Engineering topics

Gin, Jennifer W

Publications and source records attributed to Gin, Jennifer W.

5 records

DOE OSTI2026

Polyketide synthase-based controlled synthesis of polycyclopropanated fuel molecules

Reducing carbon emissions from aviation and long-distance transportation sectors requires the development of sustainable biofuels with suitable energy density, freezing point, and other physical properties. We previously demonstrated biological production of high energy polycyclopropanated fatty acids (POP-FAs, class I) using an iterative polyketide synthase (iPKS) pathway in a Streptomyces host. Here, we used a computational model of fuel properties to identify chain length and cyclopropanation control as critical steps to engineer this iPKS for biofuel applications. We next explored the natural diversity of POP biosynthesis by investigating homologous pathways. Then, by in vivo gene exchange, we determined cyclopropanase (CP) catalysis to be key for POP-FA engineering. Leveraging both natural and engineered pathway product diversity, we demonstrate targeted production of improved POP-FAs, namely shortened POP-FAs with predicted superior freezing point properties for aviation, as well as fully cyclopropane-saturated POP-FAs which should have superior energy-density. These precise and controllable modifications to POP-FA structure open the door for bioproduction of designer POP fuels.

Yin, Kevin↗

DOE OSTI2026

Predictive CRISPR-mediated gene downregulation for enhanced production of sustainable aviation fuel precursor in Pseudomonas putida

CRISPR interference (CRISPRi) has emerged as a valuable tool for redirecting metabolic flux to enhance bioproduction. However, its application is often constrained by two challenges: (i) rationally identifying effective gene targets for downregulation and (ii) efficiently constructing multiplexed CRISPRi systems. In this study, we address both challenges by integrating a computational prioritization tool with a versatile assembly method for building multiplexed CRISPRi systems. FluxRETAP (Flux-Reaction Target Prioritization) accurately identified gene targets whose knockdown led to substantial increase of isoprenol titers in Pseudomonas putida KT2440, outperforming a conventional non-computational, pathway-guided target selection. The highest isoprenol titer of nearly 1.5 g/L was achieved by knocking down PP_4118 (a gene encoding α-ketoglutarate dehydrogenase). The use of VAMMPIRE (Versatile Assembly Method for MultiPlexing CRISPRi-mediated downREgulation) enabled accurate assembly of CRISPRi constructs containing up to five sgRNA arrays, reducing context dependency and achieving uniform, position-independent gene downregulation. The integration of FluxRETAP and VAMMPIRE has the potential to advance metabolic engineering by rapidly identifying CRISPRi-mediated knockdowns and knockdown combinations that enhance bioproduction titers, with potential applicability to other microbial systems.

CRISPR interference↗

DOE OSTI2025

Engineering controllable alteration of malonyl-CoA levels to enhance polyketide production

Heterologous expression of polyketide synthase (PKS) genes in Escherichia coli has enabled the production of various valuable natural and synthetic products. However, the limited availability of malonyl-CoA (M-CoA) in E. coli remains a substantial impediment to high-titer polyketide production. Here we address this limitation by disrupting the native M-CoA biosynthetic pathway and introducing an orthogonal pathway comprising a malonate transporter and M-CoA ligase, enabling efficient M-CoA biosynthesis under malonate supplementation. This approach substantially increases M-CoA levels, enhancing fatty acid and polyketide titers while reducing the promiscuous activity of PKSs toward undesired acyl-CoA substrates. Subsequent adaptive laboratory evolution of these strains provides insights into M-CoA regulation and identifies mutations that further boost M-CoA and polyketide production. This strategy improves E. coli as a host for polyketide biosynthesis and advances understanding of M-CoA metabolism in microbial systems.

Klass, Sarah H↗

DOE OSTI2025

Fast growth and high-titer bioproduction from renewable formate via metal-dependent formate dehydrogenase in Escherichia coli

Microbial bioproduction using one-carbon (C1) feedstocks has the potential to decarbonize the manufacturing of materials, fuels, and chemicals. Formate is a promising C1 feedstock, and the realization of industrial, formatotrophic platform organisms is a key goal for C1-based bioproduction. So far, a major limitation for synthetic formatotrophy has been slow energy supply due to slow formate dehydrogenase activity. Here, we implement a fast, metal-dependent formate dehydrogenase complex in a synthetic formatotrophic Escherichia coli utilizing the reductive glycine pathway. After a short-term evolution, we demonstrate formatotrophic growth of E. coli with a doubling time of less than 4.5 h, comparable to the fastest natural formatotrophs. To further explore the potential of a formate-based bioeconomy, this strain is engineered to produce mevalonate, as well as the terpenoid and aviation fuel precursor isoprenol, using formate we generate directly from the electrochemical reduction of CO2. This work demonstrates an improvement in bioproduct titer from formate, achieving the production of 3.8 g/L of mevalonate. Additionally, the abundant and recalcitrant polymer lignin is chemically decomposed into a formate-rich mixture of small organic acids and subsequently bioconverted into mevalonate. Overall, the described fast-growing, formatotrophic bioproduction strain demonstrates that a sustainable formate bioeconomy is within reach.

Cowan, Aidan E↗

DOE OSTI2025

Automation and machine learning drive rapid optimization of isoprenol production in Pseudomonas putida

Advances in genome engineering have improved our ability to perturb microbial metabolic networks, yet bioproduction campaigns often struggle with parsing complex metabolic datasets to efficiently enhance product titers. We address this challenge by coupling laboratory automation with machine learning to systematically optimize the production of isoprenol, a sustainable aviation fuel precursor, in Pseudomonas putida. The simultaneous downregulation through CRISPR interference of combinations of up to four gene targets, guided by machine learning, permitted us to increase isoprenol titer 5-fold in six consecutive design-build-test-learn cycles. Moreover, machine learning enabled us to swiftly explore a vast experimental design space of 800,000 possible combinations by strategically recommending approximately 400 priority constructs. High-throughput proteomics allowed us to validate CRISPRi downregulation and identify biological mechanisms driving production increases. Our work demonstrates that ML-driven automated design-build-test-learn cycles, when combined with rigorous data validation, can rapidly enhance titers without specific biological knowledge, suggesting that it can be applied to any host, product, or pathway.

Carruthers, David N↗