Fat for biodiesel: Optimization of lipid production in yeast

Biodiesel is a sustainable alternative to conventional diesel as a product of crude oil distillation. Regarding its performance in combustion engines, biodiesel is very similar to conventional diesel. Since it is produced from renewable sources, however, its price and availability are less dependent on political and economic developments. The currently most widely used feedstocks for biodiesel production are fats and oils (triacylglycerol, TAG) from plant and animal sources. The use of large areas of arable land for biodiesel production, however, resulted in an increase in prices for agricultural products. In addition, long cultivation times and modest yields have led to the current efforts to establish oil derived from microorganisms as a sustainable feedstock for biodiesel plants. Some unicellular organisms, especially some yeasts and microalgae species, are able to produce large amounts of TAG under certain conditions. The goal of this project is the maximization of lipid production with yeasts. Optimization of TAG synthesis is as a prerequisite for a cost efficient process aiming at MO-based production of oil for biodiesel plants. Yarrowia lipolytica (Y.l.) is one of the yeast species that are able to produce and store TAG in high quantities. The well known baker`s yeast (Saccharomyces cerevisiae, S.c.), on the other hand, belongs to the group of non-oleaginous yeasts and is not able to accumulate large fat reserves. In this project we will elucidate the reasons for this difference. By identification of these factors we expect to gain a broader understanding of the mechanisms that influence accumulation of storage lipids. Methodically, we propose to reconstruct the metabolic network of Y.l. for use in flux balance analysis (FBA). FBA is a constraint-based method to analyze metabolic fluxes on the basis of a stoichiometric matrix. The results from these simulations will be compared to the fluxes in the metabolic network reconstruction of S.c. This comparative analysis will allow for the identification of significantly different fluxes in the metabolism of these two yeast species. Based on the hypothesis that these fluxes influence the ability to synthesize fatty acids, we will evaluate the results from these in silico analyses by experimental methods. Furthermore, we will perform 13C-flux and metabolomic analyses with the two model systems to gain a comprehensive understanding of the factors that influence lipid accumulation. The knowledge gained from this work will be used to engineer yeast strains that are able to synthesize and store higher amounts of neutral lipids than the wild type. The aim of this work is to identify all major factors that are necessary to direct carbon fluxes in a cell towards TAG synthesis. By combining these factors in one cell we expect to obtain a syxstem with optimized lipid accumulation.

Biodiesel is biodegradable and can contribute to the development of our society towards a sustainable bioeconomy, through the partial replacement of conventional diesel obtained from crude oil. Currently, most of the biodiesel is produced from oils (triacylglycerol, TAG) that are extracted from plant sources. This led to concerns about a competition between the production of fuel feedstocks and the cultivation of food plants on arable land (food versus fuel debate) and resulted in efforts to replace these plant oils with oils of similar quality produced biotechnologically with microorganisms, such as microalgae or yeasts.In this project, we aimed at the optimization of TAG biosynthesis and storage in yeast. Yeasts are unicellular eukaryotic microorganisms. Although all eukaryotes produce fat to store energy and carbon, they differ considerably with regard to the degree of its synthesis. Some yeasts, which are commonly termed 'oleaginous', store huge amounts of TAG under appropriate conditions and are, therefore, the most promising candidates for the biotechnological bulk fat production with yeast. One of them is Yarrowia lipolytica, a non-pathogenic yeast for which some methods for genetic modification and the whole genome sequence are available. To gain a better understanding of neutral lipid synthesis in Y. lipolytica under various conditions, we reconstructed the genome-scale metabolic network of this yeast. In such a reconstruction, all known chemical reactions within a cell are assembled in a computational model. By using a method called Flux Balance Analysis (FBA) the whole cell metabolism of the species can be simulated with this model and it can be used to predict the effects of changes in the environment or due to genetic modifications. Therefore, FBA facilitates the process of hypothesis generation and experimental design. Through this combination of computational simulation and experimental verification, we developed fermentation conditions that resulted in a considerable improvement of TAG storage in Y. lipolytica and in higher yields. Furthermore, we used the model to identify those components of the TAG synthesis pathway that might limit its efficiency and productivity. We identified several potential candidates and introduced the respective genetic modifications to obtain strains with a better performance. Among others, glycogen synthesis was eliminated because we found that glycogen is stored to a similar extent as TAG. If glycogen synthesis is deleted, the cell responds to this perturbation with increased synthesis of TAG. Furthermore, we characterized and cloned a gene that encodes a protein which is required to prevent other enzymes from degrading TAG. If this gene is overexpressed, the cell's capacity to store TAG is increased.Overall, we were able to considerably improve the TAG accumulation properties of Y. lipolytica, through a combination of genetic modifications and the optimization of process conditions.