Computational predicting the human infectivity of H7N9 influenza viruses isolated from avian hosts.

The genome composition of a given avian influenza virus is the primary determinant of its potential for cross‐species transmission from birds to humans. Here, we introduce a viral genome‐based computational tool that can be used to evaluate the human infectivity of avian isolates of influenza A H7N9 viruses, which can enable prediction of the potential risk of these isolates infecting humans. This tool, which is based on a novel class weight‐biased logistic regression (CWBLR) algorithm, uses the sequences of the eight genome segments of an H7N9 strain as the input and gives the probability of this strain infecting humans (reflecting its human infectivity). We examined the replication efficiency and the pathogenicity of several H7N9 avian isolates that were predicted to have very low or high human infectivity by the CWBLR model in cell culture and in mice, and found that the strains with high predicted human infectivity replicated more efficiently in mammalian cells and were more infective in mice than those that were predicted to have low human infectivity. These results demonstrate that our CWBLR model can serve as a powerful tool for predicting the human infectivity and cross‐species transmission risks of H7N9 avian strains.

特定禽流感病毒的基因组组成是其从鸟类跨物种传播至人类潜力的主要决定因素。在此，我们引入一种基于病毒基因组的计算工具，该工具可用于评估甲型H7N9禽流感病毒分离株对人类的感染性，从而能够预测这些分离株感染人类的潜在风险。此工具基于一种新型的类别权重偏差逻辑回归（CWBLR）算法，将H7N9毒株的8个基因组片段序列作为输入，并给出该毒株感染人类的概率（反映其对人类的感染性）。我们检测了几种经CWBLR模型预测对人类感染性极低或极高的H7N9禽流感分离株在细胞培养和小鼠中的复制效率及致病性，发现预测对人类感染性高的毒株在哺乳动物细胞中复制效率更高，且在小鼠中的感染性比预测对人类感染性低的毒株更强。这些结果表明，我们的CWBLR模型可作为一种强大的工具，用于预测H7N9禽流感毒株对人类的感染性以及跨物种传播风险。