Computational metabolic engineering strategies for growth-coupled biofuel production by Synechocystis

Tailoring regulatory components for metabolic engineering in cyanobacteria

The looming climate crisis has prompted an ever-growing interest in cyanobacteria due to their potential as sustainable production platforms for the synthesis of energy carriers and value-added chemicals from CO2 and sunlight. Nonetheless, cyanobacteria are yet to compete with heterotrophic systems in terms of space-time yields and consequently production costs. One major drawback leading to the low production performance observed in cyanobacteria is the limited ability to utilize the full capacity of the photosynthetic apparatus and its associated systems, i.e. CO2 fixation and the directly connected metabolism. In this review, novel insights into various levels of metabolic regulation of cyanobacteria are discussed, including the potential of targeting these regulatory mechanisms to create a chassis with a phenotype favorable for photoautotrophic production. Compared to conventional metabolic engineering approaches, minor perturbations of regulatory mechanisms can have wide-ranging effects.

Uses of Multi-Objective Flux Analysis for Optimization of Microbial Production of Secondary Metabolites

Secondary metabolites are not essential for the growth of microorganisms, but they play a critical role in how microbes interact with their surroundings. In addition to this important ecological role, secondary metabolites also have a variety of agricultural, medicinal, and industrial uses, and thus the examination of secondary metabolism of plants and microbes is a growing scientific field. While the chemical production of certain secondary metabolites is possible, industrial-scale microbial production is a green and economically attractive alternative. This is even more true, given the advances in bioengineering that allow us to alter the workings of microbes in order to increase their production of compounds of interest. This type of engineering requires detailed knowledge of the "chassis" organism's metabolism. Since the resources and the catalytic capacity of enzymes in microbes is finite, it is important to examine the tradeoffs between various bioprocesses in an engineered system and alter its working in a manner that minimally perturbs the robustness of the system while allowing for the maximum production of a product of interest. The in silico multi-objective analysis of metabolism using genome-scale models is an ideal method for such examinations.

A systematic overexpression approach reveals native targets to increase squalene production in Synechocystis sp. PCC 6803

Cyanobacteria are a promising platform for the production of the triterpene squalene (C30), a precursor for all plant and animal sterols, and a highly attractive intermediate towards triterpenoids, a large group of secondary plant metabolites. Synechocystis sp. PCC 6803 natively produces squalene from CO₂ through the MEP pathway. Based on the predictions of a constraint-based metabolic model, we took a systematic overexpression approach to quantify native Synechocystis gene's impact on squalene production in a squalene-hopene cyclase gene knock-out strain (Δshc). Our in silico analysis revealed an increased flux through the Calvin-Benson-Bassham cycle in the Δshc mutant compared to the wildtype, including the pentose phosphate pathway, as well as lower glycolysis, while the tricarboxylic acid cycle predicted to be downregulated. Further, all enzymes of the MEP pathway and terpenoid synthesis, as well as enzymes from the central carbon metabolism, Gap2, Tpi and PyrK, were predicted to positively contribute to squalene production upon their overexpression. Each identified target gene was integrated into the genome of Synechocystis Δshc under the control of the rhamnose-inducible promoter P_rha. Squalene production was increased in an inducer concentration dependent manner through the overexpression of most predicted genes, which are genes of the MEP pathway, ispH, ispE, and idi, leading to the greatest improvements. Moreover, we were able to overexpress the native squalene synthase gene (sqs) in Synechocystis Δshc, which reached the highest production titer of 13.72 mg l^-1 reported for squalene in Synechocystis sp. PCC 6803 so far, thereby providing a promising and sustainable platform for triterpene production.

Assessing and reducing phenotypic instability in cyanobacteria

Cyanobacteria have promising potential as sustainable cell factories. However, one challenge that is still largely unreported in scaling-up cyanobacteria bioproduction is phenotypic instability, where the emergence and selection of nonproducing cells leading to loss in production has longer evolutionary timescales to take place in industrial-scale bioreactors. Quantifying phenotypic instability early on in strain development allows researchers to make informed decisions on whether to proceed with scalable designs, or if present, devise countermeasures to reduce instability. One particularly effective strategy to mitigate instability is the use of genome-scale metabolic models to design growth-coupled production strains. In silico studies have predicted that creating certain cofactor imbalances or removing recycling reactions in cyanobacteria can be exploited to stably produce a wide variety of metabolites.

Mapping competitive pathways to terpenoid biosynthesis in Synechocystis sp. PCC 6803 using an antisense RNA synthetic tool

BACKGROUND

Synechocystis sp. PCC 6803 utilizes pyruvate and glyceraldehyde 3-phosphate via the methylerythritol 4-phosphate (MEP) pathway for the biosynthesis of terpenoids. Considering the deep connection of the MEP pathway to the central carbon metabolism, and the low carbon partitioning towards terpenoid biosynthesis, significant changes in the metabolic network are required to increase cyanobacterial production of terpenoids.

RESULTS

We used the Hfq-MicC antisense RNA regulatory tool, under control of the nickel-inducible P_nrsB promoter, to target 12 different genes involved in terpenoid biosynthesis, central carbon metabolism, amino acid biosynthesis and ATP production, and evaluated the changes in the performance of an isoprene-producing cyanobacterial strain. Six candidate targets showed a positive effect on isoprene production: three genes involved in terpenoid biosynthesis (crtE, chlP and thiG), two involved in amino acid biosynthesis (ilvG and ccmA) and one involved in sugar catabolism (gpi). The same strategy was applied to interfere with different parts of the terpenoid biosynthetic pathway in a bisabolene-producing strain. Increased bisabolene production was observed not only when interfering with chlorophyll a biosynthesis, but also with carotenogenesis.

CONCLUSIONS

We demonstrated that the Hfq-MicC synthetic tool can be used to evaluate the effects of gene knockdown on heterologous terpenoid production, despite the need for further optimization of the technique. Possible targets for future engineering of Synechocystis aiming at improved terpenoid microbial production were identified.

Current Metabolic Engineering Strategies for Photosynthetic Bioproduction in Cyanobacteria

Cyanobacteria are photosynthetic microorganisms capable of using solar energy to convert CO₂ and H₂O into O₂ and energy-rich organic compounds, thus enabling sustainable production of a wide range of bio-products. More and more strains of cyanobacteria are identified that show great promise as cell platforms for the generation of bioproducts. However, strain development is still required to optimize their biosynthesis and increase titers for industrial applications. This review describes the most well-known, newest and most promising strains available to the community and gives an overview of current cyanobacterial biotechnology and the latest innovative strategies used for engineering cyanobacteria. We summarize advanced synthetic biology tools for modulating gene expression and their use in metabolic pathway engineering to increase the production of value-added compounds, such as terpenoids, fatty acids and sugars, to provide a go-to source for scientists starting research in cyanobacterial metabolic engineering.

Introduction to Cyanobacteria

Cyanobacteria are highly interesting microbes with the capacity for oxygenic photosynthesis. They fulfill an important purpose in nature but are also potent biocatalysts. This chapter gives a brief overview of this diverse phylum and shortly addresses the functions these organisms have in the natural ecosystems. Further, it introduces the main topics covered in this volume, which is dealing with the development and application of cyanobacteria as solar cell factories for the production of chemicals including potential fuels. We discuss cyanobacteria as industrial workhorses, present established chassis strains, and give an overview of the current target products. Genetic engineering strategies aiming at the photosynthetic efficiency as well as approaches to optimize carbon fluxes are summarized. Finally, main cultivation strategies are sketched.

Role of regulatory pathways and multi-omics approaches for carbon capture and mitigation in cyanobacteria

Cyanobacteria are known for their metabolic potential and carbon capture and sequestration capabilities. These cyanobacteria are not only an effective source for carbon minimization and resource mobilization into value-added products for biotechnological gains. The present review focuses on the detailed description of carbon capture mechanisms exerted by the various cyanobacterial strains, the role of important regulatory pathways, and their subsequent genes responsible for such mechanisms. Moreover, this review will also describe effectual mechanisms of central carbon metabolism like isoprene synthesis, ethylene production, MEP pathway, and the role of Glyoxylate shunt in the carbon sequestration mechanisms. This review also describes some interesting facets of using carbon assimilation mechanisms for valuable bio-products. The role of regulatory pathways and multi-omics approaches in cyanobacteria will not only be crucial towards improving carbon utilization but also will give new insights into utilizing cyanobacterial bioresource for carbon neutrality.

In Silico Design Strategies for the Production of Target Chemical Compounds Using Iterative Single-Level Linear Programming Problems

The optimization of metabolic reaction modifications for the production of target compounds is a complex computational problem whose execution time increases exponentially with the number of metabolic reactions. Therefore, practical technologies are needed to identify reaction deletion combinations to minimize computing times and promote the production of target compounds by modifying intracellular metabolism. In this paper, a practical metabolic design technology named AERITH is proposed for high-throughput target compound production. This method can optimize the production of compounds of interest while maximizing cell growth. With this approach, an appropriate combination of metabolic reaction deletions can be identified by solving a simple linear programming problem. Using a standard CPU, the computation time could be as low as 1 min per compound, and the system can even handle large metabolic models. AERITH was implemented in MATLAB and is freely available for non-profit use.

pSHDY: A New Tool for Genetic Engineering of Cyanobacteria

Genetic engineering of cyanobacteria is currently limited to genomic integration via homologous recombination and RSF1010-based conjugative vector systems. Here, we introduce a rationally designed conjugative vector with two BioBrick-based cloning sites which enables facilitated and modular cloning. This streamlined vector is suitable for a variety of synthetic biology applications, such as expression of multiple enzymes from metabolic pathways for the production of biofuels or secondary metabolites, or screening of modular parts such as promoters, further facilitating applications to improve crop plants using synthetic biology. Finally, we present a general approach to cloning of constructs, as well as detailed protocols for conjugation and culturing of strains carrying said constructs.

Combinatorial assembly platform enabling engineering of genetically stable metabolic pathways in cyanobacteria

Cyanobacteria are simple, efficient, genetically-tractable photosynthetic microorganisms which in principle represent ideal biocatalysts for CO2 capture and conversion. However, in practice, genetic instability and low productivity are key, linked problems in engineered cyanobacteria. We took a massively parallel approach, generating and characterising libraries of synthetic promoters and RBSs for the cyanobacterium Synechocystis sp. PCC 6803, and assembling a sparse combinatorial library of millions of metabolic pathway-encoding construct variants. Genetic instability was observed for some variants, which is expected when variants cause metabolic burden. Surprisingly however, in a single combinatorial round without iterative optimisation, 80% of variants chosen at random and cultured photoautotrophically over many generations accumulated the target terpenoid lycopene from atmospheric CO2, apparently overcoming genetic instability. This large-scale parallel metabolic engineering of cyanobacteria provides a new platform for development of genetically stable cyanobacterial biocatalysts for sustainable light-driven production of valuable products directly from CO2, avoiding fossil carbon or competition with food production.

Cycling between growth and production phases increases cyanobacteria bioproduction of lactate

Decoupling growth from product synthesis is a promising strategy to increase carbon partitioning and maximize productivity in cell factories. However, reduction in both substrate uptake rate and metabolic activity in the production phase are an underlying problem for upscaling. Here, we used CRISPR interference to repress growth in lactate-producing Synechocystis sp. PCC 6803. Carbon partitioning to lactate in the production phase exceeded 90%, but CO₂ uptake was severely reduced compared to uptake during the growth phase. We characterized strains during the onset of growth arrest using transcriptomics and proteomics. Multiple genes involved in ATP homeostasis were regulated once growth was inhibited, which suggests an alteration of energy charge that may lead to reduced substrate uptake. In order to overcome the reduced metabolic activity and take advantage of increased carbon partitioning, we tested a novel production strategy that involved alternating growth arrest and recovery by periodic addition of an inducer molecule to activate CRISPRi. Using this strategy, we maintained lactate biosynthesis in Synechocystis for 30 days in a constant light turbidostat cultivation. Cumulative lactate titers were also increased by 100% compared to a constant growth-arrest regime, and reached 1 g/L. Further, the cultivation produced lactate for 30 days, compared to 20 days for the non-growth arrest cultivation. Periodic growth arrest could be applicable for other products, and in cyanobacteria, could be linked to internal circadian rhythms that persist in constant light.

Development of CRISPR-Cas9 knock-in tools for free fatty acid production using the fast-growing cyanobacterial strain Synechococcus elongatus UTEX 2973

Synechococcus elongatus UTEX 2973 has one of the fastest measured doubling time of cyanobacteria making it an important candidate for metabolic engineering. Traditional genetic engineering methods, which rely on homologous recombination, however, are inefficient, labor-intensive, and time-consuming due to the oligoploidy or polyploidy nature of cyanobacteria and the reliance on unique antibiotic resistance markers. CRISPR-Cas9 has emerged as an effective and versatile editing platform in a wide variety of organisms, but its application for cyanobacterial engineering is limited by the inherent toxicity of Cas9 resulting in poor transformation efficiencies. Here, we demonstrated that a single-plasmid CRISPR-Cas9 system, pCRISPOmyces-2, can effectively knock-in a truncated thioesterase gene from Escherichia coli to generate free fatty acid (FFA) producing mutants of Syn2973. To do so, three parameters were evaluated on the effect of generating recipient colonies after conjugation with pCRISPOmyces-2-based plasmids: 1) a modified conjugation protocol termed streaked conjugation, 2) the deletion of the gene encoding RecJ exonuclease, and 3) single guide RNA (sgRNA) sequence. With the use of the streaked conjugation protocol and a ΔrecJ mutant strain of Syn2973, the conjugation efficiency for the pCRISPomyces-2 plasmid could be improved by 750-fold over the wildtype (WT) for a conjugation efficiency of 2.0 × 10^-6 transconjugants/recipient cell. While deletion of the RecJ exonuclease alone increased the conjugation efficiency by 150-fold over the WT, FFA generation was impaired in FFA-producing mutants with the ΔrecJ background, and the large number of poor FFA-producing isolates indicated the potential increase in spontaneous mutation rates. The sgRNA sequence was found to be critical in achieving the desired CRISPR-Cas9-mediated knock-in mutation as the sgRNA impacts conjugation efficiency, likelihood of homogenous recombinants, and free fatty acid production in engineered strains.

Systematizing the different notions of growth-coupled product synthesis and a single framework for computing corresponding strain designs

A widely used design principle for metabolic engineering of microorganisms aims to introduce interventions that enforce growth-coupled product synthesis such that the product of interest becomes a (mandatory) by-product of growth. However, different variants and partially contradicting notions of growth-coupled production (GCP) exist. Herein, we propose an ontology for the different degrees of GCP and clarify their relationships. Ordered by coupling degree, we distinguish four major classes: potentially, weakly, and directionally growth-coupled production (pGCP, wGCP, dGCP) as well as substrate-uptake coupled production (SUCP). We then extend the framework of Minimal Cut Sets (MCS), previously used to compute dGCP and SUCP strain designs, to allow inclusion of implicit optimality constraints, a feature required to compute pGCP and wGCP designs. This extension closes the gap between MCS-based and bilevel-based strain design approaches and enables computation (and comparison) of designs for all GCP classes within a single framework. By computing GCP strain designs for a range of products, we illustrate the hierarchical relationships between the different coupling degrees. We find that feasibility of coupling is not affected by the chosen GCP degree and that strongest coupling (SUCP) requires often only one or two more interventions than wGCP and dGCP. Finally, we show that the principle of coupling can be generalized to couple product synthesis with other cellular functions than growth, for example, with net ATP formation. This work provides important theoretical results and algorithmic developments and a unified terminology for computational strain design based on GCP.

A metabolic model of Lipomyces starkeyi for predicting lipogenesis potential from diverse low-cost substrates

BACKGROUND

Lipomyces starkeyi has been widely regarded as a promising oleaginous yeast with broad industrial application prospects because of its wide substrate spectrum, good adaption to fermentation inhibitors, excellent fatty acid composition for high-quality biodiesel, and negligible lipid remobilization. However, the currently low experimental lipid yield of L. starkeyi prohibits its commercial success. Metabolic model is extremely valuable to comprehend the complex biochemical processes and provide great guidance for strain modification to facilitate the lipid biosynthesis.

RESULTS

A small-scale metabolic model of L. starkeyi NRRL Y-11557 was constructed based on the genome annotation information. The theoretical lipid yields of glucose, cellobiose, xylose, glycerol, and acetic acid were calculated according to the flux balance analysis (FBA). The optimal flux distribution of the lipid synthesis showed that pentose phosphate pathway (PPP) independently met the necessity of NADPH for lipid synthesis, resulting in the relatively low lipid yields. Several targets (NADP-dependent oxidoreductases) beneficial for oleaginicity of L. starkeyi with significantly higher theoretical lipid yields were compared and elucidated. The combined utilization of acetic acid and other carbon sources and a hypothetical reverse β-oxidation (RBO) pathway showed outstanding potential for improving the theoretical lipid yield.

CONCLUSIONS

The lipid biosynthesis potential of L. starkeyi can be significantly improved through appropriate modification of metabolic network, as well as combined utilization of carbon sources according to the metabolic model. The prediction and analysis provide valuable guidance to improve lipid production from various low-cost substrates.

In silico Design for Systems-Based Metabolic Engineering for the Bioconversion of Valuable Compounds From Industrial By-Products

Selecting appropriate metabolic engineering targets to build efficient cell factories maximizing the bioconversion of industrial by-products to valuable compounds taking into account time restrictions is a significant challenge in industrial biotechnology. Microbial metabolism engineering following a rational design has been widely studied. However, it is a cost-, time-, and laborious-intensive process because of the cell network complexity; thus, it is important to use tools that allow predicting gene deletions. An in silico experiment was performed to model and understand the metabolic engineering effects on the cell factory considering a second complexity level by transcriptomics data integration. In this study, a systems-based metabolic engineering target prediction was used to increase glycerol bioconversion to succinic acid based on Escherichia coli. Transcriptomics analysis suggests insights on how to increase cell glycerol utilization to further design efficient cell factories. Three E. coli models were used: a core model, a second model based on the integration of transcriptomics data obtained from growth in an optimized culture media, and a third one obtained after integration of transcriptomics data from adaptive laboratory evolution (ALE) experiments. A total of 2,402 strains were obtained with fumarase and pyruvate dehydrogenase being frequently predicted for all the models, suggesting these reactions as essential to increase succinic acid production. Finally, based on using flux balance analysis (FBA) results for all the mutants predicted, a machine learning method was developed to predict new mutants as well as to propose optimal metabolic engineering targets and mutants based on the measurement of the importance of each knockout's (feature's) contribution. Glycerol has become an interesting carbon source for industrial processes due to biodiesel business growth since it has shown promising results in terms of biomass/substrate yields. The combination of transcriptome, systems metabolic modeling, and machine learning analyses revealed the versatility of computational models to predict key metabolic engineering targets in a less cost-, time-, and laborious-intensive process. These data provide a platform to improve the prediction of metabolic engineering targets to design efficient cell factories. Our results may also work as a guide and platform for the selection/engineering of microorganisms for the production of interesting chemical compounds.

Biofuels for a sustainable future

Rapid increases of energy consumption and human dependency on fossil fuels have led to the accumulation of greenhouse gases and consequently, climate change. As such, major efforts have been taken to develop, test, and adopt clean renewable fuel alternatives. Production of bioethanol and biodiesel from crops is well developed, while other feedstock resources and processes have also shown high potential to provide efficient and cost-effective alternatives, such as landfill and plastic waste conversion, algal photosynthesis, as well as electrochemical carbon fixation. In addition, the downstream microbial fermentation can be further engineered to not only increase the product yield but also expand the chemical space of biofuels through the rational design and fine-tuning of biosynthetic pathways toward the realization of "designer fuels" and diverse future applications.

Opportunities and Challenges for Microbial Synthesis of Fatty Acid-Derived Chemicals (FACs)

Global warming and uneven distribution of fossil fuels worldwide concerns have spurred the development of alternative, renewable, sustainable, and environmentally friendly resources. From an engineering perspective, biosynthesis of fatty acid-derived chemicals (FACs) is an attractive and promising solution to produce chemicals from abundant renewable feedstocks and carbon dioxide in microbial chassis. However, several factors limit the viability of this process. This review first summarizes the types of FACs and their widely applications. Next, we take a deep look into the microbial platform to produce FACs, give an outlook for the platform development. Then we discuss the bottlenecks in metabolic pathways and supply possible solutions correspondingly. Finally, we highlight the most recent advances in the fast-growing model-based strain design for FACs biosynthesis.

Biocatalysis in Green and Blue: Cyanobacteria

Recently, several studies have proven the potential of cyanobacteria as whole-cell biocatalysts for biotransformation. Compared to heterotrophic hosts, cyanobacteria show unique advantages thanks to their photoautotrophic metabolism. Their ability to use light as energy and CO₂ as carbon source promises a truly sustainable production platform. Their photoautotrophic metabolism offers an encouraging source of reducing power, which makes them attractive for redox-based biotechnological purposes. To exploit the full potential of these whole-cell biocatalysts, cyanobacterial cells must be considered in their entirety. With this emphasis, this review summarizes the latest developments in cyanobacteria research with a strong focus on the benefits associated with their unique metabolism. Remaining bottlenecks and recent strategies to overcome them are evaluated for their potential in future applications.

Bioprospecting of microbial strains for biofuel production: metabolic engineering, applications, and challenges

The issues of global warming, coupled with fossil fuel depletion, have undoubtedly led to renewed interest in other sources of commercial fuels. The search for renewable fuels has motivated research into the biological degradation of lignocellulosic biomass feedstock to produce biofuels such as bioethanol, biodiesel, and biohydrogen. The model strain for biofuel production needs the capability to utilize a high amount of substrate, transportation of sugar through fast and deregulated pathways, ability to tolerate inhibitory compounds and end products, and increased metabolic fluxes to produce an improved fermentation product. Engineering microbes might be a great approach to produce biofuel from lignocellulosic biomass by exploiting metabolic pathways economically. Metabolic engineering is an advanced technology for the construction of highly effective microbial cell factories and a key component for the next-generation bioeconomy. It has been extensively used to redirect the biosynthetic pathway to produce desired products in several native or engineered hosts. A wide range of novel compounds has been manufactured through engineering metabolic pathways or endogenous metabolism optimizations by metabolic engineers. This review is focused on the potential utilization of engineered strains to produce biofuel and gives prospects for improvement in metabolic engineering for new strain development using advanced technologies.

Recent advances in synthetic biology of cyanobacteria for improved chemicals production

Cyanobacteria are Gram-negative photoautotrophic prokaryotes and have shown great importance to the Earth’s ecology. Based on their capability in oxygenic photosynthesis and genetic merits, they can be engineered as microbial chassis for direct conversion of carbon dioxide to value-added biofuels and chemicals. In the last decades, attempts have given to the application of synthetic biology tools and approaches in the development of cyanobacterial cell factories. Despite the successful proof-of-principle studies, large-scale application is still a technical challenge due to low yields of bioproducts. Therefore, recent efforts are underway to characterize and develop genetic regulatory parts and strategies for the synthetic biology applications in cyanobacteria. In this review, we present the recent advancements and application in cyanobacterial synthetic biology toolboxes. We also discuss the limitations and future perspectives for using such novel tools in cyanobacterial biotechnology.

Genome-scale metabolic rewiring improves titers rates and yields of the non-native product indigoidine at scale

High titer, rate, yield (TRY), and scalability are challenging metrics to achieve due to trade-offs between carbon use for growth and production. To achieve these metrics, we take the minimal cut set (MCS) approach that predicts metabolic reactions for elimination to couple metabolite production strongly with growth. We compute MCS solution-sets for a non-native product indigoidine, a sustainable pigment, in Pseudomonas putida KT2440, an emerging industrial microbe. From the 63 solution-sets, our omics guided process identifies one experimentally feasible solution requiring 14 simultaneous reaction interventions. We implement a total of 14 genes knockdowns using multiplex-CRISPRi. MCS-based solution shifts production from stationary to exponential phase. We achieve 25.6 g/L, 0.22 g/l/h, and ~50% maximum theoretical yield (0.33 g indigoidine/g glucose). These phenotypes are maintained from batch to fed-batch mode, and across scales (100-ml shake flasks, 250-ml ambr®, and 2-L bioreactors).

Reconstruction and analysis of genome-scale metabolic model of weak Crabtree positive yeast Lachancea kluyveri

Lachancea kluyveri, a weak Crabtree positive yeast, has been extensively studied for its unique URC pyrimidine catabolism pathway. It produces more biomass than Saccharomyces cerevisiae due to the underlying weak Crabtree effect and resorts to fermentation only in oxygen limiting conditions that renders it as a suitable industrial host. The yeast also produces ethyl acetate as a major overflow metabolite in aerobic conditions. Here, we report the first genome-scale metabolic model, iPN730, of L. kluyveri comprising of 1235 reactions, 1179 metabolites, and 730 genes distributed in 8 compartments. The in silico viability in different media conditions and the growth characteristics in various carbon sources show good agreement with experimental data. Dynamic flux balance analysis describes the growth dynamics, substrate utilization and product formation kinetics in various oxygen-limited conditions. We have also demonstrated the effect of switching carbon sources on the production of ethyl acetate under varying oxygen uptake rates. A phenotypic phase plane analysis described the energetic cost penalty of ethyl acetate and ethanol production on the specific growth rate of L. kluyveri. We generated the context specific models of L. kluyveri growing on uracil or ammonium salts as the sole nitrogen source. Differential flux calculated using flux variability analysis helped us in highlighting pathways like purine, histidine, riboflavin and pyrimidine metabolism associated with uracil degradation. The genome-scale metabolic construction of L. kluyveri will provide a better understanding of metabolism behind ethyl acetate production as well as uracil catabolism (pyrimidine degradation) pathway. iPN730 is an addition to genome-scale metabolic models of non-conventional yeasts that will facilitate system-wide omics analysis to understand fungal metabolic diversity.

Pooled CRISPRi screening of the cyanobacterium Synechocystis sp PCC 6803 for enhanced industrial phenotypes

Cyanobacteria are model organisms for photosynthesis and are attractive for biotechnology applications. To aid investigation of genotype-phenotype relationships in cyanobacteria, we develop an inducible CRISPRi gene repression library in Synechocystis sp. PCC 6803, where we aim to target all genes for repression. We track the growth of all library members in multiple conditions and estimate gene fitness. The library reveals several clones with increased growth rates, and these have a common upregulation of genes related to cyclic electron flow. We challenge the library with 0.1 M L-lactate and find that repression of peroxiredoxin bcp2 increases growth rate by 49%. Transforming the library into an L-lactate-secreting Synechocystis strain and sorting top lactate producers enriches clones with sgRNAs targeting nutrient assimilation, central carbon metabolism, and cyclic electron flow. In many examples, productivity can be enhanced by repression of essential genes, which are difficult to access by transposon insertion.

Heterologous Expression of Ethanol Synthesis Pathway in Glycogen Deficient Synechococcus elongatus PCC 7942 Resulted in Enhanced Production of Ethanol and Exopolysaccharides

In this study, the Synechococcus elongatus PCC 7942 (hereafter S. elongatus) was engineered by the glgC knockout as well as the insertion of the pdc-adh genes from two different microorganisms. The insertion of pdc-adh genes increased the ethanol synthesis with further improvement in the productivity upon the destruction of glycogen synthesis pathway and the supplementation of cofactor. The abolition of glycogen synthesis pathway led to a considerable increase of the engineered S. elongatus metabolites involved in the ethanol synthesis pathway. Moreover, the studies on cofactor addition highlighted the importance of Mg+2, Zn+2, thiamine pyrophosphate, and NADP+ in ethanol synthesis. The yields of 3856 mg/L ethanol and 109.5 µg/108 cells exopolysaccharides were obtained in the engineered S. elongatus using a photo-bioreactor under optimized conditions. This enhanced production in ethanol and exopolysaccharides are attributed to the flux of carbon from glycogen synthesis pathway and proper availability of essential components.

CF-Targeter: A Rational Biological Cell Factory Targeting Platform for Biosynthetic Target Chemicals

Biosynthesis is a promising method for chemical synthesis. However, due to varieties between different microorganism hosts, yield and heterologous pathways needed for production of target chemical may also vary from different strains. One of the main challenges in metabolic engineering is to select an appropriate chassis host for specified target chemical production. However, with thousands of microorganisms existing in nature and extremely complicated metabolism within them, it is still time-consuming and error-prone work to achieve such a goal only through experimental methods, even with some existing computational methods. Hence, more efficient methods should be proposed to assist in selecting appropriate chassis hosts. In this article, based on symbolic reaction repositories and a pathway search algorithm which performed 1 400 000 searches for per target compound, we established a biological reasoning system for appropriate chassis host selection by coupling with various GEM-models. By using a supercomputer to calculate the biosynthetic pathways for more than 1 month, nearly 50 000 000 biosynthetic pathways are computed for production of 6026 compounds within 70 microorganisms. With retrieved organisms for specified target production, several heterologous biosynthetic pathways can be shown in length order, and then the maximum theoretical yields and thermodynamic feasibility can be calculated in real time under customized growth conditions and physiological states. From the computation results, the system not only identifies experimentally validated pathways but also outputs more efficient solutions with less heterologous steps or higher maximum possible theoretical yield by engineering other organism hosts. CF-targeter is available at http://www.rxnfinder.org/cf_targeter/.

Metabolic model guided strain design of cyanobacteria

Cyanobacteria are oxygenic photoautotrophs that serve as potential platforms for the production of biochemicals from cheap and renewable raw materials - sunlight, water, and carbon dioxide. Systems level analysis of the metabolic network of these organisms could enable the successful engineering of these organisms for the enhanced production of target chemicals. Metabolic modeling techniques including both stoichiometric and kinetic modeling with a genome-wide coverage enable a global assessment of metabolic capabilities. Recent studies guided by such modeling techniques have engineered strains for the enhanced production of valuable chemicals such as ethanol, n-butanol, 1,3-propanediol, glycerol, limonene, and isoprene from CO₂.

Emerging Species and Genome Editing Tools: Future Prospects in Cyanobacterial Synthetic Biology

Recent advances in synthetic biology and an emerging algal biotechnology market have spurred a prolific increase in the availability of molecular tools for cyanobacterial research. Nevertheless, work to date has focused primarily on only a small subset of model species, which arguably limits fundamental discovery and applied research towards wider commercialisation. Here, we review the requirements for uptake of new strains, including several recently characterised fast-growing species and promising non-model species. Furthermore, we discuss the potential applications of new techniques available for transformation, genetic engineering and regulation, including an up-to-date appraisal of current Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR associated protein (CRISPR/Cas) and CRISPR interference (CRISPRi) research in cyanobacteria. We also provide an overview of several exciting molecular tools that could be ported to cyanobacteria for more advanced metabolic engineering approaches (e.g., genetic circuit design). Lastly, we introduce a forthcoming mutant library for the model species Synechocystis sp. PCC 6803 that promises to provide a further powerful resource for the cyanobacterial research community.

Determination of growth-coupling strategies and their underlying principles

BACKGROUND

Metabolic coupling of product synthesis and microbial growth is a prominent approach for maximizing production performance. Growth-coupling (GC) also helps stabilizing target production and allows the selection of superior production strains by adaptive laboratory evolution. To support the implementation of growth-coupling strain designs, we seek to identify biologically relevant, metabolic principles that enforce strong growth-coupling on the basis of reaction knockouts.

RESULTS

We adapted an established bilevel programming framework to maximize the minimally guaranteed production rate at a fixed, medium growth rate. Using this revised formulation, we identified various GC intervention strategies for metabolites of the central carbon metabolism, which were examined for GC generating principles under diverse conditions. Curtailing the metabolism to render product formation an essential carbon drain was identified as one major strategy generating strong coupling of metabolic activity and target synthesis. Impeding the balancing of cofactors and protons in the absence of target production was the underlying principle of all other strategies and further increased the GC strength of the aforementioned strategies.

CONCLUSION

Maximizing the minimally guaranteed production rate at a medium growth rate is an attractive principle for the identification of strain designs that couple growth to target metabolite production. Moreover, it allows for controlling the inevitable compromise between growth coupling strength and the retaining of microbial viability. With regard to the corresponding metabolic principles, generating a dependency between the supply of global metabolic cofactors and product synthesis appears to be advantageous in enforcing strong GC for any metabolite. Deriving such strategies manually, is a hard task, due to which we suggest incorporating computational metabolic network analyses in metabolic engineering projects seeking to determine GC strain designs.

A non-dominated sorting Differential Search Algorithm Flux Balance Analysis (ndsDSAFBA) for in silico multiobjective optimization in identifying reactions knockout

Metabolic engineering is defined as improving the cellular activities of an organism by manipulating the metabolic, signal or regulatory network. In silico reaction knockout simulation is one of the techniques applied to analyse the effects of genetic perturbations on metabolite production. Many methods consider growth coupling as the objective function, whereby it searches for mutants that maximise the growth and production rate. However, the final goal is to increase the production rate. Furthermore, they produce one single solution, though in reality, cells do not focus on one objective and they need to consider various different competing objectives. In this work, a method, termed ndsDSAFBA (non-dominated sorting Differential Search Algorithm and Flux Balance Analysis), has been developed to find the reaction knockouts involved in maximising the production rate and growth rate of the mutant, by incorporating Pareto dominance concepts. The proposed ndsDSAFBA method was validated using three genome-scale metabolic models. We obtained a set of non-dominated solutions, with each solution representing a different mutant strain. The results obtained were compared with the single objective optimisation (SOO) and multi-objective optimisation (MOO) methods. The results demonstrate that ndsDSAFBA is better than the other methods in terms of production rate and growth rate.

Biological conversion of methane to putrescine using genome-scale model-guided metabolic engineering of a methanotrophic bacterium Methylomicrobium alcaliphilum 20Z

BACKGROUND

Methane is the primary component of natural gas and biogas. The huge abundance of methane makes it a promising alternative carbon source for industrial biotechnology. Herein, we report diamine compound, putrescine, production from methane by an industrially promising methanotroph Methylomicrobium alcaliphilum 20Z.

RESULTS

We conducted adaptive evolution to improve putrescine tolerance of M. alcaliphilum 20Z because putrescine highly inhibits the cell growth. The evolved strain 20ZE was able to grow in the presence of 400 mM of putrescine dihydrochloride. The expression of linear pathway ornithine decarboxylase genes from Escherichia coli and Methylosinus trichosporium OB3b allowed the engineered strain to produce putrescine. A higher putrescine titer of 12.44 mg/L was obtained in the strain 20ZE-pACO with ornithine decarboxylase from M. trichosporium OB3b. For elimination of the putrescine utilization pathway, spermidine synthase (MEALZ_3408) was knocked out, resulting in no spermidine formation in the strain 20ZES1-pACO with a putrescine titer of 18.43 mg/L. Next, a genome-scale metabolic model was applied to identify gene knockout strategies. Acetate kinase (MEALZ_2853) and subsequently lactate dehydrogenase (MEALZ_0534) were selected as knockout targets, and the deletion of these genes resulted in an improvement of the putrescine titer to 26.69 mg/L. Furthermore, the putrescine titer was improved to 39.04 mg/L by overexpression of key genes in the ornithine biosynthesis pathway under control of the pTac promoter. Finally, suitable nitrogen sources for growth of M. alcaliphilum 20Z and putrescine production were optimized with the supplement of 2 mM ammonium chloride to nitrate mineral salt medium, and this led to the production of 98.08 mg/L putrescine, almost eightfold higher than that from the initial strain. Transcriptome analysis of the engineered strains showed upregulation of most genes involved in methane assimilation, citric acid cycle, and ammonia assimilation in ammonia nitrate mineral salt medium, compared to nitrate mineral salt medium.

CONCLUSIONS

The engineered M. alcaliphilum 20ZE4-pACO strain was able to produce putrescine up to 98.08 mg/L, almost eightfold higher than the initial strain. This study represents the bioconversion of methane to putrescine-a high value-added diamine compound.

Cyanobacteria as an eco-friendly resource for biofuel production: A critical review

Cyanobacteria are photosynthetic microorganisms which can be found in various environmental habitats. These photosynthetic bacteria are considered as promising feedstock for the production of the third- and the fourth-generation biofuels. The main subject of this review is highlighting the significant aspects of the biofuel production from cyanobacteria. The most recent investigations about the extraction or separation of the bio-oil from cyanobacteria are also adduced in the present review. Moreover, the genetic engineering of cyanobacteria for improving biofuel production and the impact of bioinformatics studies on the designing better-engineered strains are mentioned. The large-scale biofuel production is challenging, so the economic considerations to provide inexpensive biofuels are also cited. It seems that the future of biofuels is strongly dependent to the following items; understanding the metabolic pathways of the cyanobacterial species, progression in the construction of the engineered cyanobacteria, and inexpensive large-scale cultivation of them.

New Applications of Synthetic Biology Tools for Cyanobacterial Metabolic Engineering

Cyanobacteria are promising microorganisms for sustainable biotechnologies, yet unlocking their potential requires radical re-engineering and application of cutting-edge synthetic biology techniques. In recent years, the available devices and strategies for modifying cyanobacteria have been increasing, including advances in the design of genetic promoters, ribosome binding sites, riboswitches, reporter proteins, modular vector systems, and markerless selection systems. Because of these new toolkits, cyanobacteria have been successfully engineered to express heterologous pathways for the production of a wide variety of valuable compounds. Cyanobacterial strains with the potential to be used in real-world applications will require the refinement of genetic circuits used to express the heterologous pathways and development of accurate models that predict how these pathways can be best integrated into the larger cellular metabolic network. Herein, we review advances that have been made to translate synthetic biology tools into cyanobacterial model organisms and summarize experimental and in silico strategies that have been employed to increase their bioproduction potential. Despite the advances in synthetic biology and metabolic engineering during the last years, it is clear that still further improvements are required if cyanobacteria are to be competitive with heterotrophic microorganisms for the bioproduction of added-value compounds.

Physiological Significance of NAD Kinases in Cyanobacteria

Unicellular cyanobacteria are thought to be the evolutionary ancestors of plant chloroplasts and are widely used both for chemical production and as model organisms in studies of photosynthesis. Although most research focused on increasing reducing power (that is, NADPH) as target of metabolic engineering, the physiological roles of NAD(P)(H) in cyanobacteria poorly understood. In cyanobacteria such as the model species Synechocystis sp. PCC 6803, most metabolic pathways share a single compartment. This complex metabolism raises the question of how cyanobacteria control the amounts of the redox pairs NADH/NAD⁺ and NADPH/NADP⁺ in the cyanobacterial metabolic pathways. For example, photosynthetic and respiratory electron transport chains share several redox components in the thylakoid lumen, including plastoquinone, cytochrome b₆f (cyt b₆f), and the redox carriers plastocyanin and cytochrome c6. In the case of photosynthesis, NADP⁺ acts as an important electron mediator on the acceptor-side of photosystem I (PSI) in the linear electron chain as well as in the plant chloroplast. Meanwhile, in respiration, most electrons derived from NADPH and NADH are transferred by NAD(P)H dehydrogenases. Therefore, it is expected that Synechocystis employs unique NAD(P)(H) -pool control mechanisms to regulate the mixed metabolic systems involved in photosynthesis and respiration. This review article summarizes the current state of knowledge of NAD(P)(H) metabolism in Synechocystis. In particular, we focus on the physiological function in Synechocystis of NAD kinase, the enzyme that phosphorylates NAD⁺ to NADP⁺.

Targeted Repression of Essential Genes To Arrest Growth and Increase Carbon Partitioning and Biofuel Titers in Cyanobacteria

Photoautotrophic production of fuels and chemicals by cyanobacteria typically gives lower volumetric productivities and titers than heterotrophic production. Cyanobacteria cultures become light limited above an optimal cell density, so that this substrate is not supplied to all cells sufficiently. Here, we investigate genetic strategies for a two-phase cultivation, where biofuel-producing Synechocystis cultures are limited to an optimal cell density through inducible CRISPR interference (CRISPRi) repression of cell growth. Fixed CO₂ is diverted to ethanol or n-butanol. Among the most successful strategies was partial repression of citrate synthase gltA. Strong repression (>90%) of gltA at low culture densities increased carbon partitioning to n-butanol 5-fold relative to a nonrepression strain, but sacrificed volumetric productivity due to severe growth restriction. CO₂ fixation continued for at least 3 days after growth was arrested. By targeting sgRNAs to different regions of the gltA gene, we could modulate GltA expression and carbon partitioning between growth and product to increase both specific and volumetric productivity. These growth arrest strategies can be useful for improving performance of other photoautotrophic processes.

Systematic overexpression study to find target enzymes enhancing production of terpenes in Synechocystis PCC 6803, using isoprene as a model compound

Of the two natural metabolic pathways for making terpenoids, biotechnological utilization of the mevalonate (MVA) pathway has enabled commercial production of valuable compounds, while the more recently discovered but stoichiometrically more efficient methylerythritol phosphate (MEP) pathway is underdeveloped. We conducted a study on the overexpression of each enzyme in the MEP pathway in the unicellular cyanobacterium Synechocystis sp. PCC 6803, to identify potential targets for increasing flux towards terpenoid production, using isoprene as a reporter molecule. Results showed that the enzymes Ipi, Dxs and IspD had the biggest impact on isoprene production. By combining and creating operons out of those genes, isoprene production was increased 2-fold compared to the base strain. A genome-scale model was used to identify targets upstream of the MEP pathway that could redirect flux towards terpenoids. A total of ten reactions from the Calvin-Benson-Bassham cycle, lower glycolysis and co-factor synthesis pathways were probed for their effect on isoprene synthesis by co-expressing them with the MEP enzymes, resulting in a 60% increase in production from the best strain. Lastly, we studied two isoprene synthases with the highest reported catalytic rates. Only by expressing them together with Dxs and Ipi could we get stable strains that produced 2.8 mg/g isoprene per dry cell weight, a 40-fold improvement compared to the initial strain.

Genetic Optimization Algorithm for Metabolic Engineering Revisited

To date, several independent methods and algorithms exist for exploiting constraint-based stoichiometric models to find metabolic engineering strategies that optimize microbial production performance. Optimization procedures based on metaheuristics facilitate a straightforward adaption and expansion of engineering objectives, as well as fitness functions, while being particularly suited for solving problems of high complexity. With the increasing interest in multi-scale models and a need for solving advanced engineering problems, we strive to advance genetic algorithms, which stand out due to their intuitive optimization principles and the proven usefulness in this field of research. A drawback of genetic algorithms is that premature convergence to sub-optimal solutions easily occurs if the optimization parameters are not adapted to the specific problem. Here, we conducted comprehensive parameter sensitivity analyses to study their impact on finding optimal strain designs. We further demonstrate the capability of genetic algorithms to simultaneously handle (i) multiple, non-linear engineering objectives; (ii) the identification of gene target-sets according to logical gene-protein-reaction associations; (iii) minimization of the number of network perturbations; and (iv) the insertion of non-native reactions, while employing genome-scale metabolic models. This framework adds a level of sophistication in terms of strain design robustness, which is exemplarily tested on succinate overproduction in Escherichia coli.

Recent advances in synthetic biology of cyanobacteria

Cyanobacteria are attractive hosts that can be engineered for the photosynthetic production of fuels, fine chemicals, and proteins from CO₂. Moreover, the responsiveness of these photoautotrophs towards different environmental signals, such as light, CO₂, diurnal cycle, and metals make them potential hosts for the development of biosensors. However, engineering these hosts proves to be a challenging and lengthy process. Synthetic biology can make the process of biological engineering more predictable through the use of standardized biological parts that are well characterized and tools to assemble them. While significant progress has been made with model heterotrophic organisms, many of the parts and tools are not portable in cyanobacteria. Therefore, efforts are underway to develop and characterize parts derived from cyanobacteria. In this review, we discuss the reported parts and tools with the objective to develop cyanobacteria as cell factories or biosensors. We also discuss the issues related to characterization, tunability, portability, and the need to develop enabling technologies to engineer this "green" chassis.

Alignment of microbial fitness with engineered product formation: obligatory coupling between acetate production and photoautotrophic growth

BACKGROUND

Microbial bioengineering has the potential to become a key contributor to the future development of human society by providing sustainable, novel, and cost-effective production pipelines. However, the sustained productivity of genetically engineered strains is often a challenge, as spontaneous non-producing mutants tend to grow faster and take over the population. Novel strategies to prevent this issue of strain instability are urgently needed.

RESULTS

In this study, we propose a novel strategy applicable to all microbial production systems for which a genome-scale metabolic model is available that aligns the production of native metabolites to the formation of biomass. Based on well-established constraint-based analysis techniques such as OptKnock and FVA, we developed an in silico pipeline-FRUITS-that specifically 'Finds Reactions Usable in Tapping Side-products'. It analyses a metabolic network to identify compounds produced in anabolism that are suitable to be coupled to growth by deletion of their re-utilization pathway(s), and computes their respective biomass and product formation rates. When applied to Synechocystis sp. PCC6803, a model cyanobacterium explored for sustainable bioproduction, a total of nine target metabolites were identified. We tested our approach for one of these compounds, acetate, which is used in a wide range of industrial applications. The model-guided engineered strain shows an obligatory coupling between acetate production and photoautotrophic growth as predicted. Furthermore, the stability of acetate productivity in this strain was confirmed by performing prolonged turbidostat cultivations.

CONCLUSIONS

This work demonstrates a novel approach to stabilize the production of target compounds in cyanobacteria that culminated in the first report of a photoautotrophic growth-coupled cell factory. The method developed is generic and can easily be extended to any other modeled microbial production system.

Photosynthetic fuel for heterologous enzymes: the role of electron carrier proteins

Plants, cyanobacteria, and algae generate a surplus of redox power through photosynthesis, which makes them attractive for biotechnological exploitations. While central metabolism consumes most of the energy, pathways introduced through metabolic engineering can also tap into this source of reducing power. Recent work on the metabolic engineering of photosynthetic organisms has shown that the electron carriers such as ferredoxin and flavodoxin can be used to couple heterologous enzymes to photosynthetic reducing power. Because these proteins have a plethora of interaction partners and rely on electrostatically steered complex formation, they form productive electron transfer complexes with non-native enzymes. A handful of examples demonstrate channeling of photosynthetic electrons to drive the activity of heterologous enzymes, and these focus mainly on hydrogenases and cytochrome P450s. However, competition from native pathways and inefficient electron transfer rates present major obstacles, which limit the productivity of heterologous reactions coupled to photosynthesis. We discuss specific approaches to address these bottlenecks and ensure high productivity of such enzymes in a photosynthetic context.

Metabolic engineering of Synechococcus elongatus PCC 7942 for improvement of 1,3-propanediol and glycerol production based on in silico simulation of metabolic flux distribution

Background

Production directly from carbon dioxide by engineered cyanobacteria is one of the promising technologies for sustainable future. Previously, we have successfully achieved 1,3-propanediol (1,3-PDO) production using Synechococcus elongatus PCC 7942 with a synthetic metabolic pathway. The strain into which the synthetic metabolic pathway was introduced produced 3.48 mM (0.265 g/L) 1,3-PDO and 14.3 mM (1.32 g/L) glycerol during 20 days of incubation. In this study, the productivities of 1,3-PDO were improved by gene disruption selected by screening with in silico simulation.

Methods

First, a stoichiometric metabolic model was applied to prediction of cellular metabolic flux distribution in a 1,3-PDO-producing strain of S. elongatus PCC 7942. A genome-scale model of S. elongatus PCC 7942 constructed by Knoop was modified by the addition of a synthetic metabolic pathway for 1,3-PDO production. Next, the metabolic flux distribution predicted by metabolic flux balance analysis (FBA) was used for in silico simulation of gene disruption. As a result of gene disruption simulation, NADPH dehydrogenase 1 (NDH-1) complexes were found by screening to be the most promising candidates for disruption to improve 1,3-PDO production. The effect of disruption of the gene encoding a subunit of the NDH-1 complex was evaluated in the 1,3-PDO-producing strain.

Results and Conclusions

During 20 days of incubation, the ndhF1-null 1,3-PDO-producing strain showed the highest titers: 4.44 mM (0.338 g/L) 1,3-PDO and 30.3 mM (2.79 g/L) glycerol. In this study, we successfully improved 1,3-PDO productivity on the basis of in silico simulation of gene disruption.

Electronic supplementary material

The online version of this article (10.1186/s12934-017-0824-4) contains supplementary material, which is available to authorized users.

Responses of Synechocystis sp. PCC 6803 to heterologous biosynthetic pathways

Background

There are an increasing number of studies regarding genetic manipulation of cyanobacteria to produce commercially interesting compounds. The majority of these works study the expression and optimization of a selected heterologous pathway, largely ignoring the wholeness and complexity of cellular metabolism. Regulation and response mechanisms are largely unknown, and even the metabolic pathways themselves are not fully elucidated. This poses a clear limitation in exploiting the rich biosynthetic potential of cyanobacteria.

Results

In this work, we focused on the production of two different compounds, the cyanogenic glucoside dhurrin and the diterpenoid 13R-manoyl oxide in Synechocystis PCC 6803. We used genome-scale metabolic modelling to study fluxes in individual reactions and pathways, and we determined the concentrations of key metabolites, such as amino acids, carotenoids, and chlorophylls. This allowed us to identify metabolic crosstalk between the native and the introduced metabolic pathways. Most results and simulations highlight the metabolic robustness of cyanobacteria, suggesting that the host organism tends to keep metabolic fluxes and metabolite concentrations steady, counteracting the effects of the heterologous pathway. However, the amino acid concentrations of the dhurrin-producing strain show an unexpected profile, where the perturbation levels were high in seemingly unrelated metabolites.

Conclusions

There is a wealth of information that can be derived by combining targeted metabolite identification and computer modelling as a frame of understanding. Here we present an example of how strain engineering approaches can be coupled to ‘traditional’ metabolic engineering with systems biology, resulting in novel and more efficient manipulation strategies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12934-017-0757-y) contains supplementary material, which is available to authorized users.

Rerouting of carbon flux in a glycogen mutant of cyanobacteria assessed via isotopically non-stationary 13 C metabolic flux analysis

Cyanobacteria, which constitute a quantitatively dominant phylum, have attracted attention in biofuel applications due to favorable physiological characteristics, high photosynthetic efficiency and amenability to genetic manipulations. However, quantitative aspects of cyanobacterial metabolism have received limited attention. In the present study, we have performed isotopically non-stationary ¹³ C metabolic flux analysis (INST-¹³ C-MFA) to analyze rerouting of carbon in a glycogen synthase deficient mutant strain (glgA-I glgA-II) of the model cyanobacterium Synechococcus sp. PCC 7002. During balanced photoautotrophic growth, 10-20% of the fixed carbon is stored in the form of glycogen via a pathway that is conserved across the cyanobacterial phylum. Our results show that deletion of glycogen synthase gene orchestrates cascading effects on carbon distribution in various parts of the metabolic network. Carbon that was originally destined to be incorporated into glycogen gets partially diverted toward alternate storage molecules such as glucosylglycerol and sucrose. The rest is partitioned within the metabolic network, primarily via glycolysis and tricarboxylic acid cycle. A lowered flux toward carbohydrate synthesis and an altered distribution at the glucose-1-phosphate node indicate flexibility in the network. Further, reversibility of glycogen biosynthesis reactions points toward the presence of futile cycles. Similar redistribution of carbon was also predicted by Flux Balance Analysis. The results are significant to metabolic engineering efforts with cyanobacteria where fixed carbon needs to be re-routed to products of interest. Biotechnol. Bioeng. 2017;114: 2298-2308. © 2017 Wiley Periodicals, Inc.

Systems analysis of ethanol production in the genetically engineered cyanobacterium Synechococcus sp. PCC 7002

BACKGROUND

Future sustainable energy production can be achieved using mass cultures of photoautotrophic microorganisms, which are engineered to synthesize valuable products directly from CO₂ and sunlight. As cyanobacteria can be cultivated in large scale on non-arable land, these phototrophic bacteria have become attractive organisms for production of biofuels. Synechococcus sp. PCC 7002, one of the cyanobacterial model organisms, provides many attractive properties for biofuel production such as tolerance of seawater and high light intensities.

RESULTS

Here, we performed a systems analysis of an engineered ethanol-producing strain of the cyanobacterium Synechococcus sp. PCC 7002, which was grown in artificial seawater medium over 30 days applying a 12:12 h day-night cycle. Biosynthesis of ethanol resulted in a final accumulation of 0.25% (v/v) ethanol, including ethanol lost due to evaporation. The cultivation experiment revealed three production phases. The highest production rate was observed in the initial phase when cells were actively growing. In phase II growth of the producer strain stopped, but ethanol production rate was still high. Phase III was characterized by a decrease of both ethanol production and optical density of the culture. Metabolomics revealed that the carbon drain due to ethanol diffusion from the cell resulted in the expected reduction of pyruvate-based intermediates. Carbon-saving strategies successfully compensated the decrease of central intermediates of carbon metabolism during the first phase of fermentation. However, during long-term ethanol production the producer strain showed clear indications of intracellular carbon limitation. Despite the decreased levels of glycolytic and tricarboxylic acid cycle intermediates, soluble sugars and even glycogen accumulated in the producer strain. The changes in carbon assimilation patterns are partly supported by proteome analysis, which detected decreased levels of many enzymes and also revealed the stress phenotype of ethanol-producing cells. Strategies towards improved ethanol production are discussed.

CONCLUSIONS

Systems analysis of ethanol production in Synechococcus sp. PCC 7002 revealed initial compensation followed by increasing metabolic limitation due to excessive carbon drain from primary metabolism.