Frustration and fidelity in influenza genome assembly

被引:2
|
作者
Farheen, Nida [1 ,3 ]
Thattai, Mukund [2 ]
机构
[1] Indian Inst Sci Educ & Res, Pune 411008, Maharashtra, India
[2] Tata Inst Fundamental Res, Natl Ctr Biol Sci, Simons Ctr Study Living Machines, Bangalore 560065, Karnataka, India
[3] Brandeis Univ, Waltham, MA 02454 USA
关键词
segmented virus; influenza; self-assembly; network evolution; MUTATIONAL ANALYSIS; SEGMENT; 7; RNA; VIRUSES;
D O I
10.1098/rsif.2019.0411
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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 measuredRNA-RNAbinding 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.
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页数:8
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