This study aims to fully evaluate the culture-based morphological identification method and the multi-primer/multi-database molecular approach in identifying microalgae diversity from an enriched environmental sample, in our case, the River Nile. The challenge presented was to identify the capability of the currently used media in the enrichment of the diversity to identify culturable microalgae. Additionally, pursuing the possibility of improving the media for better enrichment of microalgae population. This could be through finding a media that would provide an evenness to the present diversity that would enable the enrichment of rare species in the community. Growth analysis studied confirmed that none of the media hindered the growth of the microalgae (data not shown).
Through morphological identification of microalgae in the four tested media (BG-11, BBM, MM and MS) used to enrich the River Nile environmental samples, a total of 37 microalgae have been identified across the three samples combined. MM media alone identified 35 microalgae, while the commonly used media BG-11 and BBM identified 30 and 27 microalgae, respectively. Through a closer look at the four tested nutrient media used, each media had all the necessary macronutrients and micronutrients (such as N, P, K, Fe, Cu, Mn, Zn, Co and Mo) that would enhance microalgae and not hinder its growth. However, each nutrient media had different macro and micronutrient composition/concentrations. Differences in media components may have caused the favoring growth of certain microalgae over the other. One of the main differences in media composition is the source of nitrogen. BG-11, BBM, and MM media contain sodium nitrate as the main source of nitrogen. While MS media, containing the highest nitrogen content, has nitrogen in two forms, ammonium nitrate, and potassium nitrate. Microalgae can use nitrogen in different forms such as nitrate, nitrite, ammonium, and urea, an organic nitrogen source. The different nitrogen sources are first reduced to ammonium which is assimilated into amino acids through different pathways in microalgae 20. While ammonium is a preferred nitrogen source due to its assimilation being a low energy process, sodium and potassium nitrates are still preferred in media over ammonium. This is attributed to ammonium ions (NH4+) being converted to ammonia gas (NH3) under aeration at alkaline conditions and thus considered a loss of nitrogen source 21. Hence, this may be considered a reason why MS media had the lowest identified diversity even though it was rich with nitrogen content.
Citric acid and EDTA are other critical media components. The citric acid present in BG-11 and MM media is responsible for solubilising salts components in the media and preventing iron precipitation, making it readily available to the microalgae, thus resulting in an enhanced growth rate as iron is considered an essential limiting micronutrient 21. Therefore, it can be a leading cause for the higher diversity present in MM and BG-11 media. BG-11, BBM, and MM media also have EDTA, disodium salt acting as a chelating agent. In contrast, MS medium only has its iron chelated in the form of Ferric sodium EDTA, which has been reported to be a better source of iron 22,23. Yet, MS medium did not uniquely enrich any microalgae over the other tested media when morphologically analysed. This indicated that the ratio of iron is relatively low compared to the other media and its increase could have resulted in a higher diversity enrichment.
Nevertheless, we have decided to continue molecular analysis, including MS medium, because morphological analysis could be limited by the volume analysed and the number of microscopic fields visualised from a single sample and the possibility of human error. The morphological analysis was conducted every seven days, and the highest diversity was observed on day 14. Hence, DNA extraction for the molecular level analysis was conducted on the samples on the 14th day. Even though nutrient content was not measured during the experiment, we assume that after day 14, nutrient depletion and accumulation of waste products from the enriched microalgae biomass can drastically increase pH. pH can also increase due to the microalgae uptake of inorganic carbon, and photosynthesis increases O2 production, which increases the pH of the culture 24,25. Exceeding the 14 days may also cause the reduction of the intermediate and rare microalgae (low in abundance) and further boost the dominant and most tolerant microalgae such as Ankistrodesmus, Chlorella, and Scenedesmus 26,27. Furthermore, this is common as green algae are usually known to dominate and push down the growth of cyanobacteria 28.
The combination of the hypervariable regions sequenced using universal primers specific to the 16S V1-V3, V4-V5 rRNA, and the 18S V4 rRNA region provided us with complete coverage of the algal diversity in the enriched population. Each primer set aligned to the three reference databases contributed substantially to profiling the different eukaryotic and prokaryotic microalgae present in the River Nile samples studied. Eighty-seven microalgae were characterised from the River Nile samples collected. Additionally, each individual reference database, along with each primer set, had a limited capacity to profile a certain microalgae diversity in the samples analysed. Combining the three primer sets results resulted in a superior coverage that would have been missed if only a single set of primers and one reference database had been used.
Yet, several microalgae (17 genera) have been identified only using morphological characteristics and were missing from the list of species identified using molecular methods. This can be explained by the possibility that some microalgae have been incorrectly morphologically classified due to the close resemblance to other microalgae in the same phylum. For example, Tetraspora (Chlorophyta) may be confused with Chlamydamonas or Gloeococcus. Other genera such as Leptolyngbya and Spirulina (Cyanobacteria) can also be incorrectly classified with other filamentous Cyanobacteria 29. Furthermore, the presence of high nutrients and asexual reproduction can hinder morphological identification of unicellular microalgae due to their phenotypic plasticity under nutrient and environmental alteration 30,31.
The presence of synonyms of genera between databases can cause a double count between morphological and molecular-level identification or result in “hit and miss” between one database and another 32. Genera such as Selenastrum identified through morphological-level identification has a synonym Monophidium profiled through SILVA 18S V4 region. Moreover, there is a complete sequence entry of the 18S rRNA gene of Selenastrum present in the SILVA database along with a sequence for Monophidium 33. This limitation can be ascribed to SILVA database being curated database, and there are several entries with different synonyms of the same genera 34. However, some of the microalgae identified only on a morphological level have characteristic features that can be easily distinguished microscopically and are not mistaken with other genera such as Actinastrum, Coelastrum, Dictyosphaerium, Largerheimia, Tetraedron, Pediastrum, and Selenastrum 35,36. Nevertheless, they were not identified on a molecular level through any of the regions analysed or through the three reference databases used. The complete 18S rRNA gene of the genera Actinastrum, Coelastrum, Dictyosphaerium, Tetraedron, Pediastrum, and Selenastrum are present in the SILVA database, while Largerheimia 18S rRNA gene was not available on SILVA but is present in a different database ENA (European Nucleotide Archive) that has not been used in this study for the analysis of the 18S rRNA sequences 33. These results suggest that the genera listed above could be new variants to the genera and, thus, the need for their isolation and characterisation. This also points out that databases that are not specific to microalgae are not constantly updated with microalgal sequences compared to the examined continuously and updated microbial taxonomies such as in studies on the gut and oral microbiomes or general microbial communities in environmental samples 37–41.
When it comes to eukaryotic microalgae, they were best identified through the SILVA database, and 18S V4 rRNA gene ~ 63.24% (n = 43 (total = 68)) of the identified genera was through the SILVA and 18S V4 region. Even though 25 eukaryotic genera were identified using the 16S V1-V3 region and PR2 database. SILVA identified a greater diversity; this can be accredited to the fact that SILVA is a bigger database 7. But the microalgae specifically identified using PR2 reference database 10 and the 16S rRNA gene can still be considered high and of added value to the profiling of microalgae diversity from an environmental sample.
Through the alpha diversity indices calculated for the OTUs analysed through the different databases, the Good’s coverage index was higher in the V4-V5 region in general. Moreover, through the V4-V5 region annotated by the three databases, the Nile2 sample had a high Shannon index indicating the presence of high diversity in terms of abundance, and the Sobs index confirms that. While, the InvSimpson index, a dominance index, indicates that Nile2 has a lower InvSImpson index than Nile3. Nile3 sample has a higher diversity with more evenness between the genera. Furthermore, this is also confirmed by the decrease of the Berger Parker index of the Nile3 sample across all the databases and regions sequenced. The diversity indices indicate that the Nile3 is the most abundant and with an even distribution of genera and the absence of the dominance of a genus. This is followed by the Nile2 and Nile1 samples. Regarding the 18S V4 region annotated by SILVA, the diversity indices indicate that Nile1 has the richest diversity with a high evenness between the different genera identified. This is followed by Nile3 and then Nile2.
The biplot constructed using the SILVA 18S V4 region dataset confirms that media usage does enrich specific diversity and enriched diversity is biased to the media. This further confirms that using one medium can lead to the enrichment of certain microorganisms over the other. Hence, the usage of one enrichment media will lead to the loss of identifying certain microalgae and provide misleading data concerning the diversity of studied environmental samples. Using BG-11, BBM and MM were the most suitable for culturing and enrichment of the highest diversity from an environmental sample, compared to MS media that did not identify any unique species. This enrichment culture-based method can also be considered an alternative to the more expensive approach of increasing the number of sequencing reads to investigate further the low abundant species that are not enough to appear due to the presence of a limiting detection threshold. The culture-based method followed by the molecular level through several different hypervariable regions eliminates these biases that can result from using one media or one sequencing region. Moreover, it is critical to recognise that using different enrichment media and sequencing regions complement each other and provide completeness to the diversity profiled.
Thereby, we may conclude that the combined use of both the culture-based method and sequence analysis of multiple rRNA regions/genes (targeting both nuclear and plastid genomes 42) facilitates a more reliable and comprehensive approach to identifying total microalgae biodiversity. This approach can be considered a more efficient methodology as it targets microalgae identification from an environmental sample over the commonly used metagenomic sequencing that utilises the environmental sample directly, which are usually high in microbial biomass and low in microalgae biomass.