Evolutionary Insights from Association Rule Mining of Co-Occurring Mutations in Influenza Hemagglutinin and Neuraminidase.
Neuraminidase
/ genetics
Hemagglutinin Glycoproteins, Influenza Virus
/ genetics
Mutation
Humans
Evolution, Molecular
Influenza, Human
/ virology
Influenza A Virus, H3N2 Subtype
/ genetics
Glycosylation
Viral Proteins
/ genetics
Antigens, Viral
/ genetics
Antigenic Drift and Shift
/ genetics
Phylogeny
H3N2
antigenic drift
association rule mining
co-occurring mutations
influenza
Journal
Viruses
ISSN: 1999-4915
Titre abrégé: Viruses
Pays: Switzerland
ID NLM: 101509722
Informations de publication
Date de publication:
25 Sep 2024
25 Sep 2024
Historique:
received:
31
07
2024
revised:
29
08
2024
accepted:
11
09
2024
medline:
26
10
2024
pubmed:
26
10
2024
entrez:
26
10
2024
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 39459850
pii: v16101515
doi: 10.3390/v16101515
pii:
doi:
Substances chimiques
Neuraminidase
EC 3.2.1.18
Hemagglutinin Glycoproteins, Influenza Virus
0
Viral Proteins
0
Antigens, Viral
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : German Academic Exchange Service
ID : TBA