A diurnal flux balance model of Synechocystis sp. PCC 6803 metabolism

Metabolic Flux Modeling in Marine Ecosystems

Ocean metabolism constitutes a complex, multiscale ensemble of biochemical reaction networks harbored within and between the boundaries of a myriad of organisms. Gaining a quantitative understanding of how these networks operate requires mathematical tools capable of solving in silico the resource allocation problem each cell faces in real life. Toward this goal, stoichiometric modeling of metabolism, such as flux balance analysis, has emerged as a powerful computational tool for unraveling the intricacies of metabolic processes in microbes, microbial communities, and multicellular organisms. Here, we provide an overview of this approach and its applications, future prospects, and practical considerations in the context of marine sciences. We explore how flux balance analysis has been employed to study marine organisms, help elucidate nutrient cycling, and predict metabolic capabilities within diverse marine environments, and highlight future prospects for this field in advancing our knowledge of marine ecosystems and their sustainability.

Conditional flux balance analysis toolbox for python: application to research metabolism in cyclic environments

Summary

We present py_cFBA, a Python-based toolbox for conditional flux balance analysis (cFBA). Our toolbox allows for an easy implementation of cFBA models using a well-documented and modular approach and supports the generation of Systems Biology Markup Language models. The toolbox is designed to be user-friendly, versatile, and freely available to non-commercial users, serving as a valuable resource for researchers predicting metabolic behaviour with resource allocation in dynamic-cyclic environments.

Availability and implementation

Extensive documentation, installation steps, tutorials, and examples are available at https://tp-watson-python-cfba.readthedocs.io/en/. The py_cFBA python package is available at https://pypi.org/project/py-cfba/.

Three enzymes governed the rise of O2 on Earth

Current views of O2 accumulation in Earth history depict three phases: The onset of O2 production by ∼2.4 billion years ago; 2 billion years of stasis at ∼1 % of modern atmospheric levels; and a rising phase, starting about 500 million years ago, in which oxygen eventually reached modern values. Purely geochemical mechanisms have been proposed to account for this tripartite time course of Earth oxygenation. In particular the second phase, the long period of stasis between the advent of O2 and the late rise to modern levels, has posed a puzzle. Proposed solutions involve Earth processes (geochemical, ecosystem, day length). Here we suggest that Earth oxygenation was not determined by geochemical processes. Rather it resulted from emergent biological innovations associated with photosynthesis and the activity of only three enzymes: 1) The oxygen evolving complex of cyanobacteria that makes O2; 2) Nitrogenase, with its inhibition by O2 causing two billion years of oxygen level stasis; 3) Cellulose synthase of land plants, which caused mass deposition and burial of carbon, thus removing an oxygen sink and therefore increasing atmospheric O2. These three enzymes are endogenously produced by, and contained within, cells that have the capacity for exponential growth. The catalytic properties of these three enzymes paved the path of Earth's atmospheric oxygenation, requiring no help from Earth other than the provision of water, CO2, salts, colonizable habitats, and sunlight.

A quantitative description of light-limited cyanobacterial growth using flux balance analysis

The metabolism of phototrophic cyanobacteria is an integral part of global biogeochemical cycles, and the capability of cyanobacteria to assimilate atmospheric CO2 into organic carbon has manifold potential applications for a sustainable biotechnology. To elucidate the properties of cyanobacterial metabolism and growth, computational reconstructions of genome-scale metabolic networks play an increasingly important role. Here, we present an updated reconstruction of the metabolic network of the cyanobacterium Synechocystis sp. PCC 6803 and its quantitative evaluation using flux balance analysis (FBA). To overcome limitations of conventional FBA, and to allow for the integration of experimental analyses, we develop a novel approach to describe light absorption and light utilization within the framework of FBA. Our approach incorporates photoinhibition and a variable quantum yield into the constraint-based description of light-limited phototrophic growth. We show that the resulting model is capable of predicting quantitative properties of cyanobacterial growth, including photosynthetic oxygen evolution and the ATP/NADPH ratio required for growth and cellular maintenance. Our approach retains the computational and conceptual simplicity of FBA and is readily applicable to other phototrophic microorganisms.

Thioredoxin A regulates protein synthesis to maintain carbon and nitrogen partitioning in cyanobacteria

Thioredoxins play an essential role in regulating enzyme activity in response to environmental changes, especially in photosynthetic organisms. They are crucial for metabolic regulation in cyanobacteria, but the key redox-regulated central processes remain to be determined. Physiological, metabolic, and transcriptomic characterization of a conditional mutant of the essential Synechocystis sp. PCC 6803 thioredoxin trxA gene (STXA2) revealed that decreased TrxA levels alter cell morphology and induce a dormant-like state. Furthermore, TrxA depletion in the STXA2 strain inhibited protein synthesis and led to changes in amino acid pools and nitrogen/carbon reserve polymers, accompanied by oxidation of the elongation factor-Tu. Transcriptomic analysis of TrxA depletion in STXA2 revealed a robust transcriptional response. Downregulated genes formed a large cluster directly related to photosynthesis, ATP synthesis, and CO2 fixation. In contrast, upregulated genes were grouped into different clusters related to respiratory electron transport, carotenoid biosynthesis, amino acid metabolism, and protein degradation, among others. These findings highlight the complex regulatory mechanisms that govern cyanobacterial metabolism, where TrxA acts as a critical regulator that orchestrates the transition from anabolic to maintenance metabolism and regulates carbon and nitrogen balance.

Light and carbon: Synthetic biology toward new cyanobacteria-based living biomaterials

Cyanobacteria are ideal candidates to use in developing carbon neutral and carbon negative technologies; they are efficient photosynthesizers and amenable to genetic manipulation. Over the past two decades, researchers have demonstrated that cyanobacteria can make sustainable, useful biomaterials, many of which are engineered living materials. However, we are only beginning to see such technologies applied at an industrial scale. In this review, we explore the ways in which synthetic biology tools enable the development of cyanobacteria-based biomaterials. First we give an overview of the ecological and biogeochemical importance of cyanobacteria and the work that has been done using cyanobacteria to create biomaterials so far. This is followed by a discussion of commonly used cyanobacteria strains and synthetic biology tools that exist to engineer cyanobacteria. Then, three case studies-bioconcrete, biocomposites, and biophotovoltaics-are explored as potential applications of synthetic biology in cyanobacteria-based materials. Finally, challenges and future directions of cyanobacterial biomaterials are discussed.

Quantitative insight into the metabolism of isoprene-producing Synechocystis sp. PCC 6803 using steady state 13C-MFA

Cyanobacteria are photosynthetic bacteria, widely studied for the conversion of atmospheric carbon dioxide to useful platform chemicals. Isoprene is one such industrially important chemical, primarily used for production of synthetic rubber and biofuels. Synechocystis sp. PCC 6803, a genetically amenable cyanobacterium, produces isoprene on heterologous expression of isoprene synthase gene, albeit in very low quantities. Rationalized metabolic engineering to re-route the carbon flux for enhanced isoprene production requires in-dept knowledge of the metabolic flux distribution in the cell. Hence, in the present study, we undertook steady state ¹³C-metabolic flux analysis of glucose-tolerant wild-type (GTN) and isoprene-producing recombinant (ISP) Synechocystis sp. to understand and compare the carbon flux distribution in the two strains. The R-values for amino acids, flux analysis predictions and gene expression profiles emphasized predominance of Calvin cycle and glycogen metabolism in GTN. Alternatively, flux analysis predicted higher activity of the anaplerotic pathway through phosphoenolpyruvate carboxylase and malic enzyme in ISP. The striking difference in the Calvin cycle, glycogen metabolism and anaplerotic pathway activity in GTN and ISP suggested a possible role of energy molecules (ATP and NADPH) in regulating the carbon flux distribution in GTN and ISP. This claim was further supported by the transcript level of selected genes of the electron transport chain. This study provides the first quantitative insight into the carbon flux distribution of isoprene-producing cyanobacterium, information critical for developing Synechocystis sp. as a single cell factory for isoprenoid/terpenoid production.

The paradoxes hidden behind the Droop model highlighted by a metabolic approach

We propose metabolic models for the haptophyte microalgae Tisochrysis lutea with different possible organic carbon excretion mechanisms. These models-based on the DRUM (Dynamic Reduction of Unbalanced Metabolism) methodology-are calibrated with an experiment of nitrogen starvation under day/night cycles, and then validated with nitrogen-limited chemostat culture under continuous light. We show that models including exopolysaccharide excretion offer a better prediction capability. It also gives an alternative mechanistic interpretation to the Droop model for nitrogen limitation, which can be understood as an accumulation of carbon storage during nitrogen stress, rather than the common belief of a nitrogen pool driving growth. Excretion of organic carbon limits its accumulation, which leads to a maximal C/N ratio (corresponding to the minimum Droop N/C quota). Although others phenomena-including metabolic regulations and dissipation of energy-are possibly at stake, excretion appears as a key component in our metabolic model, that we propose to include in the Droop model.

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.

Cyanobacteria as cell factories: the roles of host and pathway engineering and translational research

Cyanobacteria, a group of photoautotrophic prokaryotes, are attractive hosts for the sustainable production of chemicals from carbon dioxide and sunlight. However, the rates, yields, and titers have remained well below those needed for commercial deployment. We argue that the following areas will be central to the development of cyanobacterial cell factories: engineered and well-characterized host strains, model-guided pathway design, and advanced synthetic biology tools. Although several foundational studies report improved strain properties, translational research will be needed to develop engineered hosts and deploy them for metabolic engineering. Further, the recent developments in metabolic modeling and synthetic biology of cyanobacteria will enable nimble strategies for strain improvement with the complete cycle of design, build, test, and learn.

Elucidation of trophic interactions in an unusual single-cell nitrogen-fixing symbiosis using metabolic modeling

Marine nitrogen-fixing microorganisms are an important source of fixed nitrogen in oceanic ecosystems. The colonial cyanobacterium Trichodesmium and diatom symbionts were thought to be the primary contributors to oceanic N₂ fixation until the discovery of the unusual uncultivated symbiotic cyanobacterium UCYN-A (Candidatus Atelocyanobacterium thalassa). UCYN-A has atypical metabolic characteristics lacking the oxygen-evolving photosystem II, the tricarboxylic acid cycle, the carbon-fixation enzyme RuBisCo and de novo biosynthetic pathways for a number of amino acids and nucleotides. Therefore, it is obligately symbiotic with its single-celled haptophyte algal host. UCYN-A receives fixed carbon from its host and returns fixed nitrogen, but further insights into this symbiosis are precluded by both UCYN-A and its host being uncultured. In order to investigate how this syntrophy is coordinated, we reconstructed bottom-up genome-scale metabolic models of UCYN-A and its algal partner to explore possible trophic scenarios, focusing on nitrogen fixation and biomass synthesis. Since both partners are uncultivated and only the genome sequence of UCYN-A is available, we used the phylogenetically related Chrysochromulina tobin as a proxy for the host. Through the use of flux balance analysis (FBA), we determined the minimal set of metabolites and biochemical functions that must be shared between the two organisms to ensure viability and growth. We quantitatively investigated the metabolic characteristics that facilitate daytime N₂ fixation in UCYN-A and possible oxygen-scavenging mechanisms needed to create an anaerobic environment to allow nitrogenase to function. This is the first application of an FBA framework to examine the tight metabolic coupling between uncultivated microbes in marine symbiotic communities and provides a roadmap for future efforts focusing on such specialized systems.

Reduction of dinitrogen gas to biologically useful forms via nitrogen fixation is a key component of the biogeochemical cycle. In the marine environment, the cyanobacteria UCYN-A (Candidatus Atelocyanobacterium thalassa) has been found to be a primary contributor to biological nitrogen fixation at a global scale. UCYN-A exhibits a highly streamlined genome which lacks genes coding for essential cyanobacterial processes such as the energy-generating TCA cycle, oxygen-producing photosystem II, the carbon-fixing RuBisCo and de novo production pathways for numerous amino acids and nucleotides. Thus, it exists in a symbiosis with unicellular planktonic algae where it exchanges fixed nitrogen for fixed carbon with its host. However, both UCYN-A and its symbiotic partner remain uncultured under laboratory conditions. This necessitates implementing a computational approach to glean insights into UCYN-A’s unique physiology and metabolic processes governing the symbiotic association. To this end, we develop a constraints-based framework that infers all possible trophic scenarios consistent with the observed data. Possible mechanisms employed by UCYN-A to accommodate diazotrophy with daytime carbon fixation by the host (i.e., two mutually incompatible processes) are also elucidated. We envision that the developed framework using UCYN-A and its algal host will be used as a roadmap and motivate the study of similarly unique microbial systems in the future.

Selective Uptake of Pelagic Microbial Community Members by Caribbean Reef Corals

We identify interactions between coral grazing behavior and the growth rates and cell abundances of pelagic microbial groups found surrounding a Caribbean reef. During incubation experiments with three reef corals, reductions in microbial cell abundance differed according to coral species and suggest specific coral or microbial mechanisms are at play.

Coral reefs are possible sinks for microbes; however, the removal mechanisms at play are not well understood. Here, we characterize pelagic microbial groups at the CARMABI reef (Curaçao) and examine microbial consumption by three coral species: Madracis mirabilis, Porites astreoides, and Stephanocoenia intersepta. Flow cytometry analyses of water samples collected from a depth of 10 m identified 6 microbial groups: Prochlorococcus, three groups of Synechococcus, photosynthetic eukaryotes, and heterotrophic bacteria. Minimum growth rates (μ) for Prochlorococcus, all Synechococcus groups, and photosynthetic eukaryotes were 0.55, 0.29, and 0.45 μ day⁻¹, respectively, and suggest relatively high rates of productivity despite low nutrient conditions on the reef. During a series of 5-h incubations with reef corals performed just after sunset or prior to sunrise, reductions in the abundance of photosynthetic picoeukaryotes, Prochlorococcus and Synechococcus cells, were observed. Of the three Synechococcus groups, one decreased significantly during incubations with each coral and the other two only with M. mirabilis. Removal of carbon from the water column is based on coral consumption rates of phytoplankton and averaged between 138 ng h⁻¹ and 387 ng h⁻¹, depending on the coral species. A lack of coral-dependent reduction in heterotrophic bacteria, differences in Synechococcus reductions, and diurnal variation in reductions of Synechococcus and Prochlorococcus, coinciding with peak cell division, point to selective feeding by corals. Our study indicates that bentho-pelagic coupling via selective grazing of microbial groups influences carbon flow and supports heterogeneity of microbial communities overlying coral reefs.

IMPORTANCE We identify interactions between coral grazing behavior and the growth rates and cell abundances of pelagic microbial groups found surrounding a Caribbean reef. During incubation experiments with three reef corals, reductions in microbial cell abundance differed according to coral species and suggest specific coral or microbial mechanisms are at play. Peaks in removal rates of Prochlorococcus and Synechococcus cyanobacteria appear highest during postsunset incubations and coincide with microbial cell division. Grazing rates and effort vary across coral species and picoplankton groups, possibly influencing overall microbial composition and abundance over coral reefs. For reef corals, use of such a numerically abundant source of nutrition may be advantageous, especially under environmentally stressful conditions when symbioses with dinoflagellate algae break down.

Computational Analysis of Dynamic Light Exposure of Unicellular Algal Cells in a Flat-Panel Photobioreactor to Support Light-Induced CO2 Bioprocess Development

Cyanobacterial cell factories trace a vibrant pathway to climate change neutrality and sustainable development owing to their ability to turn carbon dioxide-rich waste into a broad portfolio of renewable compounds, which are deemed valuable in green chemistry cross-sectorial applications. Cell factory design requires to define the optimal operational and cultivation conditions. The paramount parameter in biomass cultivation in photobioreactors is the light intensity since it impacts cellular physiology and productivity. Our modeling framework provides a basis for the predictive control of light-limited, light-saturated, and light-inhibited growth of the Synechocystis sp. PCC 6803 model organism in a flat-panel photobioreactor. The model here presented couples computational fluid dynamics, light transmission, kinetic modeling, and the reconstruction of single cell trajectories in differently irradiated areas of the photobioreactor to relate key physiological parameters to the multi-faceted processes occurring in the cultivation environment. Furthermore, our analysis highlights the need for properly constraining the model with decisive qualitative and quantitative data related to light calibration and light measurements both at the inlet and outlet of the photobioreactor in order to boost the accuracy and extrapolation capabilities of the model.

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.

Dynamic Inventory of Intermediate Metabolites of Cyanobacteria in a Diurnal Cycle

Cyanobacteria are gaining importance both as hosts for photoautotrophic production of chemicals and as model systems for studies of diurnal lifestyle. The proteome and transcriptome of cyanobacteria have been closely examined under diurnal growth, whereas the downstream effects on the intermediary metabolism have not received sufficient attention. The present study focuses on identifying the cellular metabolites whose inventories undergo dramatic changes in a fast-growing cyanobacterium, Synechococcus elongatus PCC 11801. We identified and quantified 67 polar metabolites, whose inventory changes significantly during diurnal growth, with some metabolites changing by 100-fold. The Calvin-Benson-Bassham cycle intermediates peak at midday to support fast growth. The hitherto unexplored γ-glutamyl peptides act as reservoirs of amino acids. Interestingly, several storage molecules or their precursors accumulate during the dark phase, dispelling the notion that all biosynthetic activity takes place in the light phase. Our results will guide metabolic modeling and strain engineering of cyanobacteria.

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We identify and quantify 67 polar intermediate metabolites in cyanobacteria via LC-MS

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A number of metabolites show large variations during the diurnal cycle

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Intermediates of the CBB cycle peak at midday, coinciding with peak in growth rate

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Gamma-glutamyl dipeptides identified as new storage compounds that peak at dawn

Modifying the Cyanobacterial Metabolism as a Key to Efficient Biopolymer Production in Photosynthetic Microorganisms

Cyanobacteria are photoautotrophic bacteria commonly found in the natural environment. Due to the ecological benefits associated with the assimilation of carbon dioxide from the atmosphere and utilization of light energy, they are attractive hosts in a growing number of biotechnological processes. Biopolymer production is arguably one of the most critical areas where the transition from fossil-derived chemistry to renewable chemistry is needed. Cyanobacteria can produce several polymeric compounds with high applicability such as glycogen, polyhydroxyalkanoates, or extracellular polymeric substances. These important biopolymers are synthesized using precursors derived from central carbon metabolism, including the tricarboxylic acid cycle. Due to their unique metabolic properties, i.e., light harvesting and carbon fixation, the molecular and genetic aspects of polymer biosynthesis and their relationship with central carbon metabolism are somehow different from those found in heterotrophic microorganisms. A greater understanding of the processes involved in cyanobacterial metabolism is still required to produce these molecules more efficiently. This review presents the current state of the art in the engineering of cyanobacterial metabolism for the efficient production of these biopolymers.

Integration of Time-Series Transcriptomic Data with Genome-Scale CHO Metabolic Models for mAb Engineering

Chinese hamster ovary (CHO) cells are the most commonly used cell lines in biopharmaceutical manufacturing. Genome-scale metabolic models have become a valuable tool to study cellular metabolism. Despite the presence of reference global genome-scale CHO model, context-specific metabolic models may still be required for specific cell lines (for example, CHO-K1, CHO-S, and CHO-DG44), and for specific process conditions. Many integration algorithms have been available to reconstruct specific genome-scale models. These methods are mainly based on integrating omics data (i.e., transcriptomics, proteomics, and metabolomics) into reference genome-scale models. In the present study, we aimed to investigate the impact of time points of transcriptomics integration on the genome-scale CHO model by assessing the prediction of growth rates with each reconstructed model. We also evaluated the feasibility of applying extracted models to different cell lines (generated from the same parental cell line). Our findings illustrate that gene expression at various stages of culture slightly impacts the reconstructed models. However, the prediction capability is robust enough on cell growth prediction not only across different growth phases but also in expansion to other cell lines.

Metabolic Analyses of Nitrogen Fixation in the Soybean Microsymbiont Sinorhizobium fredii Using Constraint-Based Modeling

Nitrogen is the most limiting macronutrient for plant growth, and rhizobia are important bacteria for agriculture because they can fix atmospheric nitrogen and make it available to legumes through the establishment of a symbiotic relationship with their host plants. In this work, we studied the nitrogen fixation process in the microsymbiont Sinorhizobium fredii at the genome level. A metabolic model was built using genome annotation and literature to reconstruct the symbiotic form of S. fredii. Genes controlling the nitrogen fixation process were identified by simulating gene knockouts. Additionally, the nitrogen-fixing capacities of S. fredii CCBAU45436 in symbiosis with cultivated and wild soybeans were evaluated. The predictions suggested an outperformance of S. fredii with cultivated soybean, consistent with published experimental evidence. The reconstruction presented here will help to understand and improve nitrogen fixation capabilities of S. fredii and will be beneficial for agriculture by reducing the reliance on fertilizer applications.

Rhizobia are soil bacteria able to establish symbiosis with diverse host plants. Specifically, Sinorhizobium fredii is a soil bacterium that forms nitrogen-fixing root nodules in diverse legumes, including soybean. The strain S. fredii CCBAU45436 is a dominant sublineage of S. fredii that nodulates soybeans in alkaline-saline soils in the Huang-Huai-Hai Plain region of China. Here, we present a manually curated metabolic model of the symbiotic form of Sinorhizobium fredii CCBAU45436. A symbiosis reaction was defined to describe the specific soybean-microsymbiont association. The performance and quality of the reconstruction had a 70% score when assessed using a standardized genome-scale metabolic model test suite. The model was used to evaluate in silico single-gene knockouts to determine the genes controlling the nitrogen fixation process. One hundred forty-one of 541 genes (26%) were found to influence the symbiotic process, wherein 121 genes were predicted as essential and 20 others as having a partial effect. Transcriptomic profiles of CCBAU45436 were used to evaluate the nitrogen fixation capacity in cultivated versus in wild soybean inoculated with the microsymbiont. The model quantified the nitrogen fixation activities of the strain in these two hosts and predicted a higher nitrogen fixation capacity in cultivated soybean. Our results are consistent with published data demonstrating larger amounts of ureides and total nitrogen in cultivated soybean than in wild soybean. This work presents the first metabolic network reconstruction of S. fredii as an example of a useful tool for exploring the potential benefits of microsymbionts to sustainable agriculture and the ecosystem.

IMPORTANCE Nitrogen is the most limiting macronutrient for plant growth, and rhizobia are important bacteria for agriculture because they can fix atmospheric nitrogen and make it available to legumes through the establishment of a symbiotic relationship with their host plants. In this work, we studied the nitrogen fixation process in the microsymbiont Sinorhizobium fredii at the genome level. A metabolic model was built using genome annotation and literature to reconstruct the symbiotic form of S. fredii. Genes controlling the nitrogen fixation process were identified by simulating gene knockouts. Additionally, the nitrogen-fixing capacities of S. fredii CCBAU45436 in symbiosis with cultivated and wild soybeans were evaluated. The predictions suggested an outperformance of S. fredii with cultivated soybean, consistent with published experimental evidence. The reconstruction presented here will help to understand and improve nitrogen fixation capabilities of S. fredii and will be beneficial for agriculture by reducing the reliance on fertilizer applications.

An integrated computational and experimental study to investigate Staphylococcus aureus metabolism

Staphylococcus aureus is a metabolically versatile pathogen that colonizes nearly all organs of the human body. A detailed and comprehensive knowledge of staphylococcal metabolism is essential to understand its pathogenesis. To this end, we have reconstructed and experimentally validated an updated and enhanced genome-scale metabolic model of S. aureus USA300_FPR3757. The model combined genome annotation data, reaction stoichiometry, and regulation information from biochemical databases and previous strain-specific models. Reactions in the model were checked and fixed to ensure chemical balance and thermodynamic consistency. To further refine the model, growth assessment of 1920 nonessential mutants from the Nebraska Transposon Mutant Library was performed, and metabolite excretion profiles of important mutants in carbon and nitrogen metabolism were determined. The growth and no-growth inconsistencies between the model predictions and in vivo essentiality data were resolved using extensive manual curation based on optimization-based reconciliation algorithms. Upon intensive curation and refinements, the model contains 863 metabolic genes, 1379 metabolites (including 1159 unique metabolites), and 1545 reactions including transport and exchange reactions. To improve the accuracy and predictability of the model to environmental changes, condition-specific regulation information curated from the existing knowledgebase was incorporated. These critical additions improved the model performance significantly in capturing gene essentiality, substrate utilization, and metabolite production capabilities and increased the ability to generate model-based discoveries of therapeutic significance. Use of this highly curated model will enhance the functional utility of omics data, and therefore, serve as a resource to support future investigations of S. aureus and to augment staphylococcal research worldwide.

Integration of in vivo experiment with a newly developed model of Staphylococcus aureus metabolism helps explore its metabolic versatility. A multidisciplinary team led by Rajib Saha at the University of Nebraska developed a new genome-scale metabolic model of the multi-drug resistant pathogen S. aureus by combining genome annotation data, reaction stoichiometry, and condition- and mutant-specific regulations from biochemical databases and previous strain-specific models. Extensive manual curation and incorporation of newly generated experimental data on growth and metabolite production improved the accuracy and predictability of the model and increased its ability to generate model-based discoveries of therapeutic significance. Use of this highly curated model will enhance the functional utility of omics data and, therefore, serve as a resource to support future investigations of S. aureus and to augment staphylococcal research worldwide.

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.

Exploiting Day- and Night-Time Metabolism of Synechocystis sp. PCC 6803 for Fitness-Coupled Fumarate Production around the Clock

Cyanobacterial cell factories are widely researched for the sustainable production of compounds directly from CO2. Their application, however, has been limited for two reasons. First, traditional approaches have been shown to lead to unstable cell factories that lose their production capability when scaled to industrial levels. Second, the alternative approaches developed so far are mostly limited to growing conditions, which are not always the case in industry, where nongrowth periods tend to occur (e.g., darkness). We tackled both by generalizing the concept of growth-coupled production to fitness coupling. The feasibility of this new approach is demonstrated for the production of fumarate by constructing the first stable dual-strategy cell factory. We exploited circadian metabolism using both systems and synthetic biology tools, resulting in the obligatorily coupling of fumarate to either biomass or energy production. Resorting to laboratory evolution experiments, we show that this engineering approach is more stable than conventional methods.

A comprehensive time-course metabolite profiling of the model cyanobacterium Synechocystis sp. PCC 6803 under diurnal light:dark cycles

Cyanobacteria are a model photoautotroph and a chassis for the sustainable production of fuels and chemicals. Knowledge of photoautotrophic metabolism in the natural environment of day/night cycles is lacking, yet has implications for improved yield from plants, algae and cyanobacteria. Here, a thorough approach to characterizing diverse metabolites-including carbohydrates, lipids, amino acids, pigments, cofactors, nucleic acids and polysaccharides-in the model cyanobacterium Synechocystis sp. PCC 6803 (S. 6803) under sinusoidal diurnal light:dark cycles was developed and applied. A custom photobioreactor and multi-platform mass spectrometry workflow enabled metabolite profiling every 30-120 min across a 24-h diurnal sinusoidal LD ('sinLD') cycle peaking at 1600 μmol photons m^-2 sec^-1 . We report widespread oscillations across the sinLD cycle with 90%, 94% and 40% of the identified polar/semi-polar, non-polar and polymeric metabolites displaying statistically significant oscillations, respectively. Microbial growth displayed distinct lag, biomass accumulation and cell division phases of growth. During the lag phase, amino acids and nucleic acids accumulated to high levels per cell followed by decreased levels during the biomass accumulation phase, presumably due to protein and DNA synthesis. Insoluble carbohydrates displayed sharp oscillations per cell at the day-to-night transition. Potential bottlenecks in central carbon metabolism are highlighted. Together, this report provides a comprehensive view of photosynthetic metabolite behavior with high temporal resolution, offering insight into the impact of growth synchronization to light cycles via circadian rhythms. Incorporation into computational modeling and metabolic engineering efforts promises to improve industrially relevant strain design.