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Identification and metabolite profiling of chemical activators of lipid accumulation in green algae

ABSTRACT: Microalgae are proposed as feedstock organisms useful for producing biofuels and co-products. However, several limitations must be overcome before algae-based production is economically feasible. Among these is the ability to induce lipid accumulation and storage without affecting biomass yield. To overcome this barrier, a chemical genetics approach was employed in which 43,783 compounds were screened against Chlamydomonas reinhardtii and 243 compounds were identified that increase triacylglyceride (TAG) accumulation without terminating growth. Identified compounds were classified by structural similarity and 15 selected for secondary analyses addressing impacts on growth fitness, photosynthetic pigments, and total cellular protein and starch concentrations. TAG accumulation was verified using GC-MS quantification of total fatty acids and targeted TAG and galactolipid (GL) measurements using LC-MRM/MS. These results demonstrated TAG accumulation does not necessarily proceed at the expense of GL. Untargeted metabolite profiling provided important insights into pathway shifts due to 5 different compound treatments and verified the anabolic state of the cells with regard to the oxidative pentose phosphate pathway, Calvin cycle, tricarboxylic acid cycle and amino acid biosynthetic pathways. Metabolite patterns were distinct from nitrogen starvation and other abiotic stresses commonly used to induce oil accumulation in algae. The efficacy of these compounds was also demonstrated in 3 other algal species. These lipid inducing compounds offer a valuable set of tools for delving into the biochemical mechanisms of lipid accumulation in algae and a direct means to improve algal oil content independent of the severe growth limitations associated with nutrient deprivation.

INSTRUMENT(S): Single quadrupole

ORGANISM(S): Chlamydomonas Reinhardtii Chlamydomonas

TISSUE(S): Algae

SUBMITTER: Nishikant Wase

PROVIDER: ST000622 | MetabolomicsWorkbench | Fri Jun 16 00:00:00 BST 2017

REPOSITORIES: MetabolomicsWorkbench

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Project description:Kavšček2015 - Genome-scale metabolic model of Yarrowia lipolytica (iMK735) This model is described in the article: Optimization of lipid production with a genome-scale model of Yarrowia lipolytica. Kavšček M, Bhutada G, Madl T, Natter K. BMC Syst Biol 2015; 9: 72 Abstract: Yarrowia lipolytica is a non-conventional yeast that is extensively investigated for its ability to excrete citrate or to accumulate large amounts of storage lipids, which is of great significance for single cell oil production. Both traits are thus of interest for basic research as well as for biotechnological applications but they typically occur simultaneously thus lowering the respective yields. Therefore, engineering of strains with high lipid content relies on novel concepts such as computational simulation to better understand the two competing processes and to eliminate citrate excretion.Using a genome-scale model (GSM) of baker's yeast as a scaffold, we reconstructed the metabolic network of Y. lipolytica and optimized it for use in flux balance analysis (FBA), with the aim to simulate growth and lipid production phases of this yeast. We validated our model and found the predictions of the growth behavior of Y. lipolytica in excellent agreement with experimental data. Based on these data, we successfully designed a fed-batch strategy to avoid citrate excretion during the lipid production phase. Further analysis of the network suggested that the oxygen demand of Y. lipolytica is reduced upon induction of lipid synthesis. According to this finding we hypothesized that a reduced aeration rate might induce lipid accumulation. This prediction was indeed confirmed experimentally. In a fermentation combining these two strategies lipid content of the biomass was increased by 80%, and lipid yield was improved more than four-fold, compared to standard conditions.Genome scale network reconstructions provide a powerful tool to predict the effects of genetic modifications and the metabolic response to environmental conditions. The high accuracy and the predictive value of a newly reconstructed GSM of Y. lipolytica to optimize growth conditions for lipid accumulation are demonstrated. Based on these findings, further strategies for engineering Y. lipolytica towards higher efficiency in single cell oil production are discussed. This model is hosted on BioModels Database and identified by: MODEL1510060001. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

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Identification and metabolite profiling of chemical activators of lipid accumulation in green algae

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