|FEI, ZHANGJUN - Boyce Thompson Institute|
Submitted to: Phytopathology
Publication Type: Abstract Only
Publication Acceptance Date: 5/8/2012
Publication Date: 8/4/2012
Citation: Li, R., Fei, Z., Ling, K. 2012. Characterization and detection of Tomato necrotic stunt virus, a novel potyvirus infecting greenhouse tomatoes in Mexico. Phytopathology. 102:S4.70.
Interpretive Summary: N/A
Technical Abstract: Greenhouse tomato production has increased significantly in recent years in North America. Nearly 40% of fresh tomato supplies in the U.S. are produced in greenhouses. The highly intensive and hydroponic production system has created some unique ecological conditions for disease epidemic, especially viruses. Using small RNA deep sequencing and assembly technologies, we previously identified a new potyvirus, tentatively named Tomato necrotic stunt virus (TNSV) from a diseased tomato sample collected in 2009 in a greenhouse near Mexico City, Mexico. The complete virus genome sequence was obtained and validated through Sanger-sequencing. In the present study, we were interested in further characterizing the molecular and biological properties of TNSV and in the development of molecular based detection technologies. TNSV had a genome with less than 60% overall identity in both nucleotide and amino acid sequences to other members in the genus Potyvirus. Typical symptoms on the infected tomato were chlorotic and necrotic leaves, and plant stunting with poor fruit production. In a host range study in a growth chamber, TNSV caused local lesions on Chenopodium spp. and a systemic infection on a number of solanaceous plants. Several molecular-based detection methods, including RT-PCR, real-time RT-PCR and loop mediated isothermal amplification (LAMP), were developed and optimized. A preliminary screening on diseased tomato samples collected from several greenhouses in Canada and the U.S. in 2012 did not detect the presence of TNSV. However, with the detection methods developed, additional survey will help us to reach a better understanding on its distribution.