Model-assisted metabolic engineering of Escherichia coli for long chain alkane and alcohol production

Abstract

Biologically-derived hydrocarbons are considered to have great potential as next-generation biofuels owing to the similarity of their chemical properties to contemporary diesel and jet fuels. However, the low yield of these hydrocarbons in biotechnological production is a major obstacle for commercialization. Several genetic and process engineering approaches have been adopted to increase the yield of hydrocarbon, but a model driven approach has not been implemented so far. Here, we applied a constraint-based metabolic modeling approach in which a variable demand for alkane biosynthesis was imposed, and co-varying reactions were considered as potential targets for further engineering of an E. coli strain already expressing cyanobacterial enzymes towards higher chain alkane production. The reactions that co-varied with the imposed alkane production were found to be mainly associated with the pentose phosphate pathway (PPP) and the lower half of glycolysis. An optimal modeling solution was achieved by imposing increased flux through the reaction catalyzed by glucose-6-phosphate dehydrogenase (zwf) and iteratively removing 7 reactions from the network, leading to an alkane yield of 94.2% of the theoretical maximum conversion determined by in silico analysis at a given biomass rate. To validate the in silico findings, we first performed pathway optimization of the cyanobacterial enzymes in E. coli via different dosages of genes, promoting substrate channelling through protein fusion and inducing substantial equivalent protein expression, which led to a 36-fold increase in alka(e)ne production from 2.8 mg/L to 102 mg/L. Further, engineering of E. coli based on in silico findings, including biomass constraint, led to an increase in the alka(e)ne titer to 425 mg/L (major components being 249 mg/L pentadecane and 160 mg/L heptadecene), a 148.6-fold improvement over the initial strain, respectively; with a yield of 34.2% of the theoretical maximum. The impact of model-assisted engineering was also tested for the production of long chain fatty alcohol, another commercially important molecule sharing the same pathway while differing only at the terminal reaction, and a titer of 1506 mg/L was achieved with a yield of 86.4% of the theoretical maximum. Moreover, the model assisted engineered strains had produced 2.54 g/L and 12.5 g/L of long chain alkane and fatty alcohol, respectively, in the bioreactor under fed-batch cultivation condition. Our study demonstrated successful implementation of a combined in silico modeling approach along with the pathway and process optimization in achieving the highest reported titers of long chain hydrocarbons in E. coli.

Alkane and wax ester production from lignin‐related aromatic compounds

“…More recently, an optimized alkane production pathway was integrated in the strain (Lehtinen, Virtanen, et al, ). Microbial wax esters and alkanes have previously been produced using carbon sources such as glucose and organic acids (Cao et al, ; Fatma et al, ; Kannisto, Aho, Karp, & Santala, ; Lehtinen, Efimova, et al, ; Lehtinen, Efimova, et al, ; Lehtinen, Santala, et al, ; Salmela, Lehtinen, Efimova, Santala, & Mangayil, ; Santala, Efimova, Karp, & Santala, ; Santala, Efimova, & Santala, ; Schirmer, Rude, Li, Popova, & del Cardayre, ).…”

Alka(e)ne synthesis in Cupriavidus necator boosted by the expression of endogenous and heterologous ferredoxin–ferredoxin reductase systems

“…No fatty alcohol was detected in the fermentation broth. The highest performance reported so far was observed in an engineered E. coli strain with 2.5 g/L alka(e)nes concomitantly with 5.7 g/L fatty alcohols from glucose (Fatma et al, ). The specific production for alka(e)nes in our best producing strain Re2061‐pMAB2 was 82 mg/g CDW .…”

“…The specific production for alka(e)nes in our best producing strain Re2061-pMAB2 was 82 mg/g CDW . Comparatively, the specific productions of alka(e)nes reported in other engineered microorganisms were 51 mg/g CDW in E. coli (Fatma et al, 2018) and far more lower in yeasts such as S. cerevisiae (22 µg/g CDW ) (Buijs, Zhou, Siewers, & Nielsen, 2015) or Y. lipolytica (45 µg/g CDW ) (unpublished data). The level of excreted organic acids in our strain can be a good indicator of the potential carbon amount still available for further improvement in targeted products.…”

NIHBA: A Network Interdiction Approach with Hybrid Benders Algorithm for Strain Design

“…With the advance of genome-scale metabolic modelling (GSMM), the past decades have witnessed a significant number of computational tools for microbial metabolic engineering [Maia et al, 2016]. These tools facilitate improved strain performance for the production of a variety of high-value biochemicals and biosynthetic precursors, including vanillin [Brochado et al, 2010], lycopene [Choi et al, 2010], malonyl-CoA [Xu et al, 2011], alkane and alcohol [Fatma et al, 2018].…”