Flux balance analysis of cyanobacteria reveals selective use of photosynthetic electron transport components under different spectral light conditions

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.

Unveiling metabolic pathways involved in the extreme desiccation tolerance of an Atacama cyanobacterium

Gloeocapsopsis dulcis strain AAB1 is an extremely xerotolerant cyanobacterium isolated from the Atacama Desert (i.e., the driest and oldest desert on Earth) that holds astrobiological significance due to its ability to biosynthesize compatible solutes at ultra-low water activities. We sequenced and assembled the G. dulcis genome de novo using a combination of long- and short-read sequencing, which resulted in high-quality consensus sequences of the chromosome and two plasmids. We leveraged the G. dulcis genome to generate a genome-scale metabolic model (iGd895) to simulate growth in silico. iGd895 represents, to our knowledge, the first genome-scale metabolic reconstruction developed for an extremely xerotolerant cyanobacterium. The model's predictive capability was assessed by comparing the in silico growth rate with in vitro growth rates of G. dulcis, in addition to the synthesis of trehalose. iGd895 allowed us to explore simulations of key metabolic processes such as essential pathways for water-stress tolerance, and significant alterations to reaction flux distribution and metabolic network reorganization resulting from water limitation. Our study provides insights into the potential metabolic strategies employed by G. dulcis, emphasizing the crucial roles of compatible solutes, metabolic water, energy conservation, and the precise regulation of reaction rates in their adaptation to water stress.

Electron transport in cyanobacterial thylakoid membranes: Are cyanobacteria simple models for photosynthetic organisms?

Cyanobacteria are structurally the simplest oxygenic phototrophs, which makes it difficult to understand the regulation of photosynthesis because the photosynthetic and respiratory processes share the same thylakoid membranes and cytosolic space. This review aimed to summarise the molecular mechanisms and in vivo activities of electron transport in cyanobacterial thylakoid membranes based on the latest progress in photosynthesis research in cyanobacteria. Photosynthetic linear electron transport for CO2 assimilation has the dominant electron flux in the thylakoid membranes. The capacity of O2 photoreduction in cyanobacteria is comparable to the photosynthetic CO2 assimilation, which is mediated by flavodiiron proteins. Additionally, cyanobacterial thylakoid membranes harbour the significant electron flux of respiratory electron transport through a homologue of respiratory complex I, which is also recognized as the part of cyclic electron transport chain if it is coupled with photosystem I in the light. Further, O2-independent alternative electron transports through hydrogenase and nitrate reductase function with reduced ferredoxin as the electron donor. Whereas all these electron transports are recently being understood one by one, the complexity as the whole regulatory system remains to be uncovered in near future.

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

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

Genome-scale metabolic network model of Eriocheir sinensis icrab4665 and nutritional requirement analysis

Background

Genome-scale metabolic network models (GEMs) provide an efficient platform for the comprehensive analysis the physical and biochemical functions of organisms due to their systematic perspective on the study of metabolic processes. Eriocheir sinensis is an important economic species cultivated on a large scale because it is delicious and nutritious and has a high economic value. Feed improvement is one of the important methods to improve the yield of E. sinensis and control water pollution caused by the inadequate absorption of feed.

Results

In this study, a GEM of E. sinensis, icrab4665, was reconstructed based on the transcriptome sequencing, combined with KEGG database, literature and experimental data. The icrab4665 comprised 4665 unigenes, 2060 reactions and 1891 metabolites, which were distributed in 12 metabolic subsystems and 113 metabolic pathways. The model was used to predict the optimal nutrient requirements of E. sinensis in feed, and suggestions for feed improvement were put forward based on the simulation results. The simulation results showed that arginine, methionine, isoleucine and phenylalanine had more active metabolism in E. sinensis. It was suggested that the amount of these essential amino acids should be proportionally higher than that of other amino acids in the feed to ensure the amino acid metabolism of E. sinensis. On the basis of the simulation results, we further suggested increasing the amount of linoleic acid, EPA and DHA in the feed to ensure the intake of essential fatty acids for the growth of E. sinensis and promote the accumulation of cell substances. In addition, the amounts of zinc and selenium in the feed were also suggested to be properly increased to ensure the basic metabolism and growth demand of E. sinensis.

Conclusion

The largest GEM of E. sinensis was reconstructed and suggestions were provide for the improvement of feed contents based on the model simulation. This study promoted the exploration of feed optimization for aquatic crustaceans from in vivo and in silico. The results provided guidance for improving the feed proportion for E. sinensis, which is of great significance to improve its yield and economic value.

Protocol for hybrid flux balance, statistical, and machine learning analysis of multi-omic data from the cyanobacterium Synechococcus sp. PCC 7002

Combining a computational framework for flux balance analysis with machine learning improves the accuracy of predicting metabolic activity across conditions, while enabling mechanistic interpretation. This protocol presents a guide to condition-specific metabolic modeling that integrates regularized flux balance analysis with machine learning approaches to extract key features from transcriptomic and fluxomic data. We demonstrate the protocol as applied to Synechococcus sp. PCC 7002; we also outline how it can be adapted to any species or community with available multi-omic data. For complete details on the use and execution of this protocol, please refer to Vijayakumar et al. (2020).

Proteome analysis of response to different spectral light irradiation in Synechocystis sp. PCC 6803

In cyanobacteria, it is known that the excitation ratios of photosystem (PS) I and PSII changes with the wavelength of irradiated light due to mobile phycobilisome (PBS) and spillover, affecting the photosynthetic ATP/NADPH synthesis ratio and metabolic flux state. However, the mechanisms by which these changes are controlled have not been well studied. In this study, we performed a targeted proteomic analysis of Synechocystis sp. PCC 6803 under different spectral light conditions to clarify the regulation mechanisms of mobile PBS, spillover and metabolisms under different light qualities at the protein level. The results showed an increase in the amount of proteins mainly involved in CO₂ fixation under Red1 light conditions with a high specific growth rate, suggesting that the rate of intracellular metabolism is controlled by the rate of carbon uptake, not by changes in the amount of each enzyme. Correlation analysis between protein levels and PSI/PSII excitation ratios revealed that PsbQUY showed high correlations and significantly increased under Blue and Red2 light conditions, where the PSI/PSII excitation ratio was higher due to spillover. In the strains lacking the genes encoding these proteins, a decrease in the PSI/PSII excitation ratio was observed, suggesting that PsbQUY contribute to spillover occurrence. SIGNIFICANCE: In cyanobacteria, the photosynthetic apparatus's responses, such as state transition [mobile PBS and spillover], occur due to the intensity and wavelength of irradiated light, resulting in changes in photosynthetic electron transport and metabolic flux states. Previous studies have analyzed the response of Synechocystis sp. PCC 6803 to light intensity from various directions, but only spectroscopic analysis of the photosynthetic apparatus has been done on the response to changes in the wavelength of irradiated light. This study analyzed the response mechanisms of mobile PBS, spillover, photosynthetic, and metabolic systems in Synechocystis sp. PCC 6803 under six different spectral light conditions by a targeted proteomic analysis. As a result, many proteins were successfully quantified, and the metabolic enzymes and photosynthetic apparatus were analyzed using an integrated approach. Principal component and correlation analyses and volcano plots revealed that the PSII subunits PsbQ, PsbU, and PsbY have a strong correlation with the PSI/PSII excitation ratio and contribute to spillover occurrence. Thus, statistical analysis based on proteome data revealed that PsbQ, PsbU, and PsbY are involved in spillover, as revealed by spectroscopic analysis.

Over-expression of an electron transport protein OmcS provides sufficient NADH for D-lactate production in cyanobacterium

BACKGROUND

An efficient supply of reducing equivalent is essential for chemicals production by engineered microbes. In phototrophic microbes, the NADPH generated from photosynthesis is the dominant form of reducing equivalent. However, most dehydrogenases prefer to utilize NADH as a cofactor. Thus, sufficient NADH supply is crucial to produce dehydrogenase-derived chemicals in cyanobacteria. Photosynthetic electron is the sole energy source and excess electrons are wasted in the light reactions of photosynthesis.

RESULTS

Here we propose a novel strategy to direct the electrons to generate more ATP from light reactions to provide sufficient NADH for lactate production. To this end, we introduced an electron transport protein-encoding gene omcS into cyanobacterium Synechococcus elongatus UTEX 2973 and demonstrated that the introduced OmcS directs excess electrons from plastoquinone (PQ) to photosystem I (PSI) to stimulate cyclic electron transfer (CET). As a result, an approximately 30% increased intracellular ATP, 60% increased intracellular NADH concentrations and up to 60% increased biomass production with fourfold increased D-lactate production were achieved. Comparative transcriptome analysis showed upregulation of proteins involved in linear electron transfer (LET), CET, and downregulation of proteins involved in respiratory electron transfer (RET), giving hints to understand the increased levels of ATP and NADH.

CONCLUSIONS

This strategy provides a novel orthologous way to improve photosynthesis via enhancing CET and supply sufficient NADH for the photosynthetic production of chemicals.

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

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

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.

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.

Estimation of linear and cyclic electron flows in photosynthesis based on 13C-metabolic flux analysis

Photosynthetic organisms produce ATP and NADPH using light as an energy source and further utilize these cofactors during metabolism. Photosynthesis involves linear and cyclic electron flows; as the cyclic electron flow produces ATP more effectively than the linear electron flow without NADPH, the cell efficiently adjusts ATP and NADPH production using the two different pathways. Nevertheless, direct measurement of ATP and NADPH production during photosynthesis has been difficult. In the present study, the photosynthetic ATP and NADPH production rates of Synechocystis sp. PCC 6803 under three different single peak wavelength lights (blue: 470 nm, R630: 630 nm, and R680: 680 nm) were evaluated based on ¹³C-metabolic flux analysis (¹³C-MFA) by considering the mass balance of ATP and NADPH between photosynthesis and metabolism. The ratios of ATP/NADPH production via photosynthesis were estimated as 3.13, 1.70, and 2.10 under blue, R630, and R680 light conditions, respectively. Moreover, the linear and cyclic electron flow ratios were estimated to be 1.1-2.2, 0.2-0.5, and 0.5-1.0 under blue, R630, and R680 light conditions, respectively. The predicted linear and cyclic electron flow ratios were consistent with the excitation ratio between photosystems I and II, as observed in the steady-state fluorescence spectra.

A Hybrid Flux Balance Analysis and Machine Learning Pipeline Elucidates Metabolic Adaptation in Cyanobacteria

Machine learning has recently emerged as a promising tool for inferring multi-omic relationships in biological systems. At the same time, genome-scale metabolic models (GSMMs) can be integrated with such multi-omic data to refine phenotypic predictions. In this work, we use a multi-omic machine learning pipeline to analyze a GSMM of Synechococcus sp. PCC 7002, a cyanobacterium with large potential to produce renewable biofuels. We use regularized flux balance analysis to observe flux response between conditions across photosynthesis and energy metabolism. We then incorporate principal-component analysis, k-means clustering, and LASSO regularization to reduce dimensionality and extract key cross-omic features. Our results suggest that combining metabolic modeling with machine learning elucidates mechanisms used by cyanobacteria to cope with fluctuations in light intensity and salinity that cannot be detected using transcriptomics alone. Furthermore, GSMMs introduce critical mechanistic details that improve the performance of omic-based machine learning methods.

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A pipeline for metabolic modeling in Synechococcus sp. PCC 7002 is presented

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Metabolic fluxes display clear differences in pathway activity across conditions

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Omic-informed GSMMs provide critical mechanistic details within machine learning

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Combining GSMM and machine learning improves methods based on transcriptomics alone