Dataset Information

Single-particle measurements of filamentous influenza virions reveal damage induced by freezing.

ABSTRACT: Clinical isolates of influenza virus produce pleiomorphic virions, ranging from small spheres to elongated filaments. The filaments are seemingly adaptive in natural infections, but their basic functional properties are poorly understood and functional studies of filaments often report contradictory results. This may be due to artefactual damage from routine laboratory handling, an issue which has been noted several times without being explored in detail. To determine whether standard laboratory techniques could damage filaments, we used immunofluorescence microscopy to rapidly and reproducibly quantify and characterize the dimensions of filaments. Most of the techniques we tested had minimal impact on filaments, but freezing to -70 °C, a standard storage step before carrying out functional studies on influenza viruses, severely reduced their concentration, median length and the infectivity of the whole virion population. We noted that damage from freezing is likely to have affected most of the functional studies of filaments performed to date, and to address this we show that it can be mitigated by snap-freezing or incorporating the cryoprotectant DMSO. We recommend that functional studies of filaments characterize virion populations prior to analysis to ensure reproducibility, and that they use unfrozen samples if possible and cryoprotectants if not. These basic measures will support the robust functional characterizations of filaments that are required to understand their roles in natural influenza virus infections.

SUBMITTER: Hirst JC

PROVIDER: S-EPMC6904231 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:With the development of single-particle tracking (SPT) microscopy and host membrane mimics called supported lipid bilayers (SLBs), stochastic virus-membrane binding interactions can be studied in depth while maintaining control over host receptor type and concentration. However, several experimental design challenges and quantitative image analysis limitations prevent the widespread use of this approach. One main challenge of SPT studies is the low signal-to-noise ratio of SPT videos, which is sometimes inevitable due to small particle sizes, low quantum yield of fluorescent dyes, and photobleaching. These situations could render current particle tracking software to yield biased binding kinetic data caused by intermittent tracking error. Hence, we developed an effective image restoration algorithm for SPT applications called STAWASP that reveals particles with a signal-to-noise ratio of 2.2 while preserving particle features. We tested our improvements to the SPT binding assay experiment and imaging procedures by monitoring X31 influenza virus binding to ?2,3 sialic acid glycolipids. Our interests lie in how slight changes to the peripheral oligosaccharide structures can affect the binding rate and residence times of viruses. We were able to detect viruses binding weakly to a glycolipid called GM3, which was undetected via assays such as surface plasmon resonance. The binding rate was around 28 folds higher when the virus bound to a different glycolipid called GD1a, which has a sialic acid group extending further away from the bilayer surface than GM3. The improved imaging allowed us to obtain binding residence time distributions that reflect an adhesion-strengthening mechanism via multivalent bonds. We empirically fitted these distributions using a time-dependent unbinding rate parameter, koff, which diverges from standard treatment of koff as a constant. We further explain how to convert these models to fit ensemble-averaged binding data obtained by assays such as surface plasmon resonance.

Project description:The development of improved and universal anti-influenza vaccines would represent a major advance in the protection of human health. In order to facilitate the development of such vaccines, understanding how viral proteins can contribute to protection from disease is critical. Much of the previous work to address these questions relied on reductionist systems (i.e. vaccinating with individual proteins or VLPs that contain only a few viral proteins); thus we have an incomplete understanding of how immunity to different subsets of viral proteins contribute to protection. Here, we report the development of a platform in which a single viral protein can be deleted from an authentic viral particle that retains the remaining full complement of structural proteins and viral RNA. As a first study with this system, we chose to delete the major IAV antigen, the hemagglutinin protein, to evaluate how the other components of the viral particle contribute en masse to protection from influenza disease. Our results show that while anti-HA immunity plays a major role in protection from challenge with a vaccine-matched strain, the contributions from other structural proteins were the major drivers of protection against highly antigenically drifted, homosubtypic strains. This work highlights the importance of evaluating the inclusion of non-HA viral proteins in the development of broadly efficacious and long-lasting influenza vaccines.ImportanceInfluenza virus vaccines currently afford short-term protection from viruses that are closely related to the vaccine strains. There is currently much effort to develop improved, next-generation influenza vaccines that elicit broader and longer-lasting protection. While the hemagglutinin protein is the major viral antigen, in this work, we developed an approach with which to evaluate the contributions of the non-hemagglutinin proteins to vaccine mediated protection. Our results indicate that other structural proteins together may help to mediate broad antiviral protection and should be considered in the development of more universal influenza vaccines.

Dataset Information

Single-particle measurements of filamentous influenza virions reveal damage induced by freezing.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets