Timing is everything: Diel metabolic and physiological changes in the diatom Cyclotella cryptica grown in simulated outdoor conditions

Resolving the dynamics of chrysolaminarin regulation in a marine diatom: A physiological and transcriptomic study

Diatom containing different active biological macromolecules are thought to be an excellent microbial cell factory. Phaeodactylum tricornutum, a model diatom, is a superb chassis organism accumulating chrysolaminarin with important bioactivities. However, the characteristic of chrysolaminarin accumulation and molecular mechanism of the fluctuated chrysolaminarin in diatom are still unknown. In this study, physiological data and transcriptomic analysis were carried out to clarify the mechanism involved in chrysolaminarin fluctuation. The results showed that chrysolaminarin content fluctuated, from 7.41 % dry weight (DW) to 40.01 % DW during one light/dark cycle, increase by day and decrease by night. The similar fluctuated characteristic was also observed in neutral lipid content. Genes related to the biosynthesis of chrysolaminarin and neutral lipid were up-regulated at the beginning of light-phase, explaining the accumulation of these biological macromolecules. Furthermore, genes involved in carbohydrate degradation, cell cycle, DNA replication and mitochondria-localized β-oxidation were up-regulated at the end of light phase and at the beginning of dark phase hinting an energy transition of carbohydrate to cell division during the dark period. Totally, our findings provide important information for the regulatory mechanism in the diurnal fluctuation of chrysolaminarin. It would also be of great help for the mass production of economical chrysolaminarin in marine diatom.

Microalgal cultivation characteristics using commercially available air-cushion packaging material as a photobioreactor

Air-cushion (AC) packaging has become widely used worldwide. ACs are air-filled, dual plastic packaging solutions commonly found surrounding and protecting items of value within shipping enclosures during transit. Herein, we report on a laboratory assessment employing ACs as a microalgal photobioreactor (PBR). Such a PBR inherently addresses many of the operational issues typically encountered with open raceway ponds and closed photobioreactors, such as evaporative water loss, external contamination, and predation. Using half-filled ACs, the performance of microalgal species Chlorella vulgaris, Nannochloropsis oculata, and Cyclotella cryptica (diatom) was examined and the ash-free dry cell weight and overall biomass productivity determined to be 2.39 g/L and 298.55 mg/L/day for N. oculata, 0.85 g/L and 141.36 mg/L/day for C. vulgaris, and 0.67 g/L and 96.08 mg/L/day for C. cryptica. Furthermore, maximum lipid productivity of 25.54 mg/L/day AFDCW and carbohydrate productivity of 53.69 mg/L/day AFDCW were achieved by C. cryptica, while maximum protein productivity of 247.42 mg/L/day AFDCW was attained by N. oculata. Data from this work will be useful in determining the applicability and life-cycle profile of repurposed and reused ACs as potential microalgal photobioreactors depending upon the end product of interest, scale utilized, and production costs.

Hierarchical deep learning model to simulate phytoplankton at phylum/class and genus levels and zooplankton at the genus level

Harmful algal blooms (HABs) have become a global issue, affecting public health and water industries in numerous countries. Because funds for monitoring HABs are limited, model development may be an alternative approach for understanding and managing HABs. Continuous monitoring based on grab sampling is time-consuming, costly, and labor-intensive. However, improving simulation performance remains a major challenge in modeling, and current methods are limited to simulating phytoplankton (e.g., Microcystis sp., Anabaena sp., Aulacoseira sp., Cyclotella sp., Pediastrum sp., and Eudorina sp.) and zooplankton (e.g., Cyclotella sp., Pediastrum sp., and Eudorina sp.) at the genus level. The traditional modeling approach is limited for evaluating the interactions between phytoplankton and zooplankton. Recently, deep learning (DL) models have been proposed for solving modeling problems because of their large data handling capabilities and model structure flexibilities. In this study, we evaluated the applicability of DL for simulating phytoplankton at the phylum/class and genus levels and zooplankton at the genus level. Our work was an explicit representation of the taxonomic and ecological hierarchy of the DL model structure. The prerequisite for this model design was the data collection at two taxonomic and hierarchical levels. Our model consisted of hierarchical DL with classification transformer (TF) and regression TF models. These DL models were hierarchically connected; the output of the phylum/class level model was transferred to the genus level simulation model, and the output of the genus level model was fed into the zooplankton simulation model. The classification TF model determined the phytoplankton occurrence initiation date, whereas the regression TF model quantified the cell concentration of plankton. The hierarchical DL showed potential to simulate phytoplankton at the phylum/class and genus levels by producing average R^2, and root mean standard error values of 0.42 and 0.83 [log(cells mL^-1)], respectively. All simulated plankton results closely matched the measured concentrations. Particularly, the simulated cyanobacteria showed good agreement with the measured cell concentration, with an R² value of 0.72. In addition, our simulated result showed good agreement in peak concentration compared to observations. However, a limitation remained in following the temporal variation of Tintinnopsis sp. and Bosmia sp. Using an importance map from the TF model, water temperature, total phosphorus, and total nitrogen were identified as significant variables influencing phytoplankton and zooplankton blooms. Overall, our study demonstrated that DL can be used for modeling HABs at the phylum/class and genus levels.

Screening and application of Chlorella strains on biosequestration of the power plant exhaust gas evolutions of biomass growth and accumulation of toxic agents

To use microalgae for the biosequestration of carbon dioxide (CO₂) emitted from the coal-fired power plants, the screening of high CO₂ tolerant microalgae and their accumulation of toxic agents have attracted significant research attention. This study evaluated 10 Chlorella strains for high CO₂ tolerance using combined growth rates and growth periods subjected to logistic parameters. We selected LAMB 31 with high r (0.89 ± 0.10 day^-1), high k (6.51 ± 0.19), and medium Tp (5.17 ± 0.15 day) as a candidate for CO₂ biosequestration. Correspondingly, six genes involving carbon fixation and metabolism processes were upregulated in LAMB 31 under high CO₂ conditions, verifying its high CO₂ tolerant ability. LAMB 31 cultures exposed to exhaust gas of power plant under different flow rates grew well, but the high flow rate (0.6 L/h) showed inhibition effects compared with low flow rates (0.2 and 0.3 L/h) at the end of the culturing period. The toxic agents in the exhaust gas including sulfur, arsenic, and mercury accumulated in LAMB 31 biomass but were deemed safe for use in the production of both human food and animal feed based on the National Food Safety Standard in China. This study showed a complete process involving high CO₂ tolerant microalgae screening, high CO₂ tolerant verification, and in situ application in a power plant. Data results provide valuable information as the basis for future research studies in microalgae application on CO₂ mitigation at emission sources.

Towards industrial production of microalgae without temperature control: The effect of diel temperature fluctuations on microalgal physiology

Regions that offer high levels of sunlight are ideal to produce microalgae. However, as a result of high light intensities, the temperature in photobioreactors can reach temperatures up to 50 °C. Control of temperature is essential to avoid losses on biomass productivity but should be limited to a minimum to avoid high energy requirements for cooling. Our objective is to develop a production process in which cooling is not required. We studied the behaviour of thermotolerant microalgae Picochlorum sp. (BPE23) under four diel temperature regimes, with peak temperatures from 30 °C up to a maximum of 47.5 °C. The highest growth rate of 0.17 h^-1 was obtained when applying a daytime peak temperature of 40 °C. Operating photobioreactors in tropical regions, with a maximal peak temperature of 40 °C, up from 30 °C, reduces microalgae production costs by 26.2 %, based on simulations with a pre-existing techno-economic model. Cell pigmentation was downregulated under increasingly stressful temperatures. The fatty acid composition of cell membranes was altered under increasing temperatures to contain shorter fatty acids with a higher level of saturation. Our findings show that the level of temperature control impacts the biomass yield and composition of the microalgae.