The evolutionary features and roles of single nucleotide variants and charged amino acid mutations in influenza outbreaks during NPI period.

The epidemic and outbreaks of influenza B Victoria lineage (Bv) during 2019-2022 led to an analysis of genetic, epitopes, charged amino acids and Bv outbreaks. Based on the National Influenza Surveillance Network (NISN), the Bv 72 strains isolated during 2019-2022 were selected by spatio-temporal sampling, then were sequenced. Using the Compare Means, Correlate and Cluster, the outbreak data were analyzed, including the single nucleotide variant (SNV), amino acid (AA), epitope, evolutionary rate (ER), Shannon entropy value (SV), charged amino acid and outbreak. With the emergence of COVID-19, the non-pharmaceutical interventions (NPIs) made Less distant transmission and only Bv outbreak. The 2021-2022 strains in the HA genes were located in the same subset, but were distinct from the 2019-2020 strains (P < 0.001). The codon G → A transition in nucleotide was in the highest ratio but the transversion of C → A and T → A made the most significant contribution to the outbreaks, while the increase in amino acid mutations characterized by polar, acidic and basic signatures played a key role in the Bv epidemic in 2021-2022. Both ER and SV were positively correlated in HA genes (R = 0.690) and NA genes (R = 0.711), respectively, however, the number of mutations in the HA genes was 1.59 times higher than that of the NA gene (2.15/1.36) from the beginning of 2020 to 2022. The positively selective sites 174, 199, 214 and 563 in HA genes and the sites 73 and 384 in NA genes were evolutionarily selected in the 2021-2022 influenza outbreaks. Overall, the prevalent factors related to 2021-2022 influenza outbreaks included epidemic timing, Tv, Ts, Tv/Ts, P137 (B → P), P148 (B → P), P199 (P → A), P212 (P → A), P214 (H → P) and P563 (B → P). The preference of amino acid mutations for charge/pH could influence the epidemic/outbreak trends of infectious diseases. Here was a good model of the evolution of infectious disease pathogens. This study, on account of further exploration of virology, genetics, bioinformatics and outbreak information, might facilitate further understanding of their deep interaction mechanisms in the spread of infectious diseases.