• Energy Research
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Saccharomyces cerevisiae, a well-known industrial yeast for alcoholic fermentation, is not historically known to accumulate lipids. Four S. cerevisiae strains used in industrial applications were screened for their ability to accumulate neutral lipids. Only one, D5A, was found to accumulate up to 20% dry cell weight (dcw) lipids. This strain was further engineered by knocking out ADP-activated serine/threonine kinase (SNF1) which increased lipid accumulation to 35% dcw lipids. In addition, we engineered D5A to utilize xylose and found that D5A accumulates up to 37% dcw lipids from xylose as the sole carbon source. Further we over-expressed different diacylglycerol acyltransferase (DGA1) genes and boosted lipid accumulation to 50%. Fatty acid speciation showed that 94% of the extracted lipids consisted of 5 fatty acid species, C16:0 (palmitic), C16:1n7 (palmitoleic), C18:0 (stearic), C18:1n7 (vaccenic), and C18:1n9 (oleic), while the relative distributions changed depending on growth conditions. In addition, this strain accumulated lipids concurrently with ethanol production.

The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine.

High-protein algae bioproduct system boundaries for environmental sustainability risk analysis.