Biodiesel production from marine microalgae has received much attention as microalgae can be cultivated on non-arable land without the use of potable water, and with the additional benefits of mitigating CO₂ emissions and yielding biomass. However, there is still a lack of effective operational strategies to promote lipid accumulation in marine microalgae, which are suitable for making biodiesel since they are mainly composed of saturated and monounsaturated fatty acids. Moreover, the regulatory mechanisms involved in lipid biosynthesis in microalgae under environmental stress are not well understood. In this work, the combined effects of salinity and nitrogen depletion stresses on lipid accumulation of a newly isolated marine microalga, Chlamydomonas sp. JSC4, were explored. Metabolic intermediates were profiled over time to observe transient changes during the lipid accumulation triggered by the combination of the two stresses. An innovative cultivation strategy (denoted salinity-gradient operation) was also employed to markedly improve the lipid accumulation and lipid quality of the microalga, which attained an optimal lipid productivity of 223.2 mg L^-1 d^-1 and a lipid content of 59.4% per dry cell weight. This performance is significantly higher than reported in most related studies. This work demonstrated the synergistic integration of biological and engineering technologies to develop a simple and effective strategy for the enhancement of oil production in marine microalgae.

Marine microalgae are among the most promising lipid sources for biodiesel production because they can be grown on nonarable land without the use of potable water. Marine microalgae also harvest solar energy efficiently with a high growth rate, converting CO2 into lipids stored in the cells. Both light intensity and nitrogen availability strongly affect the growth, lipid accumulation, and fatty acid composition of oleaginous microalgae. However, very few studies have systematically examined how to optimize lipid productivity by adjusting irradiance intensity, and the metabolic dynamics that may lead to improved lipid accumulation in microalgae have not been elucidated. Little is known about the mechanism of lipid synthesis regulation in microalgae. Moreover, few studies have assessed the potential of using marine microalgae as oil producers. In this work, a newly isolated marine microalga, Chlamydomonas sp. JSC4, was selected as a potential lipid producer, and the effect of photobioreactor operations on cell growth and lipid production was investigated. The combined effects of light intensity and nitrogen depletion stresses on growth and lipid accumulation were further explored in an effort to markedly improve lipid production and quality. The optimal lipid productivity and content attained were 312 mg L−1 d−1 and 43.1% per unit dry cell weight, respectively. This lipid productivity is the highest ever reported for marine microalgae. Metabolic intermediates were profiled over time to observe transient changes during lipid accumulation triggered by combined stresses. Finally, metabolite turnover was also assessed using an in vivo 13C-labeling technique to directly measure the flow of carbon during lipid biosynthesis under stress associated with light intensity and nitrogen deficiency. This work demonstrates the synergistic integration of cultivation and dynamic metabolic profiling technologies to develop a simple and effective strategy for enhancing oil production in a marine microalga. The knowledge obtained from this study could be useful in assessing the feasibility of marine microalgae biodiesel production and for understanding the links between dynamic metabolic profiles and lipid biosynthesis during the course of microalgal cultivation.

Microflora is actively used to produce value-added materials in industry, and each cell density should be controlled for stable microflora use. In this study, a simple system evaluating the cell density was constructed with artificial intelligence (AI) using the absorbance spectra data of microflora. To set up the system, the prediction system for cell density based on machine learning was constructed using the spectra data as the feature from the mixture of Saccharomyces cerevisiae and Chlamydomonas reinhardtii. As the results of predicting cell density by extremely randomized trees, when the cell densities of S. cerevisiae and C. reinhardtii were shifted and fixed, the coefficient of determination (R²) was 0.8495; on the other hand, when the cell densities of S. cerevisiae and C. reinhardtii were fixed and shifted, the R² was 0.9232. To explain the prediction system, the randomized trees regressor of the decision tree-based ensemble learning method as the machine learning algorithm and Shapley additive explanations (SHAPs) as the explainable AI (XAI) to interpret the features contributing to the prediction results were used. As a result of the SHAP analyses, not only the optical density, but also the absorbance of the Soret and Q bands derived from the chloroplasts of C. reinhardtii could contribute to the prediction as the features. The simple cell density evaluating system could have an industrial impact.

Green alga Chlamydomonas reinhardtii has gained interest as a sustainable resource because it can be easily grown using CO₂ as a carbon source owing to its high CO₂ assimilating activity. Although the robustness of the cell wall of C. reinhardtii makes it difficult to extract its intracellular products, such property is beneficial when using the cell as an ingredient to fabricate “cell-plastic” in this study. The cell layer, which is a component of the cell-plastic, was prepared with an intercellular filler to connect each cell because C. reinhardtii is a single-cell strain. The cell layers were then repeatedly piled to increase the strength of the cell-plastic. To avoid slippage between the cell layers, they were covered with a small amount of a two-dimensional polymer to maintain the flat surface structure of the cell-plastic. Based on the evaluation, the cell-plastic has the potential to be a novel, sustainable plastic using ubiquitous green algal cells in nature.

Saccharomyces cerevisiae has the potential to produce value-added chemicals; however, this strain is restricted by using glycerol as a carbon source. Although acclimatization of S. cerevisiae as a glycerol-assimilating strain was confirmed so far, the reason why S. cerevisiae can be acclimatized was not clear in detail with limited information on the metabolic changes. In this report, glycerol-assimilating strains from S. cerevisiae BY4741 were isolated, and the biomass production, ethanol fermentation, and transcription levels related to glycolysis and the tricarboxylic acid cycle under aerobic and slightly anaerobic conditions were analyzed. As the results show, although µmax was equal to 0.15 h⁻¹ between wildtype and glycerol-assimilating strains in an aerobic culture including glucose, the differences in max biomass production and percentage yields of ethanol and transcription levels between the two strains were shown. In slightly anaerobic culture, the differences in transcription levels downstream of glycolysis were also displayed. In the case of the glycerol-assimilating strain with glycerol under aerobic conditions, although the transcription levels related to ethanol production were sufficient, the ethanol production was not detected. Additionally, the biomass production reached a plateau even in the culture containing sufficient glycerol, indicating that the redox imbalance even in the cells of the glycerol-acclimatized strain could disturb the utilization of glycerol. The obtained knowledge will promote the use of glycerol resources with the glycerol-acclimatized S. cerevisiae in view of carbon recycling.

In the current system, the disposal of plastic materials causes serious environmental pollution such as the generation of carbon dioxide and destruction of the ecosystem by micro-plastics. To solve this problem, bioplastics, biomass and biodegradable plastics have been developed. As part of our research, we have developed novel bioplastics called “cell-plastics”, in which a unicellular green algal cell serves as a fundamental resource. The production of the cell-plastics would be expected to reduce environmental impact due to the usage of a natural product. Herein, to overcome the mechanical strength of cell-plastics, we used thermosetting epoxy and urethane resins containing Chlorella sp. as the green algae. We successfully fabricated thermosetting resins with a Chlorella sp. content of approximately 70 wt% or more. IR measurements revealed that the chemical structure of an epoxide or isocyanate monomer mixed with Chlorella sp. was modified, which suggests that the resins were hardened by the chemical reaction. In addition, we investigated the effect of thermosetting conditions such as temperature and compression for curing both resins. It was revealed that the Young’s moduli and tensile strengths were controlled by thermosetting temperature and compression, whereas the elongation ratios of the resins were constant at low values regardless of the conditions.