An adjustable algal chloroplast plug-and-play model for genome-scale metabolic models

Novel context-specific genome-scale modelling explores the potential of triacylglycerol production by Chlamydomonas reinhardtii

Gene expression data of cell cultures is commonly measured in biological and medical studies to understand cellular decision-making in various conditions. Metabolism, affected but not solely determined by the expression, is much more difficult to measure experimentally. Finding a reliable method to predict cell metabolism for expression data will greatly benefit metabolic engineering. We have developed a novel pipeline, OVERLAY, that can explore cellular fluxomics from expression data using only a high-quality genome-scale metabolic model. This is done through two main steps: first, construct a protein-constrained metabolic model (PC-model) by integrating protein and enzyme information into the metabolic model (M-model). Secondly, overlay the expression data onto the PC-model using a novel two-step nonconvex and convex optimization formulation, resulting in a context-specific PC-model with optionally calibrated rate constants. The resulting model computes proteomes and intracellular flux states that are consistent with the measured transcriptomes. Therefore, it provides detailed cellular insights that are difficult to glean individually from the omic data or M-model alone. We apply the OVERLAY to interpret triacylglycerol (TAG) overproduction by Chlamydomonas reinhardtii, using time-course RNA-Seq data. We show that OVERLAY can compute C. reinhardtii metabolism under nitrogen deprivation and metabolic shifts after an acetate boost. OVERLAY can also suggest possible 'bottleneck' proteins that need to be overexpressed to increase the TAG accumulation rate, as well as discuss other TAG-overproduction strategies.

Recent Progress on Systems and Synthetic Biology of Diatoms for Improving Algal Productivity

Microalgae have drawn much attention for their potential applications as a sustainable source for developing bioactive compounds, functional foods, feeds, and biofuels. Diatoms, as one major group of microalgae with high yields and strong adaptability to the environment, have shown advantages in developing photosynthetic cell factories to produce value-added compounds, including heterologous bioactive products. However, the commercialization of diatoms has encountered several obstacles that limit the potential mass production, such as the limitation of algal productivity and low photosynthetic efficiency. In recent years, systems and synthetic biology have dramatically improved the efficiency of diatom cell factories. In this review, we discussed first the genome sequencing and genome-scale metabolic models (GEMs) of diatoms. Then, approaches to optimizing photosynthetic efficiency are introduced with a focus on the enhancement of biomass productivity in diatoms. We also reviewed genome engineering technologies, including CRISPR (clustered regularly interspaced short palindromic repeats) gene-editing to produce bioactive compounds in diatoms. Finally, we summarized the recent progress on the diatom cell factory for producing heterologous compounds through genome engineering to introduce foreign genes into host diatoms. This review also pinpointed the bottlenecks in algal engineering development and provided critical insights into the future direction of algal production.

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.