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Basic Characteristics of Mutations
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Mutation Site
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N189K |
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Mutation Site Sentence
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The case of na_D339N and ha_N189K is not unique; in many other instances, an emerging asparagine is coupled with a disappearing one. |
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Mutation Level
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Amino acid level |
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Mutation Type
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Nonsynonymous substitution |
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Gene/Protein/Region
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HA |
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Standardized Encoding Gene
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HA
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Genotype/Subtype
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H3N2 |
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Viral Reference
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-
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Functional Impact and Mechanisms
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Disease
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Influenza A
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Immune
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- |
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Target Gene
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-
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Clinical and Epidemiological Correlations
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Clinical Information
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- |
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Treatment
|
- |
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Location
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North America |
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Literature Information
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PMID
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39459850
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Title
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Evolutionary Insights from Association Rule Mining of Co-Occurring Mutations in Influenza Hemagglutinin and Neuraminidase
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Author
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Galeone V,Lee C,Monaghan MT,Bauer DC,Wilson LOW
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Journal
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Viruses
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Journal Info
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2024 Sep 25;16(10):1515
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Abstract
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Seasonal influenza viruses continuously evolve via antigenic drift. This leads to recurring epidemics, globally significant mortality rates, and the need for annually updated vaccines. Co-occurring mutations in hemagglutinin (HA) and neuraminidase (NA) are suggested to have synergistic interactions where mutations can increase the chances of immune escape and viral fitness. Association rule mining was used to identify temporal relationships of co-occurring HA-NA mutations of influenza virus A/H3N2 and its role in antigenic evolution. A total of 64 clusters were found. These included well-known mutations responsible for antigenic drift, as well as previously undiscovered groups. A majority (41/64) were associated with known antigenic sites, and 38/64 involved mutations across both HA and NA. The emergence and disappearance of N-glycosylation sites in the pattern of N-X-[S/T] were also identified, which are crucial post-translational processes to maintain protein stability and functional balance (e.g., emergence of NA:339ASP and disappearance of HA:187ASP). Our study offers an alternative approach to the existing mutual-information and phylogenetic methods used to identify co-occurring mutations, enabling faster processing of large amounts of data. Our approach can facilitate the prediction of critical mutations given their occurrence in a previous season, facilitating vaccine development for the next flu season and leading to better preparation for future pandemics.
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Sequence Data
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-
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