Genome-scale modeling of light-driven reductant partitioning and carbon fluxes in diazotrophic unicellular cyanobacterium Cyanothece sp. ATCC 51142

Genome-Scale Metabolic Model Reconstruction and Investigation into the Fluxome of the Fast-Growing Cyanobacterium Synechococcus sp. PCC 11901

The ability to convert atmospheric CO2 and light into biomass and value-added chemicals makes cyanobacteria a promising resource microbial host for biotechnological applications. A newly discovered fastest-growing cyanobacterial strain, Synechococcus sp. PCC 11901, has been reported to have the highest biomass accumulation rate, making it a preferred target host for producing renewable fuels, value-added biochemicals, and natural products. System-level knowledge of an organism is imperative to understand the metabolic potential of the strain, which can be attained by developing genome-scale metabolic models (GEMs). We present the first genome-scale metabolic model of Synechococcus sp. PCC 11901 (iRS840), which contains 840 genes, 1001 reactions, and 944 metabolites. The model has been optimized and validated under different trophic modes, i.e., autotrophic and mixotrophic, by conducting an in vivo growth experiment. The robustness of the metabolic network was evaluated by changing the biomass coefficient of the model, which showed a higher sensitivity toward pigments under the photoautotrophic condition, whereas under the heterotrophic condition, amino acids were found to be more influential. Furthermore, it was discovered that PCC 11901 synthesizes succinyl-CoA via succinic semialdehyde due to its imperfect TCA cycle. Subsequent flux balance analysis (FBA) revealed a quantum yield of 0.16 in silico, which is higher compared to that of PCC 6803. Under mixotrophic conditions (with glycerol and carbon dioxide), the flux through the Calvin cycle increased compared to autotrophic conditions. This model will be useful for gaining insights into the metabolic potential of PCC 11901 and developing effective metabolic engineering strategies for product development.

The Special and General Mechanism of Cyanobacterial Harmful Algal Blooms

Cyanobacterial harmful algal blooms (CyanoHABs) are longstanding aquatic hazards worldwide, of which the mechanism is not yet fully understood, i.e., the process in which cyanobacteria establish dominance over coexisting algae in the same eutrophic waters. The dominance of CyanoHABs represents a deviation from their low abundance under conventional evolution in the oligotrophic state, which has been the case since the origin of cyanobacteria on early Earth. To piece together a comprehensive mechanism of CyanoHABs, we revisit the origin and adaptive radiation of cyanobacteria in oligotrophic Earth, demonstrating ubiquitous adaptive radiation enabled by corresponding biological functions under various oligotrophic conditions. Next, we summarize the biological functions (ecophysiology) which drive CyanoHABs and ecological evidence to synthesize a working mechanism at the population level (the special mechanism) for CyanoHABs: CyanoHABs are the consequence of the synergistic interaction between superior cyanobacterial ecophysiology and elevated nutrients. Interestingly, these biological functions are not a result of positive selection by water eutrophication, but an adaptation to a longstanding oligotrophic state as all the genes in cyanobacteria are under strong negative selection. Last, to address the relative dominance of cyanobacteria over coexisting algae, we postulate a "general" mechanism of CyanoHABs at the community level from an energy and matter perspective: cyanobacteria are simpler life forms and thus have lower per capita nutrient demand for growth than coexisting eukaryotic algae. We prove this by comparing cyanobacteria and eukaryotic algae in cell size and structure, genome size, size of genome-scale metabolic networks, cell content, and finally the golden standard-field studies with nutrient supplementation in the same waters. To sum up, the comprehensive mechanism of CyanoHABs comprises a necessary condition, which is the general mechanism, and a sufficient condition, which is the special mechanism. One prominent prediction based on this tentative comprehensive mechanism is that eukaryotic algal blooms will coexist with or replace CyanoHABs if eutrophication continues and goes over the threshold nutrient levels for eukaryotic algae. This two-fold comprehensive mechanism awaits further theoretic and experimental testing and provides an important guide to control blooms of all algal species.

Metabolic systems biology and multi-omics of cyanobacteria: Perspectives and future directions

Cyanobacteria are oxygenic photoautotrophs whose metabolism contains key biochemical pathways to fix atmospheric CO₂ and synthesize various metabolites. The development of bioengineering tools has enabled the manipulation of cyanobacterial chassis to produce various valuable bioproducts photosynthetically. However, effective utilization of cyanobacteria as photosynthetic cell factories needs a detailed understanding of their metabolism and its interaction with other cellular processes. Implementing systems and synthetic biology tools has generated a wealth of information on various metabolic pathways. However, to design effective engineering strategies for further improvement in growth, photosynthetic efficiency, and enhanced production of target biochemicals, in-depth knowledge of their carbon/nitrogen metabolism, pathway fluxe distribution, genetic regulation and integrative analyses are necessary. In this review, we discuss the recent advances in the development of genome-scale metabolic models (GSMMs), omics analyses (metabolomics, transcriptomics, proteomics, fluxomics), and integrative modeling approaches to showcase the current understanding of cyanobacterial metabolism.

Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacterium Synechococcus sp. PCC 7002

Combining a computational framework for flux balance analysis with machine learning improves the accuracy of predicting metabolic activity across conditions, while enabling mechanistic interpretation. This protocol presents a guide to condition-specific metabolic modeling that integrates regularized flux balance analysis with machine learning approaches to extract key features from transcriptomic and fluxomic data. We demonstrate the protocol as applied to Synechococcus sp. PCC 7002; we also outline how it can be adapted to any species or community with available multi-omic data. For complete details on the use and execution of this protocol, please refer to Vijayakumar et al. (2020).

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.

Quantitative models of nitrogen-fixing organisms

Nitrogen-fixing organisms are of importance to the environment, providing bioavailable nitrogen to the biosphere. Quantitative models have been used to complement the laboratory experiments and in situ measurements, where such evaluations are difficult or costly. Here, we review the current state of the quantitative modeling of nitrogen-fixing organisms and ways to enhance the bridge between theoretical and empirical studies.

Flux balance analysis of cyanobacteria reveals selective use of photosynthetic electron transport components under different spectral light conditions

Cyanobacteria acclimate and adapt to changing light conditions by controlling the energy transfer between photosystem I (PSI) and II (PSII) and pigment composition. Photosynthesis is driven by balancing the excitation between PSI and PSII. To predict the detailed electron transfer flux of cyanobacteria, we refined the photosynthesis-related reactions in our previously reconstructed genome-scale model. Two photosynthetic bacteria, Arthrospira and Synechocystis, were used as models. They were grown under various spectral light conditions and flux balance analysis (FBA) was performed using photon uptake fluxes into PSI and PSII, which were converted from each light spectrum by considering the photoacclimation of pigments and the distribution ratio of phycobilisome to PSI and PSII. In Arthrospira, the FBA was verified with experimental data using six types of light-emitting diodes (White, Blue, Green, Yellow, Red1, and Red2). FBA predicted the cell growth of Synechocystis for the LEDs, excepting Red2. In an FBA simulation, cells used respiratory terminal oxidases and two NADH dehydrogenases (NDH-1 and NDH-2) to balance the PSI and PSII excitations depending on the light conditions. FBA simulation with our refined model functionally implicated NDH-1 and NDH-2 as a component of cyclic electron transport in the varied light environments.

Applications of stable isotope-based metabolomics and fluxomics toward synthetic biology of cyanobacteria

Unique features of cyanobacteria (e.g., photosynthesis and nitrogen fixation) make them potential candidates for production of biofuels and other value-added biochemicals. As prokaryotes, they can be readily engineered using synthetic and systems biology tools. Metabolic engineering of cyanobacteria for the synthesis of desired compounds requires in-depth knowledge of central carbon and nitrogen metabolism, pathway fluxes, and their regulation. Metabolomics and fluxomics offer the comprehensive analysis of metabolism by directly characterizing the biochemical activities of cells. This information is acquired by measuring the abundance of key metabolites and their rates of interconversion, which can be achieved by labeling cells with stable isotopes, quantifying metabolite pool sizes and isotope incorporation by gas chromatography/liquid chromatography-mass spectrometry GC/LC-MS or nuclear magnetic resonance (NMR), and mathematical modeling to estimate in vivo metabolic fluxes. Herein, we review progress that has been made to adapt metabolomics and fluxomics tools to examine model cyanobacterial species. We summarize the application of metabolic flux analysis (MFA) strategies to identify metabolic bottlenecks that can be targeted to boost cell growth, improve stress tolerance, or enhance biochemical production in cyanobacteria. Despite the advances in metabolomics, fluxomics, and other synthetic and systems biology tools during the past years, further efforts are required to increase our understanding of cyanobacterial metabolism in order to create efficient photosynthetic hosts for the production of value-added compounds. This article is categorized under: Laboratory Methods and Technologies > Metabolomics Biological Mechanisms > Metabolism Analytical and Computational Methods > Analytical Methods.

Metabolic Modeling of Streptococcus mutans Reveals Complex Nutrient Requirements of an Oral Pathogen

Streptococcus mutans is a Gram-positive bacterium that thrives under acidic conditions and is a primary cause of tooth decay (dental caries). To better understand the metabolism of S. mutans on a systematic level, we manually constructed a genome-scale metabolic model of the S. mutans type strain UA159. The model, called iSMU, contains 675 reactions involving 429 metabolites and the products of 493 genes. We validated iSMU by comparing simulations with growth experiments in defined medium. The model simulations matched experimental results for 17 of 18 carbon source utilization assays and 47 of 49 nutrient depletion assays. We also simulated the effects of single gene deletions. The model's predictions agreed with 78.1% and 84.4% of the gene essentiality predictions from two experimental data sets. Our manually curated model is more accurate than S. mutans models generated from automated reconstruction pipelines and more complete than other manually curated models. We used iSMU to generate hypotheses about the S. mutans metabolic network. Subsequent genetic experiments confirmed that (i) S. mutans catabolizes sorbitol via a sorbitol-6-phosphate 2-dehydrogenase (SMU_308) and (ii) the Leloir pathway is required for growth on complex carbohydrates such as raffinose. We believe the iSMU model is an important resource for understanding the metabolism of S. mutans and guiding future experiments.IMPORTANCE Tooth decay is the most prevalent chronic disease in the United States. Decay is caused by the bacterium Streptococcus mutans, an oral pathogen that ferments sugars into tooth-destroying lactic acid. We constructed a complete metabolic model of S. mutans to systematically investigate how the bacterium grows. The model provides a valuable resource for understanding and targeting S. mutans' ability to outcompete other species in the oral microbiome.

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.

Current states and challenges of salt-affected soil remediation by cyanobacteria

Natural and human activities lead to soil degradation and soil salinization. The decrease of farmlands threatens food security. There are approximately 1 billion ha salt-affected soils all over of world, which can be made available resources after chemical, physical and biological remediation. Nostoc, Anabaena and other cyanobacterial species have outstanding capabilities, such as the ability to fix nitrogen from the air, produce an extracellular matrix and produce compatible solutes. The remediation of salt-affected soil is a complex and difficult task. During the past years, much new research has been conducted that shows that cyanobacteria are effective for salt-affected soil remediation in laboratory studies and field trials. The related mechanisms for both salt tolerance and salt-affected soil remediation were also evaluated from the perspective of biochemistry, molecular biology and systems biology. The effect of cyanobacteria on salt-affected soil is related to nitrogen fixation and other mechanisms. There are complicated interactions among cyanobacteria, bacteria, fungi and the soil. The interaction between cyanobacteria and salt-tolerant plants should be considered if the cyanobacterium is utilized to improve the soil fertility in addition to performing soil remediation. It is critical to re-establish the micro-ecology in salt-affected soils and improve the salt affected soil remediation efficiency. The first challenge is the selection of suitable cyanobacterial strain. The co-culture of cyanobacteria and bacteria is also potential approach. The cultivation of cyanobacteria on a large scale should be optimized to improve productivity and decrease cost. The development of bio-remediating agents for salt-affected soil remediation also relies on other technical problems, such as harvesting and contamination control. The application of cyanobacteria in salt-affected soil remediation will reconstruct green agriculture and promote the sustainable development of human society.

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.

Genome-Scale Fluxome of Synechococcus elongatus UTEX 2973 Using Transient 13C-Labeling Data

Synechococcus elongatus UTEX 2973 (Synechococcus 2973) has the shortest reported doubling time (2.1 h) among cyanobacteria, making it a promising platform for the solar-based production of biochemicals. In this meta-analysis, its intracellular flux distribution was recomputed using genome-scale isotopic nonstationary ¹³C-metabolic flux analysis given the labeling dynamics of 13 metabolites reported in an earlier study. To achieve this, a genome-scale mapping model, namely imSyu593, was constructed using the imSyn617 mapping model for Synechocystis sp. PCC 6803 (Synechocystis 6803) as the starting point encompassing 593 reactions. The flux elucidation revealed nearly complete conversion (greater than 96%) of the assimilated carbon into biomass in Synechococcus 2973. In contrast, Synechocystis 6803 achieves complete conversion of only 86% of the assimilated carbon. This high biomass yield was enabled by the reincorporation of the fixed carbons lost in anabolic and photorespiratory pathways in conjunction with flux rerouting through a nondecarboxylating reaction such as phosphoketolase. This reincorporation of lost CO₂ sustains a higher flux through the photorespiratory C2 cycle that fully meets the glycine and serine demands for growth. In accordance with the high carbon efficiency drive, acetyl-coenzyme A was entirely produced using the carbon-efficient phosphoketolase pathway. Comparison of the Synechococcus 2973 flux map with that of Synechocystis 6803 revealed differences in the use of Calvin cycle and photorespiratory pathway reactions. The two species used different reactions for the synthesis of metabolites such as fructose-6-phosphate, glycine, sedoheptulose-7-phosphate, and Ser. These findings allude to a highly carbon-efficient metabolism alongside the fast carbon uptake rate in Synechococcus 2973, which explains its faster growth rate.

In Situ Diazotroph Population Dynamics Under Different Resource Ratios in the North Pacific Subtropical Gyre

Major advances in understanding the diversity, distribution, and activity of marine N2-fixing microorganisms (diazotrophs) have been made in the past decades, however, large gaps in knowledge remain about the environmental controls on growth and mortality rates. In order to measure diazotroph net growth rates and microzooplankton grazing rates on diazotrophs, nutrient perturbation experiments and dilution grazing experiments were conducted using free-floating in situ incubation arrays in the vicinity of Station ALOHA in March 2016. Net growth rates for targeted diazotroph taxa as well as Prochlorococcus, Synechococcus and photosynthetic picoeukaryotes were determined under high (H) and low (L) nitrate:phosphate (NP) ratio conditions at four depths in the photic zone (25, 45, 75, and 100 m) using quantitative PCR and flow cytometry. Changes in the prokaryote community composition in response to HNP and LNP treatments were characterized using 16S rRNA variable region tag sequencing. Microzooplankton grazing rates on diazotrophs were measured using a modified dilution technique at two depths in the photic zone (15 and 125 m). Net growth rates for most of the targeted diazotrophs after 48 h were not stimulated as expected by LNP conditions, rather enhanced growth rates were often measured in HNP treatments. Interestingly, net growth rates of the uncultivated prymnesiophyte symbiont UCYN-A1 were stimulated in HNP treatments at 75 and 100 m, suggesting that N used for growth was acquired through continuing to fix N2 in the presence of nitrate. Net growth rates for UCYN-A1, UCYN-C, Crocosphaera sp. (UCYN-B) and the diatom symbiont Richelia (associated with Rhizosolenia) were uniformly high at 45 m (up to 1.6 ± 0.5 d-1), implying that all were growing optimally at the onset of the experiment at that depth. Differences in microzooplankton grazing rates on UCYN-A1 and UCYN-C in 15 m waters indicate that the grazer assemblage preyed preferentially on UCYN-A1. Deeper in the water column (125 m), both diazotrophs were grazed at substantial rates, suggesting grazing pressure may increase with depth in the photic zone. Constraining in situ diazotroph growth and mortality rates are important steps for improving parameterization for diazotrophs in global ecosystem models.

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.

Synthetic biology approaches for the production of plant metabolites in unicellular organisms

Synthetic biology is the repurposing of biological systems for novel objectives and applications. Through the co-ordinated and balanced expression of genes, both native and those introduced from other organisms, resources within an industrial chassis can be siphoned for the commercial production of high-value commodities. This developing interdisciplinary field has the potential to revolutionize natural product discovery from higher plants, by providing a diverse array of tools, technologies, and strategies for exploring the large chemically complex space of plant natural products using unicellular organisms. In this review, we emphasize the key features that influence the generation of biorefineries and highlight technologies and strategic solutions that can be used to overcome engineering pitfalls with rational design. Also presented is a succinct guide to assist the selection of unicellular chassis most suited for the engineering and subsequent production of the desired natural product, in order to meet the global demand for plant natural products in a safe and sustainable manner.

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.

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.

A Comprehensively Curated Genome-Scale Two-Cell Model for the Heterocystous Cyanobacterium Anabaena sp. PCC 7120

Anabaena sp. PCC 7120 is a nitrogen-fixing filamentous cyanobacterium. Under nitrogen-limiting conditions, a fraction of the vegetative cells in each filament terminally differentiate to nongrowing heterocysts. Heterocysts are metabolically and structurally specialized to enable O₂-sensitive nitrogen fixation. The functionality of the filament, as an association of vegetative cells and heterocysts, is postulated to depend on metabolic exchange of electrons, carbon, and fixed nitrogen. In this study, we compile and evaluate a comprehensive curated stoichiometric model of this two-cell system, with the objective function based on the growth of the filament under diazotrophic conditions. The predicted growth rate under nitrogen-replete and -deplete conditions, as well as the effect of external carbon and nitrogen sources, was thereafter verified. Furthermore, the model was utilized to comprehensively evaluate the optimality of putative metabolic exchange reactions between heterocysts and vegetative cells. The model suggested that optimal growth requires at least four exchange metabolites. Several combinations of exchange metabolites resulted in predicted growth rates that are higher than growth rates achieved by only considering exchange of metabolites previously suggested in the literature. The curated model of the metabolic network of Anabaena sp. PCC 7120 enhances our ability to understand the metabolic organization of multicellular cyanobacteria and provides a platform for further study and engineering of their metabolism.

Toward Multiscale Models of Cyanobacterial Growth: A Modular Approach

Oxygenic photosynthesis dominates global primary productivity ever since its evolution more than three billion years ago. While many aspects of phototrophic growth are well understood, it remains a considerable challenge to elucidate the manifold dependencies and interconnections between the diverse cellular processes that together facilitate the synthesis of new cells. Phototrophic growth involves the coordinated action of several layers of cellular functioning, ranging from the photosynthetic light reactions and the electron transport chain, to carbon-concentrating mechanisms and the assimilation of inorganic carbon. It requires the synthesis of new building blocks by cellular metabolism, protection against excessive light, as well as diurnal regulation by a circadian clock and the orchestration of gene expression and cell division. Computational modeling allows us to quantitatively describe these cellular functions and processes relevant for phototrophic growth. As yet, however, computational models are mostly confined to the inner workings of individual cellular processes, rather than describing the manifold interactions between them in the context of a living cell. Using cyanobacteria as model organisms, this contribution seeks to summarize existing computational models that are relevant to describe phototrophic growth and seeks to outline their interactions and dependencies. Our ultimate aim is to understand cellular functioning and growth as the outcome of a coordinated operation of diverse yet interconnected cellular processes.

Dinitrogenase-Driven Photobiological Hydrogen Production Combats Oxidative Stress in Cyanothece sp. Strain ATCC 51142

Photobiologically synthesized hydrogen (H₂) gas is carbon neutral to produce and clean to combust, making it an ideal biofuel. Cyanothece sp. strain ATCC 51142 is a cyanobacterium capable of performing simultaneous oxygenic photosynthesis and H₂ production, a highly perplexing phenomenon because H₂ evolving enzymes are O₂ sensitive. We employed a system-level in vivo chemoproteomic profiling approach to explore the cellular dynamics of protein thiol redox and how thiol redox mediates the function of the dinitrogenase NifHDK, an enzyme complex capable of aerobic hydrogenase activity. We found that NifHDK responds to intracellular redox conditions and may act as an emergency electron valve to prevent harmful reactive oxygen species formation in concert with other cell strategies for maintaining redox homeostasis. These results provide new insight into cellular redox dynamics useful for advancing photolytic bioenergy technology and reveal a new understanding for the biological function of NifHDK.

IMPORTANCE

Here, we demonstrate that high levels of hydrogen synthesis can be induced as a protection mechanism against oxidative stress via the dinitrogenase enzyme complex in Cyanothece sp. strain ATCC 51142. This is a previously unknown feature of cyanobacterial dinitrogenase, and we anticipate that it may represent a strategy to exploit cyanobacteria for efficient and scalable hydrogen production. We utilized a chemoproteomic approach to capture the in situ dynamics of reductant partitioning within the cell, revealing proteins and reactive thiols that may be involved in redox sensing and signaling. Additionally, this method is widely applicable across biological systems to achieve a greater understanding of how cells navigate their environment and how redox chemistry can be utilized to alter metabolism and achieve homeostasis.

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.

Metabolic Network Modeling of Microbial Interactions in Natural and Engineered Environmental Systems

We review approaches to characterize metabolic interactions within microbial communities using Stoichiometric Metabolic Network (SMN) models for applications in environmental and industrial biotechnology. SMN models are computational tools used to evaluate the metabolic engineering potential of various organisms. They have successfully been applied to design and optimize the microbial production of antibiotics, alcohols and amino acids by single strains. To date however, such models have been rarely applied to analyze and control the metabolism of more complex microbial communities. This is largely attributed to the diversity of microbial community functions, metabolisms, and interactions. Here, we firstly review different types of microbial interaction and describe their relevance for natural and engineered environmental processes. Next, we provide a general description of the essential methods of the SMN modeling workflow including the steps of network reconstruction, simulation through Flux Balance Analysis (FBA), experimental data gathering, and model calibration. Then we broadly describe and compare four approaches to model microbial interactions using metabolic networks, i.e., (i) lumped networks, (ii) compartment per guild networks, (iii) bi-level optimization simulations, and (iv) dynamic-SMN methods. These approaches can be used to integrate and analyze diverse microbial physiology, ecology and molecular community data. All of them (except the lumped approach) are suitable for incorporating species abundance data but so far they have been used only to model simple communities of two to eight different species. Interactions based on substrate exchange and competition can be directly modeled using the above approaches. However, interactions based on metabolic feedbacks, such as product inhibition and synthropy require extensions to current models, incorporating gene regulation and compounding accumulation mechanisms. SMN models of microbial interactions can be used to analyze complex “omics” data and to infer and optimize metabolic processes. Thereby, SMN models are suitable to capitalize on advances in high-throughput molecular and metabolic data generation. SMN models are starting to be applied to describe microbial interactions during wastewater treatment, in-situ bioremediation, microalgae blooms methanogenic fermentation, and bioplastic production. Despite their current challenges, we envisage that SMN models have future potential for the design and development of novel growth media, biochemical pathways and synthetic microbial associations.

PSAMM: A Portable System for the Analysis of Metabolic Models

The genome-scale models of metabolic networks have been broadly applied in phenotype prediction, evolutionary reconstruction, community functional analysis, and metabolic engineering. Despite the development of tools that support individual steps along the modeling procedure, it is still difficult to associate mathematical simulation results with the annotation and biological interpretation of metabolic models. In order to solve this problem, here we developed a Portable System for the Analysis of Metabolic Models (PSAMM), a new open-source software package that supports the integration of heterogeneous metadata in model annotations and provides a user-friendly interface for the analysis of metabolic models. PSAMM is independent of paid software environments like MATLAB, and all its dependencies are freely available for academic users. Compared to existing tools, PSAMM significantly reduced the running time of constraint-based analysis and enabled flexible settings of simulation parameters using simple one-line commands. The integration of heterogeneous, model-specific annotation information in PSAMM is achieved with a novel format of YAML-based model representation, which has several advantages, such as providing a modular organization of model components and simulation settings, enabling model version tracking, and permitting the integration of multiple simulation problems. PSAMM also includes a number of quality checking procedures to examine stoichiometric balance and to identify blocked reactions. Applying PSAMM to 57 models collected from current literature, we demonstrated how the software can be used for managing and simulating metabolic models. We identified a number of common inconsistencies in existing models and constructed an updated model repository to document the resolution of these inconsistencies.

The broad application of genome-scale metabolic modeling has made it a useful technique for tackling fundamental questions in biological research and engineering. Today over 100 models have been constructed for organisms that carry out a diverse array of metabolic activities spanning all three kingdoms of life. These models, however, have been curated independently following different conventions. The maintenance of model consistency has been challenging due to the lack of consensus in model representation and the absence of integrated modeling software for associating mathematical simulations with the annotation and biological interpretation of metabolic models. To solve this problem, we developed a new software package, PSAMM, and a new model format that incorporates heterogeneous, model-specific annotation information into modular representations of model definitions and simulation settings. PSAMM provides significant advances in standardizing the workflow of model annotation and consistency checking. Compared to existing tools, PSAMM supports more flexible configurations and is more efficient in running constraint-based simulations. All functions of PSAMM are freely available for academic users and can be downloaded from a public Git repository (https://zhanglab.github.io/psamm/) under the GNU General Public License.

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.

Bridging the gap between omics and earth system science to better understand how environmental change impacts marine microbes

The advent of genomic-, transcriptomic- and proteomic-based approaches has revolutionized our ability to describe marine microbial communities, including biogeography, metabolic potential and diversity, mechanisms of adaptation, and phylogeny and evolutionary history. New interdisciplinary approaches are needed to move from this descriptive level to improved quantitative, process-level understanding of the roles of marine microbes in biogeochemical cycles and of the impact of environmental change on the marine microbial ecosystem. Linking studies at levels from the genome to the organism, to ecological strategies and organism and ecosystem response, requires new modelling approaches. Key to this will be a fundamental shift in modelling scale that represents micro-organisms from the level of their macromolecular components. This will enable contact with omics data sets and allow acclimation and adaptive response at the phenotype level (i.e. traits) to be simulated as a combination of fitness maximization and evolutionary constraints. This way forward will build on ecological approaches that identify key organism traits and systems biology approaches that integrate traditional physiological measurements with new insights from omics. It will rely on developing an improved understanding of ecophysiology to understand quantitatively environmental controls on microbial growth strategies. It will also incorporate results from experimental evolution studies in the representation of adaptation. The resulting ecosystem-level models can then evaluate our level of understanding of controls on ecosystem structure and function, highlight major gaps in understanding and help prioritize areas for future research programs. Ultimately, this grand synthesis should improve predictive capability of the ecosystem response to multiple environmental drivers.

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.

Multi-Omic Dynamics Associate Oxygenic Photosynthesis with Nitrogenase-Mediated H2 Production in Cyanothece sp. ATCC 51142

To date, the proposed mechanisms of nitrogenase-driven photosynthetic H₂ production by the diazotrophic unicellular cyanobacterium Cyanothece sp. ATCC 51142 have assumed that reductant and ATP requirements are derived solely from glycogen oxidation and cyclic-electron flow around photosystem I. Through genome-scale transcript and protein profiling, this study presents and tests a new hypothesis on the metabolic relationship between oxygenic photosynthesis and nitrogenase-mediated H₂ production in Cyanothece 51142. Our results show that net-positive rates of oxygenic photosynthesis and increased expression of photosystem II reaction centers correspond and are synchronized with nitrogenase expression and H₂ production. These findings provide a new and more complete view on the metabolic processes contributing to the energy budget of photosynthetic H₂ production and highlight the role of concurrent photocatalytic H₂O oxidation as a participating process.

Reconstruction and analysis of the genome-scale metabolic model of schizochytrium limacinum SR21 for docosahexaenoic acid production

Background

Schizochytrium limacinum SR21 is a potential industrial strain for docosahexaenoic acid (DHA) production that contains more than 30–40 % DHA among its total fatty acids.

Methods

To resolve the DHA biosynthesis mechanism and improve DHA production at a systematic level, a genomescale metabolic model (GSMM), named iCY1170_DHA, which contains 1769 reactions, 1659 metabolites, and 1170 genes, was reconstructed.

Results

Based on genome annotation results and literature reports, a new DHA synthesis pathway based on a polyketide synthase (PKS) system was detected in S. limacinum. Similarly to conventional fatty acid synthesis, the biosynthesis of DHA via PKS requires abundant acetyl-CoA and NADPH. The in silico addition of malate and citrate led to increases of 24.5 % and 37.1 % in DHA production, respectively. Moreover, based on the results predicted by the model, six amino acids were shown to improve DHA production by experiment. Finally, 30 genes were identified as potential targets for DHA over-production using a Minimization of Metabolic Adjustment algorithm.

Conclusions

The reconstructed GSMM, iCY1170_DHA, could be used to elucidate the mechanism by which DHA is synthesized in S. limacinum and predict the requirements of abundant acetyl-CoA and NADPH for DHA production as well as the enhanced yields achieved via supplementation with six amino acids, malate, and citrate.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2042-y) contains supplementary material, which is available to authorized users.

Cyanobacterial Alkanes Modulate Photosynthetic Cyclic Electron Flow to Assist Growth under Cold Stress

All cyanobacterial membranes contain diesel-range C15-C19 hydrocarbons at concentrations similar to chlorophyll. Recently, two universal but mutually exclusive hydrocarbon production pathways in cyanobacteria were discovered. We engineered a mutant of Synechocystis sp. PCC 6803 that produces no alkanes, which grew poorly at low temperatures. We analyzed this defect by assessing the redox kinetics of PSI. The mutant exhibited enhanced cyclic electron flow (CEF), especially at low temperature. CEF raises the ATP:NADPH ratio from photosynthesis and balances reductant requirements of biosynthesis with maintaining the redox poise of the electron transport chain. We conducted in silico flux balance analysis and showed that growth rate reaches a distinct maximum for an intermediate value of CEF equivalent to recycling 1 electron in 4 from PSI to the plastoquinone pool. Based on this analysis, we conclude that the lack of membrane alkanes causes higher CEF, perhaps for maintenance of redox poise. In turn, increased CEF reduces growth by forcing the cell to use less energy-efficient pathways, lowering the quantum efficiency of photosynthesis. This study highlights the unique and universal role of medium-chain hydrocarbons in cyanobacterial thylakoid membranes: they regulate redox balance and reductant partitioning in these oxygenic photosynthetic cells under stress.

Sphagnum physiology in the context of changing climate: emergent influences of genomics, modelling and host-microbiome interactions on understanding ecosystem function

Peatlands harbour more than one-third of terrestrial carbon leading to the argument that the bryophytes, as major components of peatland ecosystems, store more organic carbon in soils than any other collective plant taxa. Plants of the genus Sphagnum are important components of peatland ecosystems and are potentially vulnerable to changing climatic conditions. However, the response of Sphagnum to rising temperatures, elevated CO2 and shifts in local hydrology have yet to be fully characterized. In this review, we examine Sphagnum biology and ecology and explore the role of this group of keystone species and its associated microbiome in carbon and nitrogen cycling using literature review and model simulations. Several issues are highlighted including the consequences of a variable environment on plant-microbiome interactions, uncertainty associated with CO2 diffusion resistances and the relationship between fixed N and that partitioned to the photosynthetic apparatus. We note that the Sphagnum fallax genome is currently being sequenced and outline potential applications of population-level genomics and corresponding plant photosynthesis and microbial metabolic modelling techniques. We highlight Sphagnum as a model organism to explore ecosystem response to a changing climate and to define the role that Sphagnum can play at the intersection of physiology, genetics and functional genomics.

A state of the art of metabolic networks of unicellular microalgae and cyanobacteria for biofuel production

The most promising and yet challenging application of microalgae and cyanobacteria is the production of renewable energy: biodiesel from microalgae triacylglycerols and bioethanol from cyanobacteria carbohydrates. A thorough understanding of microalgal and cyanobacterial metabolism is necessary to master and optimize biofuel production yields. To this end, systems biology and metabolic modeling have proven to be very efficient tools if supported by an accurate knowledge of the metabolic network. However, unlike heterotrophic microorganisms that utilize the same substrate for energy and as carbon source, microalgae and cyanobacteria require light for energy and inorganic carbon (CO2 or bicarbonate) as carbon source. This double specificity, together with the complex mechanisms of light capture, makes the representation of metabolic network nonstandard. Here, we review the existing metabolic networks of photoautotrophic microalgae and cyanobacteria. We highlight how these networks have been useful for gaining insight on photoautotrophic metabolism.

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.

Quantifying the effects of light intensity on bioproduction and maintenance energy during photosynthetic growth of Rhodobacter sphaeroides

Obtaining a better understanding of the physiology and bioenergetics of photosynthetic microbes is an important step toward optimizing these systems for light energy capture or production of valuable commodities. In this work, we analyzed the effect of light intensity on bioproduction, biomass formation, and maintenance energy during photoheterotrophic growth of Rhodobacter sphaeroides. Using data obtained from steady-state bioreactors operated at varying dilution rates and light intensities, we found that irradiance had a significant impact on biomass yield and composition, with significant changes in photopigment, phospholipid, and biopolymer storage contents. We also observed a linear relationship between incident light intensity and H2 production rate between 3 and 10 W m(-2), with saturation observed at 100 W m(-2). The light conversion efficiency to H2 was also higher at lower light intensities. Photosynthetic maintenance energy requirements were also significantly affected by light intensity, with links to differences in biomass composition and the need to maintain redox homeostasis. Inclusion of the measured condition-dependent biomass and maintenance energy parameters and the measured photon uptake rate into a genome-scale metabolic model for R. sphaeroides (iRsp1140) significantly improved its predictive performance. We discuss how our analyses provide new insights into the light-dependent changes in bioenergetic requirements and physiology during photosynthetic growth of R. sphaeroides and potentially other photosynthetic organisms.

Photosynthetic terpene hydrocarbon production for fuels and chemicals

Photosynthetic hydrocarbon production bypasses the traditional biomass hydrolysis process and represents the most direct conversion of sunlight energy into the next-generation biofuels. As a major class of biologically derived hydrocarbons with diverse structures, terpenes are also valuable in producing a variety of fungible bioproducts in addition to the advanced 'drop-in' biofuels. However, it is highly challenging to achieve the efficient redirection of photosynthetic carbon and reductant into terpene biosynthesis. In this review, we discuss four major scientific and technical barriers for photosynthetic terpene production and recent advances to address these constraints. Collectively, photosynthetic terpene production needs to be optimized in a systematic fashion, in which the photosynthesis improvement, the optimization of terpene biosynthesis pathway, the improvement of key enzymes and the enhancement of sink effect through terpene storage or secretion are all important. New advances in synthetic biology also offer a suite of potential tools to design and engineer photosynthetic terpene platforms. The systemic integration of these solutions may lead to 'disruptive' technologies to enable biofuels and bioproducts with high efficiency, yield and infrastructure compatibility.

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.

Generation and Evaluation of a Genome-Scale Metabolic Network Model of Synechococcus elongatus PCC7942

The reconstruction of genome-scale metabolic models and their applications represent a great advantage of systems biology. Through their use as metabolic flux simulation models, production of industrially-interesting metabolites can be predicted. Due to the growing number of studies of metabolic models driven by the increasing genomic sequencing projects, it is important to conceptualize steps of reconstruction and analysis. We have focused our work in the cyanobacterium Synechococcus elongatus PCC7942, for which several analyses and insights are unveiled. A comprehensive approach has been used, which can be of interest to lead the process of manual curation and genome-scale metabolic analysis. The final model, iSyf715 includes 851 reactions and 838 metabolites. A biomass equation, which encompasses elementary building blocks to allow cell growth, is also included. The applicability of the model is finally demonstrated by simulating autotrophic growth conditions of Synechococcus elongatus PCC7942.

Toward a systems-level understanding of gene regulatory, protein interaction, and metabolic networks in cyanobacteria

Cyanobacteria are essential primary producers in marine ecosystems, playing an important role in both carbon and nitrogen cycles. In the last decade, various genome sequencing and metagenomic projects have generated large amounts of genetic data for cyanobacteria. This wealth of data provides researchers with a new basis for the study of molecular adaptation, ecology and evolution of cyanobacteria, as well as for developing biotechnological applications. It also facilitates the use of multiplex techniques, i.e., expression profiling by high-throughput technologies such as microarrays, RNA-seq, and proteomics. However, exploration and analysis of these data is challenging, and often requires advanced computational methods. Also, they need to be integrated into our existing framework of knowledge to use them to draw reliable biological conclusions. Here, systems biology provides important tools. Especially, the construction and analysis of molecular networks has emerged as a powerful systems-level framework, with which to integrate such data, and to better understand biological relevant processes in these organisms. In this review, we provide an overview of the advances and experimental approaches undertaken using multiplex data from genomic, transcriptomic, proteomic, and metabolomic studies in cyanobacteria. Furthermore, we summarize currently available web-based tools dedicated to cyanobacteria, i.e., CyanoBase, CyanoEXpress, ProPortal, Cyanorak, CyanoBIKE, and CINPER. Finally, we present a case study for the freshwater model cyanobacteria, Synechocystis sp. PCC6803, to show the power of meta-analysis, and the potential to extrapolate acquired knowledge to the ecologically important marine cyanobacteria genus, Prochlorococcus.

Comparisons of Shewanella strains based on genome annotations, modeling, and experiments

BACKGROUND

Shewanella is a genus of facultatively anaerobic, Gram-negative bacteria that have highly adaptable metabolism which allows them to thrive in diverse environments. This quality makes them an attractive bacterial target for research in bioremediation and microbial fuel cell applications. Constraint-based modeling is a useful tool for helping researchers gain insights into the metabolic capabilities of these bacteria. However, Shewanella oneidensis MR-1 is the only strain with a genome-scale metabolic model constructed out of 21 sequenced Shewanella strains.

RESULTS

In this work, we updated the model for Shewanella oneidensis MR-1 and constructed metabolic models for three other strains, namely Shewanella sp. MR-4, Shewanella sp. W3-18-1, and Shewanella denitrificans OS217 which span the genus based on the number of genes lost in comparison to MR-1. We also constructed a Shewanella core model that contains the genes shared by all 21 sequenced strains and a few non-conserved genes associated with essential reactions. Model comparisons between the five constructed models were done at two levels - for wildtype strains under different growth conditions and for knockout mutants under the same growth condition. In the first level, growth/no-growth phenotypes were predicted by the models on various carbon sources and electron acceptors. Cluster analysis of these results revealed that the MR-1 model is most similar to the W3-18-1 model, followed by the MR-4 and OS217 models when considering predicted growth phenotypes. However, a cluster analysis done based on metabolic gene content revealed that the MR-4 and W3-18-1 models are the most similar, with the MR-1 and OS217 models being more distinct from these latter two strains. As a second level of comparison, we identified differences in reaction and gene content which give rise to different functional predictions of single and double gene knockout mutants using Comparison of Networks by Gene Alignment (CONGA). Here, we showed how CONGA can be used to find biomass, metabolic, and genetic differences between models.

CONCLUSIONS

We developed four strain-specific models and a general core model that can be used to do various in silico studies of Shewanella metabolism. The developed models provide a platform for a systematic investigation of Shewanella metabolism to aid researchers using Shewanella in various biotechnology applications.

Rapid construction of metabolic models for a family of Cyanobacteria using a multiple source annotation workflow

Background

Cyanobacteria are photoautotrophic prokaryotes that exhibit robust growth under diverse environmental conditions with minimal nutritional requirements. They can use solar energy to convert CO₂ and other reduced carbon sources into biofuels and chemical products. The genus Cyanothece includes unicellular nitrogen-fixing cyanobacteria that have been shown to offer high levels of hydrogen production and nitrogen fixation. The reconstruction of quality genome-scale metabolic models for organisms with limited annotation resources remains a challenging task.

Results

Here we reconstruct and subsequently analyze and compare the metabolism of five Cyanothece strains, namely Cyanothece sp. PCC 7424, 7425, 7822, 8801 and 8802, as the genome-scale metabolic reconstructions iCyc792, iCyn731, iCyj826, iCyp752, and iCyh755 respectively. We compare these phylogenetically related Cyanothece strains to assess their bio-production potential. A systematic workflow is introduced for integrating and prioritizing annotation information from the Universal Protein Resource (Uniprot), NCBI Protein Clusters, and the Rapid Annotations using Subsystems Technology (RAST) method. The genome-scale metabolic models include fully traced photosynthesis reactions and respiratory chains, as well as balanced reactions and GPR associations. Metabolic differences between the organisms are highlighted such as the non-fermentative pathway for alcohol production found in only Cyanothece 7424, 8801, and 8802.

Conclusions

Our development workflow provides a path for constructing models using information from curated models of related organisms and reviewed gene annotations. This effort lays the foundation for the expedient construction of curated metabolic models for organisms that, while not being the target of comprehensive research, have a sequenced genome and are related to an organism with a curated metabolic model. Organism-specific models, such as the five presented in this paper, can be used to identify optimal genetic manipulations for targeted metabolite overproduction as well as to investigate the biology of diverse organisms.

Metabolic flux analysis of Cyanothece sp. ATCC 51142 under mixotrophic conditions

Cyanobacteria are a group of photosynthetic prokaryotes capable of utilizing solar energy to fix atmospheric carbon dioxide to biomass. Despite several "proof of principle" studies, low product yield is an impediment in commercialization of cyanobacteria-derived biofuels. Estimation of intracellular reaction rates by (13)C metabolic flux analysis ((13)C-MFA) would be a step toward enhancing biofuel yield via metabolic engineering. We report (13)C-MFA for Cyanothece sp. ATCC 51142, a unicellular nitrogen-fixing cyanobacterium, known for enhanced hydrogen yield under mixotrophic conditions. Rates of reactions in the central carbon metabolism under nitrogen-fixing and -non-fixing conditions were estimated by monitoring the competitive incorporation of (12)C and (13)C from unlabeled CO2 and uniformly labeled glycerol, respectively, into terminal metabolites such as amino acids. The observed labeling patterns suggest mixotrophic growth under both the conditions, with a larger fraction of unlabeled carbon in nitrate-sufficient cultures asserting a greater contribution of carbon fixation by photosynthesis and an anaplerotic pathway. Indeed, flux analysis complements the higher growth observed under nitrate-sufficient conditions. On the other hand, the flux through the oxidative pentose phosphate pathway and tricarboxylic acid cycle was greater in nitrate-deficient conditions, possibly to supply the precursors and reducing equivalents needed for nitrogen fixation. In addition, an enhanced flux through fructose-6-phosphate phosphoketolase possibly suggests the organism's preferred mode under nitrogen-fixing conditions. The (13)C-MFA results complement the reported predictions by flux balance analysis and provide quantitative insight into the organism's distinct metabolic features under nitrogen-fixing and -non-fixing conditions.

Metabolic modeling for multi-objective optimization of ethanol production in a Synechocystis mutant

Cyanobacteria have potential to produce drop-in bio-fuels such as ethanol via photoautotrophic metabolism. Although model cyanobacterial strains have been engineered to produce such products, systematic metabolic engineering studies to identify optimal strains for the same have not been performed. In this work, we identify optimal ethanol producing mutants corresponding to appropriate gene deletions that result in a suitable redirection in the carbon flux. In particular, we systematically simulate exhaustive single and double gene deletions considering a genome scale metabolic model of a mutant strain of the unicellular cyanobacterium Synechocystis species strain PCC 6803. Various optimization based metabolic modeling techniques, such as flux balance analysis (FBA), method of minimization of metabolic adjustment (MOMA) and regulatory on/off minimization (ROOM) were used for this analysis. For single gene deletion MOMA simulations, the Pareto front with biomass and ethanol fluxes as the two objectives to be maximized was obtained and analyzed. Points on the Pareto front represent maximal utilization of resources constrained by substrate uptake thereby representing an optimal trade-off between the two fluxes. Pareto analysis was also performed for double gene deletion MOMA and single and double gene deletion ROOM simulations. Based on these analyses, two mutants, with combined gene deletions in ethanol and purine metabolism pathways, were identified as promising candidates for ethanol production. The relevant genes were adk, pta and ackA. An ethanol productivity of approximately 0.15 mmol/(gDW h) was predicted for these mutants which appears to be reasonable based on experimentally reported values in literature for other strains.

Synechocystis sp. PCC6803 metabolic models for the enhanced production of hydrogen

In the present economy, difficulties to access energy sources are real drawbacks to maintain our current lifestyle. In fact, increasing interests have been gathered around efficient strategies to use energy sources that do not generate high CO2 titers. Thus, science-funding agencies have invested more resources into research on hydrogen among other biofuels as interesting energy vectors. This article reviews present energy challenges and frames it into the present fuel usage landscape. Different strategies for hydrogen production are explained and evaluated. Focus is on biological hydrogen production; fermentation and photon-fuelled hydrogen production are compared. Mathematical models in biology can be used to assess, explore and design production strategies for industrially relevant metabolites, such as biofuels. We assess the diverse construction and uses of genome-scale metabolic models of cyanobacterium Synechocystis sp. PCC6803 to efficiently obtain biofuels. This organism has been studied as a potential photon-fuelled production platform for its ability to grow from carbon dioxide, water and photons, on simple culture media. Finally, we review studies that propose production strategies to weigh this organism's viability as a biofuel production platform. Overall, the work presented in this review unveils the industrial capabilities of cyanobacterium Synechocystis sp. PCC6803 to evolve interesting metabolites as a clean biofuel production platform.

Synthetic biology of cyanobacteria: unique challenges and opportunities

Photosynthetic organisms, and especially cyanobacteria, hold great promise as sources of renewably-produced fuels, bulk and specialty chemicals, and nutritional products. Synthetic biology tools can help unlock cyanobacteria's potential for these functions, but unfortunately tool development for these organisms has lagged behind that for S. cerevisiae and E. coli. While these organisms may in many cases be more difficult to work with as “chassis” strains for synthetic biology than certain heterotrophs, the unique advantages of autotrophs in biotechnology applications as well as the scientific importance of improved understanding of photosynthesis warrant the development of these systems into something akin to a “green E. coli.” In this review, we highlight unique challenges and opportunities for development of synthetic biology approaches in cyanobacteria. We review classical and recently developed methods for constructing targeted mutants in various cyanobacterial strains, and offer perspective on what genetic tools might most greatly expand the ability to engineer new functions in such strains. Similarly, we review what genetic parts are most needed for the development of cyanobacterial synthetic biology. Finally, we highlight recent methods to construct genome-scale models of cyanobacterial metabolism and to use those models to measure properties of autotrophic metabolism. Throughout this paper, we discuss some of the unique challenges of a diurnal, autotrophic lifestyle along with how the development of synthetic biology and biotechnology in cyanobacteria must fit within those constraints.