Publications by authors named "J A Runstadler"

Background: Though receptor binding specificity is well established as a contributor to host tropism and spillover potential of influenza A viruses, determining receptor binding preference of a specific virus still requires expensive and time-consuming laboratory analyses. In this study, we pilot a machine learning approach for prediction of binding preference.

Methods: We trained a convolutional neural network to predict the α2,6-linked sialic acid preference of influenza A viruses given the hemagglutinin amino acid sequence.

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How avian influenza virus will continue to spread and circulate among wildlife is unclear.

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While diverse strains of low-pathogenicity avian influenza have circulated in wild birds for a long period of time, there has previously been little pathology in wild birds, ducks have been the primary and largely asymptomatic wild reservoir, and spillover into mammals has been limited and rare. In recent years, a high-pathogenicity avian influenza (HPAI) virus has emerged on the global scene and shifted the previously established dogmas for influenza infection. High-pathogenicity avian influenza has expanded into wildlife in unprecedented numbers and species diversity, with unmatched disease severity for influenza in wildlife.

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Highly pathogenic avian influenza (HPAI) has persisted as a One Health threat whose current circulation and impact are addressed in the companion Currents in One Health by Puryear and Runstadler, JAVMA, May 2024. Highly pathogenic avian influenza emerged as a by-product of agricultural practices and adapted to endemic circulation in wild bird species. Over more than 20 years, continued evolution in a complex ecology involving multiple hosts has produced a lineage that expanded globally over the last 2 years.

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Article Synopsis
  • The Influenza A virus (IAV) has a segmented genome that allows for reassortment when co-infected by different strains, which can lead to new pandemic strains.
  • A new method called DE-flowSVP has been developed to analyze multiple IAV particles quickly and quantitatively, enhancing the study of reassortment between divergent strains.
  • Research using this method found that aggregation of viral particles is a key factor in genome mixing, and reassortment is influenced by factors like co-infection timing and the types of viral surface proteins involved.
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