A simple 18S rDNA approach for the identification of cultured eukaryotic microalgae with an emphasis on primers

• Main Authors: Khaw, Yam Sim, Khong, Nicholas Mun Hoe, Shaharuddin, Noor Azmi, Md.Yusoff, Fatimah
• Format: Article
• Language: English
• Published: Elsevier 2020
• Online Access: http://psasir.upm.edu.my/id/eprint/87687/1/ABSTRACT.pdf; http://psasir.upm.edu.my/id/eprint/87687/; https://www.sciencedirect.com/science/article/pii/S0167701219306736

Any forms of valorization of microorganisms would require accurate identity recognition to ensure repeatability, reproducibility and quality assurance. This study aimed to evaluate the effectiveness of different primers for identifying cultured eukaryotic microalgae using a simple 18S rDNA approach. A total of 34 isolated microalgae and one culture collection were utilized in the search for an effective molecular identification method for microalgae. Ammonium formate was applied to marine microalgae prior to DNA extraction. The microalgal DNA was extracted using a commercial kit and subjected directly to PCR amplification using four different published 18S rDNA primers. The DNA sequences were analysed using Basic Local Alignment Search Tool (BLAST) and phylogenetic trees to determine the microalgae identity. The identity was further validated with conventional morphological taxonomic identification, and the relationship of microalgal morphology and genetic materials was also determined. The microalgal DNA was successfully amplified, including marine species without prior cleaning. In addition, the ss5 + ss3 primer pair was found to be an ideal primer set among the tested primers for identifying microalgae. Overall, molecular identification showed relative matching with morphological identification (82.86%). This study is important because it serves as a platform to develop a standardized eukaryotic microalgae identification method. In addition, this method could help to ease the eukaryotic microalgae identification process and enrich the current reference databases such as GenBank.