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Quantification of the paralytic shellfish poisoning dinoflagellate Alexandrium species using a digital PCR

Cited 23 time in Web of Science Cited 27 time in Scopus
Authors

Lee, Hyun-Gwan; Kim, Hye Mi; Min, Juhee; Park, Chungoo; Jeong, Hae Jin; Lee, Kitack; Kim, Kwang Young

Issue Date
2020-02
Publisher
Elsevier BV
Citation
Harmful Algae, Vol.92, p. 101726
Abstract
A ubiquitous dinoflagellate, Alexandrium, produces paralytic shellfish toxin (PST), and its outbreaks have negative impacts on aquaculture, fisheries, human health, and the marine ecosystem. To minimize such damages, a routine monitoring program of toxic species must be implemented with a suitable analytical technique for their identification and quantification. However, the taxonomic identification and cell quantification of Alexandrium species based on their external morphology under a light microscope, or by using conventional molecular approaches have limited sensitivity and reproducibility. To address these challenges, we have developed an advanced protocol using droplet-digital PCR (ddPCR) for the discrimination and enumeration of three co-occurring Alexandrium species (A. affine, A. catenella, and A. pacificum) in environmental samples. Copies of species-specific internal transcribed spacer (ITS) per cell, which were calculated from environmental samples spiked with various numbers of culture cells, were used to estimate the abundance of species in the field samples. There were no significant differences in ITS copies estimated by the digital PCR assay between environmental samples from different localities, spiked artificially with a consistent number of cells from Alexandrium cultures. This sensitive assay was applied to determine the abundance and vertical distribution of those populations in the southern coastal waters of Korea. In spring, A. catenella was the dominant species, followed by the non-toxic A. affine in summers. A novel digital PCR assay can also be used to monitor other harmful marine protists that require high sample throughput and low detection limit with high accuracy and precision.
ISSN
1568-9883
URI
https://hdl.handle.net/10371/192628
DOI
https://doi.org/10.1016/j.hal.2019.101726
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  • College of Natural Sciences
  • Department of Earth and Environmental Sciences
Research Area Aquatic Microbial Ecology, Biological Oceanography, Plankton

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