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Carbohydrate and lipid yield in Microcystis aeruginosa for biofuel production under different light qualities

Biotechnology for Biofuels and Bioproducts volume 18, Article number: 36 (2025) Cite this article

Abstract

Background

Biofuels produced from algae have enormous advantages in replacing fossil fuels, and Microcystis aeruginosa has a great potential for biofuel production, due to growing fast to form large amounts of biomass. Light is essential for algal growth, and the optimum light quality can promote the biomass and lipid accumulation for increasing feedstock for biofuel production.

Results

We investigated the biomass accumulation, photosynthetic ability, carbohydrate, and lipid yield as well as related gene expression in M. aeruginosa under red, blue, purple, and white light to promote biofuel production using this alga under the optimal light quality. Compared with white light, purple light promoted the cell growth during the 5 days, while blue light showed inhibitory effect. Red light had no effect on the cell growth, but improved the biomass content to the highest level. Red light improved the photosynthetic ability by raising chlorophyll level, and up-regulating expression of the genes in chlorophyll biosynthesis, photosynthetic electron transfer, and CO2 fixation. Among these light qualities, red light showed the maximum effect on soluble, insoluble, and total carbohydrate accumulation by up-regulating the genes in polysaccharide and starch formation, and down-regulating the genes in glycolysis and tricarboxylic acid cycle. Red light also exhibited the maximum effect on lipid accumulation, which might be caused by up-regulating five genes in fatty acid biosynthesis.

Conclusion

Red light can promote M. aeruginosa accumulating carbohydrates and lipids by regulating related gene expression, which should be the optimal light quality for improving feedstock yield for biofuel production.

Background

Over the past few decades, the global energy consumption raises speedily due to the increase of industrialization and population as well as the improvements in human lifestyles, which results in a huge consumption of fossil fuel, greenhouse gas emissions, and global warming [1]. As the current consuming rate, the fossil fuels will be used up within the next 150 years, due to its non-renewability [2]. To overcome the issue threatening society development, biofuels have aroused the worldwide interest in the research and application, owing to replacing fossil fuel and contributing to resolving global warming [3]. It has been predicted that the global biofuel market size will exceed $201 billion by 2030 with the annual growth rate of 8.3% [4].

According to the raw materials, the biofuels are categorized into three generations [5]. For the first generation, the biofuels are produced by food sources, which lead to the competition with food production and requirement to large cultivation areas, fertilizer, and water. The second-generation biofuels are produced using the inedible lignocellulosic materials, such as rice straw, crop waste, switchgrass, and sawdust. However, there are some defects in the production process, such as inconvenient pretreatment, high energy consumption, and low yield [6]. For the third-generation biofuels, they are produced using algal biomass as feedstock [5]. Algae are photosynthetic organisms with a fast growth rate and high CO2 assimilation efficiency, whose biomass accumulation rate is 5–10 times that of terrestrial plants [5]. Algae grow in water without needing arable land, which is beneficial for ensuring food security [7]. Meanwhile, algae can be cultured in bioreactors to increase biomass content, and have a potential for producing biofuels all year round [8]. Owning to these advantages, the third-generation biofuels are regarded as the renewable and environmentally favorable resource [7].

Compared with eukaryotic algae, prokaryotic cyanobacteria have superior abilities in photosynthesis and biomass production, and can convert up to 10% solar energy into biomass, which is higher than higher plants (about 1%) and eukaryotic algae (about 5%) [9, 10]. Lots of cyanobacteria can fix atmospheric nitrogen, reducing the cost of nitrogen fertilizer in cultivation. In addition, cyanobacteria have simple genetic background, which are easily carried out genetic manipulation to further improve the levels of lipids and biomass [11]. Up to now, some cyanobacterial species with the potential for bioethanol and biodiesel production have been identified, such as Anabaena variabilis, Oscillatoria sp., Nostoc sp., and Synechococcus elongatus [12,13,14]. Cyanobacteria encompass an enormous diversity, so abundant screening efforts are still needed for supplying preferable choices for biofuel production.

For third-generation biofuels, bioethanol and biodiesel are produced using algal carbohydrates and neutral lipids, respectively [13]. Enormous efforts have been made to improve the yield of both primary metabolites in algae and cyanobacteria. It has been reported that adverse conditions are beneficial for the yield of carbohydrates and lipids in algal cells. Then, several stressful conditions have been detected, such as nutrient (nitrogen, phosphorus, or sulfur) deficiency [12, 15], H2O2 stress [16], high light [17], high temperature [18], and pH [19]. In wastewater cultivation, the carbohydrates and lipids were accumulated in some cyanobacteria, such as Oscillatoria sp., Acaryochloris marina, Pleurocapsa sp., and Synechocystis sp. [19, 20], due to their broad adaptability. In the treatment with volatile fatty acids, the accumulation of the two primary metabolites was detected in Pseudanabaena mucicola, with butyric acid being more effective than acetic acid [21].

When Nannochloropsis sp. strain MUR266 and Chlamydomonas reinhardtii strain CC-125 were cultured under different light qualities, blue light showed negative effect on the cell growth [22, 23]. In contrast to blue light, red light showed positive effect on the growth of Nannochloropsis sp. and Anabaena fertilissima strain PUPCCC410.5 [22, 24]. When C. reinhardtii was cultured under different light qualities, red–orange light promoted cell growth, biomass accumulation, and lipid formation [23]. For 3 cyanobacteria (Nostoc muscorum, Arthrospira platensis, and Phormidium foveolarum), low-intensity UV-B (0.045 W m−2) exhibited promoting effect on their growth [25]. For Spirulina platensis, red light was beneficial for the biomass accumulation, while blue light showed inhibitory effect [26]. Among 6 light qualities, red and purple light improved the extracellular polysaccharide yield in Nostoc flagelliforme [27]. Among white, blue, blue–green, red, and pink light, blue light showed the best effect on lipid accumulation in Nannochloropsis sp. [22]. For Nannochloropsis oceanica [28] and Chlorella sp. [29], blue light also improved their lipid content. These results suggest that optimal light quality can promote algal and cyanobacterial growth, biomass accumulation and lipid formation. However, the promoting mechanism basing on related gene expression and adjustment is not well uncovered, which is not beneficial for applying optimal light quality to improve feedstock yield for biofuel production.

Microcystis aeruginosa is a typical species in cyanobacteria for easily forming water blooms in eutrophicated waters [30]. It grows fast to form large amounts of biomass, and accumulates abundant lipids. The biodiesel that was produced from M. aeruginosa lipids met the standard of American ASTMD6751 [31], suggesting that this alga has a potential for biofuel production by providing feedstock. In the previous studies, the accumulation of carbohydrates and lipids was found in this cyanobacterial species under nitrogen or phosphorus deficiency [12, 15], co-culture with green algae [32], and high temperature [18]. Light is the essential condition for algal and cyanobacterial growth, and optimal light quality can promote their biomass and lipid accumulation without increasing auxiliary investment. However, the optimal light quality and its regulatory mechanism on primary metabolism in M. aeruginosa are still unknown. To promote biofuel production using M. aeruginosa under optimal light quality, the biomass accumulation, photosynthetic ability, carbohydrate and lipid yield, as well as related gene expression were investigated under red, blue, purple, and white light.

Results and discussion

Biomass yield under different light qualities

When M. aeruginosa cells were cultured under white, red, purple, and blue light, their density gradually increased with prolonging the culturing time. Compared with white light, purple light significantly (P < 0.05) promoted the cell growth, but blue light lowered the cell growth. During the 5 days, red light weakly promoted the cell growth in contrast to white light, with no significant difference between them (Fig. 1A). These findings were similar with the previous studies. Compared with white light, blue light inhibited the growth of Nannochloropsis sp. strain MUR266 and C. reinhardtii strain CC-125 [22, 23], while red light had no impact on the growth of Nannochloropsis sp. strain MUR266 [22]. In contrast to blue light, red light markedly promoted the growth of A. fertilissima strain PUPCCC410.5 and Nannochloropsis sp. strain MUR266 [22, 24].

Fig. 1
figure 1

Effects of different light qualities on the cell growth (A) and biomass content (B) on M. aeruginosa. White: White light, the control; purple: purple light; blue: blue light; red: red light. On the same day, the different lowercase letters indicate the significant difference at P < 0.05. Means ± SE (n = 4)

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In the treatments with the four light qualities, the biomass content in M. aeruginosa cultures gradually increased with prolonging the culturing time. Under red and purple light, the biomass content was significantly (P < 0.05) higher than that under white light, and red light showed the maximum promoting effect on the biomass accumulation since the 2nd day. At the 5th day, the biomass content under red light increased by 41.1% (P < 0.05), 15.0% (P < 0.05), and 99.2% (P < 0.05) compared to that under white, purple, and blue light, respectively. In contrast to white light, blue light significantly (P < 0.05) lowered the biomass content (Fig. 1B). Similarly, red light also promoted biomass accumulation in S. platensis cells, and blue light showed inhibitory effect [26]. In contrast to blue light, red light improved the biomass content in A. fertilissima, Dunaliella salina, D. tertiolecta, Nodularia sphaerocarpa, and H. pluvialis [24, 33,34,35], and red–orange light improved the biomass content in C. reinhardtii [23]. Among the treatments with the four light qualities, M. aeruginosa cell length and width not significantly changed (Supplementary Fig. 1), indicating that the variations of the algal biomass under the three monochromatic light qualities are caused by the cell contents but not the cell size.

Photosynthetic abilities under different light qualities

Chlorophylls are essential photosynthetic pigments, which serve crucial functions in absorbing solar energy and converting it into electric energy. Under red light, an increase was found in the chlorophyll content in N. sphaeroides and S. platensis cells [36, 37], while a decrease was found in S. platensis, N. sphaeroides, and N. sphaerocarpa under blue light [33, 36, 37]. Under purple light, the chlorophyll a and b content in strawberry was not changed in contrast to white light [38].

Compared with white light, the increase was found in chlorophyll content in M. aeruginosa cultured under red light since the 1st day, whereas a decrease was detected under blue light. For purple light, no obvious variation was detected during the 5-day treatment (Fig. 2A). These results demonstrate that red light has positive effect on chlorophyll formation, which is beneficial for light energy absorption and photosynthetic electron generation.

Fig. 2
figure 2

Effects of different light qualities on the chlorophyll content (A), ETo/RC (B), and O2 evolution rate (C) in M. aeruginosa. White: white light, the control; purple: purple light; blue: blue light; red: red light. On the same day, the different lowercase letters indicate the significant difference at P < 0.05. Means ± SE (n = 4)

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Among white, red, blue, yellow, and green light, Porphyra leucosticta cells exhibited the highest photosynthetic electron transport rate under red light, while the lowest under blue light [39]. When N. flagelliforme was cultured under white, red, blue, purple, yellow, and green light, the highest photosynthetic rate was found under red light, while the lowest under green and blue light [27]. In addition, red light promoted photosynthetic electron production and transfer in strawberry, but blue light exhibited adverse effect [38]. For 3 cyanobacteria, UV-B raised their photosynthetic abilities by promoting photosynthetic electron production and transfer [26].

The ETo/RC in M. aeruginosa raised under red and purple light compared with white light, with the increase of 33.6% (P < 0.05) and 31.2% (P < 0.05) after 5 days, respectively (Fig. 2B). This indicates that the two light qualities are beneficial for photosynthetic electron production and transfer, which can supply abundant assimilatory power (ATP and NADPH) for CO2 fixation. Similarly, red and purple light also improved O2 evolution rate (Fig. 2C), demonstrating that the two light qualities promoted the increase in photosynthetic rate [40]. For photosynthetic products, they generated from the high photosynthesis under purple light could be distributed to cell growth, leading to the highest cell density, while they might be converted into carbohydrates and lipids under red light, resulting in the biomass accumulating to the highest level (Fig. 1). Under blue light, the decrease was detected in ETo/RC and O2 evolution rate, which may result in the low photosynthetic ability, cell growth, and biomass accumulation (Figs. 1, 2).

Carbohydrate accumulation under different light qualities

Carbohydrates are one of the main types of primary metabolites, which mainly include sugar monomers and polymers as well as sugar derivatives (amino sugars and uronic acids) [15]. Sugar polymers exhibit widely varied molecular weights for the different polymerization degree. They function as storage (starch) or structural (cellulose) compounds with accounting for large proportion in biomass sources, and are used to produce bioethanol through hydrolysis [41]. In contrast to blue light, red light promoted the accumulation of carbohydrates in Chlorella sp. [29]. For C. reinhardtii, an increase was found in carbohydrate content under red–orange light [23]. Among 6 light qualities, red and purple light improved the extracellular polysaccharide yield from N. flagelliforme in contrast to white light, with the maximum promoting effect under red light, while blue light did not impact the polysaccharide yield [27]. Under blue light, a decrease was found in carbohydrate content in Nannochloropsis sp., due to the increase in respiration consumption [22]. Compared with white, purple, and blue light, red light remarkably promoted extracellular and intracellular polysaccharide formation for raising carbon flow into the synthetic process [42].

In M. aeruginosa, the soluble carbohydrates primarily contain monosaccharides and some oligosaccharides, while the insoluble carbohydrates primarily contain stored and structural polysaccharides [15]. In this study, M. aeruginosa cells significantly (P < 0.05) increased the soluble carbohydrate content since the 1st day under red and purple light, but declined the content under blue light. Among the treatments with the four light qualities, the highest soluble carbohydrate content was always detected under red light during the 5 days, and it was increased by 60.4% (P < 0.05), 20.3% (P < 0.05), and 93.0% (P < 0.05), respectively, compared to that under white, purple, and blue light after 5 days (Fig. 3A). Compared with white light, red and purple light significantly (P < 0.05) improved the soluble carbohydrate concentration during the 5 days, with the maximum effect under red light, whereas blue light significantly (P < 0.05) lowered the soluble carbohydrate concentration, with the decrease of 36.9% after 5 days (Fig. 3B). Among the four light qualities, the highest soluble carbohydrate productivity was found under red light, which was increased by 2.4 folds (P < 0.05), 43.3% (P < 0.05) and 7.1 folds (P < 0.05) compared to that under white, purple, and blue light, respectively (Fig. 3C).

Fig. 3
figure 3

Effects of different light qualities on the accumulation of soluble (A–C), insoluble (D–F), and total (G–I) carbohydrate in M. aeruginosa. White: white light, the control; purple: purple light; blue: blue light; red: red light. On the same day, the different lowercase letters indicate the significant difference at P < 0.05. Means ± SE (n = 4)

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Insoluble carbohydrates (Fig. 3D–F), total carbohydrates (Fig. 3G–I), and polysaccharides (mainly intracellular polysaccharides) (Supplementary Fig. 2) also showed the similar variations under white, red, purple, and blue light. Red light exhibited the maximum effect on the accumulation of these carbohydrates, which may be caused by the conversion of massive photosynthetic products from high photosynthetic ability. Blue light declined the carbohydrate accumulation, indicating that this light was not beneficial for carbohydrate production.

Lipid accumulation under different light qualities

Lipids are another major type of primary metabolites, and used as feedstock to produce biodiesel. It has been reported that the biodiesel produced from M. aeruginosa lipid transesterification conforms to the standard of American ASTMD6751 [31]. For Nannochloropsis sp., light quality played an important role in the lipid accumulation, and the highest lipid content was found under blue light among five light qualities [22]. Similarly, blue light also increased the eicosapentaenoic acid content in N. oceanica [28] and fatty acid (C16:2 and C18:2) content in Chlorella sp. cells [29]. For H. pluvialis, white–red light was beneficial for the formation of saturated fatty acids [43]. For Cyanobium sp., supplementation of red light can induce the accumulation of saturated and unsaturated fatty acids as well as lipids [44]. Compared with blue light, red–orange light was more effective for lipid accumulation in C. reinhardtii [23]. In addition, different ratios of red and blue light also affected fatty acid and lipid biosynthesis in Phaeodactylum tricornutum, with the highest saturated fatty acid content, lipid content, and lipid production under red–blue (5:2) light [45]. These results suggest that the optimal light quality for lipid formation varied with algal species.

Lipid accumulation in M. aeruginosa was also markedly influenced by light quality. With prolonging the culturing time, the lipid content and concentration in the cells gradually increased under purple, blue, and red light. Among these light qualities, blue light always showed the maximum promoting effect on the increase in lipid content, but had minor effect on lipid concentration and productivity (Fig. 4). This demonstrated that blue light was beneficial for lipid formation, but the low cell density and biomass content limited the lipid yield.

Fig. 4
figure 4

Effects of different light qualities on the lipid content (A), lipid concentration (B) and lipid productivity (C) in M. aeruginosa. White: white light, the control; Purple: purple light; blue: blue light; red: red light. On the same day, the different lowercase letters indicate the significant difference at P < 0.05. Means ± SE (n = 4)

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For red light, it improved the lipid content to the highest level at the 5th day, with no significant difference from that under blue light (Fig. 4A). However, the increase in lipid content under the two light qualities showed different patterns, with a gradual increase under red light and a fast increase to the plateau under blue light. Blue light may have stimulated the immediately formed and stored photosynthetic products to form lipids quickly, with a fast increase at the 1st day. However, the low photosynthetic ability under blue light might limit the supply of photosynthetic products, resulting in a slow increase trend after 3 days. The lipid conversation from photosynthetic products was slowly initiated under red light, but the high photosynthetic ability might supply abundant photosynthetic products, leading to a gradually increasing trend in lipid content. Under red light, M. aeruginosa raised the lipid concentration to the highest level since the 3rd day (Fig. 4B). For lipid productivity, the highest value was detected under red light, with the increase of 2.6-fold (P < 0.05), 74.0% (P < 0.05), and 1.7-fold (P < 0.05), respectively, compared to that under white, purple, and blue light (Fig. 4C). These results suggest that red light is suitable for culturing M. aeruginosa to harvest lipids for biodiesel production.

Related gene expression in photosynthesis under red light

For algae, light quality can act as a signal to adjust gene expression in energy and matter metabolism, resulting in the variation of primary and secondary metabolites [40]. In chlorophyll biosynthesis, glutamate is the precursor, and 15 enzymes take part in the catalysis [46]. In N. oceanica, red light improved the chlorophyll content by up-regulating 11 genes in the biosynthetic process [47]. When M. aeruginosa was kept under red light for 5 days, the up-regulation was found in ten genes associated with chlorophyll biosynthesis in contrast to white light, including gltX, hemB, hemE, hemF, hemJ, bchI, bchM, acsF, por, and chlG (Fig. 5A, the gene functions and expression levels in Supplementary Table 1). For chlG, it encodes chlorophyll synthase, which catalyzes the formation of chlorophyll a and b. Under red light, its expression level in M. aeruginosa was increased by 2.6-fold (P < 0.05) (Fig. 6). Then, the up-regulation of these genes under red light should promote the formation of chlorophylls and improve their levels in M. aeruginosa (Fig. 2A).

Fig. 5
figure 5

Expression of the genes in chlorophyll biosynthesis (A) and photosynthesis (B) in M. aeruginosa under red light. White: White light, the control; red: red light. fpkm: Fragments per kilobase per million mapped reads. Means (n = 3) are shown

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Fig. 6
figure 6

Expression of three genes (chlG, psb29, and PRK) related with photosynthesis in M. aeruginosa. White: White light, the control; Red: Red light. Different lowercase letters indicate the significant difference at P < 0.05. Means ± SE (n = 4)

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In Synechococcus sp., the up-regulation was found in three genes related with photosynthetic electron transfer under red light, which led to the improvement of the photosynthetic ability [48]. Under red light, Eustigmatos cf. polyphem improved the expression of 11 genes in PSII assembly, 6 genes in cytochrome (Cyt) b6/f, 2 genes in PSI assembly, and 2 gene in ATP synthase, while blue light lowered their expression [49]. Similarly, red light also raised expression of the genes associated with light-harvesting complex, PSII, Cyt b6/f, PSI, photosynthetic electron transfer, and ATP synthase in grape seedlings [50].

In this study, red light up-regulated the expression of 4 genes (psba, psbn, psb27, and psb29) in PSII assembly, 1 gene (psaM) in PSI assembly, and 3 genes (xfp, rpiA, and PRK) in CO2 fixation compared with white light (Fig. 5B, Supplementary Table 1). psb29 and PRK encoded PSII biogenesis protein Psb29 and phosphoribulokinase, respectively, whose expression levels were increased by 11.6 (P < 0.05) and 8.1 folds (P < 0.05) under red light, respectively (Fig. 6). The up-regulation of these genes was beneficial for M. aeruginosa adsorbing light, transporting photosynthetic electron and fixing CO2, resulting in the high photosynthetic ability under red light (Fig. 2B). In the previous study, purple light also raised the expression of 12 photosynthesis-related genes in M. aeruginosa [48], which should contribute to the improvement of the photosynthetic ability under this light (Fig. 2B).

Related gene expression in primary metabolism under red light

In algal cells, glycolysis and tricarboxylic acid cycle (TCA) are two main pathways for sugar degradation that can be adjusted by light quality. Under red light, the expression of several genes in glycolysis and TCA cycle was down-regulated according to the transcriptome analysis [47, 50], and the activities of several key enzymes, such as hexokinase, pyruvate kinase, and succinic dehydrogenase, in the two pathways were declined [51, 52]. These were not beneficial for sugar degradation under red light, leading to the reduction of intermediate product levels, such as malic acid, citrate, succinic acid, 2-hydroxyglutaric acid, and fumarate [27, 53].

In this study, the down-regulation was found in the expression of eight genes (OA58_RS22055, OA58_RS13225, OA58_RS12055, fbaA, OA58_RS00740, pgk, eno, and OA58_RS17895) in glycolysis and 5 genes (acoB, OA58_RS02030, acnB, OA58_RS16450, and mdh) in TCA cycle under red light (Fig. 7A, Supplementary Table 2). This was consistent with the previous findings in N. oceanica and grape seedlings under red light [47, 50]. fbaA codes for fructose-diphosphate aldolase that cleaves fructose-1,6-bisphosphate to generate glyceraldehyde-3-phosphate and dihydroxyacetone phosphate. Under red light, its relative expression level was declined by 41.9% (P < 0.05) compared to that under white light (Fig. 8). The down-regulation of these genes may reduce the consumption of photosynthetic products in M. aeruginosa under red light, resulting in the biomass accumulation (Fig. 1B).

Fig. 7
figure 7

Expression of the genes in sugar degradation (A), fucose, and starch biosynthesis (B) as well as fatty acid biosynthesis (C) in M. aeruginosa under red light. White: White light, the control; Red: Red light. fpkm: Fragments per kilobase per million mapped reads. Means (n = 3) are shown

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Fig. 8
figure 8

Expression of four genes (fbaA, gmd, glgB, and accD) related with primary metabolism in M. aeruginosa. White: white light, the control; red: red light. Different lowercase letters indicate the significant difference at P < 0.05. Means ± SE (n = 4)

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OA58_RS06690, rfbM, and gmd code for mannose-6-phosphate isomerase, mannose-1-phosphate guanylate transferase, and GDP-d-mannose dehydratase in fucose biosynthesis, respectively. Compared with white light, their expression was significantly raised under red light (Fig. 7B, Supplementary Table 2), and the expression level of gmd was increased by 7.1 folds (P < 0.05) (Fig. 8), which should lead to the synthesis and accumulation of polysaccharides. In exposure to red light, an increase was detected in the content of extracellular polysaccharides in N. flagelliforme by raising expression of mannose-6-phosphate isomerase and improving its activity [29, 42]. In addition, red light also improved monosaccharide composition by raising UDP-glucose pyrophosphorylase activity for providing various sugar nucleotides as the substrates [42].

In starch biosynthesis, phosphoglucomutase is encoded by OA58_RS05120, and catalyzes glucose-6-phosphate to form glucose-1-phosphate. When phosphoglucomutase activity was inhibited, a decrease was detected in the starch content in Gracilariopsis lemaneiformis [54]. For phosphoglucomutase-deficient mutant of Arabidopsis thaliana, no starch was formed in the guard cells [55], whereas phosphoglucomutase overexpression promoted the formation of glucose-1-phosphate, β-1,3-glucan, chrysolaminarin, and starch in Phaeodactylum tricornutum [56]. glgB encodes 1,4-α-glucan branching enzyme, which plays a key role in catalyzing amylose to form starch in plants and glycogen in cyanobacteria. Streptomyces aureofaciens and S. elongatus were two species in cyanobacteria, and the former glgB-disrupted strains and the latter glgB mutant lowered glycogen content by blocking its formation [57, 58]. Under red light, the up-regulation was found in OA58_RS05120 and glgB expression (Fig. 7B, Supplementary Table 2), with the increase of 3.6-fold (P < 0.05) in glgB expression level (Fig. 8). This was consistent with the up-regulation of the two genes and other genes in starch biosynthesis in grape seedlings under red light [50]. The up-regulation of the two genes may facilitate glycogen biosynthesis in M. aeruginosa under red light.

Compared with white light, the expressions of five genes (accD, fabD, fabF, fabG, and fabZ) in fatty acid biosynthesis in M. aeruginosa were remarkably up-regulated under red light (Fig. 7C). accD codes for β-carboxyl transferase subunit of acetyl-CoA carboxylase, which is a rate-limiting enzyme in fatty acid formation [49]. Its expression level was increased by 9.0 folds (P < 0.05) under red light (Fig. 8). Similarly, the up-regulation of these genes was also detected in E. cf. polyphem under red light. Meanwhile, several genes related with lipid formation were also up-regulated [49]. For C. reinhardtii, red light improved three gene expression in fatty acid biosynthesis [59]. The up-regulation of these genes may promote the generation and accumulation of fatty acids and lipids in algal cells (Fig. 4) [43, 49, 59].

In contrast to white light, red light may decline the sugar degradation in M. aeruginosa by lowering the gene expression in glycolysis and TCA cycle. Then, the abundant photosynthetic products that were generated from high photosynthetic abilities might be converted into sugars and glycogen by raising the related gene expression (Figs. 7A, B, 8), leading to the accumulation of soluble and insoluble carbohydrates (Fig. 3). Meanwhile, the abundant photosynthetic products can also be used to form fatty acids by raising the related gene expression (Figs. 7C, 8), resulting in the accumulation of lipids (Fig. 4). This is beneficial for the production of bioethanol and biodiesel by supplying feedstock. Similar with red light, purple light also declined the expression of the genes in glycolysis and TCA cycle (OA58_RS13225, OA58_RS00740, eno, and acoB), and increased expression of the genes in biosynthesis of polysaccharide (rfbm and gmd), glycogen (glgB), and fatty acids (accD) (Supplementary Fig. 3). However, the less altered gene numbers might lead to the lower accumulation of carbohydrates and lipids under purple light in contrast to red light (Figs. 3, 4).

Conclusion

In contrast to white light, the photosynthetic ability in M. aeruginosa was improved under red light by increasing chlorophyll content and promoting related gene expression. Among red, blue, and purple light, red light showed the maximum effect on the biomass accumulation as well as carbohydrate and lipid yield, which may result from the high photosynthetic ability, up-regulation of primary metabolite synthesis-related genes, and down-regulation of sugar degradation-related genes. Therefore, red light should be the optimal light quality for M. aeruginosa accumulating carbohydrates and lipids, which is beneficial for supplying feedstock for biofuel production.

Materials and methods

M. aeruginosa kept under different light qualities

M. aeruginosa (FACHB-912) cells with unicellular morphology were kept in BG11 medium with 16-h light (30 μmol m−2 s−1) and 8-h dark at 25 °C, and the shaker speed was 100 rpm. During mid-logarithmic phase, the cells harvested by centrifugation were transferred into 500 mL conical flask with 300 mL fresh medium under sterile condition, and the cell density was 1 × 107 cells mL−1. Then, the conical flasks were kept under red (630–640 nm), blue (456–466 nm), purple (424–432 nm), and white (400–700 nm) light supplied with LEDs (Shenzhen Changfang Group Co., Ltd., Shenzhen, China). For each light quality, low light intensity (30 μmol m−2 s−1) was set, as high light intensity inhibited the algal growth by causing oxidative stress [60,61,62]. Meanwhile, four conical flasks were treated, with each as a replicate. Among the four treatments, the control was set as the treatment with white light. During the 5-day treatment, the algal cells always existed as unicellular morphology, and their growth, biomass content, photosynthetic ability, carbohydrate, and lipid yield as well as related gene expression were investigated.

Measurement of cell growth and biomass content

M. aeruginosa cell numbers were recorded with a hemocytometer (25 × 16), and the cell density was calculated to indicate the algal growth. The cell cultures in each conical flask were centrifuged at 6000g, and the harvested cells were dried in a freeze dryer. Then, their dry biomass was measured, and the biomass content (mg L−1) was calculated.

Determination of chlorophyll content

Dried algal cells of 5 mg were added into 96% ethanol of 3 mL, and placed in the dark for extracting chlorophyll for 24 h. After centrifugation at 8000g, the absorbance of the extracts was determined at 649 and 665 nm. Then, the chlorophyll content was calculated following the equation of Lichtenthaler and Wellburn [63].

Assay of chlorophyll fluorescence

Chlorophyll fluorescence can well reflect the electron production and transfer in photosystem II (PSII), and indicate plant and alga photosynthetic variations in response to environmental conditions. After centrifugation, the cells obtained from 25 mL of M. aeruginosa cultures were resuspended in 10 µL BG11 medium, and prepared a spot of 0.5 cm2 onto a piece of filter paper [15]. Then, it was kept in darkness for 20 min, and the YAQ-500 chlorophyll fluorescence analyzer (YZQ Technology Co., Beijing, China) was used to measure the PSII electron transfer flux per reaction center (RC) at t = 0 (ETo/RC) according to the description by Gao et al. [64].

Measurement of O2 evolution

M. aeruginosa cultures of 20 mL were added into the measurement chamber of oxygen electrode with magnetic stirring. In the chamber, the light was provided by LED lamp, and the temperature was set at 25 °C. After stabilization, the O2 evolution was measured. Then, the M. aeruginosa cells were collected and dried, and their weight was used to calculate O2 evolution rate (µM µg−1 h−1).

Determination of lipid content

The lipid content was determined according to the method described by Yu et al. [65] with some modifications. Dried algal cells of 80 mg (M0) were used to extract the lipids by adding into 4 mL extracting solution (chloroform: methanol, 2:1) with the aid of a shaker at 80 rpm for 1 h. The mixture was centrifuged at 8000g, and the precipitate was used to extract the lipids again. The deposited algal cells were dried, and their weight was measured and recorded as M1. Then, the lipid content (M) was calculated according to the formula M (%) = (M0 − M1)/M0 × 100%. The lipid concentration (LC) was calculated following the formula LC (mg L−1) = M × BC, where M and BC were the lipid content and biomass content (mg L−1), respectively. For lipid productivity (LP), it was calculated following the equation LP (mg L−1 d−1) = (LC5 − LC0)/5, where LC5 and LC0 were the lipid concentration at day 5 and the initial state, respectively.

Measurement of carbohydrate content

Dried algal cells of 80 mg were ground with a mortar, from which soluble carbohydrates were extracted with 5 mL distilled water at 40 °C for 0.5 h. After centrifugation at 8000g, the supernatant was collected, and the OD490 of the mixture of 1 mL supernatant, 5 mL H2SO4, and 1 mL 6% phenol was recorded to calculate the soluble carbohydrate content (%) following the previous method [15]. Moreover, the precipitate was added to 6 M HCl to degrade the polysaccharides at 100 °C for 0.5 h. After centrifugation at 8000g, the supernatant was collected to determine the insoluble carbohydrate content (%) following the above method for determining soluble carbohydrate content. The total carbohydrate content was the sum of soluble and insoluble carbohydrate content. Carbohydrate concentration (mg L−1) and productivity (mg L−1 d−1) were calculated according to the above formulae for lipid concentration and productivity, respectively.

Transcriptome analysis

Following the previous method [62], 300 mL of M. aeruginosa cultures kept under red and white light for 5 days were centrifuged at 5000g, and used to extract the total RNA. After reverse transcription, the cDNA was carried out adenylation at the 3′ ends, and performed amplification through polymerase chain reaction (PCR) using DNA polymerase, universal PCR primers, and index primers. Then, their sequence was analyzed, and the original data have been uploaded into the NCBI SRA database (PRJNA1058456). The clean data obtained from the original data were used to map the contigs with referring to this species genome [66]. The expression levels of the genes related to chlorophyll biosynthesis, photosynthesis, carbohydrate, and lipid metabolism were analyzed, and the differentially expressed genes with P value < 0.05 and fold change > 1 were annotated.

qRT-PCR analysis

Following above method, the total RNA was extracted from M. aeruginosa cells and carried out reverse transcription to form cDNA. Then, the cDNA was used to determine the expression of seven genes involved in chlorophyll biosynthesis (chlG), photosynthesis (psb29 and PRK), carbohydrate (fbaA, gnd, and glgb), and lipid (accD) metabolism, due to their vital role in the corresponding metabolic process and significant variations in transcriptome data. This analysis was carried out through quantitative real-time PCR (qRT-PCR) method, with the 16 s rRNA gene as the reference gene. Its amplification was performed with an RT-PCR Instrument (QuantStuddlo™ 3, Thermo, USA) using the specific primers which were listed in Supplementary Table 3. The expression changes of these genes were evaluated following the 2−ΔΔCt method [67], ΔΔCt = (Ct target gene − Ct reference gene)red light − (Ct target gene − Ct reference gene)control, where Ct was cycle threshold value and the control was white light treatment. Then, the gene expression level under white light was assigned the value to 1.

Statistical analyses

The differences among the treatments were analyzed with origin 8.0 following the Tukey test in one-way ANOVA.

Availability of data and materials

No datasets were generated or analyzed during the current study.

References

  1. Constantino A, Rodrigues B, Leon R, et al. Alternative chemo-enzymatic hydrolysis strategy applied to different microalgae species for bioethanol production. Algal Res. 2021;56: 102329.

    Article  Google Scholar 

  2. Ferdous K, Uddin MR, Islam R, et al. Potentiality of biodiesel production from non-edible oil: Bangladesh perspective. J Chem Eng. 2014;27:1–5.

    Article  Google Scholar 

  3. Maity S, Mallick N. Bioprospecting marine microalgae and cyanobacteria as alternative feedstocks for bioethanol production. Sustainable Chem Pharm. 2022;29: 100798.

    Article  CAS  Google Scholar 

  4. Joshi S, Mishra S. Recent advances in biofuel production through metabolic engineering. Bioresour Technol. 2022;352: 127037.

    Article  CAS  PubMed  Google Scholar 

  5. Rempel A, de Souza SF, Margarites AC, et al. Bioethanol from Spirulina platensis biomass and the use of residuals to produce biomethane: an energy efficient approach. Bioresour Technol. 2019;288: 121588.

    Article  CAS  PubMed  Google Scholar 

  6. Oyelami S, Olafimihan EO, Okedere OB. Emission characteristics of agricultural and industrial cellulosic wastes blended with gasoline. J Energy Saf Technol. 2020;3:31–6.

    Google Scholar 

  7. Muthuraman VS, Kasianantham N. Valorization opportunities and adaptability assessment of algae based biofuels for futuristic sustainability—a review. Process Saf Environ Prot. 2023;174:694–721.

    Article  CAS  Google Scholar 

  8. Mittal V, Ghosh UK. Optimization of biodiesel production from Spirulina microalgae via nanocatalytic transesterification process. Bioresour Technol Rep. 2023;23: 101504.

    Article  CAS  Google Scholar 

  9. Parmar A, Singh NK, Pandey A, et al. Cyanobacteria and microalgae: a positive prospect for biofuels. Bioresour Technol. 2011;102:10163–72.

    Article  CAS  PubMed  Google Scholar 

  10. Farrokh P, Sheikhpour M, Kasaeian A, et al. Cyanobacteria as an eco-friendly resource for biofuel production: a critical review. Biotechnol Prog. 2019;35: e2835.

    Article  CAS  PubMed  Google Scholar 

  11. Luan G, Zhang S, Lu X. Engineering cyanobacteria chassis cells toward more efficient photosynthesis. Curr Opin Biotechnol. 2019;62:1–6.

    Article  PubMed  Google Scholar 

  12. Deb D, Mallick N, Bhadoria PBS. Analytical studies on carbohydrates of two cyanobacterial species for enhanced bioethanol production along with poly-b-hydroxybutyrate, C-phycocyanin, sodium copper chlorophyllin, and exopolysaccharides as co-products. J Clean Prod. 2019;221:695–709.

    Article  CAS  Google Scholar 

  13. Kim JY, Jung JM, Jung S, et al. Biodiesel from microalgae: recent progress and key challenges. Prog Energy Combust Sci. 2022;93: 101020.

    Article  Google Scholar 

  14. Singh P, Kumar D. Biomass and lipid production potential of cyanobacteria and microalgae isolated from the diverse habitats of Garhwal Himalaya, Uttarakhand, India. Biomass Bioenerg. 2022;162: 106469.

    Article  CAS  Google Scholar 

  15. Zuo Z, Ni B, Yang L. Production of primary metabolites in Microcystis aeruginosa in regulation of nitrogen limitation. Bioresour Technol. 2018;270:588–95.

    Article  CAS  PubMed  Google Scholar 

  16. Burch AR, Franz AK. Combined nitrogen limitation and hydrogen peroxide treatment enhances neutral lipid accumulation in the marine diatom Phaeodactylum tricornutum. Bioresour Technol. 2016;219:559–65.

    Article  CAS  PubMed  Google Scholar 

  17. Ho SH, Chen CY, Chang JS. Effect of light intensity and nitrogen starvation on CO2 fixation and lipid/carbohydrate production of an indigenous microalga Scenedesmus obliquus CNW-N. Bioresour Technol. 2012;113:244–52.

    Article  CAS  PubMed  Google Scholar 

  18. de la Rosa F, De Troch M, Malanga G, et al. Differential sensitivity of fatty acids and lipid damage in Microcystis aeruginosa (cyanobacteria) exposed to increased temperature. Comp Biochem Physiol C Toxicol Pharmacol. 2020;235: 108773.

    Article  PubMed  Google Scholar 

  19. Shahid A, Usman M, Atta Z, et al. Impact of wastewater cultivation on pollutant removal, biomass production, metabolite biosynthesis, and carbon dioxide fixation of newly isolated cyanobacteria in a multiproduct biorefinery paradigm. Bioresour Technol. 2021;333: 125194.

    Article  CAS  PubMed  Google Scholar 

  20. Senatore V, Rueda E, Bellver M, et al. Production of phycobiliproteins, bioplastics and lipids by the cyanobacteria Synechocystis sp. treating secondary effluent in a biorefinery approach. Sci Total Environ. 2023;857: 159343.

    Article  CAS  PubMed  Google Scholar 

  21. Kim SM, Kim JY, Park J, et al. Volatile fatty acid-treated mixotrophic cultivation of lipid/carbohydrate-rich cyanobacterial species, Pseudanabaena mucicola GO0704, for the enhancement of biofuel production. Bioresour Technol. 2023;367: 128066.

    Article  CAS  PubMed  Google Scholar 

  22. Vadiveloo A, Moheimani NR, Cosgrove JJ, et al. Effect of different light spectra on the growth and productivity of acclimated Nannochloropsis sp. (Eustigmatophyceae). Algal Res. 2015;8:121–7.

    Article  Google Scholar 

  23. Li X, Slavens S, Crunkleton DW, et al. Interactive effect of light quality and temperature on Chlamydomonas reinhardtii growth kinetics and lipid synthesis. Algal Res. 2021;53: 102127.

    Article  Google Scholar 

  24. Khattar JIS, Kaur S, Kaushal S, et al. Hyperproduction of phycobiliproteins by the cyanobacterium Anabaena fertilissima PUPCCC 410.5 under optimized culture conditions. Algal Res. 2015;12:463–9.

    Article  Google Scholar 

  25. Kumar J, Parihar P, Singh R, et al. UV-B induces biomass production and nonenzymatic antioxidant compounds in three cyanobacteria. J Appl Phycol. 2016;28:131–40.

    Article  CAS  Google Scholar 

  26. Bachchhav MB, Kulkarni MV, Ingale AG. Enhanced phycocyanin production from Spirulina platensis using light emitting diode. J Inst Eng India Ser E. 2017;98:41–5.

    Article  CAS  Google Scholar 

  27. Han P, Shen S, Wang H, et al. Comparative metabolomic analysis of the effects of light quality on polysaccharide production of cyanobacterium Nostoc flagelliforme. Algal Res. 2015;9:143–50.

    Article  Google Scholar 

  28. Chen CY, Chen YC, Huang HC, et al. Engineering strategies for enhancing the production of eicosapentaenoic acid (EPA) from an isolated microalga Nannochloropsis oceanica CY2. Bioresour Technol. 2013;147:160–7.

    Article  CAS  PubMed  Google Scholar 

  29. Li D, Yuan Y, Cheng D, et al. Effect of light quality on growth rate, carbohydrate accumulation, fatty acid profile and lutein biosynthesis of Chlorella sp. AE10. Bioresour Technol. 2019;291: 121783.

    Article  CAS  PubMed  Google Scholar 

  30. Huang T, Lai M, Lin Z, et al. Identification of algicidal monoterpenoids from four chemotypes of Cinnamomum camphora and their algicidal mechanisms on Microcystis aeruginosa. Environ Res. 2024;241: 117714.

    Article  CAS  PubMed  Google Scholar 

  31. Ashokkumar V, Agila E, Salam Z, et al. A study on large scale cultivation of Microcystis aeruginosa under open raceway pond at semicontinuous mode for biodiesel production. Bioresour Technol. 2014;172:186–93.

    Article  CAS  PubMed  Google Scholar 

  32. Gonçalves AL, Pires JCM, Simões M. Biotechnological potential of Synechocystis salina co-cultures with selected microalgae and cyanobacteria: nutrients removal, biomass and lipid production. Bioresour Technol. 2016;200:279–86.

    Article  PubMed  Google Scholar 

  33. Kaushal S, Singh Y, Khattar JIS, et al. Phycobiliprotein production by a novel cold desert cyanobacterium Nodularia sphaerocarpa PUPCCC 420.1. J Appl Phycol. 2017;29:1819–27.

    Article  CAS  Google Scholar 

  34. Pereira S, Otero A. Effect of light quality on carotenogenic and noncarotenogenic species of the genus Dunaliella under nitrogen deficiency. Algal Res. 2019;44: 101725.

    Article  Google Scholar 

  35. Pereira S, Otero A. Haematococcus pluvialis bioprocess optimization: effect of light quality, temperature and irradiance on growth, pigment content and photosynthetic response. Algal Res. 2020;51: 102027.

    Article  Google Scholar 

  36. Chen HB, Wu JY, Wang CF, et al. Modeling on chlorophyll a and phycocyanin production by Spirulina platensis under various light-emitting diodes. Biochem Eng J. 2010;53:52–6.

    Article  Google Scholar 

  37. Ma R, Lu F, Bi Y, et al. Effects of light intensity and quality on phycobiliprotein accumulation in the cyanobacterium Nostoc sphaeroides Kützing. Biotechnol Lett. 2015;37:1663–9.

    Article  CAS  PubMed  Google Scholar 

  38. Peng X, Wang B, Wang X, et al. Effects of different colored light quality selective plastic films on growth, photosynthetic abilities, and fruit qualities of strawberry. Hortic Sci Technol. 2020;38:462–73.

    CAS  Google Scholar 

  39. Korbee N, Figueroa FL, Aguilera J. Effect of light quality on the accumulation of photosynthetic pigments, proteins and mycosporine-like amino acids in the red alga Porphyra leucosticta (Bangiales, Rhodophyta). J Photoch Photobio B. 2005;80:71–8.

    Article  CAS  Google Scholar 

  40. Zhou X, Zheng T, Xie Y, et al. Astaxanthin accumulation in Microcystis aeruginosa under different light quality. Bioresour Technol. 2022;346: 126629.

    Article  CAS  PubMed  Google Scholar 

  41. Templeton DW, Quinn M, Wychen SV, et al. Separation and quantification of microalgal carbohydrates. J Chromatogr A. 2012;1270:225–34.

    Article  CAS  PubMed  Google Scholar 

  42. Han P, Guo R, Shen S, et al. Proteomic profiling of Nostoc flagelliforme reveals the common mechanism in promoting polysaccharide production by different light qualities. Biochem Eng J. 2018;132:68–78.

    Article  CAS  Google Scholar 

  43. Pang N, Fu X, Fernandez JSM, et al. Multilevel heuristic LED regime for stimulating lipid and bioproducts biosynthesis in Haematococcus pluvialis under mixotrophic conditions. Bioresour Technol. 2019;288:1–8.

    Article  Google Scholar 

  44. Pagels F, Bonomi-Barufi J, Vega J, et al. Light quality triggers biochemical modulation of Cyanobium sp.—photobiology as tool for biotechnological optimization. J Appl Phycol. 2020;32:2851–61.

    Article  CAS  Google Scholar 

  45. Dong M, Zhang Y, Yu Q, et al. Regulation of light quality on lipid production, biodiesel quality, and nutritional quality of Phaeodactylum tricornutum. Aquacult Int. 2023;31:1231–51.

    Article  CAS  Google Scholar 

  46. Yue C, Wang Z, Yang P. Review: the effect of light on the key pigment compounds of photosensitive etiolated tea plant. Bot Stud. 2021;62:21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Wei L, You W, Gong Y, et al. Transcriptomic and proteomic choreography in response to light quality variation reveals key adaption mechanisms in marine Nannochloropsis oceanica. Sci Total Environ. 2020;720: 137667.

    Article  CAS  PubMed  Google Scholar 

  48. Kim NN, Shin HS, Park HG, et al. Profiles of photosynthetic pigment accumulation and expression of photosynthesis-related genes in the marine cyanobacteria Synechococcus sp.: effects of LED wavelengths. Biotechnol Bioproc E. 2014;19:250–6.

    Article  CAS  Google Scholar 

  49. Zhang H, Chen A, Huang L, et al. Transcriptomic analysis unravels the modulating mechanisms of the biomass and value-added bioproducts accumulation by light spectrum in Eustigmatos cf. polyphem (Eustigmatophyceae). Bioresour Technol. 2021;338: 125523.

    Article  CAS  PubMed  Google Scholar 

  50. Dong T, Zhang P, Hakeem A, et al. Integrated transcriptome and metabolome analysis reveals the physiological and molecular mechanisms of grape seedlings in response to red, green, blue, and white LED light qualities. Environ Exp Bot. 2023;21: 105441.

    Article  Google Scholar 

  51. Gao X, Zhang M, Tian H, et al. Effect of LED light quality on respiratory metabolism and activities of related enzymes of Haliotis discus hannai. Aquaculture. 2016;452:52–61.

    Article  CAS  Google Scholar 

  52. Zhong M, Liu X, Xu R, et al. Effects of light quality and intensity on the juvenile physiological metabolism of Babylonia areolate. Aquaculture Rep. 2023;33: 101758.

    Article  Google Scholar 

  53. Palma CFF, Castro-Alves V, Morales LO, et al. Metabolic changes in cucumber leaves are enhanced by blue light but differentially affected by UV interactions with light signalling pathways in the visible spectrum. Plant Sci. 2022;321: 111326.

    Article  CAS  PubMed  Google Scholar 

  54. Chen Q, Yu X, Liu S, et al. Identification, characteristics and function of phosphoglucomutase (PGM) in the agar biosynthesis and carbon flux in the agarophyte Gracilariopsis lemaneiformis (Rhodophyta). Mar Drugs. 2022;2:442.

    Article  Google Scholar 

  55. Lascève G, Leymarie J, Vavasseur A. Alterations in light-induced stomatal opening in a starch-deficient mutant of Arabidopsis thaliana L. deficient in chloroplast phosphoglucomutase activity. Plant Cell Environ. 1997;20:350–8.

    Article  Google Scholar 

  56. Yang YF, Li DW, Chen TT, et al. Overproduction of bioactive algal chrysolaminarin by the critical carbon flux regulator phosphoglucomutase. Biotechnol J. 2019;14:1800220.

    Article  Google Scholar 

  57. Homerová D, Benada O, Kofroňová O, et al. Disruption of a glycogen-branching enzyme gene, glgB, specifically affects the sporulation-associated phase of glycogen accumulation in Streptomyces aureofaciens. Microbiology. 1996;142:1201–8.

    Article  PubMed  Google Scholar 

  58. Shinde S, Zhang X, Singapuri SP, et al. Glycogen metabolism supports photosynthesis start through the oxidative pentose phosphate pathway in cyanobacteria. Plant Physiol. 2020;182:507–17.

    Article  CAS  PubMed  Google Scholar 

  59. Gaytán-Luna DE, Ochoa-Alfaro AE, Rocha-Uribe A, et al. Effect of green and red light in lipid accumulation and transcriptional profile of genes implicated in lipid biosynthesis in Chlamydomonas reinhardtii. Biotechnol Prog. 2016;32:1404–11.

    Article  PubMed  Google Scholar 

  60. Yang Z, Geng L, Wang W, et al. Combined effects of temperature, light intensity, and nitrogen concentration on the growth and polysaccharide content of Microcystis aeruginosa in batch culture. Biochem Systemat Ecol. 2012;41:130–5.

    Article  CAS  Google Scholar 

  61. Zheng T, Zhou M, Yang L, et al. Effects of high light and temperature on Microcystis aeruginosa cell growth and β-cyclocitral emission. Ecotox Environ Safe. 2020;192: 110313.

    Article  CAS  Google Scholar 

  62. Huang L, Hu R, Xie Y, et al. High light and temperature enhancing astaxanthin production in Microcystis aeruginosa—providing a potential thought for controlling cyanobacterial blooms through resource utilization. J Water Process Eng. 2023;56: 104318.

    Article  Google Scholar 

  63. Lichtenthaler HK, Wellburn AR. Determination of total carotenoids and chlorophylls a and b of leaf in different solvents. Biochem Soc Trans. 1983;11:591–2.

    Article  CAS  Google Scholar 

  64. Gao P, Zuo Z, Wu X, et al. Effects of cycloheximide on photosynthetic abilities, reflectance spectra and fluorescence emission spectra in Phyllostachys edulis. Trees - Struct Funct. 2016;30:719–32.

    Article  CAS  Google Scholar 

  65. Yu X, Zhao P, He C, et al. Isolation of a novel strain of Monoraphidium sp. and characterization of its potential application as biodiesel feedstock. Bioresour Technol. 2012;121:256–62.

    Article  CAS  PubMed  Google Scholar 

  66. Yamaguchi H, Suzuki S, Osana Y, et al. Complete genome sequence of Microcystis aeruginosa NIES-2481 and common genomic features of group G M. aeruginosa. J Genomics. 2018;6:30–3.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Huang L, Du X, Jin Z, et al. Accumulation of astaxanthin in Microcystis aeruginosa under NaCl and KCl stresses. Bioresour Technol. 2024;403: 130898.

    Article  CAS  PubMed  Google Scholar 

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Funding

This research was supported by the National Natural Science Foundation of China (No. 32371830, 31870585), Zhejiang Provincial Natural Science Foundation of China (LZ24C160001), National Students’ Innovation and Entrepreneurship Training Program (No. 202410341043, 202410341081X, 202310341030), and Student Science and Technology Innovation Activity and New Talent Plan of Zhejiang Province (No. 2023R412027).

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  1. Wangbo Chen and Sun Xu have contributed equally to this work.

Authors and Affiliations

  1. State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou, 311300, China

    Wangbo Chen, Sun Xu, Shuzhen Zou, Zijian Liu, Yichi Liu, Haozhe Xu, Jiayue Wang, Junjie Ma & Zhaojiang Zuo

  2. Zhejiang Provincial Key Laboratory of Forest Aromatic Plants-Based Healthcare Functions, Zhejiang A&F University, Hangzhou, 311300, China

    Wangbo Chen, Sun Xu, Shuzhen Zou, Zijian Liu, Yichi Liu, Haozhe Xu, Jiayue Wang, Junjie Ma & Zhaojiang Zuo

  3. College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou, 311300, China

    Wangbo Chen, Sun Xu, Shuzhen Zou, Zijian Liu, Yichi Liu, Haozhe Xu, Jiayue Wang, Junjie Ma, Rong Chen & Zhaojiang Zuo

  4. Jixian Honors College, Zhejiang A&F University, Hangzhou, 311300, China

    Wangbo Chen

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Wangbo Chen: methodology, investigation, and data curation. Sun Xu: methodology, investigation, and data curation. Shuzhen Zou: investigation. Zijian Liu: investigation. Yichi Liu: investigation. Haozhe Xu: investigation. Jiayue Wang: investigation. Junjie Ma: investigation. Rong Chen: supervision. Zhaojiang Zuo: writing—review & editing, writing—original draft, supervision, and conceptualization.

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Supplementary Information

13068_2025_2615_MOESM1_ESM.docx

Supplementary Material 1. Fig. 1 The size of M. aeruginosa under white (A), purple (B), blue (C) and red (D) light for 5 days. E and F: The length and width, respectively. The algal cells were taken photos with a photomicrographic system, and their length and width were measured with the software of ImageJ. There were no significant (P < 0.05) difference in the length and width among the treatments. Means ± SE (n = 4). Fig. 2 Staining images of polysaccharides in M. aeruginosa under white (A), purple (B), blue (C) and red (D) light for 5 days. M. aeruginosa cells were harvested and stained with Periodic Acid-Schiff Staining Kit (Beyotime Biotechnology Co., LTD, China) according to the instruction, and the purple color indicated polysaccharides. Among the 4 light qualities, the cells under red light showed dark purple color, following by the treatment with purple, white and blue light. This indicated that red light had the strongest effect on polysaccharide accumulation, but the weakest effect under blue light. There was remarkable purple color in the cells rather than the outside, suggesting that the accumulated polysaccharides should be mainly intracellular polysaccharides. Fig. 3 Alterations of genes in sugar degradation (A), fucose and starch biosynthesis (B) as well as fatty acid biosynthesis (C) in M. aeruginosa under purple light for 5 days. These gene expression was from previous transcriptome data for revealing the astaxanthin accumulation in the alga under purple light [40], but without publication. Purple light was also altered the gene expression in primary metabolism, but the altered gene numbers were less than red light treatment, with 4 in glycolysis, 2 in fucose synthesis, 1 in starch synthesis, and 1 in fatty acid synthesis. This might cause the weaker promoting effect of purple light on primary metabolite accumulation in contrast to red light. White: White light, the control; Purple: Purple light. fpkm: Fragments per kilobase per million mapped reads. Means (n = 3) are shown.

Supplementary Material 2.

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Chen, W., Xu, S., Zou, S. et al. Carbohydrate and lipid yield in Microcystis aeruginosa for biofuel production under different light qualities. Biotechnol. Biofuels Bioprod. 18, 36 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13068-025-02615-8

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Biotechnology for Biofuels and Bioproducts

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