Metabolic modelling and simulation of the light and dark metabolism of Chlamydomonas reinhardtii

A high throughput array microhabitat platform reveals how light and nitrogen colimit the growth of algal cells

A mechanistic understanding of algal growth is essential for maintaining a sustainable environment in an era of climate change and population expansion. It is known that algal growth is tightly controlled by complex interactive physical and chemical conditions. Many mathematical models have been proposed to describe the relation of algal growth and environmental parameters, but experimental verification has been difficult due to the lack of tools to measure cell growth under precise physical and chemical conditions. As such, current models depend on the specific testing systems, and the fitted growth kinetic constants vary widely for the same organisms in the existing literature. Here, we present a microfluidic platform where both light intensity and nutrient gradients can be well controlled for algal cell growth studies. In particular, light shading is avoided, a common problem in macroscale assays. Our results revealed that light and nitrogen colimit the growth of algal cells, with each contributing a Monod growth kinetic term in a multiplicative model. We argue that the microfluidic platform can lead towards a general culture system independent algal growth model with systematic screening of many environmental parameters. Our work advances technology for algal cell growth studies and provides essential information for future bioreactor designs and ecological predictions.

Optimization of nutrient utilization efficiency and productivity for algal cultures under light and dark cycles using genome-scale model process control

Algal cultivations are strongly influenced by light and dark cycles. In this study, genome-scale metabolic models were applied to optimize nutrient supply during alternating light and dark cycles of Chlorella vulgaris. This approach lowered the glucose requirement by 75% and nitrate requirement by 23%, respectively, while maintaining high final biomass densities that were more than 80% of glucose-fed heterotrophic culture. Furthermore, by strictly controlling glucose feeding during the alternating cycles based on model-input, yields of biomass, lutein, and fatty acids per gram of glucose were more than threefold higher with cycling compared to heterotrophic cultivation. Next, the model was incorporated into open-loop and closed-loop control systems and compared with traditional fed-batch systems. Closed-loop systems which incorporated a feed-optimizing algorithm increased biomass yield on glucose more than twofold compared to standard fed-batch cultures for cycling cultures. Finally, the performance was compared to conventional proportional-integral-derivative (PID) controllers. Both simulation and experimental results exhibited superior performance for genome-scale model process control (GMPC) compared to traditional PID systems, reducing the overall measured value and setpoint error by 80% over 8 h. Overall, this approach provides researchers with the capability to enhance nutrient utilization and productivity of cell factories systematically by combining genome-scale models and controllers into an integrated platform with superior performance to conventional fed-batch and PID methodologies.

Visualising the Emerging Platform of Using Microalgae as a Sustainable Bio-Factory for Healthy Lipid Production through Biocompatible AIE Probes

Nowadays, a particular focus is using microalgae to get high-valued health beneficiary lipids. The precise localisation of the lipid droplets (LDs) and biochemical changes are crucial to portray the lipid production strategy in algae, but it requires an in vivo tool to rapidly visualise LD distribution. As a novel strategy, this study focuses on detecting lipid bioaccumulation in a green microalga, Chlamydomonas reinhardtii using the aggregation-induced emission (AIE) based probe, 2-DPAN (C24H18N2O). As the messenger molecule and stress biomarker, hydrogen peroxide (H2O2) activity was detected in lipid synthesis with the AIE probe, TPE-BO (C38H42B2O4). Distinctive LDs labelled with 2-DPAN have elucidated the lipid inducing conditions, where more health beneficiary α-linolenic acid has been produced. TPE-BO labelled H2O2 have clarified the involvement of H2O2 during lipid biogenesis. The co-staining procedure with traditional green BODIPY dye and red chlorophyll indicates that 2-DPAN is suitable for multicolour LD imaging. Compared with BODIPY, 2-DPAN was an efficient sample preparation technique without the washing procedure. Thus, 2-DPAN could improve traditional fluorescent probes currently used for lipid imaging. In addition, the rapid, wash-free, multicolour AIE-based in vivo probe in the study of LDs with 2-DPAN could advance the research of lipid production in microalgae.

Production of polyunsaturated fatty acids by Schizochytrium (Aurantiochytrium) spp

Diverse health benefits are associated with dietary consumption of omega-3 long-chain polyunsaturated fatty acids (ω-3 LC-PUFA), particularly docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA). Traditionally, these fatty acids have been obtained from fish oil, but limited supply, variably quality, and an inability to sustainably increase production for a rapidly growing market, are driving the quest for alternative sources. DHA derived from certain marine protists (heterotrophic thraustochytrids) already has an established history of commercial production for high-value dietary use, but is too expensive for use in aquaculture feeds, a much larger potential market for ω-3 LC-PUFA. Sustainable expansion of aquaculture is prevented by its current dependence on wild-caught fish oil as the source of ω-3 LC-PUFA nutrients required in the diet of aquacultured animals. Although several thraustochytrids have been shown to produce DHA and EPA, there is a particular interest in Schizochytrium spp. (now Aurantiochytrium spp.), as some of the better producers. The need for larger scale production has resulted in development of many strategies for improving productivity and production economics of ω-3 PUFA in Schizochytrium spp. Developments in fermentation technology and metabolic engineering for enhancing LC-PUFA production in Schizochytrium spp. are reviewed.

Data Management and Modeling in Plant Biology

The study of plant-environment interactions is a multidisciplinary research field. With the emergence of quantitative large-scale and high-throughput techniques, amount and dimensionality of experimental data have strongly increased. Appropriate strategies for data storage, management, and evaluation are needed to make efficient use of experimental findings. Computational approaches of data mining are essential for deriving statistical trends and signatures contained in data matrices. Although, current biology is challenged by high data dimensionality in general, this is particularly true for plant biology. Plants as sessile organisms have to cope with environmental fluctuations. This typically results in strong dynamics of metabolite and protein concentrations which are often challenging to quantify. Summarizing experimental output results in complex data arrays, which need computational statistics and numerical methods for building quantitative models. Experimental findings need to be combined by computational models to gain a mechanistic understanding of plant metabolism. For this, bioinformatics and mathematics need to be combined with experimental setups in physiology, biochemistry, and molecular biology. This review presents and discusses concepts at the interface of experiment and computation, which are likely to shape current and future plant biology. Finally, this interface is discussed with regard to its capabilities and limitations to develop a quantitative model of plant-environment interactions.

Towards model-driven characterization and manipulation of plant lipid metabolism

Plant lipids have versatile applications and provide essential fatty acids in human diet. Therefore, there has been a growing interest to better characterize the genetic basis, regulatory networks, and metabolic pathways that shape lipid quantity and composition. Addressing these issues is challenging due to context-specificity of lipid metabolism integrating environmental, developmental, and tissue-specific cues. Here we systematically review the known metabolic pathways and regulatory interactions that modulate the levels of storage lipids in oilseeds. We argue that the current understanding of lipid metabolism provides the basis for its study in the context of genome-wide plant metabolic networks with the help of approaches from constraint-based modeling and metabolic flux analysis. The focus is on providing a comprehensive summary of the state-of-the-art of modeling plant lipid metabolic pathways, which we then contrast with the existing modeling efforts in yeast and microalgae. We then point out the gaps in knowledge of lipid metabolism, and enumerate the recent advances of using genome-wide association and quantitative trait loci mapping studies to unravel the genetic regulations of lipid metabolism. Finally, we offer a perspective on how advances in the constraint-based modeling framework can propel further characterization of plant lipid metabolism and its rational manipulation.

Improved Algal Toxicity Test System for Robust Omics-Driven Mode-of-Action Discovery in Chlamydomonas reinhardtii

Algae are key components of aquatic food chains. Consequently, they are internationally recognised test species for the environmental safety assessment of chemicals. However, existing algal toxicity test guidelines are not yet optimized to discover molecular modes of action, which require highly-replicated and carefully controlled experiments. Here, we set out to develop a robust, miniaturised and scalable Chlamydomonas reinhardtii toxicity testing approach tailored to meet these demands. We primarily investigated the benefits of synchronised cultures for molecular studies, and of exposure designs that restrict chemical volatilisation yet yield sufficient algal biomass for omics analyses. Flow cytometry and direct-infusion mass spectrometry metabolomics revealed significant and time-resolved changes in sample composition of synchronised cultures. Synchronised cultures in sealed glass vials achieved adequate growth rates at previously unachievably-high inoculation cell densities, with minimal pH drift and negligible chemical loss over 24-h exposures. Algal exposures to a volatile test compound (chlorobenzene) yielded relatively high reproducibility of metabolic phenotypes over experimental repeats. This experimental test system extends existing toxicity testing formats to allow highly-replicated, omics-driven, mode-of-action discovery.

Light/dark cycling causes delayed lipid accumulation and increased photoperiod-based biomass yield by altering metabolic flux in oleaginous Chlamydomonas sp

BACKGROUND

Light/dark cycling is an inevitable outdoor culture condition for microalgal biofuel production; however, the influence of this cycling on cellular lipid production has not been clearly established. The general aim of this study was to determine the influence of light/dark cycling on microalgal biomass production and lipid accumulation. To achieve this goal, specific causative mechanisms were investigated using a metabolomics approach. Laboratory scale photoautotrophic cultivations of the oleaginous green microalga Chlamydomonas sp. JSC4 were performed under continuous light (LL) and light/dark (LD) conditions.

RESULTS

Lipid accumulation and carbohydrate degradation were delayed under the LD condition compared with that under the LL condition. Metabolomic analysis revealed accumulation of phosphoenolpyruvate and decrease of glycerol 3-phosphate under the LD condition, suggesting that the imbalance of these metabolites is a source of delayed lipid accumulation. When accounting for light dosage, biomass yield under the LD condition was significantly higher than that under the LL condition. Dynamic metabolic profiling showed higher levels of lipid/carbohydrate anabolism (including production of 3-phosphoglycerate, fructose 6-phosphate, glucose 6-phosphate, phosphoenolpyruvate and acetyl-CoA) from CO₂ under the LD condition, indicating higher CO₂ fixation than that of the LL condition.

CONCLUSIONS

Photoperiods define lipid accumulation and biomass production, and light/dark cycling was determined as a critical obstacle for lipid production in JSC4. Conversions of phosphoenolpyruvate to pyruvate and 3-phosphoglycerate to glycerol 3-phosphate are the candidate rate-limiting steps responsible for delayed lipid accumulation. The accumulation of substrates including ribulose 5-phosphate could be explained by the close relationship of increased biomass yield with enhanced CO₂ fixation. The present study investigated the influence of light/dark cycling on lipid production by direct comparison with continuous illumination for the first time, and revealed underlying metabolic mechanisms and candidate metabolic rate-limiting steps during light/dark cycling. These findings suggest promising targets to metabolically engineer improved lipid production.