencing Analysis of Serial Passage and Effects of Sialic Acid Structural Variation.

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