(264a) An Improved Genome-Scale Metabolic Network Model for Scheffersomyces Stipitis
Due to its important role in the production of lignocellulosic ethanol, a second generation biofuel, Scheffersomyces stipitis has drawn tremendous research interest in the last few decades. This is because S. stipitis has the highest native capacity to ferment xylose into ethanol and how to efficiently utilize the pentose sugars in lignocellulosic hydrolysate remains one of the major barriers in producing cost-effective lignocellulosic ethanol through biological conversion. Although S. stipitis can produce ethanol at a yield close to the theoretical maximum under microaerobic condition, its xylose consumption rate is only half of that under fully respiratory conditions. In addition, low ethanol tolerance and no growth under anaerobic condition also limit its direct utilization in industrial applications. To address these limitations, attempts have been made to improve xylose pick up rate of S. stipitis under microaerobic condition, as well as introducing xylose fermentation pathways possessed by S. stipitis to engineered Saccharomyces cerevisiae. However, these attempts have not been entirely successful: for engineered S. stipitis strains.
Because of the complexity involved in the two yeast strains, a better understanding on cellular metabolism of S. stipitis would be helpful for improving both S. stipitis and engineered S. cerevisiae. Because genome-scale metabolic models connect the genotype with phenotype, they provide a holistic view of the cellular metabolism. Once validated, genome-scale models provide a platform to effectively interrogate cellular metabolism, such as characterizing metabolic resource allocation, predicting phenotype, designing experiments to verify model predictions, as well as designing mutant strains with desired properties. More importantly, genome-scale models allow systematic assessment of how a (genetic or environmental) perturbation would affect the organism as a whole.
The complete genome of S. stipitis has been sequenced, which provides the foundation for genome-scale metabolic network reconstruction. Recently, two genome-scale models have been published, iSS8441 and iBB8142, which represents a significant step forward in gaining systems understanding of cellular metabolism of S. stipitis. Both models have fair amount of experimental validations and both performed well in matching the model predictions with some experimental measurements. However, both models have certain limitations. For example, neither of them predicts the production of xylitol under any condition, aerobic, microaerobic or anaerobic. It is well-known that xylitol is a key byproduct produced by S. Stipitis under microaerobic condition due the redox imbalance caused by xylose fermentation step (XR and XDH). Therefore, being able to predict xylitol production under microaerobic condition is very important for a model to identify candidates for balancing cellular redox potential.
In our previous work, we have conducted comprehensive evaluation of the two genome-scale models. The evaluation consists of three steps: first is the manual comparison of the reactions included in both models; second is the conduction of in silico experiments to examine model’s global behavior as well as comparing the model predictions with additional experimental results reported in literature; Final step is the application of the system identification framework we developed to extract biological knowledge from carefully designed in silico experiments, and to examine whether the extracted knowledge agrees with existing understandings. Our finds indicate that even though model iBB814 show worse agreement with addition experimental results, it agrees better with existing biological knowledge, and therefore was chosen as the base model for further improvement.
In this work, guided by the findings obtained through the system identification framework, we developed a modified genome-scale mode for S. stipitis. The modified model not only shows better agreement with various experimental results, but also fully agrees with existing knowledge on how different pathways (glycolysis and pentose phosphate pathway) would respond to increased substrate pickup rates (such as oxygen pickup rate). In addition, the improved model predicts xylitol production under oxygen-limited conditions, and our analysis show that the purine metabolism in conjunction with the pentose phosphate pathway is critical for xylitol production. The modified genome-scale model allows us to examine the details of cofactor imbalance under oxygen-limited conditions, as well as examine the effect of different redox ratio of xylose reductase and xylitol dehydrogenase on ethanol production. Based on these analyses, metabolic engineering strategies were proposed for enhanced ethanol production.