Protocol optimization of eDNA analysis workflow for detecting Hucho bleekeri.

doi:10.13287/j.1001-9332.201607.017

Abstract

Abstract: Environmental DNA (eDNA) analysis in water, consisting of water sample collection, DNA extraction and analysis, is increasingly important in detecting rare aquatic species and determining their distribution. Optimization of protocols used in an eDNA molecular workflow is critical to reduce its uncertainty in detecting target species, especially rare species. In this study, 250 mL, 500 mL, 1 L and 2 L water samples were collected by filtering method from a pond for Sichuan taimen (Hucho bleekeri) culture, followed by extraction and purification of eDNA using DNeasy Tissue and Blood DNA extraction kit or MoBio Power Water DNA extraction kit. A PCR procedure was then applied using specific primers from the D-loop region of mtDNA in Sichuan taimen. To explore an appropriate eDNA analysis protocol, the effect of filtering method, sample size and extraction protocols on the detection rate of target gene in water eDNA was studied. The results showed that the target gene was 100% detected from the water sample by using DNeasy Tissue and Blood DNA extraction kit, which was much better that the MoBio Power Water DNA extraction kit with a detection rate of 0%. Larger volume of water samples yielded better PCR products, with 2 L being the most appropriate volume. Sequence alignment results showed that target sequence from the D-loop region of mtDNA in Sichuan taimen was successfully amplified. The results indicated that DNA extracting method and water sample volume could strongly affect the detection rate. The protocol combining filtration, 2 L water sample, DNeasy Tissue and Blood DNA extraction kit was appropriate for eDNA analysis, and mtDNA D-loop region was recommended for detecting DNA of Sichuan taimen from water samples.

JIANG Wei, WANG Qi-jun, DENG Jie, ZHAO Hu, KONG Fei, ZHANG Hong-xing. Protocol optimization of eDNA analysis workflow for detecting Hucho bleekeri.[J]. Chinese Journal of Applied Ecology, 2016, 27(7): 2372-2378.

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