Computational predicting the human infectivity of H7N9 influenza viruses isolated from avian hosts.
Computational predicting the human infectivity of H7N9 influenza viruses isolated from avian hosts.
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计算预测从禽类宿主分离的 H7N9 流感病毒的人类感染性
DOI：
10.1111/tbed.13750
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发表时间：
2021-03
影响因子：
4.3
通讯作者：
Li J
中科院分区：
文献类型：
--
作者：
Sun Y;Zhang K;Qi H;Zhang H;Zhang S;Bi Y;Wu L;Sun L;Qi J;Liu D;Ma J;Tien P;Liu W;Li J
特定禽流感病毒的基因组组成是其从鸟类到人类跨物种传播潜力的主要决定因素。在这里，我们介绍了一种基于病毒基因组的计算工具，可用于评估甲型H7N9流感病毒禽分离株的人类感染性，从而能够预测这些分离株感染人类的潜在风险。该工具基于一种新的类权重偏倚逻辑回归（CWBLR）算法，使用H7N9毒株的8个基因组片段的序列作为输入，并给出该毒株感染人类的概率（反映其人类感染性）。我们在细胞培养物和小鼠中检查了几种H7 N9禽分离株的复制效率和致病性，这些分离株通过CWBLR模型预测具有非常低或很高的人类感染性，发现具有高预测人类感染性的菌株在细胞培养物和小鼠中的复制效率更高。哺乳动物细胞和小鼠的感染性比预测具有低人类感染性的菌株更强。这些结果表明，我们的CWBLR模型可以作为预测H7N9禽流感病毒株人类感染性和跨物种传播风险的有力工具。
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.
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