Indian Institute of Science Education and Research, Pune 411008, India.
Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore 560065, India.
J R Soc Interface. 2019 Nov 29;16(160):20190411. doi: 10.1098/rsif.2019.0411. Epub 2019 Nov 6.
The genome of the influenza virus consists of eight distinct single-stranded RNA segments, each encoding proteins essential for the viral life cycle. When the virus infects a host cell, these segments must be replicated and packaged into new budding virions. The viral genome is assembled with remarkably high fidelity: experiments reveal that most virions contain precisely one copy of each of the eight RNA segments. Cell-biological studies suggest that genome assembly is mediated by specific reversible and irreversible interactions between the RNA segments and their associated proteins. However, the precise inter-segment interaction network remains unresolved. Here, we computationally predict that tree-like irreversible interaction networks guarantee high-fidelity genome assembly, while cyclic interaction networks lead to futile or frustrated off-pathway products. We test our prediction against multiple experimental datasets. We find that tree-like networks capture the nearest-neighbour statistics of RNA segments in packaged virions, as observed by electron tomography. Just eight tree-like networks (of a possible 262 144) optimally capture both the nearest-neighbour data and independently measured RNA-RNA binding and co-localization propensities. These eight do not include the previously proposed hub-and-spoke and linear networks. Rather, each predicted network combines hub-like and linear features, consistent with evolutionary models of interaction gain and loss.
流感病毒的基因组由八个独特的单链 RNA 片段组成，每个片段都编码病毒生命周期所必需的蛋白质。当病毒感染宿主细胞时，这些片段必须被复制并包装成新的出芽病毒。病毒基因组的组装具有极高的保真度：实验表明，大多数病毒粒子中都恰好含有每个 RNA 片段的一份拷贝。细胞生物学研究表明，基因组的组装是由 RNA 片段与其相关蛋白之间的特定可逆和不可逆相互作用介导的。然而，确切的片段间相互作用网络仍然没有解决。在这里，我们通过计算预测，树状不可逆相互作用网络保证了高保真度的基因组组装，而环状相互作用网络则导致无用或受阻的非途径产物。我们针对多个实验数据集对我们的预测进行了检验。我们发现，树状网络可以捕捉到电子断层扫描中观察到的包装病毒粒子中 RNA 片段的最近邻统计数据。仅通过八个树状网络（可能的 262144 个网络中）就可以最佳地捕获最近邻数据以及独立测量的 RNA-RNA 结合和共定位倾向。这八个网络不包括之前提出的中心辐射状和线性网络。相反，每个预测的网络都结合了中心辐射状和线性特征，与相互作用获得和丧失的进化模型一致。
