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Published on: November 10, 2023
Mapping the development of a Dinophysis bloom in a shellfish aquaculture area using a novel molecular qPCR assay
Penelope A Ajani1, Hernan F Henriquez-Nunez1, Arjun Verma1
1University of Technology Sydney, School of Life Sciences, Sydney, PO Box 123, Broadway, NSW 2007, Australia; Food Agility CRC Ltd, 175 Pitt St, Sydney, NSW 2000, Australia.
Abstract:
Diarrhetic shellfish toxins produced by certain species of the marine dinoflagellate Dinophysis can accumulate in shellfish in high concentrations, representing a significant food safety issue worldwide. This risk is routinely managed by monitoring programs in shellfish producing areas, however the methods used to detect these harmful marine microbes are not usually automated nor conducted onsite, and are often expensive and require specialized expertise. Here we designed a quantitative real-time polymerase chain reaction (qPCR) assay based on the ITS-5.8S ribosomal region of Dinophysis spp. and evaluated its specificity, efficiency, and sensitivity to detect species belonging to this genus. We designed and tested twenty sets of primers pairs using three species of Dinophysis - D. caudata, D. fortii and D. acuminata. We optimized a qPCR assay using the primer pair that sufficiently amplified each of the target species (Dacu_11F/Dacu_11R), and tested this assay for cross-reactivity with other dinoflagellates and diatoms in the laboratory (11 species) and in silico 8 species (15 strains) of Dinophysis, 3 species of Ornithocercus and 2 species of Phalacroma. The qPCR assay returned efficiencies of 92.4% for D. caudata, 91.3% for D fortii, and 91.5% for D. acuminata, while showing no cross-reactivity with other phytoplankton taxa. Finally, we applied this assay to a D. acuminata bloom which occurred in an oyster producing estuary in south eastern Australia, and compared cell numbers inferred by qPCR to those determined by microscopy counts (max abund. ∼6.3 × 103 and 5.3 × 103 cells L-1 respectively). Novel molecular tools such as qPCR have the potential to be used on-farm, be automated, and provide an early warning for the management of harmful algal blooms.
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