Abstract
BackgroundCyanobacteria are increasingly recognized as promising cell factories for the production of renewable biofuels and chemical feedstocks from sunlight, CO2, and water. However, most biotechnological applications of these organisms are still characterized by low yields. Increasing the production performance of cyanobacteria remains therefore a crucial step.ResultsIn this work we use a stoichiometric network model of Synechocystis sp. PCC 6803 in combination with CASOP and minimal cut set analysis to systematically identify and characterize suitable strain design strategies for biofuel synthesis, specifically for ethanol and isobutanol. As a key result, improving upon other works, we demonstrate that higher-order knockout strategies exist in the model that lead to coupling of growth with high-yield biofuel synthesis under phototrophic conditions. Enumerating all potential knockout strategies (cut sets) reveals a unifying principle behind the identified strain designs, namely to reduce the ratio of ATP to NADPH produced by the photosynthetic electron transport chain. Accordingly, suitable knockout strategies seek to block cyclic and other alternate electron flows, such that ATP and NADPH are exclusively synthesized via the linear electron flow whose ATP/NADPH ratio is below that required for biomass synthesis. The products of interest are then utilized by the cell as sinks for reduction equivalents in excess. Importantly, the calculated intervention strategies do not rely on the assumption of optimal growth and they ensure that maintenance metabolism in the absence of light remains feasible. Our analyses furthermore suggest that a moderately increased ATP turnover, realized, for example, by ATP futile cycles or other ATP wasting mechanisms, represents a promising target to achieve increased biofuel yields.ConclusionOur study reveals key principles of rational metabolic engineering strategies in cyanobacteria towards biofuel production. The results clearly show that achieving obligatory coupling of growth and product synthesis in photosynthetic bacteria requires fundamentally different intervention strategies compared to heterotrophic organisms.Electronic supplementary materialThe online version of this article (doi:10.1186/s12934-014-0128-x) contains supplementary material, which is available to authorized users.
Highlights
Cyanobacteria are increasingly recognized as promising cell factories for the production of renewable biofuels and chemical feedstocks from sunlight, CO2, and water
The linear electron flow (LEF) through the photosystem II (PSII) and photosystem I (PSI) in cyanobacteria generates an ATP/NADPH ratio of approximately 1.28 (9 ATP and 7 NADPH) whereas binding one molecule of CO2 in the CBB cycle consumes 3 ATP and 2 NADPH yielding a ratio of 1.5. This imbalance can be resolved by the cell by a number of alternate electron flow (AEF) pathways [36,53] which recycle electrons leading to a net production of ATP without generation of NADPH (Figure 1)
In this work, we investigated promising strain design strategies for the production of biofuels with cyanobacteria based on a genome-scale stoichiometric network reconstruction of Synechocystis sp
Summary
Cyanobacteria are increasingly recognized as promising cell factories for the production of renewable biofuels and chemical feedstocks from sunlight, CO2, and water. While proof-of-concept for cyanobacterial biofuel production has been established for a variety of potential fuels, such as hydrogen [21,22], ethanol [23], and isobutanol [24,25], among others, these approaches as yet mostly rely on simple ad-hoc strategies to improve product yield In this respect, computational methods for calculating a suitable strain design based on genomescale metabolic models hold great promise to significantly improve product yield and establish cyanobacteria as a universal production chassis. Most previous approaches did not succeed to identify suitable coupling strategies for phototrophic growth or were restricted to cyanobacteria grown heterotrophically on an additional carbon source [40,41]
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