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The Korean Society of Phycology (한국조류학회(藻類))
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Evaluating nuclear (18S, ITS, and 28S) and chloroplast (psbB, psbC, rbcL, and tufA) genes of the green algae Monoraphidium (Sphae-ropleales, Chlorophyta) for DNA barcoding

  • Ha-Eun Lee (Department of Life Science, Sangmyung University) ;
  • Taehee Kim (Department of Life Science, Sangmyung University) ;
  • Han-Sol Kim (Department of Life Science, Sangmyung University) ;
  • Ok-Min Lee (Department of Life Science, Kyonggi University) ;
  • Jang-Seu Ki (Department of Life Science, Sangmyung University)
  • Received : 2024.11.12
  • Accepted : 2025.04.15
  • Published : 2025.06.15
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Abstract

The genus Monoraphidium is a green microalgae distributed globally in freshwater ecosystems, and it is difficult to classify morphologically. Molecular genetic markers have been used for their taxonomy, although they have not been thoroughly evaluated. Here, we investigated three nuclear rRNA molecules (18S, internal transcribed spacer [ITS] + 5.8S, and 28S) and four chloroplast genes (psbB, psbC, rbcL, and tufA) using 16 Monoraphidium strains consisting of 9 species. Upon comparisons of genetic diversity and marker performance evaluation, we found that ITS was the best marker for Monoraphidium to discriminate each species, followed by tufA. The taxonomic discrimination power of the ITS was supported by the neighbor-joining tree. In addition, a secondary structure of ITS2 combined with compensatory base changes showed the distinct differences among individual species of Monoraphidium. These suggest that ITS may be the best marker for species differentiation of the coccoid green algae Monoraphidium.

Keywords

Acknowledgement

We thank Dr. BL Muhammad for valuable comments to the early version of our manuscript. This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Aquatic Ecosystem Conservation Research Program funded by Korea Ministry of Environment (MOE) (2022003050002 or RS2022-KE002119211530) and by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00354842).

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