Metabolic modeling of Halomonas campaniensis improves polyhydroxybutyrate production under nitrogen limitation.

Poly-hydroxybutyrate (PHB) is an environmentally friendly alternative for conventional fossil fuel-based plastics that is produced by various microorganisms. Large-scale PHB production is challenging due to the comparatively higher biomanufacturing costs. A PHB overproducer is the haloalkaliphilic bacterium Halomonas campaniensis, which has low nutritional requirements and can grow in cultures with high salt concentrations, rendering it resistant to contamination. Despite its virtues, the metabolic capabilities of H. campaniensis as well as the limitations hindering higher PHB production remain poorly studied. To address this limitation, we present HaloGEM, the first high-quality genome-scale metabolic network reconstruction, which encompasses 888 genes, 1528 reactions (1257 gene-associated), and 1274 metabolites. HaloGEM not only displays excellent agreement with previous growth data and experiments from this study, but it also revealed nitrogen as a limiting nutrient when growing aerobically under high salt concentrations using glucose as carbon source. Among different nitrogen source mixtures for optimal growth, HaloGEM predicted glutamate and arginine as a promising mixture producing increases of 54.2% and 153.4% in the biomass yield and PHB titer, respectively. Furthermore, the model was used to predict genetic interventions for increasing PHB yield, which were consistent with the rationale of previously reported strategies. Overall, the presented reconstruction advances our understanding of the metabolic capabilities of H. campaniensis for rationally engineering this next-generation industrial biotechnology platform. KEY POINTS: A comprehensive genome-scale metabolic reconstruction of H. campaniensis was developed. Experiments and simulations predict N limitation in minimal media under aerobiosis. In silico media design increased experimental biomass yield and PHB titer.

Genome-scale metabolic modeling of Thraustochytrium sp. RT2316-16: Effects of nutrients on metabolism.

Marine thraustochytrids produce metabolically important lipids such as the long-chain omega-3 polyunsaturated fatty acids, carotenoids, and sterols. The growth and lipid production in thraustochytrids depends on the composition of the culture medium that often contains yeast extract as a source of amino acids. This work discusses the effects of individual amino acids provided in the culture medium as the only source of nitrogen, on the production of biomass and lipids by the thraustochytrid Thraustochytrium sp. RT2316-16. A reconstructed metabolic network based on the annotated genome of RT2316-16 in combination with flux balance analysis was used to explain the observed growth and consumption of the nutrients. The culture kinetic parameters estimated from the experimental data were used to constrain the flux via the nutrient consumption rates and the specific growth rate of the triacylglycerol-free biomass in the genome-scale metabolic model (GEM) to predict the specific rate of ATP production for cell maintenance. A relationship was identified between the specific rate of ATP production for maintenance and the specific rate of glucose consumption. The GEM and the derived relationship for the production of ATP for maintenance were used in linear optimization problems, to successfully predict the specific growth rate of RT2316-16 in different experimental conditions.

Protein constraints in genome-scale metabolic models: Data integration, parameter estimation, and prediction of metabolic phenotypes.

Genome-scale metabolic models provide a valuable resource to study metabolism and cell physiology. These models are employed with approaches from the constraint-based modeling framework to predict metabolic and physiological phenotypes. The prediction performance of genome-scale metabolic models can be improved by including protein constraints. The resulting protein-constrained models consider data on turnover numbers (kcat ) and facilitate the integration of protein abundances. In this systematic review, we present and discuss the current state-of-the-art regarding the estimation of kinetic parameters used in protein-constrained models. We also highlight how data-driven and constraint-based approaches can aid the estimation of turnover numbers and their usage in improving predictions of cellular phenotypes. Finally, we identify standing challenges in protein-constrained metabolic models and provide a perspective regarding future approaches to improve the predictive performance.

Computational assessment of lipid production in Rhodosporidium toruloides in two-stage and one-stage batch bioprocesses.

Oleaginous yeasts are promising platforms for microbial lipids production as a renewable and sustainable alternative to vegetable oils in biodiesel production. In this paper, a thorough in silico assessment of lipid production in batch cultivation by Rhodosporidium toruloides was developed. By means of dynamic flux balance analysis, the traditional two-stage bioprocess (TSB) performed by the native strain was contrasted with one-stage bioprocess (OSB) using four designed strains obtained by gene knockout strategies. Lipid titer, yield, content, and productivity were analyzed at different initial C/N ratios as relevant performance indicators used in bioprocesses. By weighting these indicators, a global lipid efficiency metric (GLEM) was defined to consider different scenarios. Under simulated conditions, designed strains for lipid overproduction in OSB outperformed the TSB in terms of lipid title (up to threefold), lipid yield (up to 2.4-fold), lipid content (up to 2.8-fold, with a maximum of 76%), and productivity (up to 1.3-fold), depending on C/N ratios. Using these efficiency parameters and the proposed GLEM, the process of selecting the most suitable candidates for lipid production could be carried out before experimental assays. This methodology holds the potential to be extended to other oleaginous microorganisms and diverse strain design techniques.

A Holistic Approach from Systems Biology Reveals the Direct Influence of the Quorum-Sensing Phenomenon on Pseudomonas aeruginosa Metabolism to Pyoverdine Biosynthesis.

Computational modeling and simulation of biological systems have become valuable tools for understanding and predicting cellular performance and phenotype generation. This work aimed to construct, model, and dynamically simulate the virulence factor pyoverdine (PVD) biosynthesis in Pseudomonas aeruginosa through a systemic approach, considering that the metabolic pathway of PVD synthesis is regulated by the quorum-sensing (QS) phenomenon. The methodology comprised three main stages: (i) Construction, modeling, and validation of the QS gene regulatory network that controls PVD synthesis in P. aeruginosa strain PAO1; (ii) construction, curating, and modeling of the metabolic network of P. aeruginosa using the flux balance analysis (FBA) approach; (iii) integration and modeling of these two networks into an integrative model using the dynamic flux balance analysis (DFBA) approximation, followed, finally, by an in vitro validation of the integrated model for PVD synthesis in P. aeruginosa as a function of QS signaling. The QS gene network, constructed using the standard System Biology Markup Language, comprised 114 chemical species and 103 reactions and was modeled as a deterministic system following the kinetic based on mass action law. This model showed that the higher the bacterial growth, the higher the extracellular concentration of QS signal molecules, thus emulating the natural behavior of P. aeruginosa PAO1. The P. aeruginosa metabolic network model was constructed based on the iMO1056 model, the P. aeruginosa PAO1 strain genomic annotation, and the metabolic pathway of PVD synthesis. The metabolic network model included the PVD synthesis, transport, exchange reactions, and the QS signal molecules. This metabolic network model was curated and then modeled under the FBA approximation, using biomass maximization as the objective function (optimization problem, a term borrowed from the engineering field). Next, chemical reactions shared by both network models were chosen to combine them into an integrative model. To this end, the fluxes of these reactions, obtained from the QS network model, were fixed in the metabolic network model as constraints of the optimization problem using the DFBA approximation. Finally, simulations of the integrative model (CCBM1146, comprising 1123 reactions and 880 metabolites) were run using the DFBA approximation to get (i) the flux profile for each reaction, (ii) the bacterial growth profile, (iii) the biomass profile, and (iv) the concentration profiles of metabolites of interest such as glucose, PVD, and QS signal molecules. The CCBM1146 model showed that the QS phenomenon directly influences the P. aeruginosa metabolism to PVD biosynthesis as a function of the change in QS signal intensity. The CCBM1146 model made it possible to characterize and explain the complex and emergent behavior generated by the interactions between the two networks, which would have been impossible to do by studying each system's individual components or scales separately. This work is the first in silico report of an integrative model comprising the QS gene regulatory network and the metabolic network of P. aeruginosa.

Metabolic switch in the aging astrocyte supported via integrative approach comprising network and transcriptome analyses.

Dysregulated central-energy metabolism is a hallmark of brain aging. Supplying enough energy for neurotransmission relies on the neuron-astrocyte metabolic network. To identify genes contributing to age-associated brain functional decline, we formulated an approach to analyze the metabolic network by integrating flux, network structure and transcriptomic databases of neurotransmission and aging. Our findings support that during brain aging: (1) The astrocyte undergoes a metabolic switch from aerobic glycolysis to oxidative phosphorylation, decreasing lactate supply to the neuron, while the neuron suffers intrinsic energetic deficit by downregulation of Krebs cycle genes, including mdh1 and mdh2 (Malate-Aspartate Shuttle); (2) Branched-chain amino acid degradation genes were downregulated, identifying dld as a central regulator; (3) Ketone body synthesis increases in the neuron, while the astrocyte increases their utilization, in line with neuronal energy deficit in favor of astrocytes. We identified candidates for preclinical studies targeting energy metabolism to prevent age-associated cognitive decline.

Analysis of the Propionate Metabolism in Bacillus subtilis during 3-Indolacetic Production.

The genera Bacillus belongs to the group of microorganisms that are known as plant growth-promoting bacteria, their metabolism has evolved to produce molecules that benefit the growth of the plant, and the production of 3-indole acetic acid (IAA) is part of its secondary metabolism. In this work, Bacillus subtilis was cultivated in a bioreactor to produce IAA using propionate and glucose as carbon sources in an M9-modified media; in both cases, tryptophan was added as a co-substrate. The yield of IAA using propionate is 17% higher compared to glucose. After 48 h of cultivation, the final concentration was 310 mg IAA/L using propionate and 230 mg IAA/L using glucose, with a concentration of 500 mg Trp/L. To gain more insight into propionate metabolism and its advantages, the genome-scale metabolic model of B. subtilis (iBSU 1147) and computational analysis were used to calculate flux distribution and evaluate the metabolic capabilities to produce IAA using propionate. The metabolic fluxes demonstrate that propionate uptake favors the production of precursors needed for the synthesis of the hormone, and the sensitivity analysis shows that the control of a specific growth rate has a positive impact on the production of IAA.

ChiMera: an easy to use pipeline for bacterial genome based metabolic network reconstruction, evaluation and visualization.

BACKGROUND: Genome-scale metabolic reconstruction tools have been developed in the last decades. They have helped to reconstruct eukaryotic and prokaryotic metabolic models, which have contributed to fields, e.g., genetic engineering, drug discovery, prediction of phenotypes, and other model-driven discoveries. However, the use of these programs requires a high level of bioinformatic skills. Moreover, the functionalities required to build models are scattered throughout multiple tools, requiring knowledge and experience for utilizing several tools. RESULTS: Here we present ChiMera, which combines tools used for model reconstruction, prediction, and visualization. ChiMera uses CarveMe in the reconstruction module, generating a gap-filled draft reconstruction able to produce growth predictions using flux balance analysis for gram-positive and gram-negative bacteria. ChiMera also contains two modules for metabolic network visualization. The first module generates maps for the most important pathways, e.g., glycolysis, nucleotides and amino acids biosynthesis, fatty acid oxidation and biosynthesis and core-metabolism. The second module produces a genome-wide metabolic map, which can be used to retrieve KEGG pathway information for each compound in the model. A module to investigate gene essentiality and knockout is also present. CONCLUSIONS: Overall, ChiMera uses automation algorithms to combine a variety of tools to automatically perform model creation, gap-filling, flux balance analysis (FBA), and metabolic network visualization. ChiMera models readily provide metabolic insights that can aid genetic engineering projects, prediction of phenotypes, and model-driven discoveries.

TCA Cycle and Its Relationship with Clavulanic Acid Production: A Further Interpretation by Using a Reduced Genome-Scale Metabolic Model of Streptomyces clavuligerus.

Streptomyces clavuligerus (S. clavuligerus) has been widely studied for its ability to produce clavulanic acid (CA), a potent inhibitor of ß-lactamase enzymes. In this study, S. clavuligerus cultivated in 2D rocking bioreactor in fed-batch operation produced CA at comparable rates to those observed in stirred tank bioreactors. A reduced model of S. clavuligerus metabolism was constructed by using a bottom-up approach and validated using experimental data. The reduced model was implemented for in silico studies of the metabolic scenarios arisen during the cultivations. Constraint-based analysis confirmed the interrelations between succinate, oxaloacetate, malate, pyruvate, and acetate accumulations at high CA synthesis rates in submerged cultures of S. clavuligerus. Further analysis using shadow prices provided a first view of the metabolites positive and negatively associated with the scenarios of low and high CA production.

A Genome-Scale Insight into the Effect of Shear Stress During the Fed-Batch Production of Clavulanic Acid by Streptomyces Clavuligerus.

Streptomyces clavuligerus is a filamentous Gram-positive bacterial producer of the ß-lactamase inhibitor clavulanic acid. Antibiotics biosynthesis in the Streptomyces genus is usually triggered by nutritional and environmental perturbations. In this work, a new genome scale metabolic network of Streptomyces clavuligerus was reconstructed and used to study the experimentally observed effect of oxygen and phosphate concentrations on clavulanic acid biosynthesis under high and low shear stress. A flux balance analysis based on experimental evidence revealed that clavulanic acid biosynthetic reaction fluxes are favored in conditions of phosphate limitation, and this is correlated with enhanced activity of central and amino acid metabolism, as well as with enhanced oxygen uptake. In silico and experimental results show a possible slowing down of tricarboxylic acid (TCA) due to reduced oxygen availability in low shear stress conditions. In contrast, high shear stress conditions are connected with high intracellular oxygen availability favoring TCA activity, precursors availability and clavulanic acid (CA) production.

Dynamic flux balance analysis of biomass and lipid production by Antarctic thraustochytrid Oblongichytrium sp. RT2316-13.

Production of biomass and lipids in batch cultures of the Antarctic thraustochytrid Oblongichytrium sp. RT2316-13, is reported. The microorganism proved capable of producing nearly 67% docosahexaenoic acid (DHA) and 15% eicosapentaenoic acid (EPA) in its total lipid fraction. Biomass with a maximum total lipid content of 33.5% (wt/wt) could be produced at 15°C in batch culture using a medium containing glucose (20 g/L), yeast extract (10.5 g/L), and other minor components. A lower culture temperature (5°C) reduced biomass and lipid productivities compared to culture at 15°C, but enhanced the DHA and EPA content of the lipids by 6.4- and 3.3-fold, respectively. Both a simple minimally structured mathematical model and a more complex genome-scale metabolic model (GEM) allowed the fermentation profiles in batch cultures to be satisfactorily simulated, but the GEM provided much greater insight in the biochemical and physiological phenomena underlying the observed behavior. Unlike the simpler model, the GEM could be interrogated for the possible effects of various external factors such as oxygen supply, on the expected outcomes. In silico predictions of oxygen effects were consistent with literature observations for DHA producing thraustochytrids.

Contextualized genome-scale model unveils high-order metabolic effects of the specific growth rate and oxygenation level in recombinant Pichia pastoris.

Pichia pastoris is recognized as a biotechnological workhorse for recombinant protein expression. The metabolic performance of this microorganism depends on genetic makeup and culture conditions, amongst which the specific growth rate and oxygenation level are critical. Despite their importance, only their individual effects have been assessed so far, and thus their combined effects and metabolic consequences still remain to be elucidated. In this work, we present a comprehensive framework for revealing high-order (i.e., individual and combined) metabolic effects of the above parameters in glucose-limited continuous cultures of P. pastoris, using thaumatin production as a case study. Specifically, we employed a rational experimental design to calculate statistically significant metabolic effects from multiple chemostat data, which were later contextualized using a refined and highly predictive genome-scale metabolic model of this yeast under the simulated conditions. Our results revealed a negative effect of the oxygenation on the specific product formation rate (thaumatin), and a positive effect on the biomass yield. Notably, we identified a novel positive combined effect of both the specific growth rate and oxygenation level on the specific product formation rate. Finally, model predictions indicated an opposite relationship between the oxygenation level and the growth-associated maintenance energy (GAME) requirement, suggesting a linear GAME decrease of 0.56 mmol ATP/gDCW per each 1% increase in oxygenation level, which translated into a 44% higher metabolic cost under low oxygenation compared to high oxygenation. Overall, this work provides a systematic framework for mapping high-order metabolic effects of different culture parameters on the performance of a microbial cell factory. Particularly in this case, it provided valuable insights about optimal operational conditions for protein production in P. pastoris.

FindTargetsWEB: A User-Friendly Tool for Identification of Potential Therapeutic Targets in Metabolic Networks of Bacteria.

Background: Healthcare-associated infections (HAIs) are a serious public health problem. They can be associated with morbidity and mortality and are responsible for the increase in patient hospitalization. Antimicrobial resistance among pathogens causing HAI has increased at alarming levels. In this paper, a robust method for analyzing genome-scale metabolic networks of bacteria is proposed in order to identify potential therapeutic targets, along with its corresponding web implementation, dubbed FindTargetsWEB. The proposed method assumes that every metabolic network presents fragile genes whose blockade will impair one or more metabolic functions, such as biomass accumulation. FindTargetsWEB automates the process of identification of such fragile genes using flux balance analysis (FBA), flux variability analysis (FVA), extended Systems Biology Markup Language (SBML) file parsing, and queries to three public repositories, i.e., KEGG, UniProt, and DrugBank. The web application was developed in Python using COBRApy and Django. Results: The proposed method was demonstrated to be robust enough to process even non-curated, incomplete, or imprecise metabolic networks, in addition to integrated host-pathogen models. A list of potential therapeutic targets and their putative inhibitors was generated as a result of the analysis of Pseudomonas aeruginosa metabolic networks available in the literature and a curated version of the metabolic network of a multidrug-resistant P. aeruginosa strain belonging to a clone endemic in Brazil (P. aeruginosa ST277). Genome-scale metabolic networks of other gram-positive and gram-negative bacteria, such as Staphylococcus aureus, Klebsiella pneumoniae, and Haemophilus influenzae, were also analyzed using FindTargetsWEB. Multiple potential targets have been found using the proposed method in all metabolic networks, including some overlapping between two or more pathogens. Among the potential targets, several have been previously reported in the literature as targets for antimicrobial development, and many targets have approved drugs. Despite similarities in the metabolic network structure for closely related bacteria, we show that the method is able to selectively identify targets in pathogenic versus non-pathogenic organisms. Conclusions: This new computational system can give insights into the identification of new candidate therapeutic targets for pathogenic bacteria and discovery of new antimicrobial drugs through genome-scale metabolic network analysis and heterogeneous data integration, even for non-curated or incomplete networks.

A genome-scale metabolic model of potato late blight suggests a photosynthesis suppression mechanism.

BACKGROUND: Phytophthora infestans is a plant pathogen that causes an important plant disease known as late blight in potato plants (Solanum tuberosum) and several other solanaceous hosts. This disease is the main factor affecting potato crop production worldwide. In spite of the importance of the disease, the molecular mechanisms underlying the compatibility between the pathogen and its hosts are still unknown. RESULTS: To explain the metabolic response of late blight, specifically photosynthesis inhibition in infected plants, we reconstructed a genome-scale metabolic network of the S. tuberosum leaf, PstM1. This metabolic network simulates the effect of this disease in the leaf metabolism. PstM1 accounts for 2751 genes, 1113 metabolic functions, 1773 gene-protein-reaction associations and 1938 metabolites involved in 2072 reactions. The optimization of the model for biomass synthesis maximization in three infection time points suggested a suppression of the photosynthetic capacity related to the decrease of metabolic flux in light reactions and carbon fixation reactions. In addition, a variation pattern in the flux of carboxylation to oxygenation reactions catalyzed by RuBisCO was also identified, likely to be associated to a defense response in the compatible interaction between P. infestans and S. tuberosum. CONCLUSIONS: In this work, we introduced simultaneously the first metabolic network of S. tuberosum and the first genome-scale metabolic model of the compatible interaction of a plant with P. infestans.

A Genome-Scale Metabolic Reconstruction of Phytophthora infestans With the Integration of Transcriptional Data Reveals the Key Metabolic Patterns Involved in the Interaction of Its Host.

Phytophthora infestans, the causal agent of late blight disease, affects potatoes and tomatoes worldwide. This plant pathogen has a hemibiotrophic lifestyle, having an initial biotrophic infection phase during which the pathogen spreads within the host tissue, followed by a necrotrophic phase in which host cell death is induced. Although increasing information is available on the molecular mechanisms, underlying the distinct phases of the hemibiotrophic lifestyle, studies that consider the entire metabolic processes in the pathogen while undergoing the biotrophic, transition to necrotrophic, and necrotrophic phases have not been conducted. In this study, the genome-scale metabolic reconstruction of P. infestans was achieved. Subsequently, transcriptional data (microarrays, RNA-seq) was integrated into the metabolic reconstruction to obtain context-specific (metabolic) models (CSMs) of the infection process, using constraint-based reconstruction and analysis. The goal was to identify specific metabolic markers for distinct stages of the pathogen's life cycle. Results indicate that the overall metabolism show significant changes during infection. The most significant changes in metabolism were observed at the latest time points of infection. Metabolic activity associated with purine, pyrimidine, fatty acid, fructose and mannose, arginine, glycine, serine, and threonine amino acids appeared to be the most important metabolisms of the pathogen during the course of the infection, showing high number of reactions associated with them and expression switches at important stages of the life cycle. This study provides a framework for future throughput studies of the metabolic changes during the hemibiotrophic life cycle of this important plant pathogen.

Comprehensive analysis of a metabolic model for lipid production in Rhodosporidium toruloides.

The yeast Rhodosporidium toruloides has been extensively studied for its application in biolipid production. The knowledge of its metabolism capabilities and the application of constraint-based flux analysis methodology provide useful information for process prediction and optimization. The accuracy of the resulting predictions is highly dependent on metabolic models. A metabolic reconstruction for R. toruloides metabolism has been recently published. On the basis of this model, we developed a curated version that unblocks the central nitrogen metabolism and, in addition, completes charge and mass balances in some reactions neglected in the former model. Then, a comprehensive analysis of network capability was performed with the curated model and compared with the published metabolic reconstruction. The flux distribution obtained by lipid optimization with flux balance analysis was able to replicate the internal biochemical changes that lead to lipogenesis in oleaginous microorganisms. These results motivate the development of a genome-scale model for complete elucidation of R. toruloides metabolism.

Streptomyces clavuligerus shows a strong association between TCA cycle intermediate accumulation and clavulanic acid biosynthesis.

Clavulanic acid (CA) is produced by Streptomyces clavuligerus (S. clavuligerus) as a secondary metabolite. Knowledge about the carbon flux distribution along the various routes that supply CA precursors would certainly provide insights about metabolic performance. In order to evaluate metabolic patterns and the possible accumulation of tricarboxylic acid (TCA) cycle intermediates during CA biosynthesis, batch and subsequent continuous cultures with steadily declining feed rates were performed with glycerol as the main substrate. The data were used to in silico explore the metabolic capabilities and the accumulation of metabolic intermediates in S. clavuligerus. While clavulanic acid accumulated at glycerol excess, it steadily decreased at declining dilution rates; CA synthesis stopped when glycerol became the limiting substrate. A strong association of succinate, oxaloacetate, malate, and acetate accumulation with CA production in S. clavuligerus was observed, and flux balance analysis (FBA) was used to describe the carbon flux distribution in the network. This combined experimental and numerical approach also identified bottlenecks during the synthesis of CA in a batch and subsequent continuous cultivation and demonstrated the importance of this type of methodologies for a more advanced understanding of metabolism; this potentially derives valuable insights for future successful metabolic engineering studies in S. clavuligerus.

An enhanced genome-scale metabolic reconstruction of Streptomyces clavuligerus identifies novel strain improvement strategies.

In this work, we expanded and updated a genome-scale metabolic model of Streptomyces clavuligerus. The model includes 1021 genes and 1494 biochemical reactions; genome-reaction information was curated and new features related to clavam metabolism and to the biomass synthesis equation were incorporated. The model was validated using experimental data from the literature and simulations were performed to predict cellular growth and clavulanic acid biosynthesis. Flux balance analysis (FBA) showed that limiting concentrations of phosphate and an excess of ammonia accumulation are unfavorable for growth and clavulanic acid biosynthesis. The evaluation of different objective functions for FBA showed that maximization of ATP yields the best predictions for cellular behavior in continuous cultures, while the maximization of growth rate provides better predictions for batch cultures. Through gene essentiality analysis, 130 essential genes were found using a limited in silico media, while 100 essential genes were identified in amino acid-supplemented media. Finally, a strain design was carried out to identify candidate genes to be overexpressed or knocked out so as to maximize antibiotic biosynthesis. Interestingly, potential metabolic engineering targets, identified in this study, have not been tested experimentally.

Metabolic modelling and flux analysis of microorganisms from the Atacama Desert used in biotechnological processes.

Metabolic modelling is a useful tool that enables the rational design of metabolic engineering experiments and the study of the unique capabilities of biotechnologically important microorganisms. The extreme abiotic conditions of the Atacama Desert have selected microbial diversity with exceptional characteristics that can be applied in the mining industry for bioleaching processes and for production of specialised metabolites with antimicrobial, antifungal, antiviral, antitumoral, among other activities. In this review we summarise the scientific data available of the use of metabolic modelling and flux analysis to improve the performance of Atacama Desert microorganisms in biotechnological applications.

Analysis of Sensitive CO2 Pathways and Genes Related to Carbon Uptake and Accumulation in Chlamydomonas reinhardtii through Genomic Scale Modeling and Experimental Validation.

The development of microalgae sustainable applications needs better understanding of microalgae biology. Moreover, how cells coordinate their metabolism toward biomass accumulation is not fully understood. In this present study, flux balance analysis (FBA) was performed to identify sensitive metabolic pathways of Chlamydomonas reinhardtii under varied CO2 inputs. The metabolic network model of Chlamydomonas was updated based on the genome annotation data and sensitivity analysis revealed CO2 sensitive reactions. Biological experiments were performed with cells cultivated at 0.04% (air), 2.5, 5, 8, and 10% CO2 concentration under controlled conditions and cell growth profiles and biomass content were measured. Pigments, lipids, proteins, and starch were further quantified for the reference low (0.04%) and high (10%) CO2 conditions. The expression level of candidate genes of sensitive reactions was measured and validated by quantitative real time PCR. The sensitive analysis revealed mitochondrial compartment as the major affected by changes on the CO2 concentrations and glycolysis/gluconeogenesis, glyoxylate, and dicarboxylate metabolism among the affected metabolic pathways. Genes coding for glycerate kinase (GLYK), glycine cleavage system, H-protein (GCSH), NAD-dependent malate dehydrogenase (MDH3), low-CO2 inducible protein A (LCIA), carbonic anhydrase 5 (CAH5), E1 component, alpha subunit (PDC3), dual function alcohol dehydrogenase/acetaldehyde dehydrogenase (ADH1), and phosphoglucomutase (GPM2), were defined, among other genes, as sensitive nodes in the metabolic network simulations. These genes were experimentally responsive to the changes in the carbon fluxes in the system. We performed metabolomics analysis using mass spectrometry validating the modulation of carbon dioxide responsive pathways and metabolites. The changes on CO2 levels mostly affected the metabolism of amino acids found in the photorespiration pathway. Our updated metabolic network was compared to previous model and it showed more consistent results once considering the experimental data. Possible roles of the sensitive pathways in the biomass metabolism are discussed.