Identification of Eukaryotic Microalgal Strains

Simple and Effective Squash-PCR for Rapid Genotyping of Industrial Microalgae

Microalgae are recognized for their versatility in providing renewable energy, biopharmaceuticals, and nutraceuticals, attributed to their sustainable, renewable, and cost-effective nature. Genetic engineering has proven highly effective in enhancing microalgae production. PCR-based genotyping is the primary method for screening genetically transformed microalgae cells. Recently, we developed a novel PCR method, namely Squash-PCR, and employed it for the molecular analysis of industrially important fungi and yeasts. In this study, we successfully implemented the Squash-PCR technique in 12 industrially significant algae species. This approach offers a quick and reliable means of obtaining DNA templates directly from squashed algal cells, eliminating the need for time-consuming and labor-intensive cultivation and genomic DNA extraction steps. Our results demonstrate the effectiveness of Squash-PCR in detecting and characterizing target genes of interest in 12 different algae species. Overall, this study establishes the Squash-PCR method as a valuable tool for molecular studies in algae, enabling researchers to rapidly screen and manipulate genetic traits in diverse algal species.

Abiotic factors improving fatty acid profiling of freshwater indigenous microalgae isolated from Kumaun region of Uttarakhand, India

Microalgae have grabbed huge attention as a potential feedstock for biofuel production in response to the rise in energy consumption and the energy crisis. In the present study, indigenous microalgal strains were isolated from four freshwater lakes in the Kumaun region, Uttarakhand, India. Based on growth and lipid profiles, the four best-performing isolates were selected for further experiments. Initial identification of isolates was done by morphological observations, which were further validated by molecular identification using ITS sequencing. The screened cultures were subjected to abiotic stress conditions (varying concentrations of nitrogen and different temperatures) to monitor the biomass, lipid accumulation, and biochemical compositions (chlorophyll and carotenoids). The quantification of fatty acids was checked via gas chromatographic analysis. The strains were identified as KU_MA3 Chlamydopodium starrii, KU_MA4 Tetradesmus nygaardii, KU_MA5 Desmodesmus intermedius, and KU_MA6 Tetradesmus nygaardii. KU_MA3 Chlamydopodium starrii showed the best results in terms of growth and lipid production at 21 °C and 0.37 g/L NaNO2 concentration. The percentage of fatty acid methyl esters (FAMEs) attained >80% and met the standard for biodiesel properties. The strain has the potential to attain higher biomass and accumulate higher lipid content, and after some more studies, it can be used for upscaling processes and large-scale biodiesel production.

Applying an internal transcribed spacer as a single molecular marker to differentiate between Tetraselmis and Chlorella species

In the realm of applied phycology, algal physiology, and biochemistry publications, the absence of proper identification and documentation of microalgae is a common concern. This poses a significant challenge for non-specialists who struggle to identify numerous eukaryotic microalgae. However, a promising solution lies in employing an appropriate DNA barcoding technique and establishing comprehensive databases of reference sequences. To address this issue, we conducted a study focusing on the molecular characterization and strain identification of Tetraselmis and Chlorella species, utilizing the internal transcribed spacer (ITS) barcode approach. By analyzing the full nuclear ITS region through the Sanger sequencing approach, we obtained ITS barcodes that were subsequently compared with other ITS sequences of various Tetraselmis and Chlorella species. To ensure the reliability of our identification procedure, we conducted a meticulous comparison of the DNA alignment, constructed a phylogenetic tree, and determined the percentage of identical nucleotides. The findings of our study reveal the significant value of the ITS genomic region as a tool for distinguishing and identifying morphologically similar chlorophyta. Moreover, our results demonstrate that both the ITS1 and ITS2 regions are capable of effectively discriminating isolates from one another; however, ITS2 is preferred due to its greater intraspecific variation. These results underscore the indispensability of employing ITS barcoding in microalgae identification, highlighting the limitations of relying solely on morphological characterization.

Genetic Markers for Metabarcoding of Freshwater Microalgae: Review

The metabarcoding methods for studying the diversity of freshwater microalgae and routine biomonitoring are actively used in modern research. A lot of experience has been accumulated already, and many methodological questions have been solved (such as the influence of the methods and time of sample conservation, DNA extraction and bioinformatical processing). The reproducibility of the method has been tested and confirmed. However, one of the main problems-choosing a genetic marker for the study-still lacks a clear answer. We analyzed 70 publications and found out that studies on eukaryotic freshwater microalgae use 12 markers (different nuclear regions 18S and ITS and plastids rbcL, 23S and 16S). Each marker has its peculiarities; they amplify differently and have various levels of efficiency (variability) in different groups of algae. The V4 and V9 18S and rbcL regions are used most often. We concentrated especially on the studies that compare the results of using different markers and microscopy. We summarize the data on the primers for each region and on how the choice of a marker affects the taxonomic composition of a community.

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

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.

Deep learning-based classification of microalgae using light and scanning electron microscopy images

Microalgae possess diverse applications, such as food production, animal feed, cosmetics, plastics manufacturing, and renewable energy sources. However, uncontrolled proliferation, known as algal bloom, can detrimentally impact ecosystems. Therefore, the accurate detection, monitoring, identification, and tracking of algae are imperative, albeit demanding considerable time, effort, and expertise, as well as financial resources. Deep learning, employing image pattern recognition, emerges as a practical and promising approach for rapid and precise microalgae cell counting and identification. In this study, we processed light microscopy (LM) and scanning electron microscopy (SEM) images of two Cyanobacteria species and three Chlorophyta species to classify them, utilizing state-of-the-art Convolutional Neural Network (CNN) models, including VGG16, MobileNet V2, Xception, NasnetMobile, and EfficientNetV2. In contrast to prior deep learning based identification studies limited to LM images, we, for the first time, incorporated SEM images of microalgae in our analysis. Both LM and SEM microalgae images achieved an exceptional classification accuracy of 99%, representing the highest accuracy attained by the VGG16 and EfficientNetV2 models to date. While NasnetMobile exhibited the lowest accuracy of 87% with SEM images, the remaining models achieved classification accuracies surpassing 93%. Notably, the VGG16 and EfficientNetV2 models achieved the highest accuracy of 99%. Intriguingly, our findings indicate that algal identification using optical microscopes, which are more cost-effective, outperformed electron microscopy techniques.

Microalgae identification: Future of image processing and digital algorithm

The identification of microalgae species is an important tool in scientific research and commercial application to prevent harmful algae blooms (HABs) and recognizing potential microalgae strains for the bioaccumulation of valuable bioactive ingredients. The aim of this study is to incorporate rapid, high-accuracy, reliable, low-cost, simple, and state-of-the-art identification methods. Thus, increasing the possibility for the development of potential recognition applications, that could identify toxic-producing and valuable microalgae strains. Recently, deep learning (DL) has brought the study of microalgae species identification to a much higher depth of efficiency and accuracy. In doing so, this review paper emphasizes the significance of microalgae identification, and various forms of machine learning algorithms for image classification, followed by image pre-processing techniques, feature extraction, and selection for further classification accuracy. Future prospects over the challenges and improvements of potential DL classification model development, application in microalgae recognition, and image capturing technologies are discussed accordingly.

Geographical distribution, molecular and toxin diversity of the dinoflagellate species Gambierdiscus honu in the Pacific region

An increase in cases of ciguatera poisoning (CP) and expansion of the causative species in the South Pacific region highlight the need for baseline data on toxic microalgal species to help identify new areas of risk and manage known hot spots. Gambierdiscus honu is a toxin producing and potential CP causing dinoflagellate species, first described in 2017. Currently no high-resolution geographical distribution, intraspecific genetic variation or toxin production diversity data is available for G. honu. This research aimed to further characterize G. honu by investigating its distribution using species-specific real-time polymerase chain reaction assays at 25 sites in an area spanning ∼8000 km of the Coral Sea/Pacific Ocean, and assessing intraspecific genetic variation, toxicity and toxin production of isolated strains. Assessment of genetic variation of the partial rRNA operon of isolates demonstrated no significant intraspecific population structure, in addition to a lack of adherence to isolation by distance (IBD) model of evolution. The detected distribution of G. honu in the Pacific region was within the expected tropical to temperate latitudinal ranges of 10° to -30° and extended from Australia to French Polynesia. In the lipophilic fractions, the neuroblastoma cell-based assay (CBA-N2a) showed no ciguatoxin (CTX)-like activity for nine of the 10 isolates, and an atypical pattern for CAWD233 isolate which showed cytotoxic activity in OV- and OV+ conditions. In the same way, liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis confirmed no Pacific-CTXs (CTX-3B, CTX-3C, CTX-4A, CTX-4B) were produced by the ten strains. The CBA-N2a assessment of the hydrophilic fractions showed moderate to high cytotoxicity in both OV- and OV+ condition for all the strains showing a cytotoxic profile similar to that of gambierone. Indeed, this study is the first to show the cytotoxic activity of gambierone on mouse neuroblastoma cells while no cytotoxicity was observed when 44-MG was analysed at the same concentrations using the CBA-N2a. Analysis of the hydrophilic via LC-MS/MS confirmed production of gambierone in all isolates, ranging from 2.1 to 38.1 pg/cell, with 44-methylgambierone (44-MG) also produced by eight of the isolates, ranging from 0.3 to 42.9 pg/cell. No maitotoxin-1 was detected in any of the isolates. Classification of the G. honu strains according to the quantities of gambierone produced aligned with the classification of their cytotoxicity using the CBA-N2a. Finally, no maitotoxin-1 (MTX) was detected in any of the isolates. This study shows G. honu is widely distributed within the Pacific region with no significant intraspecific population structure present. This aligns with the view of microalgal populations as global metapopulations, however more in-depth assessment with other genetic markers could detect further structure. Toxicity diversity across 10 isolates assessed did not display any geographical patterns.

Thorsmoerkia curvula gen. et spec. nov. (Trebouxiophyceae, Chlorophyta), a semi-terrestrial microalga from Iceland exhibits high levels of unsaturated fatty acids

A terrestrial green alga was isolated at Iceland, and the strain (SAG 2627) was described for its morphology and phylogenetic position and tested for biotechnological capabilities. Cells had a distinctly curved, crescent shape with conical poles and a single parietal chloroplast. Phylogenetic analyses of 18S rDNA and rbcL markers placed the strain into the Trebouxiophyceae (Chlorophyta). The alga turned out to belong to an independent lineage without an obvious sister group within the Trebouxiophyceae. Based on morphological and phylogenetic data, the strain was described as a new genus and species, Thorsmoerkia curvula gen. et sp. nov. Biomass was generated in column reactors and subsequently screened for promising metabolites. Growth was optimized by pH-regulated, episodic CO₂ supplement during the logarithmic growth-phase, and half of the biomass was thereafter exposed to nitrogen and phosphate depletion. The biomass yield reached up to 53.5 mg L^-1 day^-1. Fatty acid (FA) production peaked at 24 mg L^-1 day^-1 and up to 83% of all FAs were unsaturated. At the end of the log phase, approximately 45% of dry mass were lipids, including eicosapentaenoic acid. Carotenoid production reached up to 2.94 mg L^-1 day^-1 but it was halted during the stress phase. The N-linked glycans of glycoproteins were assessed to reveal chemotaxonomic patterns. The study demonstrated that new microalgae can be found at Iceland, potentially suitable for applied purposes. The advantage of T. curvula is its robustness and that significant amounts of lipids are already accumulated during log phase, making a subsequent stress exposure dispensable.

Monodopsis and Vischeria Genomes Shed New Light on the Biology of Eustigmatophyte Algae

Members of eustigmatophyte algae, especially Nannochloropsis and Microchloropsis, have been tapped for biofuel production owing to their exceptionally high lipid content. Although extensive genomic, transcriptomic, and synthetic biology toolkits have been made available for Nannochloropsis and Microchloropsis, very little is known about other eustigmatophytes. Here we present three near-chromosomal and gapless genome assemblies of Monodopsis strains C73 and C141 (60 Mb) and Vischeria strain C74 (106 Mb), which are the sister groups to Nannochloropsis and Microchloropsis in the order Eustigmatales. These genomes contain unusually high percentages of simple repeats, ranging from 12% to 21% of the total assembly size. Unlike Nannochloropsis and Microchloropsis, long interspersed nuclear element repeats are abundant in Monodopsis and Vischeria and might constitute the centromeric regions. We found that both mevalonate and nonmevalonate pathways for terpenoid biosynthesis are present in Monodopsis and Vischeria, which is different from Nannochloropsis and Microchloropsis that have only the latter. Our analysis further revealed extensive spliced leader trans-splicing in Monodopsis and Vischeria at 36-61% of genes. Altogether, the high-quality genomes of Monodopsis and Vischeria not only serve as the much-needed outgroups to advance Nannochloropsis and Microchloropsis research, but also shed new light on the biology and evolution of eustigmatophyte algae.

Uncovering Unique Green Algae and Cyanobacteria Isolated from Biocrusts in Highly Saline Potash Tailing Pile Habitats, Using an Integrative Approach

Potash tailing piles caused by fertilizer production shape their surroundings because of the associated salt impact. A previous study in these environments addressed the functional community "biocrust" comprising various micro- and macro-organisms inhabiting the soil surface. In that previous study, biocrust microalgae and cyanobacteria were isolated and morphologically identified amongst an ecological discussion. However, morphological species identification maybe is difficult because of phenotypic plasticity, which might lead to misidentifications. The present study revisited the earlier species list using an integrative approach, including molecular methods. Seventy-six strains were sequenced using the markers small subunit (SSU) rRNA gene and internal transcribed spacer (ITS). Phylogenetic analyses confirmed some morphologically identified species. However, several other strains could only be identified at the genus level. This indicates a high proportion of possibly unknown taxa, underlined by the low congruence of the previous morphological identifications to our results. In general, the integrative approach resulted in more precise species identifications and should be considered as an extension of the previous morphological species list. The majority of taxa found were common in saline habitats, whereas some were more likely to occur in nonsaline environments. Consequently, biocrusts in saline environments of potash tailing piles contain unique microalgae and cyanobacteria that will possibly reveal several new taxa in more detailed future studies and, hence, provide new data on the biodiversity, as well as new candidates for applied research.