WeChat Mini Program
Old Version Features

Quantitative Detection of Pythium Porphyrae and Pythium Chondricola (oomycota), the Causative Agents of Red Rot Disease in Pyropia Farms in China

ALGAE(2024)

Cited 0|Views5
Abstract
Red rot disease is one of the notorious algal diseases that threaten the cultivation of Pyropia in China, and two Pythium pathogens, i.e., Pythium porphyrae and P. chondricola, have been reported as causative agents. To monitor the pathogens, a fluorescent quantitative polymerase chain reaction (PCR) method was developed to quantitatively detect their abundance. Using overlapping PCR and pathogen-specific primer pairs, two pathogen-specific fragments were concatenated to construct an internal standard plasmid, which was used for quantification. For zoospores of known numbers, the results showed that this method can detect as less as 100 and 10 zoospores mL-1 in a 200 mL solution for P. porphyrae and P. chondricola, respectively. Using monthly collected seawater at 10 sites in Haizhou Bay, a typical aquaculture farm in China, a significantly higher temperature and a significantly lower salinity were determined in December 2021. P. porphyrae was determined to be more abundant than P. chondricola, though with similar temporal distribution patterns from December 2021 to February 2022. When a red rot disease occurred in December 2021, the two pathogens were significantly more abundant at two infected sub-sites than the uninfected sub-site within both seawater and sediment, though they were all significantly more enriched in sediment than in seawater. The present method provides the capability to quantify and compare the abundance of two pathogens and also has the potential to forecast the occurrence of red rot disease, which is of much significance in managing and controlling the disease.
More
Translated text
Key words
Pyropia,Pythium chondricola,Pythium porphyrae,quantification,red rot disease
PDF
Bibtex
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined