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Nhan, Melissa

Publications and source records attributed to Nhan, Melissa.

Engineering high-level production of fatty alcohols by Saccharomyces cerevisiae from lignocellulosic feedstocks

Fatty alcohols in the C12-C18 range are used in personal care products, lubricants, and potentially biofuels. These compounds can be produced from the fatty acid pathway by a fatty acid reductase (FAR), yet yields from the preferred industrial host Saccharomyces cerevisiae remain under 2% of the theoretical maximum from glucose. Here we improved titer and yield of fatty alcohols using an approach involving quantitative analysis of protein levels and metabolic flux, engineering enzyme level and localization, pull-push-block engineering of carbon flux, and cofactor balancing. We compared four heterologous FARs, finding highest activity and endoplasmic reticulum localization from a Mus musculus FAR. After screening an additional twenty-one single-gene edits, we identified increasing FAR expression; deleting competing reactions encoded by DGA1, HFD1, and ADH6; overexpressing a mutant acetyl-CoA carboxylase; limiting NADPH and carbon usage by the glutamate dehydrogenase encoded by GDH1; and overexpressing the Δ9-desaturase encoded by OLE1 as successful strategies to improve titer. Our final strain produced 1.2 g/L fatty alcohols in shake flasks, and 6.0 g/L in fed-batch fermentation, corresponding to ~ 20% of the maximum theoretical yield from glucose, the highest titers and yields reported to date in S. cerevisiae. We further demonstrate high-level production from lignocellulosic feedstocks derived from ionic-liquid treated switchgrass and sorghum, reaching 0.7 g/L in shake flasks. Together, our work represents progress towards efficient and renewable microbial production of fatty acid-derived products.

09 BIOMASS FUELS↗

Analytics for Metabolic Engineering

Realizing the promise of metabolic engineering has been slowed by challenges related to moving beyond proof-of-concept examples to robust and economically viable systems. Key to advancing metabolic engineering beyond trial-and-error research is access to parts with well-defined performance metrics that can be readily applied in vastly different contexts with predictable effects. As the field now stands, research depends greatly on analytical tools that assay target molecules, transcripts, proteins, and metabolites across different hosts and pathways. Screening technologies yield specific information for many thousands of strain variants, while deep omics analysis provides a systems-level view of the cell factory. Efforts focused on a combination of these analyses yield quantitative information of dynamic processes between parts and the host chassis that drive the next engineering steps. Overall, the data generated from these types of assays aid better decision-making at the design and strain construction stages to speed progress in metabolic engineering research.

59 BASIC BIOLOGICAL SCIENCES↗