Seasonal influenza epidemics are associated with high mortality and morbidity in the human population. Influenza surveillance is critical for providing information to national influenza programmes and for making vaccine composition predictions. Vaccination prevents viral infections, but rapid influenza evolution results in emerging mutants that differ antigenically from vaccine strains. Current influenza surveillance relies on Sanger sequencing of the haemagglutinin (HA) gene. Its classification according to World Health Organization (WHO) and European Centre for Disease Prevention and Control (ECDC) guidelines is based on combining certain genotypic amino acid mutations and phylogenetic analysis. Next-generation sequencing technologies enable a shift to whole-genome sequencing (WGS) for influenza surveillance, but this requires laboratory workflow adaptations and advanced bioinformatics workflows. In this study, 253 influenza A(H3N2) positive clinical specimens from the 2016–2017 Belgian season underwent WGS using the Illumina MiSeq system. HA-based classification according to WHO/ECDC guidelines did not allow classification of all samples. A new approach, considering the whole genome, was investigated based on using powerful phylogenomic tools including beast and Nextstrain, which substantially improved phylogenetic classification. Moreover, Bayesian inference via beast facilitated reassortment detection by both manual inspection and computational methods, detecting intra-subtype reassortants at an estimated rate of 15 %. Real-time analysis (i.e. as an outbreak is ongoing) via Nextstrain allowed positioning of the Belgian isolates into the globally circulating context. Finally, integration of patient data with phylogenetic groups and reassortment status allowed detection of several associations that would have been missed when solely considering HA, such as hospitalized patients being more likely to be infected with A(H3N2) reassortants, and the possibility to link several phylogenetic groups to disease severity indicators could be relevant for epidemiological monitoring. Our study demonstrates that WGS offers multiple advantages for influenza monitoring in (inter)national influenza surveillance, and proposes an improved methodology. This allows leveraging all information contained in influenza genomes, and allows for more accurate genetic characterization and reassortment detection.

中文翻译：

---

对比利时 2016-2017 年甲型 H3N2 流感爆发季节的基于全基因组的系统基因组分析可以改进监测

季节性流感流行与人群中的高死亡率和发病率有关。流感监测对于向国家流感计划提供信息和预测疫苗成分至关重要。疫苗接种可以防止病毒感染，但流感的快速进化会导致新出现的突变体与疫苗株的抗原性不同。目前的流感监测依赖于血凝素 (HA) 基因的 Sanger 测序。根据世界卫生组织 (WHO) 和欧洲疾病预防和控制中心 (ECDC) 指南的分类是基于结合某些基因型氨基酸突变和系统发育分析。下一代测序技术使流感监测转向全基因组测序 (WGS)，但这需要实验室工作流程的调整和先进的生物信息学工作流程。在这项研究中，来自 2016-2017 年比利时季节的 253 份甲型流感（H3N2）阳性临床标本使用 Illumina MiSeq 系统进行了 WGS。根据 WHO/ECDC 指南基于 HA 的分类不允许对所有样本进行分类。一种考虑全基因组的新方法基于使用强大的系统基因组工具进行了研究，包括beast和 Nextstrain，大大改进了系统发育分类。此外，通过野兽的贝叶斯推理通过人工检查和计算方法促进重配检测，以 15% 的估计比率检测亚型内重配。通过 Nextstrain 进行的实时分析（即正在爆发）允许将比利时分离株定位到全球循环环境中。最后，将患者数据与系统发育组和重排状态相结合，可以检测到仅考虑 HA 时可能会遗漏的几个关联，例如住院患者更可能感染 A(H3N2) 重排，以及将几种关联联系起来的可能性系统发育组与疾病严重程度指标可能与流行病学监测相关。我们的研究表明，WGS 为（国际）国家流感监测中的流感监测提供了多种优势，并提出一种改进的方法。这允许利用流感基因组中包含的所有信息，并允许更准确的遗传表征和重组检测。