Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China.
Sci Rep. 2024 Sep 3;14(1):20418. doi: 10.1038/s41598-024-71349-8.
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.
在 2019-2022 年期间，乙型流感 B 维多利亚谱系（Bv）的流行和暴发促使我们对遗传、抗原表位、带电氨基酸和 Bv 暴发进行了分析。基于国家流感监测网络（NISN），我们通过时空采样选择了 2019-2022 年分离的 Bv72 株进行测序。使用比较均值、相关性和聚类，分析暴发数据，包括单核苷酸变异（SNV）、氨基酸（AA）、抗原表位、进化率（ER）、香农熵值（SV）、带电氨基酸和暴发。随着 COVID-19 的出现，非药物干预（NPIs）减少了远距离传播，仅暴发了 Bv。HA 基因中的 2021-2022 年株位于同一亚群中，但与 2019-2020 年株不同（P<0.001）。核苷酸中的 G→A 转换在碱基中占比最高，但 C→A 和 T→A 的颠换对暴发的贡献最大，而 2021-2022 年 Bv 流行中氨基酸突变特征的增加以极性、酸性和碱性签名为特征发挥了关键作用。HA 基因（R=0.690）和 NA 基因（R=0.711）中的 ER 和 SV 均呈正相关，但从 2020 年初到 2022 年，HA 基因的突变数量是 NA 基因的 1.59 倍（2.15/1.36）。HA 基因中的 174、199、214 和 563 个以及 NA 基因中的 73 和 384 个位点在 2021-2022 年流感暴发中受到了正选择。总体而言，与 2021-2022 年流感暴发相关的流行因素包括流行时间、Tv、Ts、Tv/Ts、P137（B→P）、P148（B→P）、P199（P→A）、P212（P→A）、P214（H→P）和 P563（B→P）。氨基酸突变对电荷/pH 的偏好可能会影响传染病的流行/暴发趋势。这是传染病病原体进化的一个很好的模型。本研究进一步探讨了病毒学、遗传学、生物信息学和暴发信息，可能有助于进一步了解它们在传染病传播过程中的深层相互作用机制。
