Institute of Pathogen Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China.
Key Laboratory of Respiratory Disease Pathogenomics, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China.
Microbiol Spectr. 2023 Aug 17;11(4):e0001923. doi: 10.1128/spectrum.00019-23. Epub 2023 Jun 28.
Respiratory viruses may interfere with each other and affect the epidemic trend of the virus. However, the understanding of the interactions between respiratory viruses at the population level is still very limited. We here conducted a prospective laboratory-based etiological study by enrolling 14,426 patients suffered from acute respiratory infection (ARI) in Beijing, China during 2005 to 2015. All 18 respiratory viruses were simultaneously tested for each nasal and throat swabs collected from enrolled patients using molecular tests. The virus correlations were quantitatively evaluated, and the respiratory viruses could be divided into two panels according to the positive and negative correlations. One included influenza viruses (IFVs) A, B, and respiratory syncytial virus (RSV), while the other included human parainfluenza viruses (HPIVs) 1/3, 2/4, adenovirus (Adv), human metapneumovirus (hMPV), and enterovirus (including rhinovirus, named picoRNA), α and β human coronaviruses (HCoVs). The viruses were positive-correlated in each panel, while negative-correlated between panels. After adjusting the confounding factors by vector autoregressive model, positive interaction between IFV-A and RSV and negative interaction between IFV-A and picoRNA are still be observed. The asynchronous interference of IFV-A significantly delayed the peak of β human coronaviruses epidemic. The binary property of the respiratory virus interactions provides new insights into the viral epidemic dynamics in human population, facilitating the development of infectious disease control and prevention strategies. Systematic quantitative assessment of the interactions between different respiratory viruses is pivotal for the prevention of infectious diseases and the development of vaccine strategies. Our data showed stable interactions among respiratory viruses at human population level, which are season irrelevant. Respiratory viruses could be divided into two panels according to their positive and negative correlations. One included influenza virus and respiratory syncytial virus, while the other included other common respiratory viruses. It showed negative correlations between the two panels. The asynchronous interference between influenza virus and β human coronaviruses significantly delayed the peak of β human coronaviruses epidemic. The binary property of the viruses indicated transient immunity induced by one kind of virus would play role on subsequent infection, which provides important data for the development of epidemic surveillance strategies.
呼吸道病毒之间可能会相互干扰，从而影响病毒的流行趋势。然而，人们对人群水平上呼吸道病毒之间相互作用的了解仍然非常有限。在这里，我们通过对 2005 年至 2015 年期间在中国北京因急性呼吸道感染（ARI）就诊的 14426 例患者进行前瞻性基于实验室的病因学研究，来了解这一情况。使用分子检测同时对采集自每位患者的鼻和咽拭子检测 18 种呼吸道病毒。定量评估病毒相关性，并根据阳性和阴性相关性将呼吸道病毒分为两组。一组包括流感病毒（IFV）A、B 和呼吸道合胞病毒（RSV），另一组包括人副流感病毒（HPIV）1/3、2/4、腺病毒（Adv）、人偏肺病毒（hMPV）和肠道病毒（包括鼻病毒，命名为 picoRNA）、α 和β 人冠状病毒（HCoV）。每组内的病毒呈正相关，而两组之间呈负相关。通过向量自回归模型调整混杂因素后，仍观察到 IFV-A 与 RSV 之间存在正相互作用，IFV-A 与 picoRNA 之间存在负相互作用。IFV-A 的异步干扰显著延迟了β 人冠状病毒流行的高峰。呼吸道病毒相互作用的二元性质为人群中病毒流行动力学提供了新的见解，有助于制定传染病控制和预防策略。系统地定量评估不同呼吸道病毒之间的相互作用对于预防传染病和制定疫苗策略至关重要。我们的数据表明，在人群水平上呼吸道病毒之间存在稳定的相互作用，这些相互作用与季节无关。呼吸道病毒可根据其阳性和阴性相关性分为两组。一组包括流感病毒和呼吸道合胞病毒，另一组包括其他常见的呼吸道病毒。两组之间呈负相关。流感病毒和β 人冠状病毒之间的异步干扰显著延迟了β 人冠状病毒流行的高峰。病毒的二元性质表明，一种病毒引起的短暂免疫会对随后的感染产生影响，这为开发传染病监测策略提供了重要数据。
