Analysis of Carbohydrate Storage Granules in the Diazotrophic Cyanobacterium Cyanothece sp. PCC 7822.

The unicellular diazotrophic cyanobacteria of the genus Cyanothece demonstrate oscillations in nitrogenase activity and H2 production when grown under 12 h light–12 h dark cycles. We established that Cyanothece sp. PCC 7822 allows for the construction of knock-out mutants and our objective was to improve the growth characteristics of this strain and to identify the nature of the intracellular storage granules. We report the physiological and morphological effects of reduction in nitrate and phosphate concentrations in BG-11 media on this strain. We developed a series of BG-11-derived growth media and monitored batch culture growth, nitrogenase activity and nitrogenase-mediated hydrogen production, culture synchronicity, and intracellular storage content. Reduction in NaNO3 and K2HPO4 concentrations from 17.6 and 0.23 to 4.41 and 0.06 mM, respectively, improved growth characteristics such as cell size and uniformity, and enhanced the rate of cell division. Cells grown in this low NP BG-11 were less complex, a parameter that related to the composition of the intracellular storage granules. Cells grown in low NP BG-11 had less polyphosphate, fewer polyhydroxybutyrate granules and many smaller granules became evident. Biochemical analysis and transmission electron microscopy using the histocytochemical PATO technique demonstrated that these small granules contained glycogen. The glycogen levels and the number of granules per cell correlated nicely with a 2.3 to 3.3-fold change from the minimum at L0 to the maximum at D0. The differences in granule morphology and enzymes between Cyanothece ATCC 51142 and CyanothecePCC 7822 provide insights into the formation of large starch-like granules in some cyanobacteria

An integrated approach to develop unicellular cyanobacteria for biofuel production

The goal of engineering cyanobacteria to produce of high-value compounds, like biofuels, comes with many challenges. Physiological characteristics such as growth rate, culture heterogeneity, and nutrient storage can impact the facility of using specific organisms for targeted chemical production. In addition, an increasing body of evidence suggests that post-transcriptional regulatory mechanisms are prevalent in cyanobacteria. The unicellular genus Cyanothece consists of diazotrophic cyanobacteria that can fix atmospheric N2 in addition to CO2. Cyanothece strain PCC 7822 is the only member of this genus that is amenable to genetic modification. This strain stores a significant amount of nutrients in intracellular granules, extrudes exopolysacchrides making macromolecule isolation difficult and cells to aggregate. Also, little is known about the expression dynamics of metabolic pathways that could be used for biofuel production. In order to further develop this strain for the industrial purposes, an integrated approach was applied consisting of three main areas; physiology – how cells grow and store carbohydrates under different conditions; gene expression – utilizing transcriptomics and proteomics to understand how different metabolic reactions occur within the cell; and genetics – monitoring the effects of different genetic modifications in order to generate novel metabolic capabilities. Nitrate and phosphate levels in BG-11 growth media were reduced and flow cytometry and transmission electron microscopy were used to determine the effects on various physiological properties. Reducing these nutrient concentrations led to a decrease in intracellular storage, improved the growth rate, and increased culture homogeneity. Global metabolic dynamics were quantitated during standard growth conditions at four time points across a 24 hour light-dark cycle through a parallel transcriptomics and proteomics investigation. This is the first time such a study has been described for Cyanothece 7822. In addition, glycogen biosynthesis mutants were generated to study the enzymatic basis for variation in carbohydrate storage among cyanobacterial species and to test whether or not altering the structure of stored sugar granules can influence the production of downstream compound

A Hard Day’s Night: Cyanobacteria in Diel Cycles

Cyanobacteria are photosynthetic prokaryotes that are influential in global geochemistry and are promising candidates for industrial applications. Because the livelihood of cyanobacteria is directly dependent upon light, a comprehensive understanding of metabolism in these organisms requires taking into account the effects of day-night transitions and circadian regulation. These events synchronize intracellular processes with the solar day. Accordingly, metabolism is controlled and structured differently in cyanobacteria than in heterotrophic bacteria. Thus, the approaches applied to engineering heterotrophic bacteria will need to be revised for the cyanobacterial chassis. Here, we summarize important findings related to diurnal metabolism in cyanobacteria and present open questions in the field

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

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

Genome-wide fitness assessment during diurnal growth reveals an expanded role of the cyanobacterial circadian clock protein KaiA.

The recurrent pattern of light and darkness generated by Earth's axial rotation has profoundly influenced the evolution of organisms, selecting for both biological mechanisms that respond acutely to environmental changes and circadian clocks that program physiology in anticipation of daily variations. The necessity to integrate environmental responsiveness and circadian programming is exemplified in photosynthetic organisms such as cyanobacteria, which depend on light-driven photochemical processes. The cyanobacterium Synechococcus elongatus PCC 7942 is an excellent model system for dissecting these entwined mechanisms. Its core circadian oscillator, consisting of three proteins, KaiA, KaiB, and KaiC, transmits time-of-day signals to clock-output proteins, which reciprocally regulate global transcription. Research performed under constant light facilitates analysis of intrinsic cycles separately from direct environmental responses but does not provide insight into how these regulatory systems are integrated during light-dark cycles. Thus, we sought to identify genes that are specifically necessary in a day-night environment. We screened a dense bar-coded transposon library in both continuous light and daily cycling conditions and compared the fitness consequences of loss of each nonessential gene in the genome. Although the clock itself is not essential for viability in light-dark cycles, the most detrimental mutations revealed by the screen were those that disrupt KaiA. The screen broadened our understanding of light-dark survival in photosynthetic organisms, identified unforeseen clock-protein interaction dynamics, and reinforced the role of the clock as a negative regulator of a nighttime metabolic program that is essential for S. elongatus to survive in the dark

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

The model cyanobacterium, Synechococcus elongatus PCC 7942, is a genetically tractable obligate phototroph that is being developed for the bioproduction of high-value chemicals. Genome-scale models (GEMs) have been successfully used to assess and engineer cellular metabolism; however, GEMs of phototrophic metabolism have been limited by the lack of experimental datasets for model validation and the challenges of incorporating photon uptake. Here, we develop a GEM of metabolism in S. elongatus using random barcode transposon site sequencing (RB-TnSeq) essential gene and physiological data specific to photoautotrophic metabolism. The model explicitly describes photon absorption and accounts for shading, resulting in the characteristic linear growth curve of photoautotrophs. GEM predictions of gene essentiality were compared with data obtained from recent dense-transposon mutagenesis experiments. This dataset allowed major improvements to the accuracy of the model. Furthermore, discrepancies between GEM predictions and the in vivo dataset revealed biological characteristics, such as the importance of a truncated, linear TCA pathway, low flux toward amino acid synthesis from photorespiration, and knowledge gaps within nucleotide metabolism. Coupling of strong experimental support and photoautotrophic modeling methods thus resulted in a highly accurate model of S. elongatus metabolism that highlights previously unknown areas of S. elongatus biology