Identification of a metabolic network structure representative of Arthrospira (spirulina) platensis metabolism

Towards a general kinetic microalgae model: Extending a semi-deterministic green microalgae model for the cyanobacterium Arthrospira platensis and red alga Porphyridium purpureum

Mathematical models for microalgae and cyanobacteria are seldomly validated for different algal species, as such limiting their applicability. Therefore, in this research, a previously developed kinetic model describing the growth of the green microalgae species Chlorella vulgaris was used to simulate the growth of the cyanobacterium Arthrospira platensis and the red alga Porphyridium purpureum. Based on a global sensitivity analysis, the model parameter µ_max,A was calibrated using respirometric-titrimetric data. Calibration yielded values of 5.76 ± 0.17 d^-1, 2.06 ± 0.16 d^-1 and 1.06 ± 0.09 d^-1 for Chlorella vulgaris, Arthrospira platensis and Porphyridium purpureum, respectively. Model simulations revealed that the biological growth equations in this model are adequate. However, increased light intensities triggered a survival mechanism for Arthrospira platensis, which is currently not taken into account by the model, leading to bad model accuracy under these circumstances. Future work should address the most important survival mechanisms and include those in the model to widen its applicability.

Dynamics of long-term continuous culture of Limnospira indica in an air-lift photobioreactor

MELiSSA (Microecological Life Support System Alternative) is a developing technology for regenerative life support to enable long-term human missions in Space and has developed a demonstration Pilot Plant. One of the components of the MELiSSA Pilot Plant system is an 83L external loop air-lift photobioreactor (PBR) where Limnospira indica (previously named Arthrospira sp. PC8005) is axenically cultivated in a continuous operation mode for long-periods. Its mission is to provide O₂ and consume CO₂ while producing edible material. Biological and process characterization of this PBR is performed by analysing the effect of two main variables, dilution rate (D) and PFD (Photon Flux Density) illumination. A maximum oxygen productivity ( r O 2 ) of 1.35 mmol l^-1  h^-1 is obtained at a D of 0.025 h^-1 and PFD of 930 µmol m^-2  s^-1 . Photoinhibition can occur when a 1 g l^-1 cell density culture is exposed to PFD higher than 1700 µmol m^-2  s^-1 . This process is reversible if the illumination is returned to dim light (150 µmol m^-2  s^-1 ), proving the cell adaptability and capacity to respond at different illumination conditions. Influence of light intensity in cell composition is also described. Specific photon flux density (qPFD) has a direct effect on phycobiliproteins and chlorophyll content causing a decrease of 62.5% and 47.8%, respectively, when qPFD increases from 6.1 to 19.2 µmol g^-1  s^-1 . The same trend is observed for proteins and the opposite for carbohydrate content. Morphological and spiral structural features of L. indica are studied by confocal microscopy, and size distribution parameters are quantified. A direct effect between trichome width and CDW/OD ratio is observed. Changes in size distribution are not correlated with environmental factors, further confirms the adaptation capacity of the cells. The systematic analysis performed provides valuable insights to understand the key performance criteria of continuous culture in air-lift PBRs.

Use of Photobioreactors in Regenerative Life Support Systems for Human Space Exploration

There are still many challenges to overcome for human space exploration beyond low Earth orbit (LEO) (e.g., to the Moon) and for long-term missions (e.g., to Mars). One of the biggest problems is the reliable air, water and food supply for the crew. Bioregenerative life support systems (BLSS) aim to overcome these challenges using bioreactors for waste treatment, air and water revitalization as well as food production. In this review we focus on the microbial photosynthetic bioprocess and photobioreactors in space, which allow removal of toxic carbon dioxide (CO2) and production of oxygen (O2) and edible biomass. This paper gives an overview of the conducted space experiments in LEO with photobioreactors and the precursor work (on ground and in space) for BLSS projects over the last 30 years. We discuss the different hardware approaches as well as the organisms tested for these bioreactors. Even though a lot of experiments showed successful biological air revitalization on ground, the transfer to the space environment is far from trivial. For example, gas-liquid transfer phenomena are different under microgravity conditions which inevitably can affect the cultivation process and the oxygen production. In this review, we also highlight the missing expertise in this research field to pave the way for future space photobioreactor development and we point to future experiments needed to master the challenge of a fully functional BLSS.

A review on photobioreactor design and modelling for microalgae production

From the cell to the photobioreactor and to the industrial exploitation of microalgae, through the controlled experiments and modelling.

Spirulina-in Silico-Mutations and Their Comparative Analyses in the Metabolomics Scale by Using Proteome-Based Flux Balance Analysis

This study used an in silico metabolic engineering strategy for modifying the metabolic capabilities of Spirulina under specific conditions as an approach to modifying culture conditions in order to generate the intended outputs. In metabolic models, the basic metabolic fluxes in steady-state metabolic networks have generally been controlled by stoichiometric reactions; however, this approach does not consider the regulatory mechanism of the proteins responsible for the metabolic reactions. The protein regulatory network plays a critical role in the response to stresses, including environmental stress, encountered by an organism. Thus, the integration of the response mechanism of Spirulina to growth temperature stresses was investigated via simulation of a proteome-based GSMM, in which the boundaries were established by using protein expression levels obtained from quantitative proteomic analysis. The proteome-based flux balance analysis (FBA) under an optimal growth temperature (35 °C), a low growth temperature (22 °C) and a high growth temperature (40 °C) showed biomass yields that closely fit the experimental data obtained in previous research. Moreover, the response mechanism was analyzed by the integration of the proteome and protein-protein interaction (PPI) network, and those data were used to support in silico knockout/overexpression of selected proteins involved in the PPI network. The Spirulina, wild-type, proteome fluxes under different growth temperatures and those of mutants were compared, and the proteins/enzymes catalyzing the different flux levels were mapped onto their designated pathways for biological interpretation.

Limnospira indica PCC8005 growth in photobioreactor: model and simulation of the ISS and ground experiments

The Arthrospira-B experiment is the first experiment in space ever allowing the online measurements of both oxygen production rate and growth rate of Limnospira indica PCC8005 in batch photobioreactors running on-board ISS. Four bioreactors were integrated in the ISS Biolab facility. Each reactor was composed of two chambers (gas and liquid) separated by a PTFE membrane and was run in batch conditions. Oxygen production was monitored by online measurement of the total pressure increase in the gas chamber. The experiments are composed of several successive batch cultures for each reactor, performed in parallel on ISS and on ground. In this work, a model for the growth of the cyanobacterium Limnospira indica PCC8005 (also known as Arthrospira or spirulina) in these space membrane photobioreactors was proposed and the simulation results obtained are compared to the experimental results gathered in space and on ground. The photobioreactor model was based on a light transfer limitation model, already used to describe and predict the growth and oxygen production in small to large scale ground photobioreactors. It was completed by a model for pH prediction in the liquid phase allowing assessment of the pH increase associated to the bicarbonate consumption for the biomass growth. A membrane gas-liquid transfer model is used to predict the gas pressure increase in the gas chamber. Substrate limitation is considered in the biological model. A quite satisfactory fit was achieved between experimental and simulation results when a suitable mixing of the liquid phase was maintained. The data showed that microgravity has no first order effect on the oxygen production rate of Limnospira indica PCC8005 in a photobioreactor operating in space in zero gravity conditions.

Effect of trophic conditions on microalga growth, nutrient removal, algal organic matter, and energy storage products in Scenedesmus (Acutodesmus) obliquus KGE-17 cultivation

This study compared the performance of microalga growth, nutrient removal, algal organic matter, and energy storage products in mixotrophic, photoautotrophic, and heterotrophic conditions. Scenedesmus obliquus was used as a model species. Mixotrophic condition showed the highest specific growth rate of 0.96 d^-1 as well as the fastest nitrogen and phosphorus removal rate of 85.17 mg-N g-cell^-1 day^-1 and 11.49 mg-P g-cell^-1 day^-1, respectively, compared with photoautotrophic and heterotrophic conditions. Mixotrophic microalgae had relatively higher carbohydrates and lipids contents (21.8 and 24.0%) than photoautotrophic and heterotrophic conditions. Meanwhile, algal organic matter (AOM) in the medium was produced at the highest level under photoautotrophic condition. Mixotrophic condition was more efficient in terms of microalga growth, nutrient removal, production of energy storage products, and suppression of AOM, and would be adaptable for wastewater treatment process.

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.

Genomics of Alkaliphiles

Alkalinicity presents a challenge for life due to a "reversed" proton gradient that is unfavourable to many bioenergetic processes across the membranes of microorganisms. Despite this, many bacteria, archaea, and eukaryotes, collectively termed alkaliphiles, are adapted to life in alkaline ecosystems and are of great scientific and biotechnological interest due to their niche specialization and ability to produce highly stable enzymes. Advances in next-generation sequencing technologies have propelled not only the genomic characterization of many alkaliphilic microorganisms that have been isolated from nature alkaline sources but also our understanding of the functional relationships between different taxa in microbial communities living in these ecosystems. In this review, we discuss the genetics and molecular biology of alkaliphiles from an "omics" point of view, focusing on how metagenomics and transcriptomics have contributed to our understanding of these extremophiles. Graphical Abstract.

An Improved Genome-Scale Metabolic Model of Arthrospira platensis C1 (iAK888) and Its Application in Glycogen Overproduction

Glycogen-enriched biomass of Arthrospiraplatensis has increasingly gained attention as a source for bioethanol production. To study the metabolic capabilities of glycogen production in A. platensis C1, a genome-scale metabolic model (GEM) could be a useful tool for predicting cellular behavior and suggesting strategies for glycogen overproduction. New experimentally validated GEM of A. platensis C1 namely iAK888, which has improved metabolic coverage and functionality was employed in this research. The iAK888 is a fully functional compartmentalized GEM consisting of 888 genes, 1,096 reactions, and 994 metabolites. This model was demonstrated to reasonably predict growth and glycogen fluxes under different growth conditions. In addition, iAK888 was further employed to predict the effect of deficiencies of NO₃⁻, PO₄³⁻, or SO₄²⁻ on the growth and glycogen production in A. platensis C1. The simulation results showed that these nutrient limitations led to a decrease in growth flux and an increase in glycogen flux. The experiment of A. platensis C1 confirmed the enhancement of glycogen fluxes after the cells being transferred from normal Zarrouk’s medium to either NO₃⁻, PO₄³⁻, or SO₄²⁻-free Zarrouk’s media. Therefore, iAK888 could be served as a predictive model for glycogen overproduction and a valuable multidisciplinary tool for further studies of this important academic and industrial organism.

Effect of nitrogen limitation on biochemical composition and photosynthetic performance for fed-batch mixotrophic cultivation of microalga Spirulina platensis

In this study, the effect of nitrogen limitation on microalgal growth, biochemical composition and photosynthetic performance was investigated in fed-batch mixotrophic cultivation of microalga Spirulina platensis, compared with that in autotrophic cultivation. The microalgal biomass productivity was greatly enhanced by mixotrophic cultivation. With nitrogen limitation, the mixotrophic culture accelerated the degradation of microalgal pigments and proteins to supply intracellular nitrogen for maintaining higher biomass productivity, simultaneously accumulating more carbohydrates. The mixotrophic cultivation amplified the adverse effect of nitrogen limitation on the microalgal photosynthetic performance in comparison with autotrophic cultivation. This fed-batch mixotrophic cultivation is an effective strategy for enhancing biomass productivity and total carbohydrates yield under nitrogen limited conditions.

Characterization of an easy-to-use method for the routine analysis of the central metabolism using an affordable low-resolution GC-MS system: application to Arthrospira platensis

We developed an easy-to-use method for the routine analysis of the central metabolism using an affordable low-resolution GC-MS system run in SIM mode. The profiling approach was optimized for the derivatization protocol of some 60 targeted metabolites. The performance of two silylation reagents (MSTFA and BSTFA) that allowed the comprehensive derivatization of 42 key intermediary metabolites of the 60 initially targeted (organic acids, phosphate derivatives, monosaccharides and amino acids) was measured. The experimental results unequivocally showed that the MSTFA reagent met mandatory criteria including ease of handling (a very simple one-step protocol was developed), comprehensiveness of derivatization (the 42 compounds covered the extended metabolic pathways of the central carbon metabolism, with a coverage percentage ranging from 17% for the worst to 90% for the best result), optimized response coefficient of the whole derivatives (median value greater than the others by one order of magnitude) and repeatability of the protocol (RSD value below 25% for the whole procedure). When tested in real conditions (cyanobacteria polar extract), the experimental results showed that the profiling methodology was adequately repeatable (RSD = 35%) to ensure quantification results comparable with much more sensitive analytical techniques (capillary electrophoresis/mass spectrometry and liquid chromatography/triple quadrupole mass spectrometry system), while needing only about twice the quantity of biomass. Graphical abstract Schematic overview of an easy-to-use profiling method for the routine analysis of the central metabolism using a low-resolution GC-MS system.

Optimization of production of C-phycocyanin and extracellular polymeric substances by Arthrospira sp

The key factors influencing the production of C-phycocyanin (C-PC) and extracellular polymeric substances (EPS) by photoautotrophic culture of Arthrospira sp. were optimized using Taguchi method. Six factors were varied at either three or two levels as follows: light intensity at three levels; three initial culture pHs; two species of Arthrospira; three concentrations of Zarrouk's medium; three rates of aeration of the culture with air mixed with 2% v/v carbon dioxide; and two incubation temperatures. All cultures ran for 14 days. The optimal conditions for the production of C-PC and EPS were different. For both products, the best cyanobacterium proved to be Arthrospira maxima IFRPD1183. The production of C-PC was maximized with the following conditions: a light intensity of 68 µmol photons m^-2 s^-1 (a diurnal cycle of 16-h photoperiod and 8-h dark period), an initial pH of 10, the full strength (100%) Zarrouk's culture medium, an aeration rate of 0.6 vvm (air mixed with 2% v/v CO₂) and a culture temperature of 30 °C. The concentration of Zarrouk's medium was the most important factor influencing the final concentration of C-PC. The optimal conditions for maximal production of EPS were as follows: a light intensity of 203 µmol photons m^-2 s^-1 with the earlier specified light-dark cycle; an initial pH of 9.5; a 50% strength of Zarrouk's medium; an aeration rate of 0.2 vvm (air mixed with 2% v/v CO₂); and a temperature of 35 °C. Production of C-PC and EPS in raceway ponds is discussed.

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.

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.

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.

Genome-based metabolic mapping and 13C flux analysis reveal systematic properties of an oleaginous microalga Chlorella protothecoides

Integrated and genome-based flux balance analysis, metabolomics, and (13)C-label profiling of phototrophic and heterotrophic metabolism in Chlorella protothecoides, an oleaginous green alga for biofuel. The green alga Chlorella protothecoides, capable of autotrophic and heterotrophic growth with rapid lipid synthesis, is a promising candidate for biofuel production. Based on the newly available genome knowledge of the alga, we reconstructed the compartmentalized metabolic network consisting of 272 metabolic reactions, 270 enzymes, and 461 encoding genes and simulated the growth in different cultivation conditions with flux balance analysis. Phenotype-phase plane analysis shows conditions achieving theoretical maximum of the biomass and corresponding fatty acid-producing rate for phototrophic cells (the ratio of photon uptake rate to CO2 uptake rate equals 8.4) and heterotrophic ones (the glucose uptake rate to O2 consumption rate reaches 2.4), respectively. Isotope-assisted liquid chromatography-mass spectrometry/mass spectrometry reveals higher metabolite concentrations in the glycolytic pathway and the tricarboxylic acid cycle in heterotrophic cells compared with autotrophic cells. We also observed enhanced levels of ATP, nicotinamide adenine dinucleotide (phosphate), reduced, acetyl-Coenzyme A, and malonyl-Coenzyme A in heterotrophic cells consistently, consistent with a strong activity of lipid synthesis. To profile the flux map in experimental conditions, we applied nonstationary (13)C metabolic flux analysis as a complementing strategy to flux balance analysis. The result reveals negligible photorespiratory fluxes and a metabolically low active tricarboxylic acid cycle in phototrophic C. protothecoides. In comparison, high throughput of amphibolic reactions and the tricarboxylic acid cycle with no glyoxylate shunt activities were measured for heterotrophic cells. Taken together, the metabolic network modeling assisted by experimental metabolomics and (13)C labeling better our understanding on global metabolism of oleaginous alga, paving the way to the systematic engineering of the microalga for biofuel production.

Modelling of Microalgae Culture Systems with Applications to Control and Optimization

Mathematical modeling is becoming ever more important to assess the potential, guide the design, and enable the efficient operation and control of industrial-scale microalgae culture systems (MCS). The development of overall, inherently multiphysics, models involves coupling separate submodels of (i) the intrinsic biological properties, including growth, decay, and biosynthesis as well as the effect of light and temperature on these processes, and (ii) the physical properties, such as the hydrodynamics, light attenuation, and temperature in the culture medium. When considering high-density microalgae culture, in particular, the coupling between biology and physics becomes critical. This chapter reviews existing models, with a particular focus on the Droop model, which is a precursor model, and it highlights the structure common to many microalgae growth models. It summarizes the main developments and difficulties towards multiphysics models of MCS as well as applications of these models for monitoring, control, and optimization purposes.

Engineering Synechococcus elongatus PCC 7942 for continuous growth under diurnal conditions

Synechococcus elongatus strain PCC 7942 strictly depends upon the generation of photosynthetically derived energy for growth and is incapable of biomass increase in the absence of light energy. Obligate phototrophs' core metabolism is very similar to that of heterotrophic counterparts exhibiting diverse trophic behavior. Most characterized cyanobacterial species are obligate photoautotrophs under examined conditions. Here we determine that sugar transporter systems are the necessary genetic factors in order for a model cyanobacterium, Synechococcus elongatus PCC 7942, to grow continuously under diurnal (light/dark) conditions using saccharides such as glucose, xylose, and sucrose. While the universal causes of obligate photoautotrophy may be diverse, installing sugar transporters provides new insight into the mode of obligate photoautotrophy for cyanobacteria. Moreover, cyanobacterial chemical production has gained increased attention. However, this obligate phototroph is incapable of product formation in the absence of light. Thus, converting an obligate photoautotroph to a heterotroph is desirable for more efficient, economical, and controllable production systems.

Systems biology and metabolic engineering of Arthrospira cell factories

Arthrospira are attractive candidates to serve as cell factories for production of many valuable compounds useful for food, feed, fuel and pharmaceutical industries. In connection with the development of sustainable bioprocessing, it is a challenge to design and develop efficient Arthrospira cell factories which can certify effective conversion from the raw materials (i.e. CO₂ and sun light) into desired products. With the current availability of the genome sequences and metabolic models of Arthrospira, the development of Arthrospira factories can now be accelerated by means of systems biology and the metabolic engineering approach. Here, we review recent research involving the use of Arthrospira cell factories for industrial applications, as well as the exploitation of systems biology and the metabolic engineering approach for studying Arthrospira. The current status of genomics and proteomics through the development of the genome-scale metabolic model of Arthrospira, as well as the use of mathematical modeling to simulate the phenotypes resulting from the different metabolic engineering strategies are discussed. At the end, the perspective and future direction on Arthrospira cell factories for industrial biotechnology are presented.

iAK692: a genome-scale metabolic model of Spirulina platensis C1

Background

Spirulina (Arthrospira) platensis is a well-known filamentous cyanobacterium used in the production of many industrial products, including high value compounds, healthy food supplements, animal feeds, pharmaceuticals and cosmetics, for example. It has been increasingly studied around the world for scientific purposes, especially for its genome, biology, physiology, and also for the analysis of its small-scale metabolic network. However, the overall description of the metabolic and biotechnological capabilities of S. platensis requires the development of a whole cellular metabolism model. Recently, the S. platensis C1 (Arthrospira sp. PCC9438) genome sequence has become available, allowing systems-level studies of this commercial cyanobacterium.

Results

In this work, we present the genome-scale metabolic network analysis of S. platensis C1, iAK692, its topological properties, and its metabolic capabilities and functions. The network was reconstructed from the S. platensis C1 annotated genomic sequence using Pathway Tools software to generate a preliminary network. Then, manual curation was performed based on a collective knowledge base and a combination of genomic, biochemical, and physiological information. The genome-scale metabolic model consists of 692 genes, 837 metabolites, and 875 reactions. We validated iAK692 by conducting fermentation experiments and simulating the model under autotrophic, heterotrophic, and mixotrophic growth conditions using COBRA toolbox. The model predictions under these growth conditions were consistent with the experimental results. The iAK692 model was further used to predict the unique active reactions and essential genes for each growth condition. Additionally, the metabolic states of iAK692 during autotrophic and mixotrophic growths were described by phenotypic phase plane (PhPP) analysis.

Conclusions

This study proposes the first genome-scale model of S. platensis C1, iAK692, which is a predictive metabolic platform for a global understanding of physiological behaviors and metabolic engineering. This platform could accelerate the integrative analysis of various “-omics” data, leading to strain improvement towards a diverse range of desired industrial products from Spirulina.

Metabolic modeling of Chlamydomonas reinhardtii: energy requirements for photoautotrophic growth and maintenance

In this study, a metabolic network describing the primary metabolism of Chlamydomonas reinhardtii was constructed. By performing chemostat experiments at different growth rates, energy parameters for maintenance and biomass formation were determined. The chemostats were run at low irradiances resulting in a high biomass yield on light of 1.25 g  mol(-1). The ATP requirement for biomass formation from biopolymers (K(x)) was determined to be 109 mmol g(-1) (18.9 mol mol(-1)) and the maintenance requirement (m(ATP)) was determined to be 2.85 mmol g(-1) h(-1). With these energy requirements included in the metabolic network, the network accurately describes the primary metabolism of C. reinhardtii and can be used for modeling of C. reinhardtii growth and metabolism. Simulations confirmed that cultivating microalgae at low growth rates is unfavorable because of the high maintenance requirements which result in low biomass yields. At high light supply rates, biomass yields will decrease due to light saturation effects. Thus, to optimize biomass yield on light energy in photobioreactors, an optimum between low and high light supply rates should be found. These simulations show that metabolic flux analysis can be used as a tool to gain insight into the metabolism of algae and ultimately can be used for the maximization of algal biomass and product yield. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10811-011-9674-3) contains supplementary material, which is available to authorized users.

Modelling cyanobacteria: from metabolism to integrative models of phototrophic growth

Cyanobacteria are phototrophic microorganisms of global importance and have recently attracted increasing attention due to their capability to convert sunlight and atmospheric CO(2) directly into organic compounds, including carbon-based biofuels. The utilization of cyanobacteria as a biological chassis to generate third-generation biofuels would greatly benefit from an increased understanding of cyanobacterial metabolism and its interplay with other cellular processes. In this respect, metabolic modelling has been proposed as a way to overcome the traditional trial and error methodology that is often employed to introduce novel pathways. In particular, flux balance analysis and related methods have proved to be powerful tools to investigate the organization of large-scale metabolic networks-with the prospect of predicting modifications that are likely to increase the yield of a desired product and thereby to streamline the experimental progress and avoid futile avenues. This contribution seeks to describe the utilization of metabolic modelling as a research tool to understand the metabolism and phototrophic growth of cyanobacteria. The focus of the contribution is on a mathematical description of the metabolic network of Synechocystis sp. PCC 6803 and its analysis using constraint-based methods. A particular challenge is to integrate the description of the metabolic network with other cellular processes, such as the circadian clock, the photosynthetic light reactions, carbon concentration mechanism, and transcriptional regulation-aiming at a predictive model of a cyanobacterium in silico.

The metabolic network of Synechocystis sp. PCC 6803: systemic properties of autotrophic growth

Unicellular cyanobacteria have attracted growing attention as potential host organisms for the production of valuable organic products and provide an ideal model to understand oxygenic photosynthesis and phototrophic metabolism. To obtain insight into the functional properties of phototrophic growth, we present a detailed reconstruction of the primary metabolic network of the autotrophic prokaryote Synechocystis sp. PCC 6803. The reconstruction is based on multiple data sources and extensive manual curation and significantly extends currently available repositories of cyanobacterial metabolism. A systematic functional analysis, utilizing the framework of flux-balance analysis, allows the prediction of essential metabolic pathways and reactions and allows the identification of inconsistencies in the current annotation. As a counterintuitive result, our computational model indicates that photorespiration is beneficial to achieve optimal growth rates. The reconstruction process highlights several obstacles currently encountered in the context of large-scale reconstructions of metabolic networks.

Flux balance analysis of primary metabolism in Chlamydomonas reinhardtii

BACKGROUND

Photosynthetic organisms convert atmospheric carbon dioxide into numerous metabolites along the pathways to make new biomass. Aquatic photosynthetic organisms, which fix almost half of global inorganic carbon, have great potential: as a carbon dioxide fixation method, for the economical production of chemicals, or as a source for lipids and starch which can then be converted to biofuels. To harness this potential through metabolic engineering and to maximize production, a more thorough understanding of photosynthetic metabolism must first be achieved. A model algal species, C. reinhardtii, was chosen and the metabolic network reconstructed. Intracellular fluxes were then calculated using flux balance analysis (FBA).

RESULTS

The metabolic network of primary metabolism for a green alga, C. reinhardtii, was reconstructed using genomic and biochemical information. The reconstructed network accounts for the intracellular localization of enzymes to three compartments and includes 484 metabolic reactions and 458 intracellular metabolites. Based on BLAST searches, one newly annotated enzyme (fructose-1,6-bisphosphatase) was added to the Chlamydomonas reinhardtii database. FBA was used to predict metabolic fluxes under three growth conditions, autotrophic, heterotrophic and mixotrophic growth. Biomass yields ranged from 28.9 g per mole C for autotrophic growth to 15 g per mole C for heterotrophic growth.

CONCLUSION

The flux balance analysis model of central and intermediary metabolism in C. reinhardtii is the first such model for algae and the first model to include three metabolically active compartments. In addition to providing estimates of intracellular fluxes, metabolic reconstruction and modelling efforts also provide a comprehensive method for annotation of genome databases. As a result of our reconstruction, one new enzyme was annotated in the database and several others were found to be missing; implying new pathways or non-conserved enzymes. The use of FBA to estimate intracellular fluxes also provides flux values that can be used as a starting point for rational engineering of C. reinhardtii. From these initial estimates, it is clear that aerobic heterotrophic growth on acetate has a low yield on carbon, while mixotrophically and autotrophically grown cells are significantly more carbon efficient.

Proteomic analysis of heterotrophy in Synechocystis sp. PCC 6803

To provide an insight into the heterotrophic metabolism of cyanobacteria, a proteomic approach has been employed with the model organism Synechocystis sp. PCC 6803. The soluble proteins from Synechocystis grown under photoautotrophic and light-activated heterotrophic conditions were separated by 2-DE and identified by MALDI-MS or LC-MS/MS analysis. 2-DE gels made using narrow- and micro-range IPG strips allowed quantitative comparison of more than 900 spots. Out of 67 abundant protein spots identified, 13 spots were increased and 9 decreased under heterotrophy, representing all the major fold changes. Proteomic alterations and activity levels of selected enzymes indicate a shift in the central carbon metabolism in response to trophic change. The significant reduction in light-saturated rate of photosynthesis as well as in the expression levels of rubisco and CO(2)-concentrating mechanism proteins under heterotrophy indicates the down-regulation of the photosynthetic machinery. Alterations in the expression level of proteins involved in carbon utilization pathways refer to enhanced glycolysis, oxidative pentose phosphate pathway as well as tricarboxylic acid cycle under heterotrophy. Proteomic evidences also suggest an enhanced biosynthesis of amino acids such as histidine and serine during heterotrophic growth.

A fully predictive model for one-dimensional light attenuation byChlamydomonas reinhardtii in a torus photobioreactor

The light attenuation in a photobioreactor is determined using a fully predictive model. The optical properties were first calculated, using a data bank of the literature, from only the knowledge of pigments content, shape, and size distributions of cultivated cells which are a function of the physiology of the current species. The radiative properties of the biological turbid medium were then deduced using the exact Lorenz-Mie theory. This method is experimentally validated using a large-size integrating sphere photometer. The radiative properties are then used in a rectangular, one-dimensional two-flux model to predict radiant light attenuation in a photobioreactor, considering a quasi-collimated field of irradiance. Combination of this radiative model with the predictive determination of optical properties is finally validated by in situ measurement of attenuation profiles in a torus photobioreactor cultivating the microalgae Chlamydomonas reinhardtii, after a complete and proper characterization of the incident light flux provided by the experimental set-up.