13 C flux analysis of cyanobacterial metabolism

Daylength variation affects growth, photosynthesis, leaf metabolism, partitioning, and metabolic fluxes

Daylength, a seasonal and latitudinal variable, exerts a substantial impact on plant growth. However, the relationship between daylength and growth is nonproportional, suggesting the existence of adaptive mechanisms. Thus, our study aimed to comprehensively investigate the adaptive strategies employed by plants in response to daylength variation. We grew false flax (Camelina sativa) plants, a model oilseed crop, under long-day (LD) and short-day (SD) conditions and used growth measurements, gas exchange measurements, and isotopic labeling techniques, including 13C, 14C, and 2H2O, to determine responses to different daylengths. Our findings revealed that daylength influences various growth parameters, photosynthetic physiology, carbon partitioning, metabolic fluxes, and metabolite levels. SD plants employed diverse mechanisms to compensate for reduced CO2 fixation in the shorter photoperiod. These mechanisms included enhanced photosynthetic rates and reduced respiration in the light (RL), leading to increased shoot investment. Additionally, SD plants exhibited reduced rates of the glucose 6-phosphate (G6P) shunt and greater partitioning of sugars into starch, thereby sustaining carbon availability during the longer night. Isotopic labeling results further demonstrated substantial alterations in the partitioning of amino acids and TCA cycle intermediates between rapidly and slowly turning over pools. Overall, the results point to multiple developmental, physiological, and metabolic ways in which plants adapt to different daylengths to maintain growth.

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.

Isotopically non-stationary 13 C metabolic flux analysis of two closely related fast-growing cyanobacteria, Synechococcus elongatus PCC 11801 and 11802

Synechococcus elongatus PCC 11801 and 11802 are closely related cyanobacterial strains that are fast-growing and tolerant to high light and temperature. These strains hold significant promise as chassis for photosynthetic production of chemicals from carbon dioxide. A detailed quantitative understanding of the central carbon pathways would be a reference for future metabolic engineering studies with these strains. We conducted isotopic non-stationary 13 C metabolic flux analysis to quantitively assess the metabolic potential of these two strains. This study highlights key similarities and differences in the central carbon flux distribution between these and other model/non-model strains. The two strains demonstrated a higher Calvin-Benson-Bassham (CBB) cycle flux coupled with negligible flux through the oxidative pentose phosphate pathway and the photorespiratory pathway and lower anaplerosis fluxes under photoautotrophic conditions. Interestingly, PCC 11802 shows the highest CBB cycle and pyruvate kinase flux values among those reported in cyanobacteria. The unique tricarboxylic acid (TCA) cycle diversion in PCC 11801 makes it ideal for the large-scale production of TCA cycle-derived chemicals. Additionally, dynamic labeling transients were measured for intermediates of amino acid, nucleotide, and nucleotide sugar metabolism. Overall, this study provides the first detailed metabolic flux maps of S. elongatus PCC 11801 and 11802, which may aid metabolic engineering efforts in these strains.

13CO2-labelling and Sampling in Algae for Flux Analysis of Photosynthetic and Central Carbon Metabolism

The flux in photosynthesis can be studied by performing 13CO2 pulse labelling and analysing the temporal labelling kinetics of metabolic intermediates using gas or liquid chromatography linked to mass spectrometry. Metabolic flux analysis (MFA) is the primary approach for analysing metabolic network function and quantifying intracellular metabolic fluxes. Different MFA approaches differ based on the metabolic state (steady vs. non-steady state) and the use of stable isotope tracers. The main methodology used to investigate metabolic systems is metabolite steady state associated with stable isotope labelling experiments. Specifically, in biological systems like photoautotrophic organisms, isotopic non-stationary 113C metabolic flux analysis at metabolic steady state with transient isotopic labelling (13C-INST-MFA) is required. The common requirement for metabolic steady state, alongside its very short half-timed reactions, complicates robust MFA of photosynthetic metabolism. While custom gas chambers design has addressed these challenges in various model plants, no similar tools were developed for liquid photosynthetic cultures (e.g., algae, cyanobacteria), where diffusion and equilibration of inorganic carbon species in the medium entails a new dimension of complexity. Recently, a novel tailor-made microfluidics labelling system has been introduced, supplying short 13CO2 pulses at steady state, and resolving fluxes across most photosynthetic metabolic pathways in algae. The system involves injecting algal cultures and medium containing pre-equilibrated inorganic 13C into a microfluidic mixer, followed by rapid metabolic quenching, enabling precise seconds-level label pulses. This was complemented by a 13CO2-bubbling-based open labelling system (photobioreactor), allowing long pulses (minutes-hours) required for investigating fluxes into central C metabolism and major products. This combined labelling procedure provides a comprehensive fluxome cover for most algal photosynthetic and central C metabolism pathways, thus allowing comparative flux analyses across algae and plants.

Isotopically non-stationary metabolic flux analysis of heterotrophic Arabidopsis thaliana cell cultures

Fluxes are the ultimate phenotype of metabolism and their accurate quantification is fundamental to any understanding of metabolic networks. Steady state metabolic flux analysis has been the method of choice for quantifying fluxes in heterotrophic cells, but it is unable to measure fluxes during short-lived metabolic states, such as a transient oxidative load. Isotopically non-stationary metabolic flux analysis (INST-MFA) can be performed over shorter timescales (minutes - hours) and might overcome this limitation. INST-MFA has recently been applied to photosynthesising leaves, but agriculturally important tissues such as roots and storage organs, or plants during the night are heterotrophic. Here we outline the application of INST-MFA to heterotrophic plant cells. Using INST-MFA we were able to identify changes in the fluxes supported by phosphoenolpyruvate carboxylase and malic enzyme under oxidative load, highlighting the potential of INST-MFA to measure fluxes during short-lived metabolic states. We discuss the challenges in applying INST-MFA, and highlight further development required before it can be routinely used to quantify fluxes in heterotrophic plant cells.

Metabolomics-based engineering for biofuel and bio-based chemical production in microalgae and cyanobacteria: A review

Metabolomics, an essential tool in modern synthetic biology based on the design-build-test-learn platform, is useful for obtaining a detailed understanding of cellular metabolic mechanisms through comprehensive analyses of the metabolite pool size and its dynamic changes. Metabolomics is critical to the design of a rational metabolic engineering strategy by determining the rate-limiting reaction and assimilated carbon distribution in a biosynthetic pathway of interest. Microalgae and cyanobacteria are promising photosynthetic producers of biofuels and bio-based chemicals, with high potential for developing a bioeconomic society through bio-based carbon neutral manufacturing. Metabolomics technologies optimized for photosynthetic organisms have been developed and utilized in various microalgal and cyanobacterial species. This review provides a concise overview of recent achievements in photosynthetic metabolomics, emphasizing the importance of microalgal and cyanobacterial cell factories that satisfy industrial requirements.

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.

Probing Light-Dependent Regulation of the Calvin Cycle Using a Multi-Omics Approach

Photoautotrophic microorganisms are increasingly explored for the conversion of atmospheric carbon dioxide into biomass and valuable products. The Calvin-Benson-Bassham (CBB) cycle is the primary metabolic pathway for net CO₂ fixation within oxygenic photosynthetic organisms. The cyanobacteria, Synechocystis sp. PCC 6803, is a model organism for the study of photosynthesis and a platform for many metabolic engineering efforts. The CBB cycle is regulated by complex mechanisms including enzymatic abundance, intracellular metabolite concentrations, energetic cofactors and post-translational enzymatic modifications that depend on the external conditions such as the intensity and quality of light. However, the extent to which each of these mechanisms play a role under different light intensities remains unclear. In this work, we conducted non-targeted proteomics in tandem with isotopically non-stationary metabolic flux analysis (INST-MFA) at four different light intensities to determine the extent to which fluxes within the CBB cycle are controlled by enzymatic abundance. The correlation between specific enzyme abundances and their corresponding reaction fluxes is examined, revealing several enzymes with uncorrelated enzyme abundance and their corresponding flux, suggesting flux regulation by mechanisms other than enzyme abundance. Additionally, the kinetics of ¹³C labeling of CBB cycle intermediates and estimated inactive pool sizes varied significantly as a function of light intensity suggesting the presence of metabolite channeling, an additional method of flux regulation. These results highlight the importance of the diverse methods of regulation of CBB enzyme activity as a function of light intensity, and highlights the importance of considering these effects in future kinetic models.

Photorespiration: The Futile Cycle?

Photorespiration, or C₂ photosynthesis, is generally considered a futile cycle that potentially decreases photosynthetic carbon fixation by more than 25%. Nonetheless, many essential processes, such as nitrogen assimilation, C₁ metabolism, and sulfur assimilation, depend on photorespiration. Most studies of photosynthetic and photorespiratory reactions are conducted with magnesium as the sole metal cofactor despite many of the enzymes involved in these reactions readily associating with manganese. Indeed, when manganese is present, the energy efficiency of these reactions may improve. This review summarizes some commonly used methods to quantify photorespiration, outlines the influence of metal cofactors on photorespiratory enzymes, and discusses why photorespiration may not be as wasteful as previously believed.

A guide to metabolic flux analysis in metabolic engineering: Methods, tools and applications

The field of metabolic engineering is primarily concerned with improving the biological production of value-added chemicals, fuels and pharmaceuticals through the design, construction and optimization of metabolic pathways, redirection of intracellular fluxes, and refinement of cellular properties relevant for industrial bioprocess implementation. Metabolic network models and metabolic fluxes are central concepts in metabolic engineering, as was emphasized in the first paper published in this journal, "Metabolic fluxes and metabolic engineering" (Metabolic Engineering, 1: 1-11, 1999). In the past two decades, a wide range of computational, analytical and experimental approaches have been developed to interrogate the capabilities of biological systems through analysis of metabolic network models using techniques such as flux balance analysis (FBA), and quantify metabolic fluxes using constrained-based modeling approaches such as metabolic flux analysis (MFA) and more advanced experimental techniques based on the use of stable-isotope tracers, i.e. ¹³C-metabolic flux analysis (¹³C-MFA). In this review, we describe the basic principles of metabolic flux analysis, discuss current best practices in flux quantification, highlight potential pitfalls and alternative approaches in the application of these tools, and give a broad overview of pragmatic applications of flux analysis in metabolic engineering practice.

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.

Characterizing glucose, illumination, and nitrogen-deprivation phenotypes of Synechocystis PCC6803 with Raman spectroscopy

Background

Synechocystis sp. PCC6803 is a model cyanobacterium that has been studied widely and is considered for metabolic engineering applications. Here, Raman spectroscopy and Raman chemometrics (Rametrix™) were used to (i) study broad phenotypic changes in response to growth conditions, (ii) identify phenotypic changes associated with its circadian rhythm, and (iii) correlate individual Raman bands with biomolecules and verify these with more accepted analytical methods.

Methods

Synechocystis cultures were grown under various conditions, exploring dependencies on light and/or external carbon and nitrogen sources. The Rametrix™ LITE Toolbox for MATLAB® was used to process Raman spectra and perform principal component analysis (PCA) and discriminant analysis of principal components (DAPC). The Rametrix™ PRO Toolbox was used to validate these models through leave-one-out routines that classified a Raman spectrum when growth conditions were withheld from the model. Performance was measured by classification accuracy, sensitivity, and specificity. Raman spectra were also subjected to statistical tests (ANOVA and pairwise comparisons) to identify statistically relevant changes in Synechocystis phenotypes. Finally, experimental methods, including widely used analytical and spectroscopic assays were used to quantify the levels of glycogen, fatty acids, amino acids, and chlorophyll a for correlations with Raman data.

Results

PCA and DAPC models produced distinct clustering of Raman spectra, representing multiple Synechocystis phenotypes, based on (i) growth in the presence of 5 mM glucose, (ii) illumination (dark, light/dark [12 h/12 h], and continuous light at 20 µE), (iii) nitrogen deprivation (0–100% NaNO₃ of native BG-11 medium in continuous light), and (iv) throughout a 24 h light/dark (12 h/12 h) circadian rhythm growth cycle. Rametrix™ PRO was successful in identifying glucose-induced phenotypes with 95.3% accuracy, 93.4% sensitivity, and 96.9% specificity. Prediction accuracy was above random chance values for all other studies. Circadian rhythm analysis showed a return to the initial phenotype after 24 hours for cultures grown in light/dark (12 h/12 h) cycles; this did not occur for cultures grown in the dark. Finally, correlation coefficients (R > 0.7) were found for glycogen, all amino acids, and chlorophyll a when comparing specific Raman bands to other experimental results.

Applications of stable isotope-based metabolomics and fluxomics toward synthetic biology of cyanobacteria

Unique features of cyanobacteria (e.g., photosynthesis and nitrogen fixation) make them potential candidates for production of biofuels and other value-added biochemicals. As prokaryotes, they can be readily engineered using synthetic and systems biology tools. Metabolic engineering of cyanobacteria for the synthesis of desired compounds requires in-depth knowledge of central carbon and nitrogen metabolism, pathway fluxes, and their regulation. Metabolomics and fluxomics offer the comprehensive analysis of metabolism by directly characterizing the biochemical activities of cells. This information is acquired by measuring the abundance of key metabolites and their rates of interconversion, which can be achieved by labeling cells with stable isotopes, quantifying metabolite pool sizes and isotope incorporation by gas chromatography/liquid chromatography-mass spectrometry GC/LC-MS or nuclear magnetic resonance (NMR), and mathematical modeling to estimate in vivo metabolic fluxes. Herein, we review progress that has been made to adapt metabolomics and fluxomics tools to examine model cyanobacterial species. We summarize the application of metabolic flux analysis (MFA) strategies to identify metabolic bottlenecks that can be targeted to boost cell growth, improve stress tolerance, or enhance biochemical production in cyanobacteria. Despite the advances in metabolomics, fluxomics, and other synthetic and systems biology tools during the past years, further efforts are required to increase our understanding of cyanobacterial metabolism in order to create efficient photosynthetic hosts for the production of value-added compounds. This article is categorized under: Laboratory Methods and Technologies > Metabolomics Biological Mechanisms > Metabolism Analytical and Computational Methods > Analytical Methods.

Combining isotopically non-stationary metabolic flux analysis with proteomics to unravel the regulation of the Calvin-Benson-Bassham cycle in Synechocystis sp. PCC 6803

Photosynthetic microorganisms are increasingly being investigated as a sustainable alternative to existing bio-industrial processes, converting CO₂ into desirable end products without the use of carbohydrate feedstock. The Calvin-Benson-Bassham (CBB) cycle is the main pathway of carbon fixation metabolism in photosynthetic organisms. In this study, we analyzed the metabolic fluxes in two strains of the unicellular cyanobacterium Synechocystis sp. PCC 6803 (Synechocystis) that overexpressed fructose-1,6/sedoheptulose-1,7-bisphosphatase (FBP/SBPase) and transketolase (TK), respectively. These two potential carbon flux control enzymes in the CBB cycle had previously been shown to improve biomass accumulation when overexpressed under air and low light (15 μmol m^-2 s^-1) conditions (Liang and Lindblad, 2016). We measured the growth rates of Synechocystis under atmospheric and high (3% v/v) CO₂ conditions at 80 μmol m^-2 s^-1. Surprisingly, the cells overexpressing transketolase (tktA) demonstrated no significant increase in growth rates when CO₂ was increased, suggesting an altered carbon flux distribution and a potential metabolic bottleneck in carbon fixation. Moreover, the tktA strain had an increased susceptibility to oxidative stress under high light as revealed by its chlorotic phenotype under high light conditions. In contrast, the fructose-1,6/sedoheptulose-1,7-bisphosphatase (70glpX) and wild-type cells demonstrated increases in growth rates as expected. To investigate the disparate phenotypical responses of these different Synechocystis strains, isotopically non-stationary metabolic flux analysis (INST-MFA) was used to estimate the carbon flux distribution of tktA, 70glpX, and a kanamycin-resistant control (Km), under atmospheric conditions. In addition, untargeted label-free proteomics, which can detect changes in relative enzymatic abundance, was employed to study the possible effects caused by overexpressing each enzyme. Fluxomic and proteomic results indicated a decrease in oxidative pentose phosphate pathway activity when either FBP/SBPase or TK were overexpressed, resulting in increased carbon fixation efficiency. These results are an example of the integration of multiple omic-level experimental techniques and can be used to guide future metabolic engineering efforts to improve performances and efficiencies.

Membrane-Inlet Mass Spectrometry Enables a Quantitative Understanding of Inorganic Carbon Uptake Flux and Carbon Concentrating Mechanisms in Metabolically Engineered Cyanobacteria

Photosynthesis uses solar energy to drive inorganic carbon (Ci) uptake, fixation, and biomass formation. In cyanobacteria, Ci uptake is assisted by carbon concentrating mechanisms (CCM), and CO₂ fixation is catalyzed by RubisCO in the Calvin-Benson-Bassham (CBB) cycle. Understanding the regulation that governs CCM and CBB cycle activities in natural and engineered strains requires methods and parameters that quantify these activities. Here, we used membrane-inlet mass spectrometry (MIMS) to simultaneously quantify Ci concentrating and fixation processes in the cyanobacterium Synechocystis 6803. By comparing cultures acclimated to ambient air conditions to cultures transitioning to high Ci conditions, we show that acclimation to high Ci involves a concurrent decline of Ci uptake and fixation parameters. By varying light input, we show that both CCM and CBB reactions become energy limited under low light conditions. A strain over-expressing the gene for the CBB cycle enzyme fructose-bisphosphate aldolase showed higher CCM and carbon fixation capabilities, suggesting a regulatory link between CBB metabolites and CCM capacity. While the engineering of an ethanol production pathway had no effect on CCM or carbon fixation parameters, additional fructose-bisphosphate aldolase gene over-expression enhanced both activities while simultaneously increasing ethanol productivity. These observations show that MIMS can be a useful tool to study the extracellular Ci flux and how CBB metabolites regulate Ci uptake and fixation.

Kinetic modeling of the Calvin cycle identifies flux control and stable metabolomes in Synechocystis carbon fixation

Biological fixation of atmospheric CO2 via the Calvin-Benson-Bassham cycle has massive ecological impact and offers potential for industrial exploitation, either by improving carbon fixation in plants and autotrophic bacteria, or by installation into new hosts. A kinetic model of the Calvin-Benson-Bassham cycle embedded in the central carbon metabolism of the cyanobacterium Synechocystis sp. PCC 6803 was developed to investigate its stability and underlying control mechanisms. To reduce the uncertainty associated with a single parameter set, random sampling of the steady-state metabolite concentrations and the enzyme kinetic parameters was employed, resulting in millions of parameterized models which were analyzed for flux control and stability against perturbation. Our results show that the Calvin cycle had an overall high intrinsic stability, but a high concentration of ribulose 1,5-bisphosphate was associated with unstable states. Low substrate saturation and high product saturation of enzymes involved in highly interconnected reactions correlated with increased network stability. Flux control, that is the effect that a change in one reaction rate has on the other reactions in the network, was distributed and mostly exerted by energy supply (ATP), but also by cofactor supply (NADPH). Sedoheptulose 1,7-bisphosphatase/fructose 1,6-bisphosphatase, fructose-bisphosphate aldolase, and transketolase had a weak but positive effect on overall network flux, in agreement with published observations. The identified flux control and relationships between metabolite concentrations and system stability can guide metabolic engineering. The kinetic model structure and parameterizing framework can be expanded for analysis of metabolic systems beyond the Calvin cycle.

Predicting the metabolic capabilities of Synechococcus elongatus PCC 7942 adapted to different light regimes

There is great interest in engineering photoautotrophic metabolism to generate bioproducts of societal importance. Despite the success in employing genome-scale modeling coupled with flux balance analysis to engineer heterotrophic metabolism, the lack of proper constraints necessary to generate biologically realistic predictions has hindered broad application of this methodology to phototrophic metabolism. Here we describe a methodology for constraining genome-scale models of photoautotrophy in the cyanobacteria Synechococcus elongatus PCC 7942. Experimental photophysiology parameters coupled to genome-scale flux balance analysis resulted in accurate predictions of growth rates and metabolic reaction fluxes at low and high light conditions. Additionally, by constraining photon uptake fluxes, we characterized the metabolic cost of excess excitation energy. The predicted energy fluxes were consistent with known light-adapted phenotypes in cyanobacteria. Finally, we leveraged the modeling framework to characterize existing photoautotrophic and photomixtotrophic engineering strategies for 2,3-butanediol production in S. elongatus. This methodology, applicable to genome-scale modeling of all phototrophic microorganisms, can facilitate the use of flux balance analysis in the engineering of light-driven metabolism.

Isotopically nonstationary metabolic flux analysis (INST-MFA): putting theory into practice

Typically, ¹³C flux analysis relies on assumptions of both metabolic and isotopic steady state. If metabolism is steady but isotope labeling is not allowed to fully equilibrate, isotopically nonstationary metabolic flux analysis (INST-MFA) can be used to estimate fluxes. This requires solution of differential equations that describe the time-dependent labeling of network metabolites, while iteratively adjusting the flux and pool size parameters to match the transient labeling measurements. INST-MFA holds a number of unique advantages over approaches that rely solely upon steady-state isotope enrichments. First, INST-MFA can be applied to estimate fluxes in autotrophic systems, which consume only single-carbon substrates. Second, INST-MFA is ideally suited to systems that label slowly due to the presence of large intermediate pools or pathway bottlenecks. Finally, INST-MFA provides increased measurement sensitivity to estimate reversible exchange fluxes and metabolite pool sizes, which represents a potential framework for integrating metabolomic analysis with ¹³C flux analysis. This review highlights the unique capabilities of INST-MFA, describes newly available software tools that automate INST-MFA calculations, presents several practical examples of recent INST-MFA applications, and discusses the technical challenges that lie ahead.

Engineering photosynthetic production of L-lysine

L-lysine and other amino acids are commonly produced through fermentation using strains of heterotrophic bacteria such as Corynebacterium glutamicum. Given the large amount of sugar this process consumes, direct photosynthetic production is intriguing alternative. In this study, we report the development of a cyanobacterium, Synechococcus sp. strain PCC 7002, capable of producing L-lysine with CO2 as the sole carbon-source. We found that heterologous expression of a lysine transporter was required to excrete lysine and avoid intracellular accumulation that correlated with poor fitness. Simultaneous expression of a feedback inhibition resistant aspartate kinase and lysine transporter were sufficient for high productivities, but this was also met with a decreased chlorophyll content and reduced growth rates. Increasing the reductant supply by using NH4+, a more reduced nitrogen source relative to NO3-, resulted in a two-fold increase in productivity directing 18% of fixed carbon to lysine. Given this advantage, we demonstrated lysine production from media formulated with a municipal wastewater treatment sidestream as a nutrient source for increased economic and environmental sustainability. Based on our results, we project that Synechococcus sp. strain PCC 7002 could produce lysine at areal productivities approaching that of sugar cane to lysine via fermentation using non-agricultural lands and low-cost feedstocks.

Isotopically Nonstationary Metabolic Flux Analysis (INST-MFA) of Photosynthesis and Photorespiration in Plants

Photorespiration is a central component of photosynthesis; however to better understand its role it should be viewed in the context of an integrated metabolic network rather than a series of individual reactions that operate independently. Isotopically nonstationary ¹³C metabolic flux analysis (INST-MFA), which is based on transient labeling studies at metabolic steady state, offers a comprehensive platform to quantify plant central metabolism. In this chapter, we describe the application of INST-MFA to investigate metabolism in leaves. Leaves are an autotrophic tissue, assimilating CO₂ over a diurnal period implying that the metabolic steady state is limited to less than 12 h and thus requiring an INST-MFA approach. This strategy results in a comprehensive unified description of photorespiration, Calvin cycle, sucrose and starch synthesis, tricarboxylic acid (TCA) cycle, and amino acid biosynthetic fluxes. We present protocols of the experimental aspects for labeling studies: transient ¹³CO₂ labeling of leaf tissue, sample quenching and extraction, mass spectrometry (MS) analysis of isotopic labeling data, measurement of sucrose and amino acids in vascular exudates, and provide details on the computational flux estimation using INST-MFA.

Metabolic flux analyses of Pseudomonas aeruginosa cystic fibrosis isolates

Pseudomonas aeruginosa is a metabolically versatile wide-ranging opportunistic pathogen. In humans P. aeruginosa causes infections of the skin, urinary tract, blood, and the lungs of Cystic Fibrosis patients. In addition, P. aeruginosa's broad environmental distribution, relatedness to biotechnologically useful species, and ability to form biofilms have made it the focus of considerable interest. We used ¹³C metabolic flux analysis (MFA) and flux balance analysis to understand energy and redox production and consumption and to explore the metabolic phenotypes of one reference strain and five strains isolated from the lungs of cystic fibrosis patients. Our results highlight the importance of the oxidative pentose phosphate and Entner-Doudoroff pathways in P. aeruginosa growth. Among clinical strains we report two divergent metabolic strategies and identify changes between genetically related strains that have emerged during a chronic infection of the same patient. MFA revealed that the magnitude of fluxes through the glyoxylate cycle correlates with growth rates.

Transcriptional regulator PrqR plays a negative role in glucose metabolism and oxidative stress acclimation in Synechocystis sp. PCC 6803

Plant and cyanobacteria can perceive signals from soluble sugar and reactive oxygen species (ROS) and then coordinate gene expression under stress acclimation, but the underlying mechanism remains unclear. In this study, we found that the transcriptional factor PrqR (Slr0895) in Synechocystis can perceive signals from ROS generated after shifting from prolonged darkness with glucose into high-light. The deletion mutant (DprqR) showed increased growth rate and decreased ROS content, whereas the complementary strain (CprqR) restored the growth characteristics, phenotypes and ROS status of WT, thereby establishing PrqR as a negative regulator of ROS.LC/GC-MS-based metabolic profiling also showed active ROS mitigation in DprqR mutant. Further study by qRT-PCR, ChIP-PCR and deletion of both prqR and prqA (DprqR-DprqA mutant) revealed that PrqR exerts this negative regulation of ROS removal by controlling the expression of sodB and prqA (slr0896). Furthermore, PrqR also found to control glucose metabolism by regulating a positive regulator of glucose metabolism, sigE, and its regulons. Results suggest that PrqR was involved in perceiving signals from ROS under physiological condition, as well as in regulating stress removal and glucose metabolism.