bioRxivMicrobiologyPreprint
Mutations, Recombination and Insertion in the Evolution of 2019-nCoV
A. Wu, P. Niu, L. Wang, H. Zhou, X. Zhao, W. Wang, J. Wang, C. Ji, X. Ding, X. Wang, R. Lu, S. Gold, S. Aliyari, S. Zhang, E. Vikram, A. Zou, E. Lenh, J. Chen, F. Ye, N. Han, Y. Peng, H. Guo, G. Wu, T. Jiang, W. Tan, G. Cheng
Abstract
BackgroundThe 2019 novel coronavirus (2019-nCoV or SARS-CoV-2) has spread more rapidly than any other betacoronavirus including SARS-CoV and MERS-CoV. However, the mechanisms responsible for infection and molecular evolution of this virus remained unclear. MethodsWe collected and analyzed 120 genomic sequences of 2019-nCoV including 11 novel genomes from patients in China. Through comprehensive analysis of the available genome sequences of 2019-nCoV strains, we have tracked multiple inheritable SNPs and determined the evolution of 2019-nCoV relative to other coronaviruses. ResultsSystematic analysis of 120 genomic sequences of 2019-nCoV revealed co-circulation of two genetic subgroups with distinct SNPs markers, which can be used to trace the 2019-nCoV spreading pathways to different regions and countries. Although 2019-nCoV, human and bat SARS-CoV share high homologous in overall genome structures, they evolved into two distinct groups with different receptor entry specificities through potential recombination in the receptor binding regions. In addition, 2019-nCoV has a unique four amino acid insertion between S1 and S2 domains of the spike protein, which created a potential furin or TMPRSS2 cleavage site. ConclusionsOur studies provided comprehensive insights into the evolution and spread of the 2019-nCoV. Our results provided evidence suggesting that 2019-nCoV may increase its infectivity through the receptor binding domain recombination and a cleavage site insertion. One Sentence SummaryNovel 2019-nCoV sequences revealed the evolution and specificity of betacoronavirus with possible mechanisms of enhanced infectivity.
- organism: virus
- assay: whole-genome sequencing
- research focus: phylogenetics / evolution
- approach: statistical model
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