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Chemoenzymatic site-specific labeling of influenza glycoproteins as a tool to observe virus budding in real time.

ABSTRACT: The influenza virus uses the hemagglutinin (HA) and neuraminidase (NA) glycoproteins to interact with and infect host cells. While biochemical and microscopic methods allow examination of the early steps in flu infection, the genesis of progeny virions has been more difficult to follow, mainly because of difficulties inherent in fluorescent labeling of flu proteins in a manner compatible with live cell imaging. We here apply sortagging as a chemoenzymatic approach to label genetically modified but infectious flu and track the flu glycoproteins during the course of infection. This method cleanly distinguishes influenza glycoproteins from host glycoproteins and so can be used to assess the behavior of HA or NA biochemically and to observe the flu glycoproteins directly by live cell imaging.

SUBMITTER: Popp MW

PROVIDER: S-EPMC3310791 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Chemoenzymatic site-specific labeling of influenza glycoproteins as a tool to observe virus budding in real time.

Popp Maximilian Wei-Lin MW Karssemeijer Roos A RA Ploegh Hidde L HL

PLoS pathogens 20120322 3

The influenza virus uses the hemagglutinin (HA) and neuraminidase (NA) glycoproteins to interact with and infect host cells. While biochemical and microscopic methods allow examination of the early steps in flu infection, the genesis of progeny virions has been more difficult to follow, mainly because of difficulties inherent in fluorescent labeling of flu proteins in a manner compatible with live cell imaging. We here apply sortagging as a chemoenzymatic approach to label genetically modified b ...[more]

PMID: 22457626

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Project description:BackgroundInfluenza causes serious illness requiring annual health system surge capacity, yet annual seasonal variation makes it difficult to forecast and plan for the severity of an upcoming season. Research shows that hospital and health system stakeholders indicate a preference for forecasting tools that are easy to use and understand to assist with surge capacity planning for influenza.ObjectiveThis study aimed to develop a simple risk prediction tool, Flucast, to predict the severity of an emerging influenza season.MethodsStudy data were obtained from the National Notifiable Diseases Surveillance System and Australian Influenza Surveillance Reports from the Department of Health, Australia. We tested Flucast using retrospective seasonal data for 11 Australian influenza seasons. We compared five different models using parameters known early in the season that may be associated with the severity of the season. To calibrate the tool, the resulting estimates of seasonal severity were validated against independent reports of influenza-attributable morbidity and mortality. The model with the highest predictive accuracy against retrospective seasonal activity was chosen as a best-fit model to develop the Flucast tool. The tool was prospectively tested against the 2018 and the emerging 2019 influenza season.ResultsThe Flucast tool predicted the severity of all retrospectively studied years correctly for influenza seasonal activity in Australia. With the use of real-time data, the tool provided a reasonable early prediction of a low to moderate season for the 2018 and severe seasonal activity for the upcoming 2019 season. The tool meets stakeholder preferences for simplicity and ease of use to assist with surge capacity planning.ConclusionsThe Flucast tool may be useful to inform future health system influenza preparedness planning, surge capacity, and intervention programs in real time, and can be adapted for different settings and geographic locations.

Project description:Recombinant proteins are of great interest in glycobiology and proteomics, known especially for their reproducibility and accessibility. However, variation in glycosylation among recombinant glycoproteins is not well understood and may depend on numerous conditions in the biomanufacturing process. In order to confidently assess variation in glycosylation measurements, it is vital to both optimize the measurement of, and determine the degree of variation between, distributions of glycosylation on specific sites of glycoproteins. This is especially important for glycoproteins that are known to have rapid sequence changes, such as with different influenza strains. In this study, eight strains of recombinant influenza hemagglutinin and neuraminidase produced from HEK293 cell line were obtained from four vendors and digestion was conducted using a series of complex multi-enzymatic methods designed to isolate glycopeptide sequons. Site-specific glycosylation profiles of intact glycopeptides were produced using mass spectrometric evaluation on an orbitrap system and compared using spectral similarity scores. Variation in glycan abundances and distribution was most pronounced between different strains of virus (similarity score = 383 out of 1000), whereas replicates resulted in low variation (similarity score = 957 out of 1000). Glycan variation was also measured based on differences between vendors, lots, batches, protease digestion, and intra-protein site. The most abundant glycans in all of these influenza glycoproteins were monofucosylated and complex, as reported by other laboratories. However, it was found that different vendors can produce very different glycan distributions for the same glycosylation site. Notably, it is demonstrated that glycan distributions are similar for conserved regions of influenza glycoproteins. Overall, these methods present a potential use in developing reproducible measurements of glycosylated biologics for quality control or making more informed decisions in biomanufacturing.

Dataset Information

Chemoenzymatic site-specific labeling of influenza glycoproteins as a tool to observe virus budding in real time.

Publications

Chemoenzymatic site-specific labeling of influenza glycoproteins as a tool to observe virus budding in real time.

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