Elucidating Mechanisms and Genotypes Underlying Robust Phenotypes in Yarrowia lipolytica

Robustness is an important phenotype for bioenergy microbes to acquire but is difficult to engineer. Hence, tools for engineering microbial robustness are critical to unlock novel phenotypes for innovative bioprocessing strategies. The oleaginous yeast, Yarrowia lipolytica, is an exceptionally robust microbe that can tolerate stressful environments, assimilate a wide range of substrates, and produce high-value chemicals. In this doctoral dissertation, the impacts of systems biology and metabolic engineering to reveal mechanisms and identify genotypes- underlying robust phenotypes are addressed.

The first approach employs adaptive laboratory engineering to generate a platform strain by which to study superior robust mechanisms. This approach generated the most solvent-tolerant microorganism reported to date, identified sterols are critical for solvent tolerance in Y. lipolytica, and reverse engineered high solvent tolerance by increasing sterol biosynthesis via overexpression of the sterol transcription factor (Chapter I). Similarly, short-term adaptation of Y. lipolytica to depolymerized plastic waste improved utilization of all hydrophobic substrates tested and acquired a mechanism enabling cellular adhesion to the hydrophobic layer (Chapter V).

A more elaborate approach is developed using temporal RNA-sequencing data to directly predict genotypes underling robust phenotypes. Here, a novel concept was established that ranks differentially expressed genes by their co-expression connectivity to predict top-performing genetic targets conferring robustness. This methodology identified genes that would normally be overlooked by common differential expression approaches yet proved to have the highest prediction of genetic targets conferring solvent tolerance in Y. lipolytica (Chapter III).

We approached engineering an absent genotype (e.g., thiamine auxotroph) using comparative genomics with a thiamine prototroph strain and discovered the missing gene for thiamine biosynthesis (Chapter II). Bioinformatics in conjunction with metabolic engineering elucidated a promoter tightly regulated by thiamine concentration that enabled strong expression of the missing gene and restoration of thiamine biosynthesis in Y. lipolytica (Chapter II).

Finally, we exploited the phenotypic diversity exhibited by non-conventional Y. lipolytica isolates to reveal key processes and regulators underlying xylose metabolism and lipid accumulation or degradation from biomass hydrolysate (Chapter IV).

In conclusion, the outcome of this research is to develop strategies to elucidate and establish genetic tractability of robust phenotypes.