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Article

Unexplored Potential: Metabolite Screening of Local Lake Algae Isolated from Al-Asfar Lake in Saudi Arabia

by
Sumayah I. Alsanie
1,* and
Faten Dhawi
2,*
1
Department of Biology, College of Science, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia
2
Agricultural Biotechnology Department, College of Agricultural and Food Sciences, King Faisal University, Hufof 3959-36362, Saudi Arabia
*
Authors to whom correspondence should be addressed.
Water 2023, 15(15), 2757; https://doi.org/10.3390/w15152757
Submission received: 10 July 2023 / Revised: 27 July 2023 / Accepted: 28 July 2023 / Published: 30 July 2023
(This article belongs to the Special Issue Algae Biomass Development for the Production of High-Value Products In Vivo and In Vitro)
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Abstract

:
In this study, we aimed to explore the commercial potential of various microalgae variants found in Al-Asfar Lake, Saudi Arabia, which have not been extensively investigated previously. We conducted a comprehensive analysis of the metabolic profiles of algae isolated from Al-Asfar Lake. The isolated algae were subjected to molecular analysis using specific primers for the Chlorophyceae class to confirm their identity. Subsequently, we compared the concentration of metabolites in the locally isolated Chlorella vulgaris from Al-Asfar Lake with five commercially available algae (Tetraselmis, Nannochloropsis, Chlorella vulgaris, Spirulina, and Isochrysis). To perform the metabolomics analysis, we employed untargeted ultra-performance liquid chromatography (UPLC) coupled with mass spectrometry (MS) analysis, which yielded a total of 168 metabolites from the microalgae samples. The data were further analyzed using MetaboAnalyst. The results revealed two distinct clusters of microalgae: the first cluster comprised Chlorella vulgaris and the microalgae isolated from the lake, while the second cluster consisted of two sub-clusters, with Isochrysis grouped with Tetraselmis, and Nannochloropsis clustered with Spirulina. Notably, the metabolites of Al-Asfar Lake algae showed a remarkable similarity to Chlorella vulgaris. These findings have significant implications for the environmental aspect of Al-Asfar Lake, shedding light on critical insights into the metabolites and commercial potential of the lake’s microalgae. The valuable insights gained from this research can be utilized to investigate the impact of nutrient abundance on the lake’s biodiversity, enhance microalgal biomass production for biofuel applications, and explore the reuse of lake water in agriculture and environmental restoration projects. Overall, our study provides important groundwork for understanding the potential of Al-Asfar Lake microalgae and their application in various industries, contributing to the sustainable development and environmental health of the region.

1. Introduction

Al Hassa Oasis, a prominent and ancient agricultural sector in Saudi Arabia, encompasses a vast expanse of 20,000 hectares of land. At its heart lies an extensive irrigation network with 1450 km of concrete channels, and the impressive Al-Asfar Lake, the largest evaporation lake in the country, located 13 km to the east. These artificial or natural evaporation lakes play a vital role in connecting water and land environments, providing habitat for wildlife, mitigating pollution impacts, replenishing groundwater resources, and offering flood protection [1,2,3]. Al-Asfar Lake, nestled in the Al-Ahsa region, is a natural reservoir teeming with diverse microalgae variants unique to Saudi Arabia. While previous research has identified various microalgae species within the lake, their commercial potential has remained largely unexplored. Microalgae, integral components of aquatic food chains, hold immense promise as value-added food products and medicinal sources for human consumption. Their metabolic pathways offer a rich array of compounds such as polysaccharides, agar-agar, alginic acid, carrageenan, mycosporine-like amino acids, carotenoids, fatty acids, lectins, polyketides, and steroids, making them lucrative avenues for revenue generation [4].
The commercial significance of microalgae biomass cannot be understated, as it serves as a sustainable source of eco-friendly renewable energy and various valuable byproducts. These tiny organisms are reservoirs of enzymes, lipids, minerals, proteins, pigments, vitamins, sugars, sterols [5,6], and bioactive peptides, boasting high protein concentrations [7]. Their lipid content is classified into two categories: those with 14 to 20 carbon chains, ideal for biofuel production, and those with 20 carbon chains, sought after for food component production [8]. Moreover, pigments extracted from microalgae, such as carotenoids, find applications as natural dyes in the food industry and as components in pharmaceutical formulations benefiting from their biological activity [9]. Compared to traditional plant biomass, microalgae exhibit higher levels of polysaccharides and lower levels of hemicellulose and lignin composition, making them suitable biomass material for biofuel production through anaerobic fermentation by solvogenic microorganisms [10,11].
Bioprospecting, a crucial process, has enabled the understanding of microalgae biodiversity and facilitated the development of novel commercial products. By collecting and screening exceptional algal strains from water and land ecosystems, bioprospecting aims to produce value-added products with diverse applications [12]. Utilizing local algal strains ensures their adaptability to their native growth habitats and climates, promoting dominant growth patterns and avoiding the proliferation of non-native species [13]. In Al-Asfar Lake, four algal classes have been identified: Bacillariophyceae, Chlorophyceae, Cyanophyceae, and Euglenophyceae, with Chlorella sp., Chlorococcus humicola, Monoraphidium contortum, Oedogonium sp., Cyclotella meneghiniana, Gyrosigma sp., Fragilaria capucina, Navicula lanceolata, Surirella obonga, Synedra acus, Tabellaria sp., and Oscillatoria sp. among the most frequently observed microalgae species [3]. However, despite this knowledge, the commercial potential of these microalgae species has not been explored, necessitating the development of effective techniques for isolating and screening the chemical and physical compositions of microalgal strains that play pivotal roles in manufacturing valuable metabolites. Technical specifications for the production of value-added microalgal products are essential in this regard [14]. In addition to their biofuel potential, microalgae demonstrate extraordinary capabilities in improving water quality. They possess the ability to assimilate nutrients and contaminants from water bodies, contributing to the restoration of ecosystems. Through bioremediation, certain microalgal species can effectively remove excess nutrients like nitrogen and phosphorus, thus preventing harmful algal outbreaks and eutrophication. Furthermore, their capacity to accumulate heavy metals and other pollutants aids in the purification of polluted waters. Another critical role of microalgae lies in carbon capture and climate change mitigation. As natural carbon sinks, they play a pivotal role in capturing and sequestering carbon dioxide from the atmosphere through photosynthesis. The large-scale cultivation of microalgae for carbon capture holds promise as an effective strategy in combating climate change. With these aspects in mind, this study aims to delve into the value of local algae from Al-Asfar Lake, identifying metabolites through comparative analysis and studying five commercially valuable microalgae species (Tetraselmis, Nannochloropsis, Chlorella vulgaris, Spirulina, and Isochrysis). By uncovering the commercial potential of these microalgae, we hope to contribute to the development of sustainable and high-value applications in various industries.

2. Materials and Methods

2.1. Sample Information

Local algae were collected from Al-Asfar Lake (25.529° N 49.800° E) in April 2022. After obtaining 1 L of water samples, they were allowed to settle for a minimum of 36 h. Following this, 10 mL of the supernatant was carefully extracted and used for culturing. For the cultivation process, 10 mL of the water sample was cultured for 15 days at 29 ± 1 °C in sterile 1000 mL Erlenmeyer flasks containing 500 mL of commercial F/2 media. This media was obtained from Algae Research and Supply Company, Carlsbad, Calfornia USA) and was placed in a water-bath shaker at 150 rpm. The culturing was conducted under a light/dark cycle of 16:8 h, with the initial cell concentration being 1.18 × 108 cells/mL. Subsequently, a specific algae species was isolated using a phase contrast Carl Zeiss (Jena Med2 microscope) at 100× and 40× magnification. These algae were found to be microscopically identical to Chlorella species, as shown in Figure 1. Therefore, further investigation focused on the cultured algae that resembled Chlorella. To confirm the classification of the isolated algae species, molecular analysis was performed at Allgenetics laboratories (https://www.allgenetics.eu; accessed on 21 June 2022). The validation process involved using specific primers (tufA, rbcL, and ITS markers, covering ITS1, 5.8S gene, and ITS2) designed for the Chlorophyceae class. Upon identity confirmation, the isolated microalgae samples were identified as Chlorella sp. The Chlorella sp. samples isolated from Al-Asfar Lake, along with five other commercially available algae (Tetraselmis, Nannochloropsis, Chlorella vulgaris, Spirulina, and Isochrysis), were tested for metabolites. These algae were also obtained from Algae Research and Supply Company.

2.2. Instruments and Reagents

For the LC-MS analysis, we employed the Waters 2695 LC in combination with the Q Exactive MS instrument from Thermo Scientific, Shirley, NY. USA. The analysis utilized the ACQUITY UPLC HSS T3 column (100 × 2.1 mm × 1.8 μm). The reagents used in the analysis included acetonitrile (Merck), methanol (Merck), and formic acid (CNW).

2.3. Sample Preparation

Microalgae samples were freeze-dried to obtain dryness, and then 500 μL of 80% methanol was added. The samples were vortexed for 60 s and subsequently sonicated for 30 min at 4 °C. Afterward, the samples were frozen at −40 °C for 1 h. Following this, the samples were vortexed for 30 s and then subjected to centrifugation at 12,000 rpm at 4 °C for 15 min. Finally, 200 μL of the supernatant was collected in a vial, and 5 μL of DL-o-Chlorophenylalanine (140 μg/mL) was added to the vial containing the microalgae supernatant. The mixture was then subjected to LC-MS analysis.

3. LC-MS Analysis

A high-resolution LC-MS-based untargeted metabolomics profiling of samples was performed. Algal metabolic extracts were chromatographically separated using a Waters 2695LC system, coupled with high-resolution Q Exactive MS from Thermo Scientific, and screened with electrospray ionization mass spectrometry (ESI-MS). The LC system consisted of an ACQUITY UPLC HSS T3 column (100 × 2.1 mm × 1.8 μm) from Waters 2695 LC. The mobile phase included solvent A (0.05% formic acid in water) and solvent B (acetonitrile) with gradient elution (0–1 min, 95–95% A; 1–12 min, 95–5% A; 12–13.5 min, 5% A; 13.5–13.6 min, 5–95% A; 13.6–16.0 min, 95% A). The mobile phase flow rate was 0.3 mL/min. The sample manager’s temperature was set at 4 °C, and the column temperature was maintained at 40 °C. For ESI+ mode in mass spectrometry, the parameters were as follows: heater temperature at 300 °C, sheath gas flow rate at 45 arb, aux gas flow rate at 15 arbs, sweep gas flow rate at 1 arb, spray voltage at 3.0 kV, capillary temperature at 350 °C, and S-lens RF level at 30%. For ESI- mode in mass spectrometry, the parameters were as follows: heater temperature at 300 °C, sheath gas flow rate at 45 arb, aux gas flow rate at 15 arbs, sweep gas flow rate at 1 arb, spray voltage at 3.2 kV, capillary temperature at 350 °C, and S-lens RF level at 60%. The detailed parameters are depicted in Figure 2.

4. Statistical Analysis

The concentration data of all the metabolites from the samples were uploaded as an Excel file to the MetaboAnalyst website (https://www.metaboanalyst.ca/; accessed on 27 May 2023). The data then underwent untargeted metabolomics analysis using ultra-performance liquid chromatography (UPLC) coupled with mass spectrometry (MS) on the UPLC-MS platform. MetaboAnalyst was employed to analyze the samples for 168 types of metabolites [15].

4.1. Principal Component Analysis (PCA)

PCA is an unsupervised methodology conducted to identify the variance in a data set (X) without relying on class labels (Y). The data were represented in fewer variables, referred to as scores, which are the weighted averages of the original variables. The weighting profiles are known as loadings.

4.2. Partial Least Squares Discriminant Analysis (PLS-DA)

PLS is a supervised method that utilizes multivariate regression techniques to extract information from the original variables (X) in a linear combination, which can predict class membership (Y). PLS regression was conducted using the partial least squares regression function (plsR) provided by the partial least square regression software R pls package4. Classification and cross-validation were performed using the corresponding wrapper function of the caret package5. To assess the significance of class discrimination, a permutation test was conducted. In each permutation test, a PLS-DA model was built between the data (X) and the permuted class labels (Y), using an optimal number of components determined by cross-validation based on the original class assignment. MetaboAnalyst offers two types of test statistics for assessing class discrimination. The first test relies on prediction accuracy during training, while the second test uses the separation distance, calculated by the ratio of the squares between and the sum of squares within (B/W-ratio). If the observed test statistic falls within the distribution obtained from the permuted class assignments, the class discrimination cannot be considered statistically significant [16].

4.3. Hierarchical Cluster Analysis

In (agglomerative) hierarchical cluster analysis, each sample starts as a separate cluster, and the algorithm proceeds to combine them until all samples belong to one cluster. Two parameters are crucial to consider when conducting hierarchical clustering. The first parameter is the similarity measure, which can be calculated using Euclidean distance, Pearson’s correlation, and Spearman’s rank correlation. The other parameter is the clustering algorithm, which includes average linkage (clustering utilizing the centroids of the observations), complete linkage (clustering utilizing the farthest pair of observations between the two groups), single linkage (clustering utilizing the closest pair of observations), and Ward’s linkage (clustering to minimize the sum of squares of any two clusters). A heatmap and dendrogram are presented as visual aids.

5. Results

Metabolic Analysis Comparison of Local Algae with Five Known Algae Species

Metabolic analysis was performed to compare the local algae obtained from Al-Asfar Lake with five known algae species: Tetraselmis, Nannochloropsis, Chlorella vulgaris, Spirulina, and Isochrysis. A total of 168 types of metabolites were investigated using MetaboAnalyst (https://www.metaboanalyst.ca/ accessed on 27 May 2023) [16].
PCA calculation was conducted based on singular value decomposition, utilizing the prcomp package, and the results are presented in Figure 3. The scores’ plot variance between PC 1 and PC 2 was recorded as 29.7% and 21.2%, respectively. Figure 4 illustrates the relative concentrations of corresponding metabolites in the six algal groups identified by PLS-DA analysis (local algae, Tetraselmis, Nannochloropsis, Chlorella vulgaris, Spirulina, and Isochrysis). The colored boxes on the right side of the chart indicate the relative concentrations of the corresponding metabolites in each microalgal group. Red color indicates the highest concentration, while blue color indicates the lowest concentration. Among the algal groups, the highest concentrations of the corresponding metabolites were observed in C. vulgaris, followed by local algae (Tetraselmis, Nannochloropsis, C. vulgaris, Spirulina, and Isochrysis).
Additionally, Figure 5 displays clustered algal groups of the six samples recorded in the PCA. The scores were plotted between PC 1 and PC 2. The first group of Chlorella vulgaris clustered with local algae, while the second group, comprising a large cluster consisting of two sub-clusters (Isochrysis clustered with Tetraselmis, and Nannochloropsis clustered with Spirulina), is shown. While Figure S1, in Supplementary, presents the results of hierarchical clustering analysis in the form of a heatmap. The distance was measured using Euclidean distance, and clustering was performed using the ward.D algorithm, utilizing the hclust function in the stats package. The heatmap observations revealed that the clustering results of the local algae were in the same clade as C. vulgaris.
In the current study, LC-MS was utilized for the metabolic analysis of microalgae, revealing different types of microalgae and various lipids, including saturated and unsaturated fatty acids, as shown in Table 1.
As shown in Figure S1 [17], a comparison of the concentrations of lipids derived from the metabolites of six groups of microalgae revealed that C. vulgaris contained the highest concentrations of lipids, followed by the local algae from Al-Asfar Lake, Nannochloropsis, and Isochrysis, respectively.

6. Discussion

Al Hassa Oasis is a significant irrigation facility in Saudi Arabia, serving 328 million m3 of water, covering almost 22,000 farms. The excess drainage water is channeled through an earthen drainage network and distributed into two evaporation lakes, Al-Asfar and Al Uyoun [18]. Previous research studies have examined the water quality of Al-Asfar and Al Uyoun evaporation lakes using methodologies such as geographical information systems (GIS), hydrogeological studies, chemical studies, and remote sensing to design strategies for reusing the lake water for agricultural activities in Al Hassa Oasis [18]. Water composition analysis of Al-Asfar and Al Uyoun lakes has established relatively low salinity levels, and future development and planning strategies have been proposed to re-treat this water for its reuse in agricultural and land irrigation activities [18].
While earlier research studies on Al-Asfar and Al Uyoun lakes confirmed the presence of a wide diversity of microalgal species, their characteristics, and commercial importance, the microalgae present have never been investigated [3]. Hence, in our study, we identified and profiled, in comparison with 5 commercial algae, 168 crucial metabolites of local microalgae. The PCA scores’ plot variance (Figure 3) between PC 1 (29.7%) and PC 2 (21.2%) showed that local algae were identical to Chlorella vulgaris; Chlorella vulgaris clustered with local algae is shown in Figure 5. PLS-DA analysis for metabolites (Figure 4) determined that the relative concentrations of metabolites of local microalgae corresponded to those of Chlorella vulgaris. Heat map observations showed that the clustering results of local algae were similar to those of Chlorella vulgaris (Figure 6). The lipid concentrations of local algae were very similar to those of Chlorella vulgaris (Figure 6); the types of lipids identified in microalgae are shown in Table 1.
Based on our analyses, we concluded that the local algae exhibit the same characteristics as Chlorella vulgaris and could potentially provide the same benefits. Chlorella is considered a valuable single-celled green algae that contains a diverse range of antioxidants, bioactive materials, chlorophylls, amino acids, protein, vitamins, and dietary fiber [17]. Microalgae are unicellular cell factories that range in size from micrometers (μm) to a few hundred micrometers, depending on the species. Microalgae are known to generate high biomass in a relatively brief time and hold promising commercial prospects for the development of value-added microalgae-based coproducts, such as proteins, essential fatty acids, pigments, and biofuels, produced on an industrial scale [14]. Microalgae-based products are extensively used in human nutrition, aquaculture, and nutraceutical industries as a high-protein supplement [19,20,21]. Expert economists propose that intensive cultivation of microalgae appears to be a timely solution to the food versus fuel dilemma in today’s world [22]. Recent bioprospecting investigations of relevant microalgae species in South America have begun to uncover the great potential of the massive diversity of microalgae on the continent [23]. Microalgae identification from various environments near the Atlantic forest area in Brazil was conducted by utilizing selected culture media, classifying 20 algal species of industrial importance from a total of 30 algal isolates. A multicriteria analysis was employed, considering process and composition dimensions, including kinetic parameters and biomass production, kinetic parameters, and biomass composition (carbohydrates, lipids, proteins, and carotenoids), in addition to kinetic parameters and biomass composition after algal cellular stress and disruption [14]. The most important starting point for the characterization of microalgae is to uncover the commercial biotechnology prospects of microalgae by following the most appropriate isolation strategy and exploratory procedures, and evaluating a myriad of chemical composition variables and physical growth parameters [14].
Pandey et al. [24] identified promising commercial prospects for native microalgae species in the treatment of dairy effluents (DE) and lipid production. Their research found that the Scenedesmus sp. ASK22 microalgal strain showed significant potential for effective DE treatment and high rates of biodiesel production [24]. Schuelter et al. [25] demonstrated robust standard operating protocols for isolation procedures, identification, and evaluation methodologies to examine the antibacterial effect of unique metabolites isolated from novel microalgae strains identified in aquatic ecosystems located in the western regions of Parana, Brazil [25].
Multicriteria selection and characterization paradigms are the most appropriate and proven approaches for identifying microalgae in natural aquatic and land ecosystems [14]. In accordance with this methodology, our bioprospecting studies, using PLS-DA analysis, determined the relative concentrations of corresponding metabolites in six algal groups. The highest concentrations of the corresponding metabolite were observed in C. vulgaris, followed by local algae, in comparison to other algal groups (Tetraselmis, Nannochloropsis, C. vulgaris, Spirulina, and Isochrysis). Our research findings, determined by thorough principal component analysis, also established that local alga exhibited nearly the same characteristics as Chlorella vulgaris in terms of metabolite profiling (Figure 3 and Figure 5), heat map observations through PLS-DA analysis (Figure 4), hierarchical clustering analysis (Figure 6), and comparative analysis of lipid concentrations (Figure S1). Hence, we concluded that local algae may have the same characteristics and commercial prospects as Chlorella vulgaris.
Chemical compounds synthesized from microalgae are characterized by their high protein content, making them a sought-after nonconventional resource for proteins. Chlorella and Spirulina were among the earliest microalgae species to be commercialized as nutritional foods in China, Japan, and Mexico [26,27]. Chlorella is an important food source due to its high protein composition and associated nutritional benefits. Chlorella is included in the diet in East Asian countries such as Japan, China, and Korea, where it is eaten in association with tea, rice, and pancakes [28]. Numerous research studies have reported the hypoglycemic effects and antitumor and anti-obesity properties of chlorella in diabetic mice [29,30,31]. Chlorella vulgaris (CV) is known to have high concentrations of protein, with a dry weight of 51% to 58%, and is a resource of notable biological and disease-preventive functions [32]. C. vulgaris is suggested as a complementary n-3/n-6 polyunsaturated fatty acid (PUFA) source to optimize the n-3/n-6 composition of the diet [33].
C. vulgaris has been shown to exhibit protective effects in acute hepatic damage conditions and is reported to have antioxidant effects [34]. Supplementation with CV has demonstrated immunomodulatory properties, leading to the inhibition of immunoglobulin E (IgE) production and triggering enhanced Th1 cell-related immunological responses [35]. In addition, oral administration of CV has been reported to promote inhibitory phenomena, leading to the suppression of histamine secretion in clinical studies [36]. Furthermore, C. vulgaris has been shown to reduce stress-associated hyperglycemia and stress-related hypothalamic–pituitary–adrenal (HPA) activation in rats [37]. Chlorella vulgaris, in association with hot water extract, has shown anti-aging effects by reducing the activity of superoxide dismutase (SOD) in human diploid fibroblasts [38]. This extract has also shown anti-inflammatory effects by downregulating IL-4 and IFN-γ mRNA expression levels in NC/Nga mice [39].

6.1. The Likelihood of Biodiesel Production

Microalgae are considered ideal biomass materials for the production of third-generation biofuels due to their faster growth rate, ability to thrive in non-arable terrestrial or aquatic environments, and higher lipid productivity [40]. Lipids obtained from microalgae can be transformed into biodiesel through transesterification reactions using heterogeneous or homogeneous catalysts [41]. Our study’s comparative analysis of lipid concentrations in microalgae groups revealed that the highest value was recorded in Chlorella vulgaris, followed by local microalgae (Figure S1). These observations suggest that local microalgae may have biofuel production prospects that are similar to those of Chlorella vulgaris. Microalgae are considered prominent sources of biofuel due to their higher levels of lipid production, and can be used as raw material for biodiesel production. Sistiafi and Putri [41] noted that biodiesel yields produced from the lipids of Chlorella vulgaris were better than those from Nannochloropsis oculate [41], while Aguoru and Okibe [42] reported that oil extracted from Chlorella vulgaris contained unsaturated and saturated fatty acids, with the most abundant chain lengths being C9:0, 10:0, 13:0, 16:0, 18:0, 8:1, and 18:2, with C18:1 dominating. They also established Chlorella vulgaris. is the most suitable species for manufacturing biodiesel due to its higher lipid profile, its composition, and level of unsaturation that meets the requirements of an oil’s suitability for biodiesel production [42]. Kaushik et al. [43] demonstrated that low concentrations of MeJA (methyl jasmonate) could potentially improve lipid productivity without altering the growth rate in Scenedesmus Sp. Low concentrations of MeJA (methyl jasmonate) have the potential to enhance lipid productivity in Chlorella vulgaris without affecting the growth rate. MeJA is a plant hormone that can stimulate various physiological processes in plants and microorganisms. In the case of Chlorella vulgaris, the application of MeJA at low concentrations triggers specific metabolic pathways that promote lipid synthesis. MeJA acts as a signaling molecule, activating certain genes and enzymes involved in lipid biosynthesis. It can enhance the expression of key enzymes, such as acetyl-CoA carboxylase and fatty acid desaturase, which are crucial for lipid production. This upregulation of lipid-related enzymes leads to an increase in lipid content within the cells of Chlorella vulgaris. Importantly, the effect of MeJA on lipid productivity occurs without negatively impacting the growth rate of the microalgae. This is significant because maintaining a high growth rate is essential for efficient microalgae cultivation. By optimizing lipid productivity without compromising growth, the use of MeJA offers a promising strategy for improving the overall lipid yield of Chlorella vulgaris and enhancing its potential as a biodiesel feedstock.

6.2. Disadvantages/Limitations of the Study

The microalgae found in Al-Asfar Lake, which is similar to Chlorella, need to be characterized for some of the limitations encountered by Chlorella species, such as toxicity, dosage, bioaccumulation, and contamination, as noted in the previous section. There have been no research studies carried out directly linking Chlorella ingestion to chronic health risks or toxicity. The intermittent risks of ingesting Chlorella by susceptible populations, including pregnant women, immunosuppressed individuals, infants, and the elderly, have not been evaluated, and these populations may need to avoid using phytotherapeutic drugs and/or health supplements without prior medical consultation and prescription. This is also recommended for any other plant extract without complete evidence of efficacy and safety studies [33]. There is a possible risk of contamination and bioaccumulation of Zn2+ and Cd2+ in Chlorella found in natural environments, which is similar to the bioaccumulation of routine plant crop variants [44]. Alam et al. [45] established that C. vulgaris bioaccumulates 80% Zn2+ and 60% Cd2+ from the culture medium and, therefore, efficiently cleans contaminated water. C. vulgaris also bioaccumulates arsenic during inoculation in paddy crops, attenuating levels of metal in crop plants. Therefore, we believe that utilizing microalgae from Al-Asfar Lake could potentially offer a safer approach for producing biofuel and serving as a liquid tool to clean the environment.

7. Conclusions

In Saudi Arabia, the Al Hassa Oasis serves a crucial role in irrigation, sustaining a large number of farms with substantial water resources. The surplus drainage water from the oasis is directed to Al-Asfar and Al Uyoun, two evaporation lakes. Prior research centered on the water quality of these lakes and explored methods for reusing the water for agricultural purposes in Al Hassa Oasis. It has been determined that the water in these lakes has relatively low salinity, and strategies for treating and reusing it have been proposed. Our study investigated a previously unexplored aspect of these lakes by identifying and characterizing the metabolites of local microalgae and comparing them to five commercial algae. Using sophisticated analytic techniques such as PCA scores’ plot variance and PLS-DA analysis, we discovered that the local algae were closely related to Chlorella vulgaris, a highly valuable single-celled green algae known for its wide variety of beneficial components including antioxidants, bioactive materials, chlorophylls, amino acids, protein, vitamins, and dietary fiber. With their rapid growth and adaptability to a variety of environments, microalgae have enormous potential for commercial applications. They can be utilized to create a variety of valuable products, including high-protein supplements, biofuels, essential fatty acids, and pigments, among others. Economists view intensive microalgae cultivation as a viable solution to the current food versus petroleum dilemma. Recent South American bioprospecting investigations have also revealed the vast diversity of microalgae with latent potential.
The metabolic analysis and comparison of local algae in Al-Asfar Lake with five commercially valuable algae (Tetraselmis, Nannochloropsis, Chlorella vulgaris, Spirulina, and Isochrysis) showed that the local algae are related to C. vulgaris. Chlorella algae are well known for their nutritional and commercial value in the production of biofuels, their environmental aspects related to the absorption of CO2, and their effectiveness as a biological treatment for wastewater.
In addition to establishing the similarities between local algae and Chlorella vulgaris, our bioprospecting studies cast light on the potential applications of these microalgae. Chlorella and Spirulina have already been marketed as nutritional foods, and Chlorella is renowned for its numerous health advantages, which include hypoglycemic, antitumor, anti-obesity, and antioxidant effects. It is a valuable addition to the diet due to its potential as a source of proteins and essential fatty acids. Biodiesel synthesis is one of the microalgae’s most promising applications. Their capacity to grow in non-arable environments and high lipid productivity make them ideal feedstocks for third-generation biofuels. Our comparative analysis of lipid concentrations revealed that local microalgae, particularly those resembling Chlorella vulgaris, may be candidates for biodiesel production. Despite the promising potential of local algae, we recognize that additional research is necessary to address potential limitations, such as toxicity, dosage, bioaccumulation, and contamination, which are typical obstacles to microalgae utilization. However, it is important to note that previous research has demonstrated that Chlorella vulgaris can effectively purify polluted water by accumulating certain metals. Nonetheless, caution should be exercised when contemplating its use for specific populations in the absence of sufficient studies on its safety and efficacy. As we delve deeper into the potential of these microalgae, we can explore innovative methods to harness their benefits for commercial and ecological applications, thereby contributing to a more sustainable and eco-friendly future.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w15152757/s1, Figure S1: The results of the hierarchical clustering analysis are presented as a heatmap, employing the Euclidean distance metric and Ward’s linkage method for the clustering algorithm. The heatmap illustrates the relationships and patterns among different metabolites extracted from six algae species (Tetraselmis, local algae, Nannochloropsis, Chlorella vulgaris, Spirulina, and Isochrysis).

Author Contributions

Authors F.D. and S.I.A. made equal contributions to the conceptualization, methodology, formal analysis, investigation, resource management, data curation, preparation of the original draft, review and editing, visualization, supervision, project administration, and funding acquisition. All authors have thoroughly reviewed and consented to the final version of the manuscript for publication.

Funding

This research received no external funding.

Data Availability Statement

The data presented in this study are available in the article [Unexplored Potential: Metabolite Screening of Local Lake Algae Isolated from Al-Asfar Lake in Saudi Arabia].

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Photomicrograph of young and mature vegetative Chlorella sp. MF1 cells (×40) from a local algae specimen collected in Al-Asfar Lake. The specimen has been identified as Chlorella vulgrais and serves as one of the diverse species contributing to the lake’s ecosystem.
Figure 1. Photomicrograph of young and mature vegetative Chlorella sp. MF1 cells (×40) from a local algae specimen collected in Al-Asfar Lake. The specimen has been identified as Chlorella vulgrais and serves as one of the diverse species contributing to the lake’s ecosystem.
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Figure 2. Total ion chromatogram (TIC) illustrating the mass spectrometry analysis of the samples, captured in both ESI− (a) and ESI+ (b) ionization modes. The TIC plot showcases the distribution and abundance of ions detected in each mode, providing valuable insights into the molecular composition and characteristics of the analyzed samples.
Figure 2. Total ion chromatogram (TIC) illustrating the mass spectrometry analysis of the samples, captured in both ESI− (a) and ESI+ (b) ionization modes. The TIC plot showcases the distribution and abundance of ions detected in each mode, providing valuable insights into the molecular composition and characteristics of the analyzed samples.
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Figure 3. Principal Component Analysis (PCA) performed on six algae species (Tetraselmis, local algae, Nannochloropsis, Chlorella vulgaris, Spirulina, and Isochrysis) using 168 metabolite types. This scores plot displays the distribution of the samples based on selected principal components (PCs), highlighting patterns of similarity and dissimilarity among the algae species. The percentage of explained variances for each PC is indicated in brackets, indicating the proportion of data variance accounted for by each principal component.
Figure 3. Principal Component Analysis (PCA) performed on six algae species (Tetraselmis, local algae, Nannochloropsis, Chlorella vulgaris, Spirulina, and Isochrysis) using 168 metabolite types. This scores plot displays the distribution of the samples based on selected principal components (PCs), highlighting patterns of similarity and dissimilarity among the algae species. The percentage of explained variances for each PC is indicated in brackets, indicating the proportion of data variance accounted for by each principal component.
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Figure 4. Principal Component Analysis (PLS-DA) reveals important features distinguishing six algae species (Tetraselmis, local algae, Nannochloropsis, Chlorella vulgaris, Spirulina, and Isochrysis) across 168 metabolite types. The colored boxes on the right side represent the relative concentrations of the corresponding metabolites within each studied algae group. These identified features offer valuable insights into the metabolic profiles that differentiate the algae species, aiding in their characterization and understanding of biochemical diversity”.
Figure 4. Principal Component Analysis (PLS-DA) reveals important features distinguishing six algae species (Tetraselmis, local algae, Nannochloropsis, Chlorella vulgaris, Spirulina, and Isochrysis) across 168 metabolite types. The colored boxes on the right side represent the relative concentrations of the corresponding metabolites within each studied algae group. These identified features offer valuable insights into the metabolic profiles that differentiate the algae species, aiding in their characterization and understanding of biochemical diversity”.
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Figure 5. Principal Component Analysis (PCA) reveals the clustering of algae groups based on the results from the analysis of 168 metabolite types for six algae species (Tetraselmis, local algae, Nannochloropsis, Chlorella vulgaris, Spirulina, and Isochrysis). This scores plot displays the distribution of samples along selected principal components (PCs). The explained variances for each PC are indicated in brackets, representing the percentage of data variance explained. Notably, Chlorella vulgaris clusters together with local algae, forming one group. Another prominent cluster comprises two sub-clusters, with Isochrysis clustering with Tetraselmis and Nannochloropsis clustering with Spirulina, indicating shared metabolic similarities between these algae pairs.
Figure 5. Principal Component Analysis (PCA) reveals the clustering of algae groups based on the results from the analysis of 168 metabolite types for six algae species (Tetraselmis, local algae, Nannochloropsis, Chlorella vulgaris, Spirulina, and Isochrysis). This scores plot displays the distribution of samples along selected principal components (PCs). The explained variances for each PC are indicated in brackets, representing the percentage of data variance explained. Notably, Chlorella vulgaris clusters together with local algae, forming one group. Another prominent cluster comprises two sub-clusters, with Isochrysis clustering with Tetraselmis and Nannochloropsis clustering with Spirulina, indicating shared metabolic similarities between these algae pairs.
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Figure 6. Comparison of lipid concentrations among six algae species (Tetraselmis, local algae, Nannochloropsis, Chlorella vulgaris, Spirulina, and Isochrysis) highlights the relative similarities in most lipids, while also emphasizing the significantly higher concentrations observed in Chlorella vulgaris and the local algae.
Figure 6. Comparison of lipid concentrations among six algae species (Tetraselmis, local algae, Nannochloropsis, Chlorella vulgaris, Spirulina, and Isochrysis) highlights the relative similarities in most lipids, while also emphasizing the significantly higher concentrations observed in Chlorella vulgaris and the local algae.
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Table
Table 1. Characterization of lipids and fatty acid extracted from microalgae of Al-Asfar lake. Analysis of fatty acid distribution in various lipid classes: Glycolipids, Phospholipids, and Triacylglycerols (represented as percentage means ± standard deviation (SD), with a sample size of n = 3).
Table 1. Characterization of lipids and fatty acid extracted from microalgae of Al-Asfar lake. Analysis of fatty acid distribution in various lipid classes: Glycolipids, Phospholipids, and Triacylglycerols (represented as percentage means ± standard deviation (SD), with a sample size of n = 3).
Lipids Molecular FormulaGlycolipid %Phospholipid %Triacylglycerol %
C14:043.5 ± 727.8 ± 5.58.9 ± 1.4
C16:030.5 ± 3.730.7 ± 2.422.1 ± 1.6
C16:1n737.3 ± 2.429.2 ± 1.88.4 ± 0.6
C16:2n460.7 ± 1.53.3 ± 0.322 ± 1.5
C16:3n490.5 ± 0.62.6 ± 0.80.4 ± 0.2
C18:023.2 ± 7.715.2 ± 3.763.9 ± 10.8
C18:1n912.4 ± 1.075.9 ± 0.87.8 ± 0.9
C18:1n729.6 ± 2.061.9 ± 2.55.8 ± 0.5
C18:2n619 ± 1.970.4 ± 1.17.7 ± 0.7
C18:3n652.2 ± 0.945.9 ± 0.90 ± 0
C18:3n334.0 ± 2.2562.1 ± 3.23.8 ± 4.0
C18:4n362.8 ± 7.033.5 ± 2.43.9 ± 5.2
C18:5n353.2 ± 1.222.9 ± 2.425.2± 2.5
C20:5n363.5 ± 1.230.9 ± 0.42.1 ± 1.6
C22:620.3 ± 2.472.5 ± 0.23.6 ± 2.1
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Alsanie, S.I.; Dhawi, F. Unexplored Potential: Metabolite Screening of Local Lake Algae Isolated from Al-Asfar Lake in Saudi Arabia. Water 2023, 15, 2757. https://doi.org/10.3390/w15152757

AMA Style

Alsanie SI, Dhawi F. Unexplored Potential: Metabolite Screening of Local Lake Algae Isolated from Al-Asfar Lake in Saudi Arabia. Water. 2023; 15(15):2757. https://doi.org/10.3390/w15152757

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Alsanie, Sumayah I., and Faten Dhawi. 2023. "Unexplored Potential: Metabolite Screening of Local Lake Algae Isolated from Al-Asfar Lake in Saudi Arabia" Water 15, no. 15: 2757. https://doi.org/10.3390/w15152757

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