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Evolution and host adaptability of plant RNA viruses: Research insights on compositional biases

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出版社

ELSEVIER
DOI: 10.1016/j.csbj.2022.05.021

关键词

Plant RNA viruses; Compositional biases; Codon usage; Host adaptation

资金

  1. Natural Science Foun-dation of Jiangsu Province of China [BK20211323]
  2. National Natu-ral Science Foundation of China [31601604]
  3. Yangzhou Municipal Key ST Program [YZ2019051]
  4. Qing Lan Project of Yangzhou University

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The article provides information on the evolution of plant RNA viruses and their strategies for adapting to hosts. It reveals that most plant RNA viruses exhibit specific nucleotide compositions and codon usage patterns.
During recent decades, many new emerging or re-emerging RNA viruses have been found in plants through the development of deep-sequencing technology and big data analysis. These findings largely changed our understanding of the origin, evolution and host range of plant RNA viruses. There is evidence that their genetic composition originates from viruses, and host populations play a key role in the evolution and host adaptability of plant RNA viruses. In this mini-review, we describe the state of our understanding of the evolution of plant RNA viruses in view of compositional biases and explore how they adapt to the host. It appears that adenine rich (A-rich) coding sequences, low CpG and UpA dinucleotide frequencies and lower codon usage patterns were found in the vast majority of plant RNA viruses. The codon usage pattern of plant RNA viruses was influenced by both natural selection and mutation pressure, and natural selection mostly from hosts was the dominant factor. The codon adaptation analyses support that plant RNA viruses probably evolved a dynamic balance between codon adaptation and deoptimization to maintain efficient replication cycles in multiple hosts with various codon usage patterns. In the future, additional combinations of computational and experimental analyses of the nucleotide composition and codon usage of plant RNA viruses should be addressed. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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