Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions

Genomic imprints of unparalleled growth

Chlorella ohadii was isolated from desert biological soil crusts, one of the harshest habitats on Earth, and is emerging as an exciting new green model for studying growth, photosynthesis and metabolism under a wide range of conditions. Here, we compared the genome of C. ohadii, the fastest growing alga on record, to that of other green algae, to reveal the genomic imprints empowering its unparalleled growth rate and resistance to various stressors, including extreme illumination. This included the genome of its close relative, but slower growing and photodamage sensitive, C. sorokiniana UTEX 1663. A larger number of ribosome-encoding genes, high intron abundance, increased codon bias and unique genes potentially involved in metabolic flexibility and resistance to photodamage are all consistent with the faster growth of C. ohadii. Some of these characteristics highlight general trends in Chlorophyta and Chlorella spp. evolution, and others open new broad avenues for mechanistic exploration of their relationship with growth. This work entails a unique case study for the genomic adaptations and costs of exceptionally fast growth and sheds light on the genomic signatures of fast growth in photosynthetic cells. It also provides an important resource for future studies leveraging the unique properties of C. ohadii for photosynthesis and stress response research alongside their utilization for synthetic biology and biotechnology aims.

Cupriavidus necator as a platform for polyhydroxyalkanoate production: An overview of strains, metabolism, and modeling approaches

Cupriavidus necator is a bacterium with a high phenotypic diversity and versatile metabolic capabilities. It has been extensively studied as a model hydrogen oxidizer, as well as a producer of polyhydroxyalkanoates (PHA), plastic-like biopolymers with a high potential to substitute petroleum-based materials. Thanks to its adaptability to diverse metabolic lifestyles and to the ability to accumulate large amounts of PHA, C. necator is employed in many biotechnological processes, with particular focus on PHA production from waste carbon sources. The large availability of genomic information has enabled a characterization of C. necator's metabolism, leading to the establishment of metabolic models which are used to devise and optimize culture conditions and genetic engineering approaches. In this work, the characteristics of available C. necator strains and genomes are reviewed, underlining how a thorough comprehension of the genetic variability of C. necator is lacking and it could be instrumental for wider application of this microorganism. The metabolic paradigms of C. necator and how they are connected to PHA production and accumulation are described, also recapitulating the variety of carbon substrates used for PHA accumulation, highlighting the most promising strategies to increase the yield. Finally, the review describes and critically analyzes currently available genome-scale metabolic models and reduced metabolic network applications commonly employed in the optimization of PHA production. Overall, it appears that the capacity of C. necator of performing CO2 bioconversion to PHA is still underexplored, both in biotechnological applications and in metabolic modeling. However, the accurate characterization of this organism and the efforts in using it for gas fermentation can help tackle this challenging perspective in the future.

Reconstruction of a Genome-Scale Metabolic Model of Scenedesmus obliquus and Its Application for Lipid Production under Three Trophic Modes

Green microalgae have emerged as beneficial feedstocks for biofuel production. A systems-level understanding of the biochemical network is needed to harness the microalgal metabolic capacity for bioproduction. Genome-scale metabolic modeling (GEM) showed immense potential in rational metabolic engineering, utilizing biochemical flux distribution analysis. Here, we report the first GEM for the green microalga, Scenedesmus obliquus (iAR632), a promising biodiesel feedstock with high lipid-storing capability. iAR632 comprises 1467 reactions, 734 metabolites, and 632 genes distributed among 7 compartments. The model was optimized under three different trophic modes of microalgal cultivation, i.e., autotrophy, mixotrophy, and heterotrophy. The robustness of the reconstructed network was confirmed by analyzing its sensitivity to the biomass components. Pathway-level flux profiles were analyzed, and significant flux space expansion was noticed majorly in reactions associated with lipid biosynthesis. In agreement with the experimental observation, iAR632 predicted about 3.8-fold increased biomass and almost 4-fold higher lipid under mixotrophy than the other trophic modes. Thus, the assessment of the condition-specific metabolic flux distribution of iAR632 suggested that mixotrophy is the preferred cultivation condition for improved microalgal growth and lipid production. Overall, the reconstructed GEM and subsequent analyses will provide a systematic framework for developing model-driven strategies to improve microalgal bioproduction.

Acclimation strategies of the green alga Chlorella vulgaris to different light regimes revealed by physiological and comparative proteomic analyses

Acclimation to different light regimes is at the basis of survival for photosynthetic organisms, regardless of their evolutionary origin. Previous research efforts largely focused on acclimation events occurring at the level of the photosynthetic apparatus and often highlighted species-specific mechanisms. Here, we investigated the consequences of acclimation to different irradiances in Chlorella vulgaris, a green alga that is one of the most promising species for industrial application, focusing on both photosynthetic and mitochondrial activities. Moreover, proteomic analysis of cells acclimated to high light (HL) or low light (LL) allowed identification of the main targets of acclimation in terms of differentially expressed proteins. The results obtained demonstrate photosynthetic adaptation to HL versus LL that was only partially consistent with previous findings in Chlamydomonas reinhardtii, a model organism for green algae, but in many cases similar to vascular plant acclimation events. Increased mitochondrial respiration measured in HL-acclimated cells mainly relied on alternative oxidative pathway dissipating the excessive reducing power produced due to enhanced carbon flow. Finally, proteins involved in cell metabolism, intracellular transport, gene expression, and signaling-including a heliorhodopsin homolog-were identified as strongly differentially expressed in HL versus LL, suggesting their key roles in acclimation to different light regimes.

The genome-scale metabolic model for the purple non-sulfur bacterium Rhodopseudomonas palustris Bis A53 accurately predicts phenotypes under chemoheterotrophic, chemoautotrophic, photoheterotrophic, and photoautotrophic growth conditions

The purple non-sulfur bacterium Rhodopseudomonas palustris is recognized as a critical microorganism in the nitrogen and carbon cycle and one of the most common members in wastewater treatment communities. This bacterium is metabolically extremely versatile. It is capable of heterotrophic growth under aerobic and anaerobic conditions, but also able to grow photoautotrophically as well as mixotrophically. Therefore R. palustris can adapt to multiple environments and establish commensal relationships with other organisms, expressing various enzymes supporting degradation of amino acids, carbohydrates, nucleotides, and complex polymers. Moreover, R. palustris can degrade a wide range of pollutants under anaerobic conditions, e.g., aromatic compounds such as benzoate and caffeate, enabling it to thrive in chemically contaminated environments. However, many metabolic mechanisms employed by R. palustris to breakdown and assimilate different carbon and nitrogen sources under chemoheterotrophic or photoheterotrophic conditions remain unknown. Systems biology approaches, such as metabolic modeling, have been employed extensively to unravel complex mechanisms of metabolism. Previously, metabolic models have been reconstructed to study selected capabilities of R. palustris under limited experimental conditions. Here, we developed a comprehensive metabolic model (M-model) for R. palustris Bis A53 (iDT1294) consisting of 2,721 reactions, 2,123 metabolites, and comprising 1,294 genes. We validated the model using high-throughput phenotypic, physiological, and kinetic data, testing over 350 growth conditions. iDT1294 achieved a prediction accuracy of 90% for growth with various carbon and nitrogen sources and close to 80% for assimilation of aromatic compounds. Moreover, the M-model accurately predicts dynamic changes of growth and substrate consumption rates over time under nine chemoheterotrophic conditions and demonstrated high precision in predicting metabolic changes between photoheterotrophic and photoautotrophic conditions. This comprehensive M-model will help to elucidate metabolic processes associated with the assimilation of multiple carbon and nitrogen sources, anoxygenic photosynthesis, aromatic compound degradation, as well as production of molecular hydrogen and polyhydroxybutyrate.

The Special and General Mechanism of Cyanobacterial Harmful Algal Blooms

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

Toward mechanistic modeling and rational engineering of plant respiration

Plant respiration not only provides energy to support all cellular processes, including biomass production, but also plays a major role in the global carbon cycle. Therefore, modulation of plant respiration can be used to both increase the plant yield and mitigate the effects of global climate change. Mechanistic modeling of plant respiration at sufficient biochemical detail can provide key insights for rational engineering of this process. Yet, despite its importance, plant respiration has attracted considerably less modeling effort in comparison to photosynthesis. In this update review, we highlight the advances made in modeling of plant respiration, emphasizing the gradual but important change from phenomenological to models based on first principles. We also provide a detailed account of the existing resources that can contribute to resolving the challenges in modeling plant respiration. These resources point at tangible improvements in the representation of cellular processes that contribute to CO2 evolution and consideration of kinetic properties of underlying enzymes to facilitate mechanistic modeling. The update review emphasizes the need to couple biochemical models of respiration with models of acclimation and adaptation of respiration for their effective usage in guiding breeding efforts and improving terrestrial biosphere models tailored to future climate scenarios.

Improvement of Lutein Production in Auxenochlorella protothecoides Using Its Genome-Scale Metabolic Model and a System-Oriented Approach

Lutein is a valuable metabolite widely used in the food, pharmaceutical, cosmetic, and aquaculture industries. Marigold flowers are the most common source of commercial lutein, but cultivation area, weather conditions, and high manpower costs are among the disadvantages of lutein production from marigold flowers. Microalgae are an excellent alternative to plant sources of lutein as they do not have the limitations of plant extraction. Auxenochlorella protothecoides is a promising candidate for commercial production of lutein. In the present research, a genome-scale metabolic model was applied to introduce some strategies to improve lutein production in A. protothecoides. The effective reactions to improve lutein production were determined based on analysis of multiple optimal solutions. The enzymatic regulators of candidate reactions were identified using the BRENDA database. The effect of 13 activators was investigated experimentally. Our results showed that sodium citrate has the greatest effect on lutein production, so it was introduced as the most effective compound for increasing lutein production by A. protothecoides.

Novel context-specific genome-scale modelling explores the potential of triacylglycerol production by Chlamydomonas reinhardtii

Gene expression data of cell cultures is commonly measured in biological and medical studies to understand cellular decision-making in various conditions. Metabolism, affected but not solely determined by the expression, is much more difficult to measure experimentally. Finding a reliable method to predict cell metabolism for expression data will greatly benefit metabolic engineering. We have developed a novel pipeline, OVERLAY, that can explore cellular fluxomics from expression data using only a high-quality genome-scale metabolic model. This is done through two main steps: first, construct a protein-constrained metabolic model (PC-model) by integrating protein and enzyme information into the metabolic model (M-model). Secondly, overlay the expression data onto the PC-model using a novel two-step nonconvex and convex optimization formulation, resulting in a context-specific PC-model with optionally calibrated rate constants. The resulting model computes proteomes and intracellular flux states that are consistent with the measured transcriptomes. Therefore, it provides detailed cellular insights that are difficult to glean individually from the omic data or M-model alone. We apply the OVERLAY to interpret triacylglycerol (TAG) overproduction by Chlamydomonas reinhardtii, using time-course RNA-Seq data. We show that OVERLAY can compute C. reinhardtii metabolism under nitrogen deprivation and metabolic shifts after an acetate boost. OVERLAY can also suggest possible 'bottleneck' proteins that need to be overexpressed to increase the TAG accumulation rate, as well as discuss other TAG-overproduction strategies.

Metabolic network reconstruction of Euglena gracilis: Current state, challenges, and applications

A metabolic model, representing all biochemical reactions in a cell, is a prerequisite for several approaches in systems biology used to explore the metabolic phenotype of an organism. Despite the use of Euglena in diverse industrial applications and as a biological model, there is limited understanding of its metabolic network capacity. The unavailability of the completed genome data and the highly complex evolution of Euglena are significant obstacles to the reconstruction and analysis of its genome-scale metabolic model. In this mini-review, we discuss the current state and challenges of metabolic network reconstruction in Euglena gracilis. We have collated and present the available relevant data for the metabolic network reconstruction of E. gracilis, which could be used to improve the quality of the metabolic model of E. gracilis. Furthermore, we deliver the potential applications of the model in metabolic engineering. Altogether, it is supposed that this mini-review would facilitate the investigation of metabolic networks in Euglena and further lay out a direction for model-assisted metabolic engineering.

Physiological and Genetic Regulation for High Lipid Accumulation by Chlorella sorokiniana Strains from Different Environments of an Arctic Glacier, Desert, and Temperate Lake under Nitrogen Deprivation Conditions

Microalgae can adapt to extreme environments with specialized metabolic mechanisms. Here, we report comparative physiological and genetic regulation analyses of Chlorella sorokiniana from different environmental regions of an arctic glacier, desert, and temperate native lake in response to N deprivation, for screening the optimal strain with high lipid accumulation. Strains from the three regions showed different growth and biochemical compositions under N deprivation. The arctic glacier and desert strains produced higher soluble sugar content than strains from the native lake. The arctic glacier strains produced the highest levels of lipid content and neutral lipids under N deprivation compared with strains from desert and native lake. At a molecular level, the arctic strain produced more differentially expressed genes related to fatty acid biosynthesis, glycolysis gluconeogenesis, and glycerolipid metabolism. The important functional genes acetyl coenzyme A (acetyl-CoA) carboxylase (ACCase), fatty acid synthase complex, pyruvate dehydrogenase component, and fatty acyl-acyl carrier protein (acyl-ACP) thioesterase were highly expressed in arctic strains. More acetyl-CoA was produced from glycolysis gluconeogenesis and glycerolipid metabolism, which then produced more fatty acid with the catalytic function of ACCase and acyl-ACP thioesterase in fatty acid biosynthesis. Our results indicated that the C. sorokiniana strains from the arctic region had the fullest potential for biodiesel production due to special genetic regulation related to fatty acid synthesis, glycolysis gluconeogenesis, and glycerolipid metabolism. IMPORTANCE It is important to reveal the physiological and genetic regulation mechanisms of microalgae for screening potential strains with high lipid production. Our results showed that Chlorella sorokiniana strains from arctic glacier, desert, and temperate native lake had different growth, biochemical composition, and genetic expression under N deprivation. The strains from an arctic glacier produced the highest lipid content (including neutral lipid), which was related to the genetic regulation of fatty acid biosynthesis, glycolysis gluconeogenesis, and glycerolipid metabolism. The functional genes for acetyl-CoA carboxylase, fatty acid synthase complex, pyruvate dehydrogenase component, and fatty acyl-ACP thioesterase in the three pathways were highly expressed in arctic strains. The revelation of physiological and genetic regulation of strains from different environmental regions will contribute to the microalgae selection for high lipid accumulation.

Re-Programing Glucose Catabolism in the Microalga Chlorella sorokiniana under Light Condition

The microalga Chlorella sorokiniana has attracted much attention for lipid production and wastewater treatment. It can perform photosynthesis and organic carbon utilization concurrently. To understand its phototrophic metabolism, a biomass compositional analysis, a 13C metabolic flux analysis, and metabolite pool size analyses were performed. Under dark condition, the oxidative pentose phosphate pathway (OPP) was the major route for glucose catabolism (88% carbon flux) and a cyclic OPP–glycolytic route for glucose catabolism was formed. Under light condition, fluxes in the glucose catabolism, tricarboxylic acid (TCA) cycle, and anaplerotic reaction (CO2 fixation via phosphoenolpyruvate carboxylase) were all suppressed. Meanwhile, the RuBisCO reaction became active and the ratio of its carbon fixation to glucose carbon utilization was determined as 7:100. Moreover, light condition significantly reduced the pool sizes of sugar phosphate metabolites (such as E4P, F6P, and S7P) and promoted biomass synthesis (which reached 0.155 h−1). In addition, light condition increased glucose consumption rates, leading to higher ATP and NADPH production and a higher protein content (43% vs. 30%) in the biomass during the exponential growth phase.

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

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

Biosynthesis and biotechnological interventions for commercial production of microalgal pigments: A review

Microalgae are photosynthetic eukaryotes that serve as microbial cell factories for the production of useful biochemicals, including pigments. These pigments are eco-friendly alternatives to synthetic dyes and reduce environmental and health risks. They also exhibit excellent anti-oxidative properties, making them a useful commodity in the nutrition and pharmaceutical industries. Light-harvesting pigments such as chlorophylls and phycobilins, and photoprotective carotenoids are some of the most common microalgal pigments. The increasing demand for these pigments in industrial applications has prompted a need to improve their metabolic yield in microalgal cells. So far, expensive cultivation methods and sensitivity to microbial contamination remain the main obstacles to the large-scale production of these pigments. This review highlights current issues and future prospects related to the production of microalgal pigments. The review also emphasizes the use of engineering approaches such as genetic engineering, and optimization of media components and physical parameters to increase their commercial-scale production.

Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data

Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.

Mixotrophic Chlorella pyrenoidosa as cell factory for ultrahigh-efficient removal of ammonium from catalyzer wastewater with valuable algal biomass coproduction through short-time acclimation

To achieve ultrahigh-efficient ammonium removal and valuable biomass coproduction, Chlorella-mediated short-time acclimation was implemented in photo-fermentation. The results demonstrated short-time acclimation of mixotrophic Chlorella pyrenoidosa could significantly improve NH₄⁺ removal and biomass production in shake flasks. After acclimation through two batch cultures in 5-L photo-fermenter, the maximum NH₄⁺ removal rate (1,400 mg L^-1 d^-1) were achieved under high NH₄⁺ level (4,750 mg L^-1) in batch 3. In 50-L photo-fermenter, through one batch acclimated culture, the maximum NH₄⁺ removal rate (2,212 mg L^-1 d^-1) and biomass concentration (58.4 g L^-1) were achieved in batch 2, with the highest productivities of protein (5.56 g L^-1 d^-1) and total lipids (5.66 g L^-1 d^-1). The hypothetical pathway of nutrients assimilation in mixotrophic cells as cell factory was proposed with detailed discussion. This study provided a novel strategy for high-ammonium wastewater treatment without dilution, facilitating the algae-based "waste-to-treasure" bioconversion process for green manufacturing.

Insights into the physiology of Chlorella vulgaris cultivated in sweet sorghum bagasse hydrolysate for sustainable algal biomass and lipid production

Supplementing cultivation media with exogenous carbon sources enhances biomass and lipid production in microalgae. Utilization of renewable organic carbon from agricultural residues can potentially reduce the cost of algae cultivation, while enhancing sustainability. In the present investigation a medium was developed from sweet sorghum bagasse for cultivation of Chlorella under mixotrophic conditions. Using response surface methodology, the optimal values of critical process parameters were determined, namely inoculum cell density (O.D.₇₅₀) of 0.786, SSB hydrolysate content of the medium 25% v/v, and zero medium salinity, to achieve maximum lipid productivity of 120 mg/L/d. Enhanced biomass (3.44 g/L) and lipid content (40% of dry cell weight) were observed when the alga was cultivated in SSB hydrolysate under mixotrophic conditions compared to heterotrophic and photoautotrophic conditions. A time course investigation revealed distinct physiological responses in terms of cellular growth and biochemical composition of C. vulgaris cultivated in the various trophic modes. The determined carbohydrate and lipid profiles indicate that sugar addition to the cultivation medium boosts neutral lipid synthesis compared to structural lipids, suggesting that carbon flux is channeled towards triacylglycerol synthesis in the cells. Furthermore, the fatty acid profile of lipids extracted from mixotrophically grown cultures contained more saturated and monosaturated fatty acids, which are suitable for biofuel manufacturing. Scale-up studies in a photobioreactor using SSB hydrolysate achieved a biomass concentration of 2.83 g/L consisting of 34% lipids and 26% carbohydrates. These results confirmed that SSB hydrolysate is a promising feedstock for mixotrophic cultivation of Chlorella and synthesis of algal bioproducts and biofuels.

The carbon partitioning of glucose and DIC in mixotrophic, heterotrophic and photoautotrophic cultures of Tetraselmis suecica

OBJECTIVE

Changes in the partitioning of dissolved inorganic (DIC) and glucose were elucidated by utilising ¹³C labelled DIC or glucose, and quantifying the biochemical profile of mixotrophic, heterotrophic and photoautotrophic cultures of the microalga Tetraselmis suecica.

RESULTS

Mixotrophic cultivation increases microalgal productivity and changes their biochemical profile, due to an alteration in the partitioning of carbon within the cell. When cultured mixotrophically and heterotrophically, there is enhanced incorporation of carbon into shorter chain saturated fatty acids and non-lipid biomass, compared to photoautotrophic cultivation. Autotrophic culture results in increased total fatty acid content of cultures (4.19% dry weight compared to 2.13%) and shifts the fatty acid profile in favour of long-chain unsaturated fatty acids, such as 18:2 n-(9,12), compared to mixotrophic culture. Quantifying the changes in partitioning between DIC and glucose facilitates tailoring of the biochemical profile to develop "designer" algae.

CONCLUSIONS

There is a condition specific shift in carbon partitioning into different fatty acid and biochemical fractions in T. suecica, with more inorganic carbon partitioned into 18:2 n-(9,12) in photoautotrophic rather than mixotrophic cultures.

The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes

For over 10 years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical 'Rosetta Stone' to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org/biochem and KBase.

The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes

For over ten years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions;; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical “Rosetta Stone” to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies, and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org and KBase.

Evolutionary divergence of photoprotection in the green algal lineage: a plant-like violaxanthin de-epoxidase enzyme activates the xanthophyll cycle in the green alga Chlorella vulgaris modulating photoprotection

The xanthophyll cycle is the metabolic process by which the carotenoid violaxanthin is de-epoxidated to zeaxanthin, a xanthophyll with a crucial photoprotective role in higher plants and mosses. The role of zeaxanthin is still unclear in green algae, and a peculiar violaxanthin de-epoxidating enzyme was found in the model organism Chlamydomonas reinhardtii. Here, we investigated the molecular details and functions of the xanthophyll cycle in the case of Chlorella vulgaris, one of the green algae most considered for industrial cultivation, where resistance to high light stress is a prerequisite for sustainable biomass production. Identification of the violaxanthin de-epoxidase enzyme in C. vulgaris was performed by genome mining and in vitro analysis of the catalytic activity of the gene product identified. The photoprotective role of zeaxanthin was then investigated in vivo and in isolated pigment-binding complexes. The results obtained demonstrate the functioning, even though with a different pH sensitivity, of a plant-like violaxanthin de-epoxidase enzyme in C. vulgaris. Differently from C. reinhardtii, zeaxanthin accumulation in C. vulgaris was found to be crucial for photoprotective quenching of excitation energy harvested by both photosystem I and II. These findings demonstrate an evolutionary divergence of photoprotective mechanisms among Chlorophyta.

Chlorella vulgaris and Its Phycosphere in Wastewater: Microalgae-Bacteria Interactions During Nutrient Removal

Microalgae-based bioenergy production is a promising field with regard to the wide variety of algal species and metabolic potential. The use of liquid wastes as nutrient clearly improves the sustainability of microalgal biofuel production. Microalgae and bacteria have an ecological inter-kingdom relationship. This microenvironment called phycosphere has a major role in the ecosystem productivity and can be utilized both in bioremediation and biomass production. However, knowledge on the effects of indigenous bacteria on microalgal growth and the characteristics of bacterial communities associated with microalgae are limited. In this study municipal, industrial and agricultural liquid waste derivatives were used as cultivation media. Chlorella vulgaris green microalgae and its bacterial partners efficiently metabolized the carbon, nitrogen and phosphorous content available in these wastes. The read-based metagenomics approach revealed a diverse microbial composition at the start point of cultivations in the different types of liquid wastes. The relative abundance of the observed taxa significantly changed over the cultivation period. The genome-centric reconstruction of phycospheric bacteria further explained the observed correlations between the taxonomic composition and biomass yield of the various waste-based biodegradation systems. Functional profile investigation of the reconstructed microbes revealed a variety of relevant biological processes like organic acid oxidation and vitamin B synthesis. Thus, liquid wastes were shown to serve as valuable resources of nutrients as well as of growth promoting bacteria enabling increased microalgal biomass production.

A Genome-Scale Metabolic Model of Thalassiosira pseudonana CCMP 1335 for a Systems-Level Understanding of Its Metabolism and Biotechnological Potential

Thalassiosira pseudonana is a transformable and biotechnologically promising model diatom with an ability to synthesise nutraceuticals such as fucoxanthin and store a significant amount of polyglucans and lipids including omega-3 fatty acids. While it was the first diatom to be sequenced, a systems-level analysis of its metabolism has not been done yet. This work presents first comprehensive, compartmentalized, and functional genome-scale metabolic model of the marine diatom Thalassiosira pseudonana CCMP 1335, which we have termed iThaps987. The model includes 987 genes, 2477 reactions, and 2456 metabolites. Comparison with the model of another diatom Phaeodactylum tricornutum revealed presence of 183 unique enzymes (belonging primarily to amino acid, carbohydrate, and lipid metabolism) in iThaps987. Model simulations showed a typical C3-type photosynthetic carbon fixation and suggested a preference of violaxanthin-diadinoxanthin pathway over violaxanthin-neoxanthin pathway for the production of fucoxanthin. Linear electron flow was found be active and cyclic electron flow was inactive under normal phototrophic conditions (unlike green algae and plants), validating the model predictions with previous reports. Investigation of the model for the potential of Thalassiosira pseudonana CCMP 1335 to produce other industrially useful compounds suggest iso-butanol as a foreign compound that can be synthesized by a single-gene addition. This work provides novel insights about the metabolism and potential of the organism and will be helpful to further investigate its metabolism and devise metabolic engineering strategies for the production of various compounds.

Linking metabolic phenotypes to pathogenic traits among "Candidatus Liberibacter asiaticus" and its hosts

Candidatus Liberibacter asiaticus (CLas) has been associated with Huanglongbing, a lethal vector-borne disease affecting citrus crops worldwide. While comparative genomics has provided preliminary insights into the metabolic capabilities of this uncultured microorganism, a comprehensive functional characterization is currently lacking. Here, we reconstructed and manually curated genome-scale metabolic models for the six CLas strains A4, FL17, gxpsy, Ishi-1, psy62, and YCPsy, in addition to a model of the closest related culturable microorganism, L. crescens BT-1. Predictions about nutrient requirements and changes in growth phenotypes of CLas were confirmed using in vitro hairy root-based assays, while the L. crescens BT-1 model was validated using cultivation assays. Host-dependent metabolic phenotypes were revealed using expression data obtained from CLas-infected citrus trees and from the CLas-harboring psyllid Diaphorina citri Kuwayama. These results identified conserved and unique metabolic traits, as well as strain-specific interactions between CLas and its hosts, laying the foundation for the development of model-driven Huanglongbing management strategies.

Synthetic microbial communities of heterotrophs and phototrophs facilitate sustainable growth

Microbial communities comprised of phototrophs and heterotrophs hold great promise for sustainable biotechnology. Successful application of these communities relies on the selection of appropriate partners. Here we construct four community metabolic models to guide strain selection, pairing phototrophic, sucrose-secreting Synechococcus elongatus with heterotrophic Escherichia coli K-12, Escherichia coli W, Yarrowia lipolytica, or Bacillus subtilis. Model simulations reveae metabolic exchanges that sustain the heterotrophs in minimal media devoid of any organic carbon source, pointing to S. elongatus-E. coli K-12 as the most active community. Experimental validation of flux predictions for this pair confirms metabolic interactions and potential production capabilities. Synthetic communities bypass member-specific metabolic bottlenecks (e.g. histidine- and transport-related reactions) and compensate for lethal genetic traits, achieving up to 27% recovery from lethal knockouts. The study provides a robust modelling framework for the rational design of synthetic communities with optimized growth sustainability using phototrophic partners.

Towards model-driven characterization and manipulation of plant lipid metabolism

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

Modeling of nitrogen fixation and polymer production in the heterotrophic diazotroph Azotobacter vinelandii DJ

Nitrogen fixation is an important metabolic process carried out by microorganisms, which converts molecular nitrogen into inorganic nitrogenous compounds such as ammonia (NH₃). These nitrogenous compounds are crucial for biogeochemical cycles and for the synthesis of essential biomolecules, i.e. nucleic acids, amino acids and proteins. Azotobacter vinelandii is a bacterial non-photosynthetic model organism to study aerobic nitrogen fixation (diazotrophy) and hydrogen production. Moreover, the diazotroph can produce biopolymers like alginate and polyhydroxybutyrate (PHB) that have important industrial applications. However, many metabolic processes such as partitioning of carbon and nitrogen metabolism in A. vinelandii remain unknown to date.

Genome-scale metabolic models (M-models) represent reliable tools to unravel and optimize metabolic functions at genome-scale. M-models are mathematical representations that contain information about genes, reactions, metabolites and their associations. M-models can simulate optimal reaction fluxes under a wide variety of conditions using experimentally determined constraints. Here we report on the development of a M-model of the wild type bacterium A. vinelandii DJ (iDT1278) which consists of 2,003 metabolites, 2,469 reactions, and 1,278 genes. We validated the model using high-throughput phenotypic and physiological data, testing 180 carbon sources and 95 nitrogen sources. iDT1278 was able to achieve an accuracy of 89% and 91% for growth with carbon sources and nitrogen source, respectively. This comprehensive M-model will help to comprehend metabolic processes associated with nitrogen fixation, ammonium assimilation, and production of organic nitrogen in an environmentally important microorganism.

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Genome-scale metabolic model of Azotobacter vinelandii DJ achives over 90% accuracy.

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iDT1278 is the most comprehensive model to simulate diazotrophy.

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Determining the most suitable culture conditions to produce polymers A. vinelandii.

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Constraint-based modeling unravels links among nitrogen fixation and production of organic nitrogen.

Dynamic resource allocation drives growth under nitrogen starvation in eukaryotes

Cells can sense changes in their extracellular environment and subsequently adapt their biomass composition. Nutrient abundance defines the capability of the cell to produce biomass components. Under nutrient-limited conditions, resource allocation dramatically shifts to carbon-rich molecules. Here, we used dynamic biomass composition data to predict changes in growth and reaction flux distributions using the available genome-scale metabolic models of five eukaryotic organisms (three heterotrophs and two phototrophs). We identified temporal profiles of metabolic fluxes that indicate long-term trends in pathway and organelle function in response to nitrogen depletion. Surprisingly, our calculations of model sensitivity and biosynthetic cost showed that free energy of biomass metabolites is the main driver of biosynthetic cost and not molecular weight, thus explaining the high costs of arginine and histidine. We demonstrated how metabolic models can accurately predict the complexity of interwoven mechanisms in response to stress over the course of growth.

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

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

Hardy S, Falciatore A, Lavaud J. Genome-Scale Metabolic Reconstruction and in Silico Perturbation Analysis of the Polar Diatom Fragilariopsis cylindrus Predicts High Metabolic Robustness

Diatoms are major primary producers in polar environments where they can actively grow under extremely variable conditions. Integrative modeling using a genome-scale model (GSM) is a powerful approach to decipher the complex interactions between components of diatom metabolism and can provide insights into metabolic mechanisms underlying their evolutionary success in polar ecosystems. We developed the first GSM for a polar diatom, Fragilariopsis cylindrus, which enabled us to study its metabolic robustness using sensitivity analysis. We find that the predicted growth rate was robust to changes in all model parameters (i.e., cell biochemical composition) except the carbon uptake rate. Constraints on total cellular carbon buffer the effect of changes in the input parameters on reaction fluxes and growth rate. We also show that single reaction deletion of 20% to 32% of active (nonzero flux) reactions and single gene deletion of 44% to 55% of genes associated with active reactions affected the growth rate, as well as the production fluxes of total protein, lipid, carbohydrate, DNA, RNA, and pigments by less than 1%, which was due to the activation of compensatory reactions (e.g., analogous enzymes and alternative pathways) with more highly connected metabolites involved in the reactions that were robust to deletion. Interestingly, including highly divergent alleles unique for F. cylindrus increased its metabolic robustness to cellular perturbations even more. Overall, our results underscore the high robustness of metabolism in F. cylindrus, a feature that likely helps to maintain cell homeostasis under polar conditions.

Effect of Delays on the Response of Microalgae When Exposed to Dynamic Environmental Conditions

During mathematical representation of microbial cultures, it is normally assumed that changes in the environment produce instantaneous effects on growth. However, reports are available indicating that sometimes this may not be the case. This work studied the existence of delays on the response of a population of microalgae when subjected to changes in energy and carbon sources, and when exposed to a growth inhibitor. Results show that no appreciable delays exist when microalgae undergo changes in the incident light intensity. For changes in carbon source concentration (inorganic carbon), a small delay in the range of minutes was detected. Finally, when exposing microalgae to inhibitory concentrations of ammonia, a significant delay of several hours was observed.

Chlorella vulgaris genome assembly and annotation reveals the molecular basis for metabolic acclimation to high light conditions

Chlorella vulgaris is a fast-growing fresh-water microalga cultivated on the industrial scale for applications ranging from food to biofuel production. To advance our understanding of its biology and to establish genetics tools for biotechnological manipulation, we sequenced the nuclear and organelle genomes of Chlorella vulgaris 211/11P by combining next generation sequencing and optical mapping of isolated DNA molecules. This hybrid approach allowed us to assemble the nuclear genome in 14 pseudo-molecules with an N50 of 2.8 Mb and 98.9% of scaffolded genome. The integration of RNA-seq data obtained at two different irradiances of growth (high light, HL versus low light, LL) enabled us to identify 10 724 nuclear genes, coding for 11 082 transcripts. Moreover, 121 and 48 genes, respectively, were found in the chloroplast and mitochondrial genome. Functional annotation and expression analysis of nuclear, chloroplast and mitochondrial genome sequences revealed particular features of Chlorella vulgaris. Evidence of horizontal gene transfers from chloroplast to mitochondrial genome was observed. Furthermore, comparative transcriptomic analyses of LL versus HL provided insights into the molecular basis for metabolic rearrangement under HL versus LL conditions leading to enhanced de novo fatty acid biosynthesis and triacylglycerol accumulation. The occurrence of a cytosolic fatty acid biosynthetic pathway could be predicted and its upregulation upon HL exposure was observed, consistent with the increased lipid amount under HL conditions. These data provide a rich genetic resource for future genome editing studies, and potential targets for biotechnological manipulation of Chlorella vulgaris or other microalgae species to improve biomass and lipid productivity.

Pulsed electric field based cyclic protein extraction of microalgae towards closed-loop biorefinery concepts

Microalgae-based biorefinery concepts can contribute to providing sufficient resources for a growing world population. However, the performance needs to be improved, which requires innovative technologies and processes. Continuous extraction from Chlorella vulgaris cultures via pulsed electric field (PEF) processing might be a viable process to increase the performance of microalgae-based biorefinery concepts. In this study, increasing protein extraction rates were observed with increasing electric field strength, up to 96.6 ± 4.8% of the free protein in the microalgae. However, increased extraction rates negatively influenced microalgae growth after PEF treatment. A free protein extraction rate up to 29.1 ± 1.1% without a significant influence on microalgal growth after 168 h was achieved (p = 0.788). Within the scope of this work, a protocol for continuous protein extraction during microalgae cultivation by PEF processing was developed. The incorporation of innovative downstream into upstream processing could be a viable future concept.

Environmental stimuli drive a transition from cooperation to competition in synthetic phototrophic communities

Phototrophic communities of photosynthetic algae or cyanobacteria and heterotrophic bacteria or fungi are pervasive throughout the environment¹. How interactions between members contribute to the resilience and affect the fitness of phototrophic communities is not fully understood^2,3. Here, we integrated metatranscriptomics, metabolomics and phenotyping with computational modelling to reveal condition-dependent secretion and cross-feeding of metabolites in a synthetic community. We discovered that interactions between members are highly dynamic and are driven by the availability of organic and inorganic nutrients. Environmental factors, such as ammonia concentration, influenced community stability by shifting members from collaborating to competing. Furthermore, overall fitness was dependent on genotype and streamlined genomes improved growth of the entire community. Our mechanistic framework provides insights into the physiology and metabolic response to environmental and genetic perturbation of these ubiquitous microbial associations.

Carbon dioxide fixation coupled with ammonium uptake by immobilized Scenedesmus obliquus and its potential for protein production

In this study, immobilized Scenedesmus obliquus (S. obliquus) was proposed to simultaneously alleviate the carbon dioxide (CO₂) and ammonium (NH₄⁺-N). Two trophic modes of autotrophy and mixotrophy were conducted by batch experiments with a period of 5 days. The results shown that NH₄⁺-N could be removed more efficiently if algal cells were immobilized, and the trophic mode change had no significant effect on immobilized S. obliquus to NH₄⁺-N removal under 5% CO₂ sparging. Specifically, immobilized S. obliquus could remove NH₄⁺-N completely at initial concentrations of 30 and 50 mg/L and reached about 80% removal rate of NH₄⁺-N at the concentration of 70 mg/L under both trophic modes. The protein synthesis was its main removal mechanism and the dominant amino acid components including glutamic acid (Glu), cystine (Cys), arginine (Arg), methionine (Met) and lysine (Lys) were sensitive to NH₄⁺-N assimilation.

Utilizing genome-scale models to optimize nutrient supply for sustained algal growth and lipid productivity

Nutrient availability is critical for growth of algae and other microbes used for generating valuable biochemical products. Determining the optimal levels of nutrient supplies to cultures can eliminate feeding of excess nutrients, lowering production costs and reducing nutrient pollution into the environment. With the advent of omics and bioinformatics methods, it is now possible to construct genome-scale models that accurately describe the metabolism of microorganisms. In this study, a genome-scale model of the green alga Chlorella vulgaris (iCZ946) was applied to predict feeding of multiple nutrients, including nitrate and glucose, under both autotrophic and heterotrophic conditions. The objective function was changed from optimizing growth to instead minimizing nitrate and glucose uptake rates, enabling predictions of feed rates for these nutrients. The metabolic model control (MMC) algorithm was validated for autotrophic growth, saving 18% nitrate while sustaining algal growth. Additionally, we obtained similar growth profiles by simultaneously controlling glucose and nitrate supplies under heterotrophic conditions for both high and low levels of glucose and nitrate. Finally, the nitrate supply was controlled in order to retain protein and chlorophyll synthesis, albeit at a lower rate, under nitrogen-limiting conditions. This model-driven cultivation strategy doubled the total volumetric yield of biomass, increased fatty acid methyl ester (FAME) yield by 61%, and enhanced lutein yield nearly 3 fold compared to nitrogen starvation. This study introduces a control methodology that integrates omics data and genome-scale models in order to optimize nutrient supplies based on the metabolic state of algal cells in different nutrient environments. This approach could transform bioprocessing control into a systems biology-based paradigm suitable for a wide range of species in order to limit nutrient inputs, reduce processing costs, and optimize biomanufacturing for the next generation of desirable biotechnology products.

Genome-scale metabolic reconstruction and metabolic versatility of an obligate methanotroph Methylococcus capsulatus str. Bath

The increase in greenhouse gases with high global warming potential such as methane is a matter of concern and requires multifaceted efforts to reduce its emission and increase its mitigation from the environment. Microbes such as methanotrophs can assist in methane mitigation. To understand the metabolic capabilities of methanotrophs, a complete genome-scale metabolic model (GSMM) of an obligate methanotroph, Methylococcus capsulatus str. Bath was reconstructed. The model contains 535 genes, 899 reactions and 865 metabolites and is named iMC535. The predictive potential of the model was validated using previously-reported experimental data. The model predicted the Entner–Duodoroff pathway to be essential for the growth of this bacterium, whereas the Embden–Meyerhof–Parnas pathway was found non-essential. The performance of the model was simulated on various carbon and nitrogen sources and found that M. capsulatus can grow on amino acids. The analysis of network topology of the model identified that six amino acids were in the top-ranked metabolic hubs. Using flux balance analysis, 29% of the metabolic genes were predicted to be essential, and 76 double knockout combinations involving 92 unique genes were predicted to be lethal. In conclusion, we have reconstructed a GSMM of a methanotroph Methylococcus capsulatus str. Bath. This is the first high quality GSMM of a Methylococcus strain which can serve as an important resource for further strain-specific models of the Methylococcus genus, as well as identifying the biotechnological potential of M. capsulatus Bath.

Sugar-stimulated CO2 sequestration by the green microalga Chlorella vulgaris

To convert waste CO₂ from flue gases of power plants into value-added products, bio-mitigation technologies show promise. In this study, we cultivated a fast-growing species of green microalgae, Chlorella vulgaris, in different sizes of photobioreactors (PBRs) and developed a strategy using small doses of sugars for enhancing CO₂ sequestration under light-emitting diode illumination. Glucose supplementation at low levels resulted in an increase of photoautotrophic growth-driven biomass generation as well as CO₂ capture by 10% and its enhancement corresponded to an increase of supplied photon flux. The utilization of urea instead of nitrate as the sole nitrogen source increased photoautotrophic growth by 14%, but change of nitrogen source didn't compromise glucose-induced enhancement of photoautotrophic growth. The optimized biomass productivity achieved was 30.4% higher than the initial productivity of purely photoautotrophic culture. The major pigments in the obtained algal biomass were found comparable to its photoautotrophic counterpart and a high neutral lipids productivity of 516.6 mg/(L·day) was achieved after optimization. A techno-economic model was also developed, indicating that LED-based PBRs represent a feasible strategy for converting CO₂ into value-added algal biomass.

Advances in metabolic modeling of oleaginous microalgae

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

Leveraging algal omics to reveal potential targets for augmenting TAG accumulation

Ongoing global efforts to commercialize microalgal biofuels have expedited the use of multi-omics techniques to gain insights into lipid biosynthetic pathways. Functional genomics analyses have recently been employed to complement existing sequence-level omics studies, shedding light on the dynamics of lipid synthesis and its interplay with other cellular metabolic pathways, thus revealing possible targets for metabolic engineering. Here, we review the current status of algal omics studies to reveal potential targets to augment TAG accumulation in various microalgae. This review specifically aims to examine and catalog systems level data related to stress-induced TAG accumulation in oleaginous microalgae and inform future metabolic engineering strategies to develop strains with enhanced bioproductivity, which could pave a path for sustainable green energy.