Development and evaluation of a novel COI targeting primer set for metabarcoding-based enhanced detection of Scyphozoa (Cnidaria: Medusozoa) species.

Authors

Sung-Ah Kim, Sang-Eun Nam, Jae-Sung Rhee

Published in

Scientific reports. Jul 01, 2026. Epub Jul 01, 2026.

Abstract

Many jellyfish species within the Scyphozoa exhibit seasonal blooms, particularly in summer, resulting in significant economic losses for fisheries and posing direct health risks to humans from toxins in their nematocysts. Although metabarcoding has proven to be an effective monitoring tool and the use of taxon-specific primers is known to enhance detection efficiency, primer sets specifically optimized for the detection of Scyphozoa remain underdeveloped. In this study, we developed a DNA metabarcoding primer set targeting the cytochrome c oxidase subunit I (COI) gene to improve the detection of Scyphozoa taxa and compared its performance with that of universal primers (mlCO1intF/HCO2198). In an in silico PCR evaluation of 36 sequences, the newly designed primers (Scy_F/R), targeting a 314 bp fragment of the COI gene, successfully amplified five additional Scyphozoan species compared to the universal primers, indicating improved taxonomic resolution. This enhanced detection was further validated through environmental DNA (eDNA) metabarcoding of samples collected from Korean coastal regions. While the universal primers detected only a single species (Aurelia coerulea), the Scy_F/R primers identified six Scyphozoa species, including two Aurelia species (A. coerulea and A. limbata), two Chrysaora species (C. pacifica and the putative false positive C. chesapeakei), Rhopilema esculentum, and Nemopilema nomurai. Notably, two species (A. limbata and R. esculentum) were detected exclusively through metabarcoding and not by conventional survey methods. These findings highlight the effectiveness of taxon-specific metabarcoding primers in improving detection sensitivity and species resolution for Scyphozoa, providing a valuable tool for ecological monitoring and the management of jellyfish blooms in marine ecosystems.

PMID:
42387088
Bibliographic data and abstract were imported from PubMed on 02 Jul 2026.

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