Comparative study of multiple approaches for identifying cultivable microalgae population diversity from freshwater samples

Abstract

The vast diversity of microalgae imposes the challenge of identifying them through the most common and economical identification method, morphological identification, or through using the more recent molecular-level identification tools. Here we report an approach combining enrichment and metagenomic molecular techniques to enhance microalgae identification and identify microalgae diversity from environmental water samples. From this perspective, we aimed to identify the most suitable culturing media and molecular approach (using different primer sets and reference databases) for detecting microalgae diversity. Using this approach, we have analyzed three water samples collected from the River Nile on several enrichment media. A total of 37 microalgae were identified morphologically to the genus level. While sequencing the three-primer sets (16S rRNA V1-V3 and V4-V5 and 18S rRNA V4 region) and aligning them to three reference databases (GG, SILVA, and PR2), a total of 87 microalgae were identified to the genus level. The highest eukaryotic microalgae diversity was identified using the 18S rRNA V4 region and alignment to the SILVA database (43 genera). The two 16S rRNA regions sequenced added to the eukaryotic microalgae identification, 26 eukaryotic microalgae. Cyanobacteria were identified through the two sequenced 16S rRNA regions. Alignment to the SILVA database served to identify 14 cyanobacteria to the genera level, followed by Greengenes, 11 cyanobacteria genera. Our multiple-media, primer, and reference database approach revealed a high microalgae diversity that would have been overlooked if a single approach had been used over the other.

Fine-Tuning of DADA2 Parameters for Multiregional Metabarcoding Analysis of 16S rRNA Genes from Activated Sludge and Comparison of Taxonomy Classification Power and Taxonomy Databases

“…In the metabarcoding approach, it is necessary to use an appropriate database for obtaining the most complete representation of microbial communities. Usually, for taxonomic classification of microbes the SILVA and Greengenes databases are used [ 23 , 24 ]. In the case of the Greengenes database, this was recently replaced by the new Greengenes2 in mid-2023.…”

Metabarcoding Analysis of Microorganisms Inside Household Washing Machines in Shanghai, China

“…Regions V4-V5 of the bacterial 16S rRNA gene were detected using the forward primer 515F (5′-GTGCCCGCMGGCGGGGGGGTAA-3′) and the reverse primer 907R (5′-CCGTCAATTCMTTTTTRAGTTT-3′) [ 18 ]. Generic primers were used to amplify fungal internal transcribed genes (ITS1F, 5′-CTGGTCTTAGAGAGAGAGGAGGAAGTAA-3′ and ITS2R, 5′-GTGCGTTCTCTCTCTCATCGGATGC-3′) [ 19 ].…”

A critical review of soil algae as a crucial soil biological component of high ecological and economic significance

“…The molecular approaches that have been used have involved analysis of small subunit ribosomal RNA (SSU rRNA: 16S rRNA gene, 18S rRNA gene); the internal transcribed spacer 2 rRNA region between the 5.8S and 28S rRNA genes (ITS2); the rbcL, rpoC1, tufA, and cox1 genes (Flechtner et al, 2002;Kabirnataj et al, 2018;Leliaert et al, 2014;Mikhailyuk et al, 2022;Pietrasiak et al, 2019); and the 16S-23S rRNA region secondary structure (Lukesova et al, 2009;Pietrasiak et al, 2021;Yan et al, 2022). In general, two 16S rRNA and one 18S rRNA gene regions have been explored and then aligned to various databases for the molecular identification of microalgae (Badr & Fouad, 2023;Nirmala & Dawn, 2020), which is now considered the best alternative or parallel method in the accounting of soil algal diversity. In identifying BSCs, such an integrated method of taxonomy has been followed by many authors Nagy et al, 2005;Roncero-Ramos et al, 2019;Samolov et al, 2020).…”