Title |
Characterization of a new apple luteovirus identified by high-throughput sequencing
|
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Published in |
Virology Journal, May 2018
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DOI | 10.1186/s12985-018-0998-3 |
Pubmed ID | |
Authors |
Huawei Liu, Liping Wu, Ekaterina Nikolaeva, Kari Peter, Zongrang Liu, Dimitre Mollov, Mengji Cao, Ruhui Li |
Abstract |
'Rapid Apple Decline' (RAD) is a newly emerging problem of young, dwarf apple trees in the Northeastern USA. The affected trees show trunk necrosis, cracking and canker before collapse in summer. In this study, we discovered and characterized a new luteovirus from apple trees in RAD-affected orchards using high-throughput sequencing (HTS) technology and subsequent Sanger sequencing. Illumina NextSeq sequencing was applied to total RNAs prepared from three diseased apple trees. Sequence reads were de novo assembled, and contigs were annotated by BLASTx. RT-PCR and 5'/3' RACE sequencing were used to obtain the complete genome of a new virus. RT-PCR was used to detect the virus. Three common apple viruses and a new luteovirus were identified from the diseased trees by HTS and RT-PCR. Sequence analyses of the complete genome of the new virus show that it is a new species of the genus Luteovirus in the family Luteoviridae. The virus is graft transmissible and detected by RT-PCR in apple trees in a couple of orchards. A new luteovirus and/or three known viruses were found to be associated with RAD. Molecular characterization of the new luteovirus provides important information for further investigation of its distribution and etiological role. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 1 | 20% |
Unknown | 4 | 80% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 4 | 80% |
Science communicators (journalists, bloggers, editors) | 1 | 20% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 24 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 5 | 21% |
Student > Bachelor | 3 | 13% |
Student > Ph. D. Student | 3 | 13% |
Student > Doctoral Student | 2 | 8% |
Professor > Associate Professor | 1 | 4% |
Other | 0 | 0% |
Unknown | 10 | 42% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 10 | 42% |
Environmental Science | 2 | 8% |
Nursing and Health Professions | 1 | 4% |
Unknown | 11 | 46% |