Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/10449
Title: Application of cytochrome P450 monooxygenases in plant for rapid detection of 2,3,7,8 tetrachlorodibenzodioxin in the contaminated sites
Authors: Leung, Ho Man 
Sung, Ka Chun 
Woo, Lai Yan 
Mo, Wing Yin 
Cheung, Kwai Chung 
Dr. AU Chi Kin 
Yung, Ken Kin Lam 
Li, Wai Chin 
Issue Date: 2024
Source: International Journal of Environmental Research, 2024, vol. 18, article no. 102.
Journal: International Journal of Environmental Research 
Abstract: The function of Cytochrome (CYP) P450 in plants to enhance detoxification of herbicide metabolism is well-known. However, the knowledge of gene quantification for detecting and detoxifying pollutants and other toxicants by an indigenous plant growing in a contaminated site is limited. The objective of this research is to evaluate the potential of detecting or degrading 2,3,7,8 Tetrachlorodibenzodioxin (TCDD) in soil using a native plant growing in a contaminated site via the gene expression of Cytochrome P450 monooxygenases (P450s) method. The novelty of this research is that P450s in native plants possibly acts as a bioindicator on contaminated land by increasing its gene expression levels induced by the presence of TCDD. In seedling toxicity test and cytochrome enzyme activity test, a significant difference in the root length (range of value: 580.2–799.2 mm) and enzyme activity (range of value: 31.2–82.3 nmolmin−1 g−1 total protein) of such indigenous plant was found in 10 µg/L TCDD treatment when compared to other treatments. 13- and 20-fold levels of gene expression in CYP71C1 and CYP79A61 of the plant growing in a contaminated site were found after 10 µg/L TCDD treatment. The results revealed that such indigenous plant is sensitive to the detection of such persistent organic pollutant in the field site and involves gene expression change facilitated by a plant‒microbe symbiotic association. The current findings can provide an insight to use another option for pollution monitoring using non-standard plant models.
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/10449
ISSN: 2008-2304
1735-6865
DOI: 10.1007/s41742-024-00640-3
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