Marine viruses are key players of ocean biogeochemistry, profoundly influencing microbial community ecology and evolution. Despite their importance, few studies have explored continuous inter-seasonal viral metagenomic time-series in marine environments. Viral dynamics are complex, influenced by multiple factors such as host population dynamics and environmental conditions. To disentangle the complexity of viral communities, we developed an unsupervised machine learning framework to classify viral contigs into "chronotypes" based on temporal abundance patterns. Analysing an inter-seasonal monthly time-series of surface viral metagenomes from the Western English Channel, we identified chronotypes and compared their functional and evolutionary profiles. Results revealed a consistent annual cycle with steep compositional changes from winter to summer and steadier transitions from summer to winter. Seasonal chronotypes were enriched in potential auxiliary metabolic genes of the ferrochelatases and 2OG-Fe(II) oxygenase orthologous groups compared to non-seasonal types. Chronotypes clustered into four groups based on their correlation profiles with environmental parameters, primarily driven by temperature and nutrients. Viral contigs exhibited a rapid turnover of polymorphisms, akin to Red Queen dynamics. However, within seasonal chronotypes, some sequences exhibited annual polymorphism recurrence, suggesting that a fraction of the seasonal viral populations evolve more slowly. Classification into chronotypes revealed viral genomic signatures linked to temporal patterns, likely reflecting metabolic adaptations to environmental fluctuations and host dynamics. This novel framework enables the identification of long-term trends in viral composition, environmental influences on genomic structure, and potential viral interactions.

中文翻译：

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宏基因组时间序列揭示了一个由具有强烈季节性信号的成员主导的西英吉利海峡病毒群落


海洋病毒是海洋生物地球化学的关键参与者，对微生物群落生态学和进化产生了深远影响。尽管它们很重要，但很少有研究探讨海洋环境中连续的季节间病毒宏基因组时间序列。病毒动力学很复杂，受宿主种群动态和环境条件等多种因素的影响。为了理清病毒群落的复杂性，我们开发了一个无监督机器学习框架，根据时间丰度模式将病毒重叠群分类为“时间型”。通过分析来自西英吉利海峡的表面病毒宏基因组的跨季节月度时间序列，我们确定了时间型并比较了它们的功能和进化特征。结果显示，这是一个一致的年度周期，从冬季到夏季的成分变化很大，从夏季到冬季的过渡更稳定。与非季节性类型相比，季节性时间型富含铁螯合酶和 2OG-Fe（II） 加氧酶直系同源组的潜在辅助代谢基因。时间型根据它们与环境参数的相关性分为四组，主要由温度和营养物质驱动。病毒重叠群表现出多态性的快速周转，类似于 Red Queen 动力学。然而，在季节性时间型中，一些序列表现出每年多态性复发，这表明一小部分季节性病毒种群的进化速度更慢。按时间型分类揭示了与时间模式相关的病毒基因组特征，这可能反映了对环境波动和宿主动力学的代谢适应。 这种新颖的框架能够识别病毒组成的长期趋势、环境对基因组结构的影响以及潜在的病毒相互作用。