Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis

Improving genome-scale metabolic models of incomplete genomes with deep learning

Deciphering microbial metabolism is essential for understanding ecosystem functions. Genome-scale metabolic models (GSMMs) predict metabolic traits from genomic data, but constructing GSMMs for uncultured bacteria is challenging due to incomplete metagenome-assembled genomes, resulting in many gaps. We introduce the deep neural network guided imputation of reactomes (DNNGIOR), which uses AI to improve gap-filling by learning from the presence and absence of metabolic reactions across diverse bacterial genomes. Key factors for prediction accuracy are: (1) reaction frequency across all bacteria and (2) phylogenetic distance of the query to the training genomes. DNNGIOR predictions achieve an average F1 score of 0.85 for reactions present in over 30% of training genomes. DNNGIOR guided gap-filling was 14 times more accurate for draft reconstructions and 2-9 times for curated models than unweighted gap-filling.

Insufficient Acetyl-CoA Pool Restricts the Phototrophic Production of Organic Acids in Model Cyanobacteria

Cyanobacteria are promising biological chassis to produce biochemicals such as carboxylic acids and their derivatives from CO2. In this manuscript, we reflected on cyanobacterial acetyl-CoA pool and TCA cycle as an important source of precursor molecules for the biosynthesis of carboxylic acids such as 3-hydroxypropionate, 3-hydroxybutyrate, succinate, malate, fumarate and free fatty acids, each of which is an important platform chemical for bioeconomy. We further highlighted specific features of the cyanobacterial TCA cycle, how it differs in structure and function from widely described TCA cycles of heterotrophic model organisms, and methods to make it more suitable for the production of carboxylic acids from CO2. Currently, the yields of these compounds are significantly lower than those in heterotrophic organisms and it was concluded that the primary cause of this can be attributed to the limited flux toward acetyl-CoA. Strategies like overexpressing pyruvate dehydrogenase complex or introducing synthetic bypasses are being explored to overcome these limitations. While significant progress has been made, further research is needed to enhance the metabolic efficiency of cyanobacteria, making them viable for the large-scale, sustainable production of carboxylic acids and their derivatives.

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

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

Central transcriptional regulator controls photosynthetic growth and carbon storage in response to high light

Carbon capture and biochemical storage are some of the primary drivers of photosynthetic yield and productivity. To elucidate the mechanisms governing carbon allocation, we designed a photosynthetic light response test system for genetic and metabolic carbon assimilation tracking, using microalgae as simplified plant models. The systems biology mapping of high light-responsive photophysiology and carbon utilization dynamics between two variants of the same Picochlorum celeri species, TG1 and TG2 elucidated metabolic bottlenecks and transport rates of intermediates using instationary 13C-fluxomics. Simultaneous global gene expression dynamics showed 73% of the annotated genes responding within one hour, elucidating a singular, diel-responsive transcription factor, closely related to the CCA1/LHY clock genes in plants, with significantly altered expression in TG2. Transgenic P. celeri TG1 cells expressing the TG2 CCA1/LHY gene, showed 15% increase in growth rates and 25% increase in storage carbohydrate content, supporting a coordinating regulatory function for a single transcription factor.

Double blocking of carbon metabolism causes a large increase of Calvin-Benson cycle compounds in cyanobacteria

Carbon-flow-regulator A (CfrA) adapts carbon flux to nitrogen conditions in nondiazotrophic cyanobacteria. Under nitrogen deficiency, CfrA leads to the storage of excess carbon, which cannot combine with nitrogen, mainly as glycogen. cfrA overexpression from the arsenite-inducible, nitrogen-independent ParsB promoter allows analysis of the metabolic effects of CfrA accumulation. Considering that the main consequence of cfrA overexpression is glycogen accumulation, we examined carbon distribution in response to cfrA expression in Synechocystis sp. PCC 6803 strains impaired in synthesizing this polymer. We carried out a comparative phenotypic analysis to evaluate cfrA overexpression in the wild-type strain and in a mutant of ADP-glucose pyrophosphorylase (ΔglgC), which is unable to synthesize glycogen. The accumulation of CfrA in the wild-type background caused a photosynthetic readjustment although growth was not affected. However, in a ΔglgC strain, growth decreased depending on CfrA accumulation and photosynthesis was severely affected. An elemental analysis of the H, C, and N content of cells revealed that cfrA expression in the wild-type caused an increase in the C/N ratio, due to decreased nitrogen assimilation. Metabolomic study indicated that these cells store sucrose and glycosylglycerol, in addition to the previously described glycogen accumulation. However, cells deficient in glycogen synthesis accumulated large amounts of Calvin-Benson cycle intermediates as cfrA was expressed. These cells also showed increased levels of some amino acids, mainly alanine, serine, valine, isoleucine, and leucine. The findings suggest that by controlling cfrA expression, in different conditions and strains, we could change the distribution of fixed carbon, with potential biotechnological benefits.

Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects

The construction of high-performance microbial cell factories (MCFs) is the centerpiece of biomanufacturing. However, the complex metabolic regulatory network of microorganisms poses great challenges for the efficient design and construction of MCFs. The genome-scale metabolic network models (GSMs) can systematically simulate the metabolic regulation process of microorganisms in silico, providing effective guidance for the rapid design and construction of MCFs. In this review, we summarized the development status of 16 important industrial microbial GSMs, and further outline the technologies or methods that continuously promote high-quality GSMs construction from five aspects: I) Databases and modeling tools facilitate GSMs reconstruction; II) evolving gap-filling technologies; III) constraint-based model reconstruction; IV) advances in algorithms; and V) developed visualization tools. In addition, we also summarized the applications of GSMs in guiding metabolic engineering from four aspects: I) exploring and explaining metabolic features; II) predicting the effects of genetic perturbations on metabolism; III) predicting the optimal phenotype; IV) guiding cell factories construction in practical experiment. Finally, we discussed the development of GSMs, aiming to provide a reference for efficiently reconstructing GSMs and guiding metabolic engineering.

Emergence of fractal geometries in the evolution of a metabolic enzyme

Fractals are patterns that are self-similar across multiple length-scales1. Macroscopic fractals are common in nature2-4; however, so far, molecular assembly into fractals is restricted to synthetic systems5-12. Here we report the discovery of a natural protein, citrate synthase from the cyanobacterium Synechococcus elongatus, which self-assembles into Sierpiński triangles. Using cryo-electron microscopy, we reveal how the fractal assembles from a hexameric building block. Although different stimuli modulate the formation of fractal complexes and these complexes can regulate the enzymatic activity of citrate synthase in vitro, the fractal may not serve a physiological function in vivo. We use ancestral sequence reconstruction to retrace how the citrate synthase fractal evolved from non-fractal precursors, and the results suggest it may have emerged as a harmless evolutionary accident. Our findings expand the space of possible protein complexes and demonstrate that intricate and regulatable assemblies can evolve in a single substitution.

Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations

Environmental perturbations are encountered by microorganisms regularly and will require metabolic adaptations to ensure an organism can survive in the newly presenting conditions. In order to study the mechanisms of metabolic adaptation in such conditions, various experimental and computational approaches have been used. Genome-scale metabolic models (GEMs) are one of the most powerful approaches to study metabolism, providing a platform to study the systems level adaptations of an organism to different environments which could otherwise be infeasible experimentally. In this review, we are describing the application of GEMs in understanding how microbes reprogram their metabolic system as a result of environmental variation. In particular, we provide the details of metabolic model reconstruction approaches, various algorithms and tools for model simulation, consequences of genetic perturbations, integration of '-omics' datasets for creating context-specific models and their application in studying metabolic adaptation due to the change in environmental conditions.

Metabolic engineering of Synechococcus elongatus 7942 for enhanced sucrose biosynthesis

The capability of cyanobacteria to produce sucrose from CO2 and light has a remarkable societal and biotechnological impact since sucrose can serve as a carbon and energy source for a variety of heterotrophic organisms and can be converted into value-added products. However, most metabolic engineering efforts have focused on understanding local pathway alterations that drive sucrose biosynthesis and secretion in cyanobacteria rather than analyzing the global flux re-routing that occurs following induction of sucrose production by salt stress. Here, we investigated global metabolic flux alterations in a sucrose-secreting (cscB-overexpressing) strain relative to its wild-type Synechococcus elongatus 7942 parental strain. We used targeted metabolomics, 13C metabolic flux analysis (MFA), and genome-scale modeling (GSM) as complementary approaches to elucidate differences in cellular resource allocation by quantifying metabolic profiles of three cyanobacterial cultures - wild-type S. elongatus 7942 without salt stress (WT), wild-type with salt stress (WT/NaCl), and the cscB-overexpressing strain with salt stress (cscB/NaCl) - all under photoautotrophic conditions. We quantified the substantial rewiring of metabolic fluxes in WT/NaCl and cscB/NaCl cultures relative to WT and identified a metabolic bottleneck limiting carbon fixation and sucrose biosynthesis. This bottleneck was subsequently mitigated through heterologous overexpression of glyceraldehyde-3-phosphate dehydrogenase in an engineered sucrose-secreting strain. Our study also demonstrates that combining 13C-MFA and GSM is a useful strategy to both extend the coverage of MFA beyond central metabolism and to improve the accuracy of flux predictions provided by GSM.

Metabolic flux phenotyping of secondary metabolism in cyanobacteria

Cyanobacteria generate energy from photosynthesis and produce various secondary metabolites with diverse commercial and pharmaceutical applications. Unique metabolic and regulatory pathways in cyanobacteria present new challenges for researchers to enhance their product yields, titers, and rates. Therefore, further advancements are critically needed to establish cyanobacteria as a preferred bioproduction platform. Metabolic flux analysis (MFA) quantitatively determines the intracellular flows of carbon within complex biochemical networks, which elucidate the control of metabolic pathways by transcriptional, translational, and allosteric regulatory mechanisms. The emerging field of systems metabolic engineering (SME) involves the use of MFA and other omics technologies to guide the rational development of microbial production strains. This review highlights the potential of MFA and SME to optimize the production of cyanobacterial secondary metabolites and discusses the technical challenges that lie ahead.

FreeFlux: A Python Package for Time-Efficient Isotopically Nonstationary Metabolic Flux Analysis

13C metabolic flux analysis is a powerful tool for metabolism characterization in metabolic engineering and synthetic biology. However, the widespread adoption of this tool is hindered by limited software availability and computational efficiency. Currently, the most widely accepted 13C-flux tools, such as INCA and 13CFLUX2, are developed in a closed-source environment. While several open-source packages or software are available, they are either computationally inefficient or only suitable for flux estimation at isotopic steady state. To address the need for a time-efficient computational tool for the more complicated flux analysis at an isotopically nonstationary state, especially for understanding the single-carbon substrate metabolism, we present FreeFlux. FreeFlux is an open-source Python package that performs labeling pattern simulation and flux analysis at both isotopic steady state and transient state, enabling a more comprehensive analysis of cellular metabolism. FreeFlux provides a set of interfaces to manipulate the objects abstracted from a labeling experiment and computational process, making it easy to integrate into other programs or pipelines. The flux estimation by FreeFlux is fast and reliable, and its validity has been confirmed by comparison with results from other computational tools using both synthetic and experimental data. FreeFlux is freely available at https://github.com/Chaowu88/freeflux with a detailed online tutorial and documentation provided at https://freeflux.readthedocs.io/en/latest/index.html.

Morphological and metabolomics profiling of intraspecific Arabidopsis hybrids in relation to biomass heterosis

Heterosis contributes greatly to the worldwide agricultural yield. However, the molecular mechanism underlying heterosis remains unclear. This study took advantage of Arabidopsis intraspecific hybrids to identify heterosis-related metabolites. Forty-six intraspecific hybrids were used to examine parental effects on seed area and germination time. The degree of heterosis was evaluated based on biomass: combinations showing high heterosis of F₁ hybrids exhibited a biomass increase from 6.1 to 44% over the better parent value (BPV), whereas that of the low- and no-heterosis hybrids ranged from - 19.8 to 9.8% over the BPV. Metabolomics analyses of F₁ hybrids with high heterosis and those with low one suggested that changes in TCA cycle intermediates are key factors that control growth. Notably, higher fumarate/malate ratios were observed in the high heterosis F₁ hybrids, suggesting they provide metabolic support associated with the increased biomass. These hybrids may produce more energy-intensive biomass by speeding up the efficiency of TCA fluxes. However, the expression levels of TCA-process-related genes in F₁ hybrids were not associated with the intensity of heterosis, suggesting that the post-transcriptional or post-translational regulation of these genes may affect the productivity of the intermediates in the TCA cycle.

A systematic overexpression approach reveals native targets to increase squalene production in Synechocystis sp. PCC 6803

Cyanobacteria are a promising platform for the production of the triterpene squalene (C30), a precursor for all plant and animal sterols, and a highly attractive intermediate towards triterpenoids, a large group of secondary plant metabolites. Synechocystis sp. PCC 6803 natively produces squalene from CO2 through the MEP pathway. Based on the predictions of a constraint-based metabolic model, we took a systematic overexpression approach to quantify native Synechocystis gene's impact on squalene production in a squalene-hopene cyclase gene knock-out strain (Δshc). Our in silico analysis revealed an increased flux through the Calvin-Benson-Bassham cycle in the Δshc mutant compared to the wildtype, including the pentose phosphate pathway, as well as lower glycolysis, while the tricarboxylic acid cycle predicted to be downregulated. Further, all enzymes of the MEP pathway and terpenoid synthesis, as well as enzymes from the central carbon metabolism, Gap2, Tpi and PyrK, were predicted to positively contribute to squalene production upon their overexpression. Each identified target gene was integrated into the genome of Synechocystis Δshc under the control of the rhamnose-inducible promoter Prha. Squalene production was increased in an inducer concentration dependent manner through the overexpression of most predicted genes, which are genes of the MEP pathway, ispH, ispE, and idi, leading to the greatest improvements. Moreover, we were able to overexpress the native squalene synthase gene (sqs) in Synechocystis Δshc, which reached the highest production titer of 13.72 mg l-1 reported for squalene in Synechocystis sp. PCC 6803 so far, thereby providing a promising and sustainable platform for triterpene production.

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.

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

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

Highlighting the potential of Synechococcus elongatus PCC 7942 as platform to produce α-linolenic acid through an updated genome-scale metabolic modeling

Cyanobacteria are prokaryotic organisms that capture energy from sunlight using oxygenic photosynthesis and transform CO2 into products of interest such as fatty acids. Synechococcus elongatus PCC 7942 is a model cyanobacterium efficiently engineered to accumulate high levels of omega-3 fatty acids. However, its exploitation as a microbial cell factory requires a better knowledge of its metabolism, which can be approached by using systems biology tools. To fulfill this objective, we worked out an updated, more comprehensive, and functional genome-scale model of this freshwater cyanobacterium, which was termed iMS837. The model includes 837 genes, 887 reactions, and 801 metabolites. When compared with previous models of S. elongatus PCC 7942, iMS837 is more complete in key physiological and biotechnologically relevant metabolic hubs, such as fatty acid biosynthesis, oxidative phosphorylation, photosynthesis, and transport, among others. iMS837 shows high accuracy when predicting growth performance and gene essentiality. The validated model was further used as a test-bed for the assessment of suitable metabolic engineering strategies, yielding superior production of non-native omega-3 fatty acids such as α-linolenic acid (ALA). As previously reported, the computational analysis demonstrated that fabF overexpression is a feasible metabolic target to increase ALA production, whereas deletion and overexpression of fabH cannot be used for this purpose. Flux scanning based on enforced objective flux, a strain-design algorithm, allowed us to identify not only previously known gene overexpression targets that improve fatty acid synthesis, such as Acetyl-CoA carboxylase and β-ketoacyl-ACP synthase I, but also novel potential targets that might lead to higher ALA yields. Systematic sampling of the metabolic space contained in iMS837 identified a set of ten additional knockout metabolic targets that resulted in higher ALA productions. In silico simulations under photomixotrophic conditions with acetate or glucose as a carbon source boosted ALA production levels, indicating that photomixotrophic nutritional regimens could be potentially exploited in vivo to improve fatty acid production in cyanobacteria. Overall, we show that iMS837 is a powerful computational platform that proposes new metabolic engineering strategies to produce biotechnologically relevant compounds, using S. elongatus PCC 7942 as non-conventional microbial cell factory.

Current Metabolic Engineering Strategies for Photosynthetic Bioproduction in Cyanobacteria

Cyanobacteria are photosynthetic microorganisms capable of using solar energy to convert CO2 and H2O into O2 and energy-rich organic compounds, thus enabling sustainable production of a wide range of bio-products. More and more strains of cyanobacteria are identified that show great promise as cell platforms for the generation of bioproducts. However, strain development is still required to optimize their biosynthesis and increase titers for industrial applications. This review describes the most well-known, newest and most promising strains available to the community and gives an overview of current cyanobacterial biotechnology and the latest innovative strategies used for engineering cyanobacteria. We summarize advanced synthetic biology tools for modulating gene expression and their use in metabolic pathway engineering to increase the production of value-added compounds, such as terpenoids, fatty acids and sugars, to provide a go-to source for scientists starting research in cyanobacterial metabolic engineering.

Application of transposon insertion site sequencing method in the exploration of gene function in microalgae

Microalgae are a large group of organisms that can produce various useful substances through photosynthesis. Microalgae need to be genetically modified at the molecular level to become "Chassis Cells" for food, medicine, energy, and environmental protection and, consequently, obtain benefits from microalgae resources. Insertional mutagenesis of microalgae using transposons is a practical possibility for understanding the function of microalgae genes. Theoretical and technical support is provided in this manuscript for applying transposons to microalgae gene function by summarizing the sequencing method of transposon insertion sites.

Role of carboxysomes in cyanobacterial CO2 assimilation: CO2 concentrating mechanisms and metabolon implications

Many carbon-fixing organisms have evolved CO₂ concentrating mechanisms (CCMs) to enhance the delivery of CO₂ to RuBisCO, while minimizing reactions with the competitive inhibitor, molecular O₂ . These distinct types of CCMs have been extensively studied using genetics, biochemistry, cell imaging, mass spectrometry, and metabolic flux analysis. Highlighted in this paper, the cyanobacterial CCM features a bacterial microcompartment (BMC) called 'carboxysome' in which RuBisCO is co-encapsulated with the enzyme carbonic anhydrase (CA) within a semi-permeable protein shell. The cyanobacterial CCM is capable of increasing CO₂ around RuBisCO, leading to one of the most efficient processes known for fixing ambient CO₂ . The carboxysome life cycle is dynamic and creates a unique subcellular environment that promotes activity of the Calvin-Benson (CB) cycle. The carboxysome may function within a larger cellular metabolon, physical association of functionally coupled proteins, to enhance metabolite channelling and carbon flux. In light of CCMs, synthetic biology approaches have been used to improve enzyme complex for CO₂ fixations. Research on CCM-associated metabolons has also inspired biologists to engineer multi-step pathways by providing anchoring points for enzyme cascades to channel intermediate metabolites towards valuable products.

Role of regulatory pathways and multi-omics approaches for carbon capture and mitigation in cyanobacteria

Cyanobacteria are known for their metabolic potential and carbon capture and sequestration capabilities. These cyanobacteria are not only an effective source for carbon minimization and resource mobilization into value-added products for biotechnological gains. The present review focuses on the detailed description of carbon capture mechanisms exerted by the various cyanobacterial strains, the role of important regulatory pathways, and their subsequent genes responsible for such mechanisms. Moreover, this review will also describe effectual mechanisms of central carbon metabolism like isoprene synthesis, ethylene production, MEP pathway, and the role of Glyoxylate shunt in the carbon sequestration mechanisms. This review also describes some interesting facets of using carbon assimilation mechanisms for valuable bio-products. The role of regulatory pathways and multi-omics approaches in cyanobacteria will not only be crucial towards improving carbon utilization but also will give new insights into utilizing cyanobacterial bioresource for carbon neutrality.

Integration of physiologically relevant photosynthetic energy flows into whole genome models of light-driven metabolism

Characterizing photosynthetic productivity is necessary to understand the ecological contributions and biotechnology potential of plants, algae, and cyanobacteria. Light capture efficiency and photophysiology have long been characterized by measurements of chlorophyll fluorescence dynamics. However, these investigations typically do not consider the metabolic network downstream of light harvesting. By contrast, genome-scale metabolic models capture species-specific metabolic capabilities but have yet to incorporate the rapid regulation of the light harvesting apparatus. Here, we combine chlorophyll fluorescence parameters defining photosynthetic and non-photosynthetic yield of absorbed light energy with a metabolic model of the pennate diatom Phaeodactylum tricornutum. This integration increases the model predictive accuracy regarding growth rate, intracellular oxygen production and consumption, and metabolic pathway usage. Through the quantification of excess electron transport, we uncover the sequential activation of non-radiative energy dissipation processes, cross-compartment electron shuttling, and non-photochemical quenching as the rapid photoacclimation strategy in P. tricornutum. Interestingly, the photon absorption thresholds that trigger the transition between these mechanisms were consistent at low and high incident photon fluxes. We use this understanding to explore engineering strategies for rerouting cellular resources and excess light energy towards bioproducts in silico. Overall, we present a methodology for incorporating a common, informative data type into computational models of light-driven metabolism and show its utilization within the design-build-test-learn cycle for engineering of photosynthetic organisms.

Combining metabolite doping and metabolic engineering to improve 2-phenylethanol production by engineered cyanobacteria

2-Phenylethanol (2-PE) is a rose-scented aromatic compound, with broad application in cosmetic, pharmaceutical, food and beverage industries. Many plants naturally synthesize 2-PE via Shikimate Pathway, but its extraction is expensive and low-yielding. Consequently, most 2-PE derives from chemical synthesis, which employs petroleum as feedstock and generates unwanted by products and health issues. The need for "green" processes and the increasing public demand for natural products are pushing biotechnological production systems as promising alternatives. So far, several microorganisms have been investigated and engineered for 2-PE biosynthesis, but a few studies have focused on autotrophic microorganisms. Among them, the prokaryotic cyanobacteria can represent ideal microbial factories thanks to their ability to photosynthetically convert CO2 into valuable compounds, their minimal nutritional requirements, high photosynthetic rate and the availability of genetic and bioinformatics tools. An engineered strain of Synechococcus elongatus PCC 7942 for 2-PE production, i.e., p120, was previously published elsewhere. The strain p120 expresses four heterologous genes for the complete 2-PE synthesis pathway. Here, we developed a combined approach of metabolite doping and metabolic engineering to improve the 2-PE production kinetics of the Synechococcus elongatus PCC 7942 p120 strain. Firstly, the growth and 2-PE productivity performances of the p120 recombinant strain were analyzed to highlight potential metabolic constraints. By implementing a BG11 medium doped with L-phenylalanine, we covered the metabolic burden to which the p120 strain is strongly subjected, when the 2-PE pathway expression is induced. Additionally, we further boosted the carbon flow into the Shikimate Pathway by overexpressing the native Shikimate Kinase in the Synechococcus elongatus PCC 7942 p120 strain (i.e., 2PE_aroK). The combination of these different approaches led to a 2-PE yield of 300 mg/gDW and a maximum 2-PE titer of 285 mg/L, 2.4-fold higher than that reported in literature for the p120 recombinant strain and, to our knowledge, the highest recorded for photosynthetic microorganisms, in photoautotrophic growth condition. Finally, this work provides the basis for further optimization of the process aimed at increasing 2-PE productivity and concentration, and could offer new insights about the use of cyanobacteria as appealing microbial cell factories for the synthesis of aromatic compounds.

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

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

Generation of miniploid cells and improved natural transformation procedure for a model cyanobacterium Synechococcus elongatus PCC 7942

The biotechnologically important and naturally transformable cyanobacterium, Synechococcus elongatus PCC 7942, possesses multiple genome copies irrespective of its growth rate or condition. Hence, segregating mutations across all genome copies typically takes several weeks. In this study, Synechococcus 7942 cultivation on a solid growth medium was optimised using different concentrations of agar, the addition of antioxidants, and overexpression of the catalase gene to facilitate the rapid acquisition of colonies and fully segregated lines. Synechococcus 7942 was grown at different temperatures and nutritional conditions. The miniploid cells were identified using flow cytometry and fluorimetry. The natural transformation was carried out using miniploid cells and validated with PCR and high performance liquid chromatography (HPLC). We identified that 0.35% agar concentration and 200 IU of catalase could improve the growth of Synechococcus 7942 on a solid growth medium. Furthermore, overexpression of a catalase gene enhanced the growth rate and supported diluted culture to grow on a solid medium. Our results reveal that high temperature and phosphate-depleted cells contain the lowest genome copies (2.4 ± 0.3 and 1.9 ± 0.2) and showed the potential to rapidly produce fully segregated mutants. In addition, higher antibiotic concentrations improve the selection of homozygous transformants while maintaining similar genome copies at a constant temperature. Based on our observation, we have an improved cultivation and natural transformation protocol for Synechococcus 7942 by optimising solid media culturing, generating low-ploidy cells that ultimately reduced the time required for the complete segregation of engineered lines.

Combinatorial CRISPR Interference Library for Enhancing 2,3-BDO Production and Elucidating Key Genes in Cyanobacteria

Cyanobacteria can convert CO₂ to chemicals such as 2,3-butanediol (2,3-BDO), rendering them promising for renewable production and carbon neutralization, but their applications are limited by low titers. To enhance cyanobacterial 2,3-BDO production, we developed a combinatorial CRISPR interference (CRISPRi) library strategy. We integrated the 2,3-BDO pathway genes and a CRISPRi library into the cyanobacterium PCC7942 using the orthogonal CRISPR system to overexpress pathway genes and attenuate genes that inhibit 2,3-BDO formation. The combinatorial CRISPRi library strategy allowed us to inhibit fbp, pdh, ppc, and sps (which catalyzes the synthesis of fructose-6-phosphate, acetyl-coenzyme A, oxaloacetate, and sucrose, respectively) at different levels, thereby allowing for rapid screening of a strain that enhances 2,3-BDO production by almost 2-fold to 1583.8 mg/L. Coupled with a statistical model, we elucidated that differentially inhibiting all the four genes enhances 2,3-BDO synthesis to varying degrees. fbp and pdh suppression exerted more profound effects on 2,3-BDO production than ppc and sps suppression, and these four genes can be repressed simultaneously without mutual interference. The CRISPRi library approach paves a new avenue to combinatorial metabolic engineering of cyanobacteria.

Kinetic modeling: A tool for temperature shift and feeding optimization in cell culture process development

Mammalian cells have dominated the biopharmaceutical industry for biotherapeutic protein production and tremendous efforts have been devoted to enhancing productivity during the cell culture process development. However, determining the optimal process conditions is still a huge challenge. Constrained by the limited resources and timeline, usually it is impossible to fully explore the optimal range of all process parameters (temperature, pH, dissolved oxygen, basal and feeding medium, additives, etc.). Kinetic modeling, which finds out the global optimum by systematically screening all potential conditions for cell culture process, provides a solution to this dilemma. However, studies on optimizing temperature shift and feeding strategies simultaneously using this approach have not been reported. In this study, we built up a kinetic model of fed-batch culture process for simultaneous optimization of temperature shift and feeding strategies. The fitting results showed high accuracy and demonstrated that the kinetic model can be used to describe the mammalian cell culture performance. In addition, five more fed-batch experiments were conducted to test this model's predicting power on different temperature shift and feeding strategies. It turned out that the predicted data matched well with experimental ones on viable cell density (VCD), metabolites, and titer for the entire culture duration and allowed selecting the same best condition with the experimental results. Therefore, adopting this approach can potentially reduce the number of experiments required for culture process optimization.

Thermodynamics contributes to high limonene productivity in cyanobacteria

Terpenoids are a large group of secondary metabolites with broad industrial applications. Engineering cyanobacteria is an attractive route for the sustainable production of commodity terpenoids. Currently, a major obstacle lies in the low productivity attained in engineered cyanobacterial strains. Traditional metabolic engineering to improve pathway kinetics has led to limited success in enhancing terpenoid productivity. In this study, we reveal thermodynamics as the main determinant for high limonene productivity in cyanobacteria. Through overexpressing the primary sigma factor, a higher photosynthetic rate was achieved in an engineered strain of Synechococcus elongatus PCC 7942. Computational modeling and wet lab analyses showed an increased flux toward both native carbon sink glycogen synthesis and the non-native limonene synthesis from photosynthate output. On the other hand, comparative proteomics showed decreased expression of terpene pathway enzymes, revealing their limited role in determining terpene flux. Lastly, growth optimization by enhancing photosynthesis has led to a limonene titer of 19 mg/L in 7 days with a maximum productivity of 4.3 mg/L/day. This study highlights the importance of enhancing photosynthesis and substrate input for the high productivity of secondary metabolic pathways, providing a new strategy for future terpenoid engineering in phototrophs.

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Pathway enzyme engineering marginally increases cyanobacterial terpene production.

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Sigma factor overexpression improves photosynthetic efficiency in cyanobacteria.

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Enhanced photosynthesis results in high limonene production in cyanobacteria.

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Enhanced photosynthesis provides high thermodynamic driving force for terpenes.

Introduction of NADH-dependent nitrate assimilation in Synechococcus sp. PCC 7002 improves photosynthetic production of 2-methyl-1-butanol and isobutanol

Cyanobacteria hold promise for renewable chemical production due to their photosynthetic nature, but engineered strains frequently display poor production characteristics. These difficulties likely arise in part due to the distinctive photoautotrophic metabolism of cyanobacteria. In this work, we apply a genome-scale metabolic model of the cyanobacteria Synechococus sp. PCC 7002 to identify strain designs accounting for this unique metabolism that are predicted to improve the production of various biofuel alcohols (e.g. 2-methyl-1-butanol, isobutanol, and 1-butanol) synthesized via an engineered biosynthesis pathway. Using the model, we identify that the introduction of a large, non-native NADH-demand into PCC 7002's metabolic network is predicted to enhance production of these alcohols by promoting NADH-generating reactions upstream of the production pathways. To test this, we construct strains of PCC 7002 that utilize a heterologous, NADH-dependent nitrite reductase in place of the native, ferredoxin-dependent enzyme to create an NADH-demand in the cells when grown on nitrate-containing media. We find that photosynthetic production of both isobutanol and 2-methyl-1-butanol is significantly improved in the engineered strain background relative to that in a wild-type background. We additionally identify that the use of high-nutrient media leads to a substantial prolongment of the production curve in our alcohol production strains. The metabolic engineering strategy identified and tested in this work presents a novel approach to engineer cyanobacterial production strains that takes advantage of a unique aspect of their metabolism and serves as a basis on which to further develop strains with improved production of these alcohols and related products.

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

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

Figures of Merit for Photocatalysis: Comparison of NiO/La-NaTaO3 and Synechocystis sp. PCC 6803 as a Semiconductor and a Bio-Photocatalyst for Water Splitting

While photocatalysis is considered a promising sustainable technology in the field of heterogeneous catalysis as well as biocatalysis, figures of merit (FOM) for comparing catalytic performance, especially between disciplines, are not well established. Here, photocatalytic water splitting was conducted using a semiconductor (NiO/La-NaTaO3) and a bio-photocatalyst (Synechocystis sp. PCC 6803) in the same setup under similar reaction conditions, eliminating the often ill-defined influence of the setup on the FOMs obtained. Comparing the results enables the critical evaluation of existing FOMs and a quantitative comparison of both photocatalytic systems. A single FOM is insufficient to compare the photocatalysts, instead a combination of multiple FOMs (reaction rate, photocatalytic space time yield and a redefined apparent quantum yield) is superior for assessing a variety of photocatalytic systems.

Computational modeling and evolutionary implications of biochemical reactions in bacterial microcompartments

Bacterial microcompartments (BMCs) are protein-encapsulated compartments found across at least 23 bacterial phyla. BMCs contain a variety of metabolic processes that share the commonality of toxic or volatile intermediates, oxygen-sensitive enzymes and cofactors, or increased substrate concentration for magnified reaction rates. These compartmentalized reactions have been computationally modeled to explore the encapsulated dynamics, ask evolutionary-based questions, and develop a more systematic understanding required for the engineering of novel BMCs. Many crucial aspects of these systems remain unknown or unmeasured, such as substrate permeabilities across the protein shell, feasibility of pH gradients, and transport rates of associated substrates into the cell. This review explores existing BMC models, dominated in the literature by cyanobacterial carboxysomes, and highlights potentially important areas for exploration.

Doing synthetic biology with photosynthetic microorganisms

The use of photosynthetic microbes as synthetic biology hosts for the sustainable production of commodity chemicals and even fuels has received increasing attention over the last decade. The number of studies published, tools implemented, and resources made available for microalgae have increased beyond expectations during the last few years. However, the tools available for genetic engineering in these organisms still lag those available for the more commonly used heterotrophic host organisms. In this mini-review, we provide an overview of the photosynthetic microbes most commonly used in synthetic biology studies, namely cyanobacteria, chlorophytes, eustigmatophytes and diatoms. We provide basic information on the techniques and tools available for each model group of organisms, we outline the state-of-the-art, and we list the synthetic biology tools that have been successfully used. We specifically focus on the latest CRISPR developments, as we believe that precision editing and advanced genetic engineering tools will be pivotal to the advancement of the field. Finally, we discuss the relative strengths and weaknesses of each group of organisms and examine the challenges that need to be overcome to achieve their synthetic biology potential.

Path to improving the life cycle and quality of genome-scale models of metabolism

Genome-scale models of metabolism (GEMs) are key computational tools for the systems-level study of metabolic networks. Here, we describe the "GEM life cycle," which we subdivide into four stages: inception, maturation, specialization, and amalgamation. We show how different types of GEM reconstruction workflows fit in each stage and proceed to highlight two fundamental bottlenecks for GEM quality improvement: GEM maturation and content removal. We identify common characteristics contributing to increasing quality of maturing GEMs drawing from past independent GEM maturation efforts. We then shed some much-needed light on the latent and unrecognized but pervasive issue of content removal, demonstrating the substantial effects of model pruning on its solution space. Finally, we propose a novel framework for content removal and associated confidence-level assignment which will help guide future GEM development efforts, reduce duplication of effort across groups, potentially aid automated reconstruction platforms, and boost the reproducibility of model development.

Resolving the Rules of Robustness and Resilience in Biology Across Scales

Why do some biological systems and communities persist while others fail? Robustness, a system's stability, and resilience, the ability to return to a stable state, are key concepts that span multiple disciplines within and outside the biological sciences. Discovering and applying common rules that govern the robustness and resilience of biological systems is a critical step toward creating solutions for species survival in the face of climate change, as well as the for the ever-increasing need for food, health, and energy for human populations. We propose that network theory provides a framework for universal scalable mathematical models to describe robustness and resilience and the relationship between them, and hypothesize that resilience at lower organization levels contribute to robust systems. Insightful models of biological systems can be generated by quantifying the mechanisms of redundancy, diversity, and connectivity of networks, from biochemical processes to ecosystems. These models provide pathways towards understanding how evolvability can both contribute to and result from robustness and resilience under dynamic conditions. We now have an abundance of data from model and non-model systems and the technological and computational advances for studying complex systems. Several conceptual and policy advances will allow the research community to elucidate the rules of robustness and resilience. Conceptually, a common language and data structure that can be applied across levels of biological organization needs to be developed. Policy advances such as cross-disciplinary funding mechanisms, access to affordable computational capacity, and the integration of network theory and computer science within the standard biological science curriculum will provide the needed research environments. This new understanding of biological systems will allow us to derive ever more useful forecasts of biological behaviors and revolutionize the engineering of biological systems that can survive changing environments or disease, navigate the deepest oceans, or sustain life throughout the solar system.

Genetic, Genomics, and Responses to Stresses in Cyanobacteria: Biotechnological Implications

Cyanobacteria are widely-diverse, environmentally crucial photosynthetic prokaryotes of great interests for basic and applied science. Work to date has focused mostly on the three non-nitrogen fixing unicellular species Synechocystis PCC 6803, Synechococcus PCC 7942, and Synechococcus PCC 7002, which have been selected for their genetic and physiological interests summarized in this review. Extensive "omics" data sets have been generated, and genome-scale models (GSM) have been developed for the rational engineering of these cyanobacteria for biotechnological purposes. We presently discuss what should be done to improve our understanding of the genotype-phenotype relationships of these models and generate robust and predictive models of their metabolism. Furthermore, we also emphasize that because Synechocystis PCC 6803, Synechococcus PCC 7942, and Synechococcus PCC 7002 represent only a limited part of the wide biodiversity of cyanobacteria, other species distantly related to these three models, should be studied. Finally, we highlight the need to strengthen the communication between academic researchers, who know well cyanobacteria and can engineer them for biotechnological purposes, but have a limited access to large photobioreactors, and industrial partners who attempt to use natural or engineered cyanobacteria to produce interesting chemicals at reasonable costs, but may lack knowledge on cyanobacterial physiology and metabolism.

Genome-scale metabolic model of the diatom Thalassiosira pseudonana highlights the importance of nitrogen and sulfur metabolism in redox balance

Diatoms are unicellular photosynthetic algae known to secrete organic matter that fuels secondary production in the ocean, though our knowledge of how their physiology impacts the composition of dissolved organic matter remains limited. Like all photosynthetic organisms, their use of light for energy and reducing power creates the challenge of avoiding cellular damage. To better understand the interplay between redox balance and organic matter secretion, we reconstructed a genome-scale metabolic model of Thalassiosira pseudonana strain CCMP 1335, a model for diatom molecular biology and physiology, with a 60-year history of studies. The model simulates the metabolic activities of 1,432 genes via a network of 2,792 metabolites produced through 6,079 reactions distributed across six subcellular compartments. Growth was simulated under different steady-state light conditions (5–200 μmol photons m^-2 s^-1) and in a batch culture progressing from exponential growth to nitrate-limitation and nitrogen-starvation. We used the model to examine the dissipation of reductants generated through light-dependent processes and found that when available, nitrate assimilation is an important means of dissipating reductants in the plastid; under nitrate-limiting conditions, sulfate assimilation plays a similar role. The use of either nitrate or sulfate uptake to balance redox reactions leads to the secretion of distinct organic nitrogen and sulfur compounds. Such compounds can be accessed by bacteria in the surface ocean. The model of the diatom Thalassiosira pseudonana provides a mechanistic explanation for the production of ecologically and climatologically relevant compounds that may serve as the basis for intricate, cross-kingdom microbial networks. Diatom metabolism has an important influence on global biogeochemistry; metabolic models of marine microorganisms link genes to ecosystems and may be key to integrating molecular data with models of ocean biogeochemistry.

Deciphering and engineering photosynthetic cyanobacteria for heavy metal bioremediation

Environmental pollution caused by heavy metals has received worldwide attentions due to their ubiquity, poor degradability and easy bioaccumulation in host cells. As one potential solution, photosynthetic cyanobacteria have been considered as promising remediation chassis and widely applied in various bioremediation processes of heavy-metals. Meanwhile, deciphering resistant mechanisms and constructing tolerant chassis towards heavy metals could greatly contribute to the successful application of the cyanobacteria-based bioremediation in the future. In this review, first we summarized recent application of cyanobacteria in heavy metals bioremediation using either live or dead cells. Second, resistant mechanisms and strategies for enhancing cyanobacterial bioremediation of heavy metals were discussed. Finally, potential challenges and perspectives for improving bioremediation of heavy metals by cyanobacteria were presented.

Role of type I NADH dehydrogenase in Synechocystis sp. PCC 6803 under phycobilisome excited red light

Cyanobacterial type I NADH dehydrogenase (NDH-1) is involved in various bioenergetic reactions including respiration, cyclic electron transport (CET), and CO₂ uptake. The role of NDH-1 is usually minor under normal growth conditions and becomes important under stress conditions. However, in our previous study, flux balance analysis (FBA) simulation predicted that the drive of NDH-1 as CET pathway with a photosystem (PS) I/PSII excitation ratio around 1.0 contributes to achieving an optimal specific growth rate. In this study, to experimentally elucidate the predicted functions of NDH-1, first, we measured the PSI/PSII excitation ratios of Synechocystis sp. PCC 6803 grown under four types of spectral light conditions. The specific growth rate was the highest and PSI/PSII excitation ratio was with 0.88 under the single-peak light at 630 nm (Red1). Considering this measured excitation ratios, FBA simulation predicted that NDH-1-dependent electron transport was the major pathway under Red1. Moreover, in actual culture, an NDH-1 deletion strain had slower growth rate than that of wild type only under Red1 light condition. Therefore, we experimentally demonstrated that NDH-1 plays an important role under optimal light conditions such as Red1 light, where Synechocystis exhibits the highest specific growth rate and PSI/PSII excitation ratio of around 1.0.

Dynamic Allocation of Carbon Storage and Nutrient-Dependent Exudation in a Revised Genome-Scale Model of Prochlorococcus

Microbial life in the oceans impacts the entire marine ecosystem, global biogeochemistry and climate. The marine cyanobacterium Prochlorococcus, an abundant component of this ecosystem, releases a significant fraction of the carbon fixed through photosynthesis, but the amount, timing and molecular composition of released carbon are still poorly understood. These depend on several factors, including nutrient availability, light intensity and glycogen storage. Here we combine multiple computational approaches to provide insight into carbon storage and exudation in Prochlorococcus. First, with the aid of a new algorithm for recursive filling of metabolic gaps (ReFill), and through substantial manual curation, we extended an existing genome-scale metabolic model of Prochlorococcus MED4. In this revised model (iSO595), we decoupled glycogen biosynthesis/degradation from growth, thus enabling dynamic allocation of carbon storage. In contrast to standard implementations of flux balance modeling, we made use of forced influx of carbon and light into the cell, to recapitulate overflow metabolism due to the decoupling of photosynthesis and carbon fixation from growth during nutrient limitation. By using random sampling in the ensuing flux space, we found that storage of glycogen or exudation of organic acids are favored when the growth is nitrogen limited, while exudation of amino acids becomes more likely when phosphate is the limiting resource. We next used COMETS to simulate day-night cycles and found that the model displays dynamic glycogen allocation and exudation of organic acids. The switch from photosynthesis and glycogen storage to glycogen depletion is associated with a redistribution of fluxes from the Entner–Doudoroff to the Pentose Phosphate pathway. Finally, we show that specific gene knockouts in iSO595 exhibit dynamic anomalies compatible with experimental observations, further demonstrating the value of this model as a tool to probe the metabolic dynamic of Prochlorococcus.

Characterization of Light-Enhanced Respiration in Cyanobacteria

In eukaryotic algae, respiratory O₂ uptake is enhanced after illumination, which is called light-enhanced respiration (LER). It is likely stimulated by an increase in respiratory substrates produced during photosynthetic CO₂ assimilation and function in keeping the metabolic and redox homeostasis in the light in eukaryotic cells, based on the interactions among the cytosol, chloroplasts, and mitochondria. Here, we first characterize LER in photosynthetic prokaryote cyanobacteria, in which respiration and photosynthesis share their metabolisms and electron transport chains in one cell. From the physiological analysis, the cyanobacterium Synechocystis sp. PCC 6803 performs LER, similar to eukaryotic algae, which shows a capacity comparable to the net photosynthetic O₂ evolution rate. Although the respiratory and photosynthetic electron transports share the interchain, LER was uncoupled from photosynthetic electron transport. Mutant analyses demonstrated that LER is motivated by the substrates directly provided by photosynthetic CO₂ assimilation, but not by glycogen. Further, the light-dependent activation of LER was observed even with exogenously added glucose, implying a regulatory mechanism for LER in addition to the substrate amounts. Finally, we discuss the physiological significance of the large capacity of LER in cyanobacteria and eukaryotic algae compared to those in plants that normally show less LER.

Heterologous Expression of Cryptomaldamide in a Cyanobacterial Host

Filamentous marine cyanobacteria make a variety of bioactive molecules that are produced by polyketide synthases, nonribosomal peptide synthetases, and hybrid pathways that are encoded by large biosynthetic gene clusters. These cyanobacterial natural products represent potential drug leads; however, thorough pharmacological investigations have been impeded by the limited quantity of compound that is typically available from the native organisms. Additionally, investigations of the biosynthetic gene clusters and enzymatic pathways have been difficult due to the inability to conduct genetic manipulations in the native producers. Here we report a set of genetic tools for the heterologous expression of biosynthetic gene clusters in the cyanobacteria Synechococcus elongatus PCC 7942 and Anabaena (Nostoc) PCC 7120. To facilitate the transfer of gene clusters in both strains, we engineered a strain of Anabaena that contains S. elongatus homologous sequences for chromosomal recombination at a neutral site and devised a CRISPR-based strategy to efficiently obtain segregated double recombinant clones of Anabaena. These genetic tools were used to express the large 28.7 kb cryptomaldamide biosynthetic gene cluster from the marine cyanobacterium Moorena (Moorea) producens JHB in both model strains. S. elongatus did not produce cryptomaldamide; however, high-titer production of cryptomaldamide was obtained in Anabaena. The methods developed in this study will facilitate the heterologous expression of biosynthetic gene clusters isolated from marine cyanobacteria and complex metagenomic samples.

Current Status and Future Strategies to Increase Secondary Metabolite Production from Cyanobacteria

Cyanobacteria, given their ability to produce various secondary metabolites utilizing solar energy and carbon dioxide, are a potential platform for sustainable production of biochemicals. Until now, conventional metabolic engineering approaches have been applied to various cyanobacterial species for enhanced production of industrially valued compounds, including secondary metabolites and non-natural biochemicals. However, the shortage of understanding of cyanobacterial metabolic and regulatory networks for atmospheric carbon fixation to biochemical production and the lack of available engineering tools limit the potential of cyanobacteria for industrial applications. Recently, to overcome the limitations, synthetic biology tools and systems biology approaches such as genome-scale modeling based on diverse omics data have been applied to cyanobacteria. This review covers the synthetic and systems biology approaches for advanced metabolic engineering of cyanobacteria.

Engineering biology approaches for food and nutrient production by cyanobacteria

As photoautotrophic organisms, cyanobacteria capture and store solar energy in the form of biomass. Cyanobacterial biomass has been an important component of diet and nutrition in several regions for centuries. Synthetic biology strategies are currently being applied to increase the yield and productivity of cyanobacterial biomass by optimizing solar energy utilization and CO₂ fixation rates for carbon storage. Likewise, engineering cyanobacteria as cellular factories to synthesize carbohydrates, amino acids, proteins, lipids and fatty acids is providing an attractive way to sustainably produce food and nutrients for human consumption. In this review, we have summarized recent progress in both aspects and prospective trends under development.

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

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

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.

Systematic identification and elimination of flux bottlenecks in the aldehyde production pathway of Synechococcus elongatus PCC 7942

Isotopically nonstationary metabolic flux analysis (INST-MFA) provides a versatile platform to quantitatively assess in vivo metabolic activities of autotrophic systems. By applying INST-MFA to recombinant aldehyde-producing cyanobacteria, we identified metabolic alterations that correlated with increased strain performance in order to guide rational metabolic engineering. We identified four reactions adjacent to the pyruvate node that varied significantly with increasing aldehyde production: pyruvate kinase (PK) and acetolactate synthase (ALS) fluxes were directly correlated with product formation, while pyruvate dehydrogenase (PDH) and phosphoenolpyruvate carboxylase (PPC) fluxes were inversely correlated. Overexpression of enzymes for PK or ALS did not result in further improvements to the previous best-performing strain, while downregulation of PDH expression (through antisense RNA expression) or PPC flux (through expression of the reverse reaction, phosphoenolpyruvate carboxykinase) provided significant improvements. These results illustrate the potential of INST-MFA to enable a systematic approach for iterative identification and removal of pathway bottlenecks in autotrophic host cells.

BiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree

The BiGG Models knowledge base (http://bigg.ucsd.edu) is a centralized repository for high-quality genome-scale metabolic models. For the past 12 years, the website has allowed users to browse and search metabolic models. Within this update, we detail new content and features in the repository, continuing the original effort to connect each model to genome annotations and external databases as well as standardization of reactions and metabolites. We describe the addition of 31 new models that expand the portion of the phylogenetic tree covered by BiGG Models. We also describe new functionality for hosting multi-strain models, which have proven to be insightful in a variety of studies centered on comparisons of related strains. Finally, the models in the knowledge base have been benchmarked using Memote, a new community-developed validator for genome-scale models to demonstrate the improving quality and transparency of model content in BiGG Models.

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.