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Lipomyces starkeyi is an oleaginous yeast with a native metabolism well-suited for production of lipids and biofuels from complex lignocellulosic and waste feedstocks. Recent advances in genetic engineering tools have facilitated the development of L. starkeyi into a microbial chassis for biofuel and chemical production. However, the feasibility of redirecting L. starkeyi lipid flux away from lipids and towards other products remains relatively unexplored. Here, we engineer the native metabolism to produce malic acid by introducing the reductive TCA pathway and a C4-dicarboxylic acid transporter to the yeast. Heterogeneous expression of two genes, the Aspergillus oryzae malate transporter and malatemore » dehydrogenase, enabled L. starkeyi malic acid production. Overexpression of a third gene, the native pyruvate carboxylase, allowed titers to reach approximately 10 g/L during shaking flasks cultivations, with production of malic acid inhibited at pH values less than 4. Corn-stover hydrolysates were found to be well-tolerated, and controlled bioreactor fermentations on the real hydrolysate produced 26.5 g/L of malic acid. Proteomic, transcriptomic and metabolomic data from real and mock hydrolysate fermentations indicated increased levels of a S. cerevisiae hsp9/hsp12 homolog (proteinID: 101453), glutathione dependent formaldehyde dehydrogenases (proteinIDs: 2047, 278215), oxidoreductases, and expression of efflux pumps and permeases during growth on the real hydrolysate. Simultaneously, machine learning based medium optimization improved production dynamics by 18% on mock hydrolysate and revealed lower tolerance to boron (a trace element included in the standard cultivation medium) than other yeasts. Together, this work demonstrated the ability to produce organic acids in L. starkeyi with minimal byproducts. The fermentation characterization and omic analyses provide a rich dataset for understanding L. starkeyi physiology and metabolic response to growth in hydrolysates. Identified upregulated genes and proteins provide potential targets for overexpression for improving growth and tolerance to concentrated hydrolysates, as well as valuable information for future L. starkeyi engineering work.« less

Abstract Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long development times. Here, we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART provides a set of recommended strains to be built in the next engineering cycle, alongside probabilistic predictions of their production levels. We demonstratemore » the capabilities of ART on simulated data sets, as well as experimental data from real metabolic engineering projects producing renewable biofuels, hoppy flavored beer without hops, fatty acids, and tryptophan. Finally, we discuss the limitations of this approach, and the practical consequences of the underlying assumptions failing.« less