Model-assisted identification of metabolic engineering strategies for Jatropha curcas lipid pathways

Modeling the effects of strigolactone levels on maize root system architecture

Maize is the most in-demand staple crop globally. Its production relies strongly on the use of fertilizers for the supply of nitrogen, phosphorus, and potassium, which the plant absorbs through its roots, together with water. The architecture of maize roots is determinant in modulating how the plant interacts with the microbiome and extracts nutrients and water from the soil. As such, attempts to use synthetic biology and modulate that architecture to make the plant more resilient to drought and parasitic plants are underway. These attempts often try to modulate the biosynthesis of hormones that determine root architecture and growth. Experiments are laborious and time-consuming, creating the need for simulation platforms that can integrate metabolic models and 3D root growth models and predict the effects of synthetic biology interventions on both, hormone levels and root system architectures. Here, we present an example of such a platform that is built using Mathematica. First, we develop a root model, and use it to simulate the growth of many unique 3D maize root system architectures (RSAs). Then, we couple this model to a metabolic model that simulates the biosynthesis of strigolactones, hormones that modulate root growth and development. The coupling allows us to simulate the effect of changing strigolactone levels on the architecture of the roots. We then integrate the two models in a simulation platform, where we also add the functionality to analyze the effect of strigolactone levels on root phenotype. Finally, using in silico experiments, we show that our models can reproduce both the phenotype of wild type maize, and the effect that varying strigolactone levels have on changing the architecture of maize roots.

Identification of gene function based on models capturing natural variability of Arabidopsis thaliana lipid metabolism

Lipids play fundamental roles in regulating agronomically important traits. Advances in plant lipid metabolism have until recently largely been based on reductionist approaches, although modulation of its components can have system-wide effects. However, existing models of plant lipid metabolism provide lumped representations, hindering detailed study of component modulation. Here, we present the Plant Lipid Module (PLM) which provides a mechanistic description of lipid metabolism in the Arabidopsis thaliana rosette. We demonstrate that the PLM can be readily integrated in models of A. thaliana Col-0 metabolism, yielding accurate predictions (83%) of single lethal knock-outs and 75% concordance between measured transcript and predicted flux changes under extended darkness. Genome-wide associations with fluxes obtained by integrating the PLM in diel condition- and accession-specific models identify up to 65 candidate genes modulating A. thaliana lipid metabolism. Using mutant lines, we validate up to 40% of the candidates, paving the way for identification of metabolic gene function based on models capturing natural variability in metabolism.

Recent advances in metabolic engineering of microorganisms for advancing lignocellulose-derived biofuels

Combating climate change and ensuring energy supply to a rapidly growing global population has highlighted the need to replace petroleum fuels with clean, and sustainable renewable fuels. Biofuels offer a solution to safeguard energy security with reduced ecological footprint and process economics. Over the past years, lignocellulosic biomass has become the most preferred raw material for the production of biofuels, such as fuel, alcohol, biodiesel, and biohydrogen. However, the cost-effective conversion of lignocellulose into biofuels remains an unsolved challenge at the industrial scale. Recently, intensive efforts have been made in lignocellulose feedstock and microbial engineering to address this problem. By improving the biological pathways leading to the polysaccharide, lignin, and lipid biosynthesis, limited success has been achieved, and still needs to improve sustainable biofuel production. Impressive success is being achieved by the retouring metabolic pathways of different microbial hosts. Several robust phenotypes, mostly from bacteria and yeast domains, have been successfully constructed with improved substrate spectrum, product yield and sturdiness against hydrolysate toxins. Cyanobacteria is also being explored for metabolic advancement in recent years, however, it also remained underdeveloped to generate commercialized biofuels. The bacterium Escherichia coli and yeast Saccharomyces cerevisiae strains are also being engineered to have cell surfaces displaying hydrolytic enzymes, which holds much promise for near-term scale-up and biorefinery use. Looking forward, future advances to achieve economically feasible production of lignocellulosic-based biofuels with special focus on designing more efficient metabolic pathways coupled with screening, and engineering of novel enzymes.

Non-targeted Lipidomics Using a Robust and Reproducible Lipid Separation Using UPLC with Charged Surface Hybrid Technology and High-Resolution Mass Spectrometry

Lipids play an important role in the energy storage, cellular signaling, and pathophysiology of diseases such as cancer, neurodegenerative diseases, infections, and diabetes. Due to high importance of diverse lipid classes in human health and disease, manipulating lipid abundance and composition is an important target for metabolic engineering. The extreme structural diversity of lipids in real biological samples is challenging for analytical techniques due to large difference in physicochemical properties of individual lipid species. This chapter describes lipidomic analysis of large sample sets requiring reliable and robust methodology. Rapid and robust methods facilitate the support of longitudinal studies allowing the transfer of methodology between laboratories. We describe a high-throughput reversed-phase LC-MS methodology using Ultra Performance Liquid Chromatography (UPLC^®) with charged surface hybrid technology and accurate mass detection for high-throughput non-targeted lipidomics. The methodology showed excellent specificity, robustness, and reproducibility for over 100 LC-MS injections.

Plant cell cultures as heterologous bio-factories for secondary metabolite production

Synthetic biology has been developing rapidly in the last decade and is attracting increasing attention from many plant biologists. The production of high-value plant-specific secondary metabolites is, however, limited mostly to microbes. This is potentially problematic because of incorrect post-translational modification of proteins and differences in protein micro-compartmentalization, substrate availability, chaperone availability, product toxicity, and cytochrome p450 reductase enzymes. Unlike other heterologous systems, plant cells may be a promising alternative for the production of high-value metabolites. Several commercial plant suspension cell cultures from different plant species have been used successfully to produce valuable metabolites in a safe, low cost, and environmentally friendly manner. However, few metabolites are currently being biosynthesized using plant platforms, with the exception of the natural pigment anthocyanin. Both Arabidopsis thaliana and Nicotiana tabacum cell cultures can be developed by multiple gene transformations and CRISPR-Cas9 genome editing. Given that the introduction of heterologous biosynthetic pathways into Arabidopsis and N. tabacum is not widely used, the biosynthesis of foreign metabolites is currently limited; however, therein lies great potential. Here, we discuss the exemplary use of plant cell cultures and prospects for using A. thaliana and N. tabacum cell cultures to produce valuable plant-specific metabolites.

Overexpression of Type 1 and 2 Diacylglycerol Acyltransferase Genes (JcDGAT1 and JcDGAT2) Enhances Oil Production in the Woody Perennial Biofuel Plant Jatropha curcas

Diacylglycerol acyltransferase (DGAT) is the only enzyme that catalyzes the acyl-CoA-dependent acylation of sn-1, 2-diacylglycerol (DAG) to form triacylglycerol (TAG). The two main types of DGAT enzymes in the woody perennial biofuel plant Jatropha curcas, JcDGAT1 and JcDGAT2, were previously characterized only in heterologous systems. In this study, we investigated the functions of JcDGAT1 and JcDGAT2 in J. curcas.JcDGAT1 and JcDGAT2 were found to be predominantly expressed during the late stages of J. curcas seed development, in which large amounts of oil accumulated. As expected, overexpression of JcDGAT1 or JcDGAT2 under the control of the CaMV35S promoter gave rise to an increase in seed kernel oil production, reaching a content of 53.7% and 55.7% of the seed kernel dry weight, respectively, which were respectively 25% and 29.6% higher than that of control plants. The increase in seed oil content was accompanied by decreases in the contents of protein and soluble sugars in the seeds. Simultaneously, there was a two- to four-fold higher leaf TAG content in transgenic plants than in control plants. Moreover, by analysis of the fatty acid (FA) profiles, we found that JcDGAT1 and JcDGAT2 had the same substrate specificity with preferences for C18:2 in seed TAGs, and C16:0, C18:0, and C18:1 in leaf TAGs. Therefore, our study confirms the important role of JcDGAT1 and JcDGAT2 in regulating oil production in J. curcas.

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.