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Article

Evolutionary Insights from Association Rule Mining of Co-Occurring Mutations in Influenza Hemagglutinin and Neuraminidase

by
Valentina Galeone
1,2,†,
Carol Lee
2,
Michael T. Monaghan
3,4,
Denis C. Bauer
2 and
Laurence O. W. Wilson
2,5,*
1
Institute of Computer Science, Freie Universität Berlin, 14195 Berlin, Germany
2
Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Sydney, NSW 2145, Australia
3
Institute of Biology, Freie Universität Berlin, 14195 Berlin, Germany
4
Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), 12587 Berlin, Germany
5
Department of Biomedical Sciences, Macquarie University, Sydney, NSW 2109, Australia
*
Author to whom correspondence should be addressed.
Current address: Genome Competence Center, Robert Koch Institute, 13353 Berlin, Germany.
Viruses 2024, 16(10), 1515; https://doi.org/10.3390/v16101515
Submission received: 31 July 2024 / Revised: 29 August 2024 / Accepted: 11 September 2024 / Published: 25 September 2024
(This article belongs to the Special Issue Virus Bioinformatics 2024)

Abstract

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.
Keywords: influenza; H3N2; association rule mining; antigenic drift; co-occurring mutations influenza; H3N2; association rule mining; antigenic drift; co-occurring mutations

Share and Cite

MDPI and ACS Style

Galeone, V.; Lee, C.; Monaghan, M.T.; Bauer, D.C.; Wilson, L.O.W. Evolutionary Insights from Association Rule Mining of Co-Occurring Mutations in Influenza Hemagglutinin and Neuraminidase. Viruses 2024, 16, 1515. https://doi.org/10.3390/v16101515

AMA Style

Galeone V, Lee C, Monaghan MT, Bauer DC, Wilson LOW. Evolutionary Insights from Association Rule Mining of Co-Occurring Mutations in Influenza Hemagglutinin and Neuraminidase. Viruses. 2024; 16(10):1515. https://doi.org/10.3390/v16101515

Chicago/Turabian Style

Galeone, Valentina, Carol Lee, Michael T. Monaghan, Denis C. Bauer, and Laurence O. W. Wilson. 2024. "Evolutionary Insights from Association Rule Mining of Co-Occurring Mutations in Influenza Hemagglutinin and Neuraminidase" Viruses 16, no. 10: 1515. https://doi.org/10.3390/v16101515

APA Style

Galeone, V., Lee, C., Monaghan, M. T., Bauer, D. C., & Wilson, L. O. W. (2024). Evolutionary Insights from Association Rule Mining of Co-Occurring Mutations in Influenza Hemagglutinin and Neuraminidase. Viruses, 16(10), 1515. https://doi.org/10.3390/v16101515

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