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Receptonics-based real-time monitoring of bacterial volatiles for onsite fire blight diagnosis

Title
Receptonics-based real-time monitoring of bacterial volatiles for onsite fire blight diagnosis
Authors
KimKyung HoAnJai EunRiuMyoungjooSonJin-SooSeoSung EunHongkiGyeong-JiLeeSoohyunYooJinParkTae ShinYong HwanTai HyunRyuChoong-MinKwonOh Seok
Ewha Authors
박태현
SCOPUS Author ID
박태현scopus
Issue Date
2024
Journal Title
Sensors and Actuators B: Chemical
ISSN
9254-4005JCR Link
Citation
Sensors and Actuators B: Chemical vol. 419
Keywords
BiomarkerBioprobeFire blightPlant diseasesPortableReceptonics
Indexed
SCIE; SCOPUS WOS scopus
Document Type
Article
Abstract
Fire blight, caused by the bacterium Erwinia amylovora, is a rapidly progressing and highly contagious disease of apple and pear, which leads to great economic losses with high recovery fees. To control fire blight, the infected trees are either treated with antibiotics or, in some countries, buried. To minimize the damage caused by E. amylovora and to manage its spread to other trees and orchards, early diagnosis of fire blight is critical. The direct extraction of E. amylovora cells or DNA from infected tissues is a laborious and time-consuming process, and a non-destructive diagnosis method has not yet been applied. In this study, we employed receptonics, a nanotechnology platform equipped with a receptor protein that perceives airborne bacterial signals and converts them into electronic signals, for onsite fire blight diagnosis. Two bacterial volatiles, 2,3-butanediol and 2-phenylethyl alcohol, were used as E. amylovora biomarkers both in vitro and in situ. To design receptor protein for identifying the target biomarkers, the bioprobe, nanodiscs human olfactory receptor family 1 subfamily D member 5 (hOR1D5), and Drosophila melanogaster or9a were engineered and fabricated. Our portable receptonics platform was able to detect both bacterial volatiles at concentrations as low as 10 pM within 10 s under field conditions. Thus, our results suggest that the receptonics platform could be used as a non-destructive diagnostic tool for the onsite, rapid, and accurate identification of plant diseases. © 2024 Elsevier B.V.
DOI
10.1016/j.snb.2024.136337
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신산업융합대학 > 식품영양학과 > Journal papers
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