Meijers Matthijs, Ruchnewitz Denis, Eberhardt Jan, Karmakar Malancha, Łuksza Marta, Lässig Michael
Institute for Biological Physics, University of Cologne, Köln, Germany.
Departments of Oncological Sciences and Genetics and Genomic Sciences, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Methods Mol Biol. 2025;2890:253-290. doi: 10.1007/978-1-0716-4326-6_14.
The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein hemagglutinin targeted by human antibodies. Here, we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to 1 year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available at https://previr.app .
季节性人流感病毒会迅速进化，导致每年流行的病毒株发生显著变化。这些变化通常由适应性突变驱动，尤其是在抗原表位，即人类抗体靶向的病毒表面蛋白血凝素区域。在此，我们描述了一套用于病毒进化数据驱动预测分析的一致方法。我们的流程整合了四种类型的数据：（1）全球范围内收集的病毒分离株的序列数据，（2）发病率的流行病学数据，（3）流行病毒的抗原特征，以及（4）病毒固有表型。通过对这些数据的综合分析，我们获得了流行株相对适应性的估计值以及长达1年期间进化枝频率的预测值。此外，我们还获得了候选疫苗株对未来病毒群体的保护作用的比较估计值，为抢先选择疫苗株提供了依据。可在https://previr.app获取从流感和SARS-CoV-2预测流程获得的持续更新的预测结果。
