Computational evaluation of Synechococcus sp. PCC 7002 metabolism for chemical production

High-Density Guide RNA Tiling and Machine Learning for Designing CRISPR Interference in Synechococcus sp. PCC 7002

While CRISPRi was previously established in Synechococcus sp. PCC 7002 (hereafter 7002), the design principles for guide RNA (gRNA) effectiveness remain largely unknown. Here, 76 strains of 7002 were constructed with gRNAs targeting three reporter systems to evaluate features that impact gRNA efficiency. Correlation analysis of the data revealed that important features of gRNA design include the position relative to the start codon, GC content, protospacer adjacent motif (PAM) site, minimum free energy, and targeted DNA strand. Unexpectedly, some gRNAs targeting upstream of the promoter region showed small but significant increases in reporter expression, and gRNAs targeting the terminator region showed greater repression than gRNAs targeting the 3' end of the coding sequence. Machine learning algorithms enabled prediction of gRNA effectiveness, with Random Forest having the best performance across all training sets. This study demonstrates that high-density gRNA data and machine learning can improve gRNA design for tuning gene expression in 7002.

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.

Prospective bacterial and fungal sources of hyaluronic acid: A review

The unique biological and rheological properties make hyaluronic acid a sought-after material for medicine and cosmetology. Due to very high purity requirements for hyaluronic acid in medical applications, the profitability of streptococcal fermentation is reduced. Production of hyaluronic acid by recombinant systems is considered a promising alternative. Variations in combinations of expressed genes and fermentation conditions alter the yield and molecular weight of produced hyaluronic acid. This review is devoted to the current state of hyaluronic acid production by recombinant bacterial and fungal organisms.

Multi-Objective Optimization of Microalgae Metabolism: An Evolutive Algorithm Based on FBA

Studies enabled by metabolic models of different species of microalgae have become significant since they allow us to understand changes in their metabolism and physiological stages. The most used method to study cell metabolism is FBA, which commonly focuses on optimizing a single objective function. However, recent studies have brought attention to the exploration of simultaneous optimization of multiple objectives. Such strategies have found application in optimizing biomass and several other bioproducts of interest; they usually use approaches such as multi-level models or enumerations schemes. This work proposes an alternative in silico multiobjective model based on an evolutionary algorithm that offers a broader approximation of the Pareto frontier, allowing a better angle for decision making in metabolic engineering. The proposed strategy is validated on a reduced metabolic network of the microalgae Chlamydomonas reinhardtii while optimizing for the production of protein, carbohydrates, and CO2 uptake. The results from the conducted experimental design show a favorable difference in the number of solutions achieved compared to a classic tool solving FBA.

Introduction of NADH-dependent nitrate assimilation in Synechococcus sp. PCC 7002 improves photosynthetic production of 2-methyl-1-butanol and isobutanol

Cyanobacteria hold promise for renewable chemical production due to their photosynthetic nature, but engineered strains frequently display poor production characteristics. These difficulties likely arise in part due to the distinctive photoautotrophic metabolism of cyanobacteria. In this work, we apply a genome-scale metabolic model of the cyanobacteria Synechococus sp. PCC 7002 to identify strain designs accounting for this unique metabolism that are predicted to improve the production of various biofuel alcohols (e.g. 2-methyl-1-butanol, isobutanol, and 1-butanol) synthesized via an engineered biosynthesis pathway. Using the model, we identify that the introduction of a large, non-native NADH-demand into PCC 7002's metabolic network is predicted to enhance production of these alcohols by promoting NADH-generating reactions upstream of the production pathways. To test this, we construct strains of PCC 7002 that utilize a heterologous, NADH-dependent nitrite reductase in place of the native, ferredoxin-dependent enzyme to create an NADH-demand in the cells when grown on nitrate-containing media. We find that photosynthetic production of both isobutanol and 2-methyl-1-butanol is significantly improved in the engineered strain background relative to that in a wild-type background. We additionally identify that the use of high-nutrient media leads to a substantial prolongment of the production curve in our alcohol production strains. The metabolic engineering strategy identified and tested in this work presents a novel approach to engineer cyanobacterial production strains that takes advantage of a unique aspect of their metabolism and serves as a basis on which to further develop strains with improved production of these alcohols and related products.

Potential of Producing Flavonoids Using Cyanobacteria As a Sustainable Chassis

Numerous plant secondary metabolites have remarkable impacts on both food supplements and pharmaceuticals for human health improvement. However, higher plants can only generate small amounts of these chemicals with specific temporal and spatial arrangements, which are unable to satisfy the expanding market demands. Cyanobacteria can directly utilize CO₂, light energy, and inorganic nutrients to synthesize versatile plant-specific photosynthetic intermediates and organic compounds in large-scale photobioreactors with outstanding economic merit. Thus, they have been rapidly developed as a "green" chassis for the synthesis of bioproducts. Flavonoids, chemical compounds based on aromatic amino acids, are considered to be indispensable components in a variety of nutraceutical, pharmaceutical, and cosmetic applications. In contrast to heterotrophic metabolic engineering pioneers, such as yeast and Escherichia coli, information about the biosynthesis flavonoids and their derivatives is less comprehensive than that of their photosynthetic counterparts. Here, we review both benefits and challenges to promote cyanobacterial cell factories for flavonoid biosynthesis. With increasing concerns about global environmental issues and food security, we are confident that energy self-supporting cyanobacteria will attract increasing attention for the generation of different kinds of bioproducts. We hope that the work presented here will serve as an index and encourage more scientists to join in the relevant research area.

A Hybrid Flux Balance Analysis and Machine Learning Pipeline Elucidates Metabolic Adaptation in Cyanobacteria

Machine learning has recently emerged as a promising tool for inferring multi-omic relationships in biological systems. At the same time, genome-scale metabolic models (GSMMs) can be integrated with such multi-omic data to refine phenotypic predictions. In this work, we use a multi-omic machine learning pipeline to analyze a GSMM of Synechococcus sp. PCC 7002, a cyanobacterium with large potential to produce renewable biofuels. We use regularized flux balance analysis to observe flux response between conditions across photosynthesis and energy metabolism. We then incorporate principal-component analysis, k-means clustering, and LASSO regularization to reduce dimensionality and extract key cross-omic features. Our results suggest that combining metabolic modeling with machine learning elucidates mechanisms used by cyanobacteria to cope with fluctuations in light intensity and salinity that cannot be detected using transcriptomics alone. Furthermore, GSMMs introduce critical mechanistic details that improve the performance of omic-based machine learning methods.

Genome-Wide Analysis of RNA Decay in the Cyanobacterium Synechococcus sp. Strain PCC 7002

RNA degradation is an important process that affects the final concentration of individual mRNAs, affecting protein expression and cellular physiology. Studies of how RNA is degraded increase our knowledge of this fundamental process as well as enable the creation of genetic tools to manipulate RNA stability. By studying global transcript turnover, we searched for sequence elements that correlated with transcript (in)stability and used these sequences to guide tool design. This study probes global RNA turnover in a cyanobacterium, Synechococcus sp. strain PCC 7002, that both has a unique array of RNases that facilitate RNA degradation and is an industrially relevant strain that could be used to convert CO₂ and sunlight into useful products.

RNA degradation is an important process that influences the ultimate concentration of individual proteins inside cells. While the main enzymes that facilitate this process have been identified, global maps of RNA turnover are available for only a few species. Even in these cases, there are few sequence elements that are known to enhance or destabilize a native transcript; even fewer confer the same effect when added to a heterologous transcript. To address this knowledge gap, we assayed genome-wide RNA degradation in the cyanobacterium Synechococcus sp. strain PCC 7002 by collecting total RNA samples after stopping nascent transcription with rifampin. We quantified the abundance of each position in the transcriptome as a function of time using RNA-sequencing data and later analyzed the global mRNA decay map using machine learning principles. Half-lives, calculated on a per-ORF (open reading frame) basis, were extremely short, with a median half-life of only 0.97 min. Despite extremely rapid turnover of most mRNA, transcripts encoding proteins involved in photosynthesis were both highly expressed and highly stable. Upon inspection of these stable transcripts, we identified an enriched motif in the 3′ untranslated region (UTR) that had similarity to Rho-independent terminators. We built statistical models for half-life prediction and used them to systematically identify sequence motifs in both 5′ and 3′ UTRs that correlate with stabilized transcripts. We found that transcripts linked to a terminator containing a poly(U) tract had a longer half-life than both those without a poly(U) tract and those without a terminator.

IMPORTANCE RNA degradation is an important process that affects the final concentration of individual mRNAs, affecting protein expression and cellular physiology. Studies of how RNA is degraded increase our knowledge of this fundamental process as well as enable the creation of genetic tools to manipulate RNA stability. By studying global transcript turnover, we searched for sequence elements that correlated with transcript (in)stability and used these sequences to guide tool design. This study probes global RNA turnover in a cyanobacterium, Synechococcus sp. strain PCC 7002, that both has a unique array of RNases that facilitate RNA degradation and is an industrially relevant strain that could be used to convert CO₂ and sunlight into useful products.

Towards model-driven characterization and manipulation of plant lipid metabolism

Plant lipids have versatile applications and provide essential fatty acids in human diet. Therefore, there has been a growing interest to better characterize the genetic basis, regulatory networks, and metabolic pathways that shape lipid quantity and composition. Addressing these issues is challenging due to context-specificity of lipid metabolism integrating environmental, developmental, and tissue-specific cues. Here we systematically review the known metabolic pathways and regulatory interactions that modulate the levels of storage lipids in oilseeds. We argue that the current understanding of lipid metabolism provides the basis for its study in the context of genome-wide plant metabolic networks with the help of approaches from constraint-based modeling and metabolic flux analysis. The focus is on providing a comprehensive summary of the state-of-the-art of modeling plant lipid metabolic pathways, which we then contrast with the existing modeling efforts in yeast and microalgae. We then point out the gaps in knowledge of lipid metabolism, and enumerate the recent advances of using genome-wide association and quantitative trait loci mapping studies to unravel the genetic regulations of lipid metabolism. Finally, we offer a perspective on how advances in the constraint-based modeling framework can propel further characterization of plant lipid metabolism and its rational manipulation.

Enhancing photosynthetic production of glycogen-rich biomass for use as a fermentation feedstock

Current sources of fermentation feedstocks, i.e. corn, sugar cane, or plant biomass, fall short of demand for liquid transportation fuels and commodity chemicals in the United States. Aquatic phototrophs including cyanobacteria have the potential to supplement the supply of current fermentable feedstocks. In this strategy, cells are engineered to accumulate storage molecules including glycogen, cellulose, and/or lipid oils that can be extracted from harvested biomass and fed to heterotrophic organisms engineered to produce desired chemical products. In this manuscript, we examine the production of glycogen in the model cyanobacteria, Synechococcus sp. strain PCC 7002, and subsequent conversion of cyanobacterial biomass by an engineered Escherichia coli to octanoic acid as a model product. In effort to maximize glycogen production, we explored the deletion of catabolic enzymes and overexpression of GlgC, an enzyme that catalyzes the first committed step towards glycogen synthesis. We found that deletion of glgP increased final glycogen titers when cells were grown in diurnal light. Overexpression of GlgC led to a temporal increase in glycogen content but not in an overall increase in final titer or content. The best strains were grown, harvested, and used to formulate media for growth of E. coli. The cyanobacterial media was able to support the growth of an engineered E. coli and produce octanoic acid at the same titer as common laboratory media.

Biochemical Characteristics and a Genome-Scale Metabolic Model of an Indian Euryhaline Cyanobacterium with High Polyglucan Content

Marine cyanobacteria are promising microbes to capture and convert atmospheric CO₂ and light into biomass and valuable industrial bio-products. Yet, reports on metabolic characteristics of non-model cyanobacteria are scarce. In this report, we show that an Indian euryhaline Synechococcus sp. BDU 130192 has biomass accumulation comparable to a model marine cyanobacterium and contains approximately double the amount of total carbohydrates, but significantly lower protein levels compared to Synechococcus sp. PCC 7002 cells. Based on its annotated chromosomal genome sequence, we present a genome scale metabolic model (GSMM) of this cyanobacterium, which we have named as iSyn706. The model includes 706 genes, 908 reactions, and 900 metabolites. The difference in the flux balance analysis (FBA) predicted flux distributions between Synechococcus sp. PCC 7002 and Synechococcus sp. BDU130192 strains mimicked the differences in their biomass compositions. Model-predicted oxygen evolution rate for Synechococcus sp. BDU130192 was found to be close to the experimentally-measured value. The model was analyzed to determine the potential of the strain for the production of various industrially-useful products without affecting growth significantly. This model will be helpful to researchers interested in understanding the metabolism as well as to design metabolic engineering strategies for the production of industrially-relevant compounds.

Engineering microbial chemical factories using metabolic models

Living organisms in analogy with chemical factories use simple molecules such as sugars to produce a variety of compounds which are necessary for sustaining life and some of which are also commercially valuable. The metabolisms of simple (such as bacteria) and higher organisms (such as plants) alike can be exploited to convert low value inputs into high value outputs. Unlike conventional chemical factories, microbial production chassis are not necessarily tuned for a single product overproduction. Despite the same end goal, metabolic and industrial engineers rely on different techniques for achieving productivity goals. Metabolic engineers cannot affect reaction rates by manipulating pressure and temperature, instead they have at their disposal a range of enzymes and transcriptional and translational processes to optimize accordingly. In this review, we first highlight how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed in systems and control engineering. Specifically, how algorithmic concepts derived in operations research can help explain the structure and organization of metabolic networks. Finally, we consider the future directions and challenges faced by the field of metabolic network modeling and the possible contributions of concepts drawn from the classical fields of chemical and control engineering. The aim of the review is to offer a current perspective of metabolic engineering and all that it entails without requiring specialized knowledge of bioinformatics or systems biology.

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.

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.

Advances in metabolic modeling of oleaginous microalgae

Production of biofuels and bioenergy precursors by phototrophic microorganisms, such as microalgae and cyanobacteria, is a promising alternative to conventional fuels obtained from non-renewable resources. Several species of microalgae have been investigated as potential candidates for the production of biofuels, for the most part due to their exceptional metabolic capability to accumulate large quantities of lipids. Constraint-based modeling, a systems biology approach that accurately predicts the metabolic phenotype of phototrophs, has been deployed to identify suitable culture conditions as well as to explore genetic enhancement strategies for bioproduction. Core metabolic models were employed to gain insight into the central carbon metabolism in photosynthetic microorganisms. More recently, comprehensive genome-scale models, including organelle-specific information at high resolution, have been developed to gain new insight into the metabolism of phototrophic cell factories. Here, we review the current state of the art of constraint-based modeling and computational method development and discuss how advanced models led to increased prediction accuracy and thus improved lipid production in microalgae.

Rerouting of Metabolism into Desired Cellular Products by Nutrient Stress: Fluxes Reveal the Selected Pathways in Cyanobacterial Photosynthesis

Boosting cellular growth rates while redirecting metabolism to make desired products are the preeminent goals of gene engineering of photoautotrophs, yet so far these goals have been hardly achieved owing to lack of understanding of the functional pathways and their choke points. Here we apply a ¹³C mass isotopic method (INST-MFA) to quantify instantaneous fluxes of metabolites during photoautotrophic growth. INST-MFA determines the globally most accurate set of absolute fluxes for each metabolite from a finite set of measured ¹³C-isotopomer fluxes by minimizing the sum of squared residuals between experimental and predicted mass isotopomers. We show that the widely observed shift in biomass composition in cyanobacteria, demonstrated here with Synechococcus sp. PCC 7002, favoring glycogen synthesis during nitrogen starvation is caused by (1) increased flux through a bottleneck step in gluconeogenesis (3PG → GAP/DHAP), and (2) flux overflow through a previously unrecognized hybrid gluconeogenesis-pentose phosphate (hGPP) pathway. Our data suggest the slower growth rate and biomass accumulation under N starvation is due to a reduced carbon fixation rate and a reduced flux of carbon into amino acid precursors. Additionally, ¹³C flux from α-ketoglutarate to succinate is demonstrated to occur via succinic semialdehyde, an alternative to the conventional TCA cycle, in Synechococcus 7002 under photoautotrophic conditions. We found that pyruvate and oxaloacetate are synthesized mainly by malate dehydrogenase with minimal flux into acetyl coenzyme-A via pyruvate dehydrogenase. Nutrient stress induces major shifts in fluxes into new pathways that deviate from historical metabolic pathways derived from model bacteria.

Light-optimized growth of cyanobacterial cultures: Growth phases and productivity of biomass and secreted molecules in light-limited batch growth

Cyanobacteria are photosynthetic microorganisms whose metabolism can be modified through genetic engineering for production of a wide variety of molecules directly from CO2, light, and nutrients. Diverse molecules have been produced in small quantities by engineered cyanobacteria to demonstrate the feasibility of photosynthetic biorefineries. Consequently, there is interest in engineering these microorganisms to increase titer and productivity to meet industrial metrics. Unfortunately, differing experimental conditions and cultivation techniques confound comparisons of strains and metabolic engineering strategies. In this work, we discuss the factors governing photoautotrophic growth and demonstrate nutritionally replete conditions in which a model cyanobacterium can be grown to stationary phase with light as the sole limiting substrate. We introduce a mathematical framework for understanding the dynamics of growth and product secretion in light-limited cyanobacterial cultures. Using this framework, we demonstrate how cyanobacterial growth in differing experimental systems can be easily scaled by the volumetric photon delivery rate using the model organisms Synechococcus sp. strain PCC7002 and Synechococcus elongatus strain UTEX2973. We use this framework to predict scaled up growth and product secretion in 1L photobioreactors of two strains of Synechococcus PCC7002 engineered for production of l-lactate or L-lysine. The analytical framework developed in this work serves as a guide for future metabolic engineering studies of cyanobacteria to allow better comparison of experiments performed in different experimental systems and to further investigate the dynamics of growth and product secretion.

Rewiring of Cyanobacterial Metabolism for Hydrogen Production: Synthetic Biology Approaches and Challenges

With the demand for renewable energy growing, hydrogen (H₂) is becoming an attractive energy carrier. Developing H₂ production technologies with near-net zero carbon emissions is a major challenge for the "H₂ economy." Certain cyanobacteria inherently possess enzymes, nitrogenases, and bidirectional hydrogenases that are capable of H₂ evolution using sunlight, making them ideal cell factories for photocatalytic conversion of water to H₂. With the advances in synthetic biology, cyanobacteria are currently being developed as a "plug and play" chassis to produce H₂. This chapter describes the metabolic pathways involved and the theoretical limits to cyanobacterial H₂ production and summarizes the metabolic engineering technologies pursued.

Flux Balance Analysis Indicates that Methane Is the Lowest Cost Feedstock for Microbial Cell Factories

The low cost of natural gas has driven significant interest in using C₁ carbon sources (e.g. methane, methanol, CO, syngas) as feedstocks for producing liquid transportation fuels and commodity chemicals. Given the large contribution of sugar and lignocellulosic feedstocks to biorefinery operating costs, natural gas and other C₁ sources may provide an economic advantage. To assess the relative costs of these feedstocks, we performed flux balance analysis on genome-scale metabolic models to calculate the maximum theoretical yields of chemical products from methane, methanol, acetate, and glucose. Yield calculations were performed for every metabolite (as a proxy for desired products) in the genome-scale metabolic models of three organisms: Escherichia coli (bacterium), Saccharomyces cerevisiae (yeast), and Synechococcus sp. PCC 7002 (cyanobacterium). The calculated theoretical yields and current feedstock prices provided inputs to create comparative feedstock cost surfaces. Our analysis shows that, at current market prices, methane feedstock costs are consistently lower than glucose when used as a carbon and energy source for microbial chemical production. Conversely, methanol is costlier than glucose under almost all price scenarios. Acetate feedstock costs could be less than glucose given efficient acetate production from low-cost syngas using nascent biological gas to liquids (BIO-GTL) technologies. Our analysis suggests that research should focus on overcoming the technical challenges of methane assimilation and/or yield of acetate via BIO-GTL to take advantage of low-cost natural gas rather than using methanol as a feedstock.

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Review of C1 assimilation strategies is presented.

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Flux balance analysis used to create relative feedstock cost surfaces.

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Methane found to be the lowest cost feedstock for conversion of C1 compounds.

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.

Constraint-based metabolic modelling of marine microbes and communities

Constraint-based metabolic modelling (CBMM) consists in the use of computational methods and tools to perform genome-scale simulations and predict metabolic features at the whole cellular level. This approach is rapidly expanding in microbiology, as it combines reliable predictive abilities with conceptually and technically simple frameworks. Among the possible outcomes of CBMM, the capability to i) guide a focused planning of metabolic engineering experiments and ii) provide a system-level understanding of (single or community-level) microbial metabolic circuits also represent primary aims in present-day marine microbiology. In this work we briefly introduce the theoretical formulation behind CBMM and then review the most recent and effective case studies of CBMM of marine microbes and communities. Also, the emerging challenges and possibilities in the use of such methodologies in the context of marine microbiology/biotechnology are discussed. As the potential applications of CBMM have a very broad range, the topics presented in this review span over a large plethora of fields such as ecology, biotechnology and evolution.

Metabolic flux analysis of heterotrophic growth in Chlamydomonas reinhardtii

Despite the wealth of knowledge available for C. reinhardtii, the central metabolic fluxes of growth on acetate have not yet been determined. In this study, ¹³C-metabolic flux analysis (¹³C-MFA) was used to determine and quantify the metabolic pathways of primary metabolism in C. reinhardtii cells grown under heterotrophic conditions with acetate as the sole carbon source. Isotopic labeling patterns of compartment specific biomass derived metabolites were used to calculate the fluxes. It was found that acetate is ligated with coenzyme A in the three subcellular compartments (cytosol, mitochondria and plastid) included in the model. Two citrate synthases were found to potentially be involved in acetyl-coA metabolism; one localized in the mitochondria and the other acting outside the mitochondria. Labeling patterns demonstrate that Acetyl-coA synthesized in the plastid is directly incorporated in synthesis of fatty acids. Despite having a complete TCA cycle in the mitochondria, it was also found that a majority of the malate flux is shuttled to the cytosol and plastid where it is converted to oxaloacetate providing reducing equivalents to these compartments. When compared to predictions by flux balance analysis, fluxes measured with ¹³C-MFA were found to be suboptimal with respect to biomass yield; C. reinhardtii sacrifices biomass yield to produce ATP and reducing equivalents.

Identifying the Metabolic Differences of a Fast-Growth Phenotype in Synechococcus UTEX 2973

The photosynthetic capabilities of cyanobacteria make them interesting candidates for industrial bioproduction. One obstacle to large-scale implementation of cyanobacteria is their limited growth rates as compared to industrial mainstays. Synechococcus UTEX 2973, a strain closely related to Synechococcus PCC 7942, was recently identified as having the fastest measured growth rate among cyanobacteria. To facilitate the development of 2973 as a model organism we developed in this study the genome-scale metabolic model iSyu683. Experimental data were used to define CO₂ uptake rates as well as the biomass compositions for each strain. The inclusion of constraints based on experimental measurements of CO₂ uptake resulted in a ratio of the growth rates of Synechococcus 2973 to Synechococcus 7942 of 2.03, which nearly recapitulates the in vivo growth rate ratio of 2.13. This identified the difference in carbon uptake rate as the main factor contributing to the divergent growth rates. Additionally four SNPs were identified as possible contributors to modified kinetic parameters of metabolic enzymes and candidates for further study. Comparisons against more established cyanobacterial strains identified a number of differences between the strains along with a correlation between the number of cytochrome c oxidase operons and heterotrophic or diazotrophic capabilities.

The use of genome-scale metabolic network reconstruction to predict fluxes and equilibrium composition of N-fixing versus C-fixing cells in a diazotrophic cyanobacterium, Trichodesmium erythraeum

Background

Computational, genome based predictions of organism phenotypes has enhanced the ability to investigate the biological phenomena that help organisms survive and respond to their environments. In this study, we have created the first genome-scale metabolic network reconstruction of the nitrogen fixing cyanobacterium T. erythraeum and used genome-scale modeling approaches to investigate carbon and nitrogen fluxes as well as growth and equilibrium population composition.

Results

We created a genome-scale reconstruction of T. erythraeum with 971 reactions, 986 metabolites, and 647 unique genes. We then used data from previous studies as well as our own laboratory data to establish a biomass equation and two distinct submodels that correspond to the two cell types formed by T. erythraeum. We then use flux balance analysis and flux variability analysis to generate predictions for how metabolism is distributed to account for the unique productivity of T. erythraeum. Finally, we used in situ data to constrain the model, infer time dependent population compositions and metabolite production using dynamic Flux Balance Analysis. We find that our model predicts equilibrium compositions similar to laboratory measurements, approximately 15.5% diazotrophs for our model versus 10-20% diazotrophs reported in literature. We also found that equilibrium was the most efficient mode of growth and that equilibrium was stoichiometrically mediated. Moreover, the model predicts that nitrogen leakage is an essential condition of optimality for T. erythraeum; cells leak approximately 29.4% total fixed nitrogen when growing at the optimal growth rate, which agrees with values observed in situ.

Conclusion

The genome-metabolic network reconstruction allows us to use constraints based modeling approaches to predict growth and optimal cellular composition in T. erythraeum colonies. Our predictions match both in situ and laboratory data, indicating that stoichiometry of metabolic reactions plays a large role in the differentiation and composition of different cell types. In order to realize the full potential of the model, advance modeling techniques which account for interactions between colonies, the environment and surrounding species need to be developed.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0383-z) contains supplementary material, which is available to authorized users.

Flux balance analysis of photoautotrophic metabolism: Uncovering new biological details of subsystems involved in cyanobacterial photosynthesis

We have constructed and experimentally tested a comprehensive genome-scale model of photoautotrophic growth, denoted iSyp821, for the cyanobacterium Synechococcus sp. PCC 7002. iSyp821 incorporates a variable biomass objective function (vBOF), in which stoichiometries of the major biomass components vary according to light intensity. The vBOF was constrained to fit the measured cellular carbohydrate/protein content under different light intensities. iSyp821 provides rigorous agreement with experimentally measured cell growth rates and inorganic carbon uptake rates as a function of light intensity. iSyp821 predicts two observed metabolic transitions that occur as light intensity increases: 1) from PSI-cyclic to linear electron flow (greater redox energy), and 2) from carbon allocation as proteins (growth) to carbohydrates (energy storage) mode. iSyp821 predicts photoautotrophic carbon flux into 1) a hybrid gluconeogenesis-pentose phosphate (PP) pathway that produces glycogen by an alternative pathway than conventional gluconeogenesis, and 2) the photorespiration pathway to synthesize the essential amino acid, glycine. Quantitative fluxes through both pathways were verified experimentally by following the kinetics of formation of ¹³C metabolites from ¹³CO₂ fixation. iSyp821 was modified to include changes in gene products (enzymes) from experimentally measured transcriptomic data and applied to estimate changes in concentrations of metabolites arising from nutrient stress. Using this strategy, we found that iSyp821 correctly predicts the observed redistribution pattern of carbon products under nitrogen depletion, including decreased rates of CO₂ uptake, amino acid synthesis, and increased rates of glycogen and lipid synthesis.

Natural and Synthetic Variants of the Tricarboxylic Acid Cycle in Cyanobacteria: Introduction of the GABA Shunt into Synechococcus sp. PCC 7002

For nearly half a century, it was believed that cyanobacteria had an incomplete tricarboxylic acid (TCA) cycle, because 2-oxoglutarate dehydrogenase (2-OGDH) was missing. Recently, a bypass route via succinic semialdehyde (SSA), which utilizes 2-oxoglutarate decarboxylase (OgdA) and succinic semialdehyde dehydrogenase (SsaD) to convert 2-oxoglutarate (2-OG) into succinate, was identified, thus completing the TCA cycle in most cyanobacteria. In addition to the recently characterized glyoxylate shunt that occurs in a few of cyanobacteria, the existence of a third variant of the TCA cycle connecting these metabolites, the γ-aminobutyric acid (GABA) shunt, was considered to be ambiguous because the GABA aminotransferase is missing in many cyanobacteria. In this study we isolated and biochemically characterized the enzymes of the GABA shunt. We show that N-acetylornithine aminotransferase (ArgD) can function as a GABA aminotransferase and that, together with glutamate decarboxylase (GadA), it can complete a functional GABA shunt. To prove the connectivity between the OgdA/SsaD bypass and the GABA shunt, the gadA gene from Synechocystis sp. PCC 6803 was heterologously expressed in Synechococcus sp. PCC 7002, which naturally lacks this enzyme. Metabolite profiling of seven Synechococcus sp. PCC 7002 mutant strains related to these two routes to succinate were investigated and proved the functional connectivity. Metabolite profiling also indicated that, compared to the OgdA/SsaD shunt, the GABA shunt was less efficient in converting 2-OG to SSA in Synechococcus sp. PCC 7002. The metabolic profiling study of these two TCA cycle variants provides new insights into carbon metabolism as well as evolution of the TCA cycle in cyanobacteria.

Metabolic model of Synechococcus sp. PCC 7002: Prediction of flux distribution and network modification for enhanced biofuel production

Flux Balance Analysis was performed with the Genome Scale Metabolic Model of a fast growing cyanobacterium Synechococcus sp. PCC 7002 to gain insights that would help in engineering the organism as a production host. Gene essentiality and synthetic lethality analysis revealed a reduced metabolic robustness under genetic perturbation compared to the heterotrophic bacteria Escherichia coli. Under glycerol heterotrophy the reducing equivalents were generated from tricarboxylic acid cycle rather than the oxidative pentose phosphate pathway. During mixotrophic growth in glycerol the photosynthetic electron transport chain was predominantly used for ATP synthesis with a photosystem I/photosystem II flux ratio higher than that observed under autotrophy. An exhaustive analysis of all possible double reaction knock outs was performed to reroute fixed carbon towards ethanol and butanol production. It was predicted that only ∼10% of fixed carbon could be diverted for ethanol and butanol production.

Unlocking the Constraints of Cyanobacterial Productivity: Acclimations Enabling Ultrafast Growth

Harnessing the metabolic potential of photosynthetic microbes for next-generation biotechnology objectives requires detailed scientific understanding of the physiological constraints and regulatory controls affecting carbon partitioning between biomass, metabolite storage pools, and bioproduct synthesis. We dissected the cellular mechanisms underlying the remarkable physiological robustness of the euryhaline unicellular cyanobacterium Synechococcus sp. strain PCC 7002 (Synechococcus 7002) and identify key mechanisms that allow cyanobacteria to achieve unprecedented photoautotrophic productivities (~2.5-h doubling time). Ultrafast growth of Synechococcus 7002 was supported by high rates of photosynthetic electron transfer and linked to significantly elevated transcription of precursor biosynthesis and protein translation machinery. Notably, no growth or photosynthesis inhibition signatures were observed under any of the tested experimental conditions. Finally, the ultrafast growth in Synechococcus 7002 was also linked to a 300% expansion of average cell volume. We hypothesize that this cellular adaptation is required at high irradiances to support higher cell division rates and reduce deleterious effects, corresponding to high light, through increased carbon and reductant sequestration.

Efficient coupling between photosynthesis and productivity is central to the development of biotechnology based on solar energy. Therefore, understanding the factors constraining maximum rates of carbon processing is necessary to identify regulatory mechanisms and devise strategies to overcome productivity constraints. Here, we interrogate the molecular mechanisms that operate at a systems level to allow cyanobacteria to achieve ultrafast growth. This was done by considering growth and photosynthetic kinetics with global transcription patterns. We have delineated putative biological principles that allow unicellular cyanobacteria to achieve ultrahigh growth rates through photophysiological acclimation and effective management of cellular resource under different growth regimes.

Extending the biosynthetic repertoires of cyanobacteria and chloroplasts

Chloroplasts in plants and algae and photosynthetic microorganisms such as cyanobacteria are emerging hosts for sustainable production of valuable biochemicals, using only inorganic nutrients, water, CO2 and light as inputs. In the past decade, many bioengineering efforts have focused on metabolic engineering and synthetic biology in the chloroplast or in cyanobacteria for the production of fuels, chemicals and complex, high-value bioactive molecules. Biosynthesis of all these compounds can be performed in photosynthetic organelles/organisms by heterologous expression of the appropriate pathways, but this requires optimization of carbon flux and reducing power, and a thorough understanding of regulatory pathways. Secretion or storage of the compounds produced can be exploited for the isolation or confinement of the desired compounds. In this review, we explore the use of chloroplasts and cyanobacteria as biosynthetic compartments and hosts, and we estimate the levels of production to be expected from photosynthetic hosts in light of the fraction of electrons and carbon that can potentially be diverted from photosynthesis. The supply of reducing power, in the form of electrons derived from the photosynthetic light reactions, appears to be non-limiting, but redirection of the fixed carbon via precursor molecules presents a challenge. We also discuss the available synthetic biology tools and the need to expand the molecular toolbox to facilitate cellular reprogramming for increased production yields in both cyanobacteria and chloroplasts.

Cyanobacterial Biofuels: Strategies and Developments on Network and Modeling

Cyanobacteria, the phototrophic microorganisms, have attracted much attention recently as a promising source for environmentally sustainable biofuels production. However, barriers for commercial markets of cyanobacteria-based biofuels concern the economic feasibility. Miscellaneous strategies for improving the production performance of cyanobacteria have thus been developed. Among these, the simple ad hoc strategies resulting in failure to optimize fully cell growth coupled with desired product yield are explored. With the advancement of genomics and systems biology, a new paradigm toward systems metabolic engineering has been recognized. In particular, a genome-scale metabolic network reconstruction and modeling is a crucial systems-based tool for whole-cell-wide investigation and prediction. In this review, the cyanobacterial genome-scale metabolic models, which offer a system-level understanding of cyanobacterial metabolism, are described. The main process of metabolic network reconstruction and modeling of cyanobacteria are summarized. Strategies and developments on genome-scale network and modeling through the systems metabolic engineering approach are advanced and employed for efficient cyanobacterial-based biofuels production.

Proteomic and metabolomic analyses reveal metabolic responses to 3-hydroxypropionic acid synthesized internally in cyanobacterium Synechocystis sp. PCC 6803

BACKGROUND

3-hydroxypropionic acid (3-HP) is an important platform chemical with a wide range of applications. In our previous study, the biosynthetic pathway of 3-HP was constructed and optimized in cyanobacterium Synechocystis sp. PCC 6803, which led to 3-HP production directly from CO₂ at a level of 837.18 mg L^-1 (348.8 mg/g dry cell weight). As the production and accumulation of 3-HP in cells affect cellular metabolism, a better understanding of cellular responses to 3-HP synthesized internally in Synechocystis will be important for further increasing 3-HP productivity in cyanobacterial chassis.

RESULTS

Using a engineered 3-HP-producing SM strain, in this study, the cellular responses to 3-HP internally produced were first determined using a quantitative iTRAQ-LC-MS/MS proteomics approach and a LC-MS-based targeted metabolomics. A total of 2264 unique proteins were identified, which represented about 63 % of all predicted protein in Synechocystis in the proteomic analysis; meanwhile intracellular abundance of 24 key metabolites was determined by a comparative metabolomic analysis of the 3-HP-producing strain SM and wild type. Among all identified proteins, 204 proteins were found up-regulated and 123 proteins were found down-regulated, respectively. The proteins related to oxidative phosphorylation, photosynthesis, ribosome, central carbon metabolism, two-component systems and ABC-type transporters were up-regulated, along with the abundance of 14 metabolites related to central metabolism. The results suggested that the supply of ATP and NADPH was increased significantly, and the precursor malonyl-CoA and acetyl-CoA may also be supplemented when 3-HP was produced at a high level in Synechocystis. Confirmation of proteomic and metabolomic results with RT-qPCR and gene-overexpression strains of selected genes was also conducted, and the overexpression of three transporter genes putatively involved in cobalt/nickel, manganese and phosphate transporting (i.e., sll0385, sll1598 and sll0679) could lead to an increased 3-HP production in Synechocystis.

CONCLUSIONS

The integrative analysis of up-regulated proteome and metabolome data showed that to ensure the high-efficient production of 3-HP and the normal growth of Synechocystis, multiple aspects of cells metabolism including energy, reducing power supply, central carbon metabolism, the stress responses and protein synthesis were enhanced in Synechocystis. The study provides an important basis for further engineering cyanobacteria for high 3-HP production.

Construction of a Genome-Scale Metabolic Model of Arthrospira platensis NIES-39 and Metabolic Design for Cyanobacterial Bioproduction

Arthrospira (Spirulina) platensis is a promising feedstock and host strain for bioproduction because of its high accumulation of glycogen and superior characteristics for industrial production. Metabolic simulation using a genome-scale metabolic model and flux balance analysis is a powerful method that can be used to design metabolic engineering strategies for the improvement of target molecule production. In this study, we constructed a genome-scale metabolic model of A. platensis NIES-39 including 746 metabolic reactions and 673 metabolites, and developed novel strategies to improve the production of valuable metabolites, such as glycogen and ethanol. The simulation results obtained using the metabolic model showed high consistency with experimental results for growth rates under several trophic conditions and growth capabilities on various organic substrates. The metabolic model was further applied to design a metabolic network to improve the autotrophic production of glycogen and ethanol. Decreased flux of reactions related to the TCA cycle and phosphoenolpyruvate reaction were found to improve glycogen production. Furthermore, in silico knockout simulation indicated that deletion of genes related to the respiratory chain, such as NAD(P)H dehydrogenase and cytochrome-c oxidase, could enhance ethanol production by using ammonium as a nitrogen source.

Inactivation of nitrate reductase alters metabolic branching of carbohydrate fermentation in the cyanobacterium Synechococcus sp. strain PCC 7002

To produce cellular energy, cyanobacteria reduce nitrate as the preferred pathway over proton reduction (H2 evolution) by catabolizing glycogen under dark anaerobic conditions. This competition lowers H2 production by consuming a large fraction of the reducing equivalents (NADPH and NADH). To eliminate this competition, we constructed a knockout mutant of nitrate reductase, encoded by narB, in Synechococcus sp. PCC 7002. As expected, ΔnarB was able to take up intracellular nitrate but was unable to reduce it to nitrite or ammonia, and was unable to grow photoautotrophically on nitrate. During photoautotrophic growth on urea, ΔnarB significantly redirects biomass accumulation into glycogen at the expense of protein accumulation. During subsequent dark fermentation, metabolite concentrations--both the adenylate cellular energy charge (∼ATP) and the redox poise (NAD(P)H/NAD(P))--were independent of nitrate availability in ΔnarB, in contrast to the wild type (WT) control. The ΔnarB strain diverted more reducing equivalents from glycogen catabolism into reduced products, mainly H2 and d-lactate, by 6-fold (2.8% yield) and 2-fold (82.3% yield), respectively, than WT. Continuous removal of H2 from the fermentation medium (milking) further boosted net H2 production by 7-fold in ΔnarB, at the expense of less excreted lactate, resulting in a 49-fold combined increase in the net H2 evolution rate during 2 days of fermentation compared to the WT. The absence of nitrate reductase eliminated the inductive effect of nitrate addition on rerouting carbohydrate catabolism from glycolysis to the oxidative pentose phosphate (OPP) pathway, indicating that intracellular redox poise and not nitrate itself acts as the control switch for carbon flux branching between pathways.

A computational analysis of stoichiometric constraints and trade-offs in cyanobacterial biofuel production

Cyanobacteria are a promising biological chassis for the synthesis of renewable fuels and chemical bulk commodities. Significant efforts have been devoted to improve the yields of cyanobacterial products. However, while the introduction and heterologous expression of product-forming pathways is often feasible, the interactions and incompatibilities of product synthesis with the host metabolism are still insufficiently understood. In this work, we investigate the stoichiometric properties and trade-offs that underlie cyanobacterial product formation using a computational reconstruction of cyanobacterial metabolism. First, we evaluate the synthesis requirements of a selection of cyanobacterial products of potential biotechnological interest. Second, the large-scale metabolic reconstruction allows us to perform in silico experiments that mimic and predict the metabolic changes that must occur in the transition from a growth-only phenotype to a production-only phenotype. Applied to the synthesis of ethanol, ethylene, and propane, these in silico transition experiments point to bottlenecks and potential modification targets in cyanobacterial metabolism. Our analysis reveals incompatibilities between biotechnological product synthesis and native host metabolism, such as shifts in ATP/NADPH demand and the requirement to reintegrate metabolic by-products. Similar strategies can be employed for a large class of cyanobacterial products to identify potential stoichiometric bottlenecks.

Applications of genome-scale metabolic network model in metabolic engineering

Genome-scale metabolic network model (GEM) is a fundamental framework in systems metabolic engineering. GEM is built upon extensive experimental data and literature information on gene annotation and function, metabolites and enzymes so that it contains all known metabolic reactions within an organism. Constraint-based analysis of GEM enables the identification of phenotypic properties of an organism and hypothesis-driven engineering of cellular functions to achieve objectives. Along with the advances in omics, high-throughput technology and computational algorithms, the scope and applications of GEM have substantially expanded. In particular, various computational algorithms have been developed to predict beneficial gene deletion and amplification targets and used to guide the strain development process for the efficient production of industrially important chemicals. Furthermore, an Escherichia coli GEM was integrated with a pathway prediction algorithm and used to evaluate all possible routes for the production of a list of commodity chemicals in E. coli. Combined with the wealth of experimental data produced by high-throughput techniques, much effort has been exerted to add more biological contexts into GEM through the integration of omics data and regulatory network information for the mechanistic understanding and improved prediction capabilities. In this paper, we review the recent developments and applications of GEM focusing on the GEM-based computational algorithms available for microbial metabolic engineering.

Cyanobacteria as photosynthetic biocatalysts: a systems biology perspective

A review of cyanobacterial biocatalysts highlighting their metabolic features that argues for the need for systems-level metabolic engineering.

Cyanobacterial biofuels: new insights and strain design strategies revealed by computational modeling

Background

Cyanobacteria 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.

Results

In 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.

Conclusion

Our 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 material

The online version of this article (doi:10.1186/s12934-014-0128-x) contains supplementary material, which is available to authorized users.

Editorial: how multiplexed tools and approaches speed up the progress of metabolic engineering

Systems metabolic engineering is becoming a widely-evoked paradigm for industrial strain design and optimization. Specifically, systems wide experimental and computational analyses of cells and their environments enable guide metabolic engineers to quickly parse the genome and creating desirable overproduction phenotypes.