Rerouting of metabolism into desired cellular products by nutrient stress

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Rerouting of metabolism into desired cellular products by nutrient stress: Fluxes reveal the selected pathways in cyanobacterial photosynthesis Xiao Qian, YUAN ZHANG, Desmond S Lun, and G Charles Dismukes ACS Synth. Biol., Just Accepted Manuscript • DOI: 10.1021/acssynbio.8b00116 • Publication Date (Web): 04 Apr 2018 Downloaded from http://pubs.acs.org on April 8, 2018

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ACS Synthetic Biology

Rerouting of metabolism into desired cellular products by nutrient stress: Fluxes reveal the selected pathways in cyanobacterial photosynthesis Xiao Qian1, Yuan Zhang1, Desmond S. Lun2,4,5*, & G. Charles Dismukes1,3* 1

Waksman Institute, Rutgers University, New Brunswick, NJ 08854, USA.

2

Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102,

USA. 3

Department of Chemistry & Chemical Biology, Rutgers University, Piscataway, NJ 08854,

USA 4

Department of Computer Science, Rutgers University, Camden, NJ 08102, USA

5

Department of Plant Biology, Rutgers University, New Brunswick, NJ 08901, USA

*

Corresponding author(s)

[email protected] [email protected] [email protected] [email protected]

ABSTRACT Boosting cellular growth rates while redirecting metabolism to make desired products are the preeminent goals sought by gene engineering of photoautotrophs, yet so far has under achieved owing to lack of understanding of the functional pathways and their choke points. Here we apply

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a 13C mass isotopic method (INST-MFA) to quantify instantaneous fluxes of metabolites during photoautotrophic growth. INST-MFA determines the globally most accurate set of absolute fluxes for each metabolite from a finite set of measured 13C-isotopomer fluxes by minimizing the sum of squared residuals between experimental and predicted mass isotopomers. We show that the widely-observed shift in biomass composition in cyanobacteria, demonstrated here with Synechococcus sp. PCC 7002, favoring glycogen synthesis during nitrogen starvation is caused by: 1) increased flux through a bottleneck step in gluconeogenesis (3PG GAP/DHAP), and 2) flux overflow through a previously unrecognized hybrid gluconeogenesis-pentose phosphate (hGPP) pathway. Our data suggest the slower growth rate and biomass accumulation under N starvation is due to a reduced carbon fixation rate and a reduced flux of carbon into amino acid precursors. Additionally, 13C flux from α-ketoglutarate to succinate is demonstrated to occur via succinic semialdehyde, an alternative to the conventional TCA cycle, in Synechococcus 7002 under photoautotrophic condition. Novel finding: pyruvate and oxaloacetate are synthesized mainly by malate dehydrogenase with minimal flux into acetyl coenzyme-A via pyruvate dehydrogenase. Nutrient stress induces major shifts in fluxes into new pathways that deviate from historical metabolic pathways derived from model bacteria. Key words: cyanobacteria, INST-MFA, photosynthesis, nitrogen starvation, hGPP pathway, malic cyclic route

Cyanobacteria and microalgae are becoming commercially important for multiple applications. They have been developed for wastewater treatment [1], biomass for biofuels [2] as feed [3] and other high value biochemicals [4]. The prokaryotic cyanobacteria are valued in


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particular for fast growth rate, smaller genomes, diversity of genetic tools and ease of transformation. One example is the recently discovered Synechococcus elongates UTEX 2973, which has the fastest photoautotrophic doubling time on record (2.3 hours), and can be genetically modified with ease [5]. However, cyanobacteria are ecologically and metabolically diverse such that combining growth productivity and metabolic selectivity for making desired products requires understanding how to control both in a single organism. This has led to a threestep strategy for strain improvement: 1) exploit the physiological potential to environmental stress (metabolic selection), 2) random mutagenesis and phenotypic selection, and 3) targeted metabolic engineering. Measurements of the amounts of nutrients consumed during growth (CHNOPS elemental precursors, e.g. glucose, CO2, etc.) and the resulting product yields (lipids, storage carbohydrates, proteins, pigments, and total biomass) give steady-state pool sizes and rates, but do not provide information on the instantaneous fluxes of intermediate metabolites that are continuously made and consumed during growth. These fluxes dictate where rate-limiting kinetic bottlenecks in biosynthetic metabolism are located and thus are the focal point for targeted engineering using the tools of synthetic biology. By feeding cultures with 13C-labeled substrates and analyzing the 13

C metabolite labeling patterns under the steady-state conditions, other useful information can

be obtained, such as qualitative changes in pathway contributions under different growth conditions [6-8]. However, to determine actual fluxes of individual metabolites, concentration changes must be determined rapidly in real-time and then analyzed using kinetic network models that include all interacting metabolites. Metabolic flux analysis (MFA) is a mathematical methodology that can provide such comprehensive quantitative data [9-11]. MFA associates several parameters with a biochemical network, and then computes the rates of all reactions in



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the metabolic map that are in best agreement with the measured data. MFA uses as input: steadystate

13

C labeling patterns, metabolite pool sizes, terminal product formation rates, nutrient

uptake rates, and cellular biomass compositions. MFA has been applied to microorganisms to measure carbon flux distributions under heterotrophic or mixotrophic growth conditions [12, 13]. Obligate photoautotrophs, however, assimilate carbon solely from CO2 and yield a uniform steady-state

13

C-labeling pattern of all metabolites. In this case, conventional MFA cannot be

used to compute global carbon fluxes of obligate photoautotrophs. Shastri & Morgan [14] first proposed a theoretical design for

13

C MFA experiments applicable to photoautotrophic

microorganisms, using 13C-labeling rates during the transient period. Young et al. [15] named the method isotopically nonstationary metabolic flux analysis (INST-MFA). The INST-MFA method measures the rate of incorporation of 13C into metabolites following a step change from unlabeled to labeled CO2, which differs from conventional MFA that uses steady-state

13

C-

labeling patterns. There have been several applications of INST-MFA to diverse biological systems [16-19]. So far, only three isotopically nonstationary metabolic flux studies of microalgal photoautotrophic growth have been published. Martzolff et al. focused on demonstrating a controlled photo-bioreactor system [20]. Actual photoautotrophic

13

C flux data

from a freshwater cyanobacterium, Synechocystis sp. PCC 6803 (hereby Synechocystis 6803), were first published by Young et al. [15]. These authors reported inefficiencies in photoautotrophic growth attributed to the oxidative pentose phosphate (OPP) pathway and pyruvate

metabolism

involving

the

oxaloacetate-decarboxylating

activity

of

malate

dehydrogenase. Later on, photoautotrophic flux data from a green algal strain, Chlorella protothecoides, were published by Wu et al. [21]. Results of this study reveal negligible photorespiratory fluxes and a metabolically low activity TCA cycle in photoautotrophic C.


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protothecoides. These two studies demonstrated the significant potential of this underutilized method. Nutrient deprivation under photoautotrophic conditions has been widely applied to increase glycogen accumulation in cyanobacteria [22, 23] and lipid content in microalgae, albeit invariably with severely reduced growth rates and biomass yields. In a fast growing model cyanobacterium strain Synechococcus sp. PCC 7002 (hereby Synechococcus 7002), nitrogen deprived photoautotrophic cultures have shown large increases in carbohydrates (12% 60 %) and decreases in proteins (71% 33%) during N starved photoautotrophic growth [24]. A major unmet need is to measure the changes of metabolite fluxes under nitrogen deprivation and to understand the factors controlling the interconnected metabolic map responsible for these changes. For this reason, we have chosen Synechococcus 7002 for a comprehensive MFA study. Synechococcus 7002 has a well-studied genomic database and mature transformation protocol [25, 26]. Previously, we and other labs have reported that the carbon metabolite pool sizes of photoautotrophically assimilated CO2 products are affected by nitrogen source and availability in microalgae and were implicated in the distribution of products [7, 27-30]. Here we extend this to include

13

C kinetics using the INST-MFA method by fitting all

13

C fluxes to a complete

metabolic reaction network. We have discovered that increased glycogen synthesis under nitrogen deprivation is caused by increased gluconeogenic flux at the 3PG GAP/DHAP enzymatically catalyzed steps, and by additional flux through a hybrid gluconeogenesis-pentose phosphate (hGPP) pathway. In addition, under nitrogen deprivation, we quantified a novel cyclic route, PEP OXA MAL PYR PEP, that potentially maintains the intracellular ATP/NAD(P)H ratio. Lastly, we demonstrate significant flux through a newly discovered enzyme that completes the previously broken TCA cycle in this microorganism.



METHODS & MATERIALS Culture growth and experimental conditions. Wild-type Synechococcus 7002 was grown in A+ medium (A medium [31] supplemented with 10 mM of NaNO3). Photoautotrophic growth was determined spectrophotometrically by measuring light scattering at 730 nm (OD730 nm). For inoculation, 100 mL of culture was first grown to OD730 nm ~2.0, concentrated by centrifugation, and then resuspended in 400 mL of A or A+ medium supplemented with 25 mM of NaH12CO3 in a photo-bioreactor (Photon System Instruments, Model FMT 150/400). This photo-bioreactor operates as a turbidostat, maintaining constant cell density, and therefore constant light intensity. Cells were adapted to either 10 mM of nitrate (+N), or without a nitrogen source (-N) for 24 hours at constant light intensity of 60 µE/m2/s at 38 ºC, mixed by continuous air bubbling. In both conditions, the culture densities were maintained at OD730 nm of 0.50 ± 0.05. Following this acclimation, the cultures were ready for 13C labeling experiments.

Intracellular metabolite extraction and analysis. We used a previous method [32] to determine steady-state metabolite pool sizes. To determine transient 13C-labeling of metabolites, we adapted the protocol from Young et al. [15]. In brief, 5 mL of culture were quenched by mixing with 10 mL of cold extraction solution (30% methanol in water at -20 ºC), then centrifuged (15 min at 7500 X g and 2 ºC), and the metabolites were extracted from the cell pellet with cold extraction solution (600 µL of 80% methanol in water at -20 ºC). The former method has a smaller loss of metabolites during the sample handling, while the latter method achieves faster quenching that is preferred for the time sensitive 13C-labeling experiments. Samples were then analyzed by the LC-MS/MS method described in Bennette et al. [32].


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Carbon labeling experiment. To quantify reaction fluxes, the concentrations (Figure 3) and labeling dynamics (Figure 4) of 17 metabolites were measured at +N and –N conditions, respectively. Before the introduction of NaH13CO3, 2 mL of culture were removed for the metabolite pool size measurement, and another 5 mL of culture were extracted for the zero-time point T0. Then, 10 mL of 1 M NaH13CO3 were injected into the bioreactor culture with rapid mixing. Subsequently, 5 mL of culture was withdrawn and rapidly quenched and measured as described above at time points of 0, 0.17, 0.5, 1, 2, 4, 8, 12, 20 and 30 minutes.

Isotopically nonstationary metabolic flux analysis (INST-MFA). We implemented the INSTMFA approach described by Young et al. in this study [15]. This approach estimates intracellular absolute metabolic fluxes based on the isotope labeling dynamics of intracellular metabolites and measured pool sizes (Figure 1). This approach relies on an elementary metabolite unit (EMU) decomposition of the underlying isotopomer network to efficiently simulate the effects of varying fluxes on the labeling state of measurable metabolites [33]. Measured carbon consumption rates for +N and -N conditions (1.33 mmol/gDW/h and 0.58 mmol/gDW/h, respectively) were set as a known parameter in the simulation. Four parameters including metabolic fluxes were estimated by minimizing the lack-of-fit between experimentally measured and computationally simulated mass isotopomer distributions (MIDs) using least-squares regression. The three other parameters are: 1) the pool sizes of unmeasured metabolites in the map (see Figure 3); 2) a dilution parameter for each metabolite between 0 and 100% that represents the fraction of the metabolite permanently not labeled; and 3) an unknown intracellular ratio of 12CO2/13CO2 was assumed but is linearly proportional to the amount of 13Cbicarbonate used. All four parameters of the isotopomer network model were adjusted using a



Levenberg-Marquardt algorithm until optimal agreement with experimental data was obtained [34]. The minimization was done using the interior point algorithm implemented by the function fmincon in Matlab R2015b (Mathworks, Natick, Mass.) with 100 randomly selected starting points. Flux evaluation was repeated a minimum of 50 times from random initial values to obtain best-fit estimates. All results were subjected to a χ2 statistical test to assess goodness-of-fit, and accurate 95% confidence intervals were computed for all estimated parameters by evaluating the sensititvity of the SSR to parameter variations [35]. Confidence intervals for each parameter were estimated by Monte Carlo simulation, as described by Antoniewicz et al. [35].

RESULTS Requirement for stable cultures for INST-MFA analysis. Ensuring the culture remains in steady state after the injection of NaH13CO3 is critical to the validity of the flux measurements. To determine valid experimental conditions, pool sizes of 12 metabolites (Figure 2) were measured at time points of 0, 1, 2, 4, 8, and 20 minutes after the injection of 25 mM of NaHCO3 in both batch cultures and bioreactor cultures. This dilution increases the total ionic strength of the growth medium by 1% from 2.350 M to 2.375 M, and therefore the additional energy burden on cells should be small. To ensure that CO2 saturation is maintained during NaHCO3 injection, we compared cultures grown in an optically thin bioreactor to those grown in batch cultures. The intracellular stability test found that metabolite pools were more stable using the bioreactor culture than batch cultures during the selected 20-minute kinetic profile. The average RMS deviation of the 12 metabolite concentrations from their average linear best fit lines was determined to be ± 13 % (the smallest was ± 6% and the largest was ± 26%) (Figure 2). Pool


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sizes of the metabolites changed more significantly in the batch culture samples (Figure S1). Accordingly, we report only the bioreactor data in this study.

How N nutrient availability changes carbon metabolite pool sizes. Pool sizes of 22 metabolites were measured just prior to each labeling experiment for both +N and -N cultures and are depicted as bar plots in Figure 3 and numerically tabulated in Table SI. These data reveal which metabolites accumulate or decrease in response to N availability under steady state growth, and thus which pathways and branching points are most responsive to change. Specifically, three metabolite loci drew immediate attention: 1) input to glycogen synthesis, 2) input to the OPP pathway, and 3) metabolites that link the TCA cycle with nitrogen assimilation (Figure 3). The first carbon precursor that is committed exclusively to glycogen synthesis, ADPGLC, had a 10fold higher pool in the -N culture, while the immediate upstream precursors (G1P and G6P) had small insignificant differences, thus signaling possible blockage downstream in glycogen metabolism. In the +N culture, 6PG, the first intermediate of the OPP pathway made from these same two precursors, had a 10-fold higher pool size. The metabolites on the counter clockwise side of the TCA cycle (AKG, SUCC, and MAL) accumulated upon N deprivation, while the opposite is true for the metabolites on the clockwise side of the TCA cycle (ACA, CIT) (Figure 3). TCA metabolites involved in nitrogen assimilation (GLU and GLN) were depleted under N starvation (Figure 3). These coordinated changes indicate a change of carbon flux distribution at the AKG branch upon nitrogen deprivation, as described in the Discussion. How N nutrient availability changes shows the

13

13

C-labeling kinetics of carbon metabolites. Figure 4

C-labeling kinetics of each metabolite (the average of all

from photoautotrophic uptake of

13

13

C isotopomers), arising

CO2 (individual isotopomers are shown in Figure S2).



Changes in 13C-labeling kinetics under the two conditions were identified at four metabolic loci: large changes (200-400%) occur in all measured TCA metabolites (CIT, AKG, SUCC, MAL) and N assimilation (GLU). Smaller changes (25-50%) occurred at glycogen synthesis (ADPGLC) and the OPP pathway (6PG, E4P, X5P).

13

C-labeling occurred much more slowly for all TCA

metabolites in the -N culture. Among all measured TCA metabolites, MAL was labeled the slowest by far under –N and this rate was 4.9-fold slower than under +N, over the first 8 minutes. By contrast, the rate of AKG labelling did not change in the first 8 minutes, but was slower in –N media after this induction period. These changes are important as discussed later.

Quantitative analysis of carbon flux distribution. Figure 5 summarizes the fluxes derived from 13C-labelling. The statistical confidences of all the flux values were listed in Supplementary File #2 in the format of “ub” (upper boundary) and “lb” (lower boundary). Such boundaries are the limits of the 95% confidence intervals calculated by Monte Carlo simulation (Antoniewicz et al., 2016). Compared to the +N culture, the -N culture showed significantly slower absolute fluxes (> 3 fold) for the RuBisCO carboxylation reaction, and the reactions of the lower glycolysis pathway (below GAPDH) and the TCA cycle, but comparable absolute fluxes for the reactions of the upper glycolysis pathway (above GAPDH), the OPP pathway, and glycogen synthesis. We compared the relative carbon flux distributions between the two different growth conditions, by normalizing all fluxes to the corresponding CO2 fixation rates. The INST-MFA results show negligible fluxes through RuBisCO oxygenation at both experimental conditions (2.5% and 1.3% of net CO2 fixation rate for the +N culture and the -N culture, respectively, as shown in Figure 5A&B). The -N culture reveals an alternative CO2-to-


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glycogen route that differs from the +N culture. Under nitrogen deprivation, a hybrid gluconeogenesis-pentose phosphate (hGPP) pathway was used exclusively to convert fixed CO2 into glycogen: GAP/DHAP S17BP S7P F6P G6P glycogen (Figure 5B). This hybrid pathway is shown by intermediates in green color in Figure 5B. For convenience, the fluxes are shown in Table S2. This hGPP pathway differs from the conventional gluconeogenesis pathway: GAP/DHAP FBP F6P G6P glycogen (Figure 5A). The net

13

C flux into

synthesis of glycogen for the +N culture was 63% slower than that of the -N culture (Table 1). The experimentally determined carbon flux distribution at 3PG shows that under -N, 5 ± 2.4% more of 3PG efflux was directed into GAP/DHAP, which contributed to a 140% increase of G6P influx (16 to 38 relative units), and consequentially led to the increased net

13

C flux into

synthesis of glycogen under -N. The relative fluxes of the reactions of lower glycolysis and the TCA cycle were higher in the +N culture (Figure 5). 11 ± 0.7% of the 3PG efflux was funneled into lower glycolysis under +N, while the percentage decreased to 7.3 ± 0.6% under -N. Our results show that during photoautotrophic metabolism PYR is not synthesized directly from PEP via pyruvate kinase (PK), the common reaction in lower glycolysis of non-phototrophs. Rather, it is synthesized exclusively through the malic enzyme route: PEP OXA MAL PYR. PEP is synthesized from PYR by phosphoenolpyruvate synthase (PPS) in a following step. We designate this pathway as the malic cyclic route in this paper. The relative flux of the malic cyclic route for the +N culture was 3.4~7.9-fold higher than for the –N culture (Table 2). In the TCA cycle, we observed substantial fluxes through the newly discovered SSA route (via the succincyl semialdehyde intermediate) in both the +N culture and the –N culture (8.1% and 6.5% of the corresponding RuBisCO carboxylation activities, respectively) [36, 37]. This result



corroborates our earlier report and quantifies the absolute flux showing the TCA cycle in not broken in cyanobacteria [37].

Discussion Changes of carbon flux distributions at metabolic branches under -N. Nutrient stress greatly remodels terminal biomass composition and slows growth rate in photoautotrophic cultures of cyanobacteria [38-40] and microalgae [41, 42]. This method is used to stimulate some products, notably glycogen and lipids, respectively, while degrading proteins and chlorophylls. Using the INST-MFA method, we identified changes in absolute fluxes and carbon flux distributions in several metabolic branches under nitrogen deprivation. Interestingly, ADPGLC accumulated under -N rather than G1P (Figure 3), which is the first downstream metabolite of G6P. This finding suggests that glycogen synthase (GS, two isoforms encoded by A1532 and A2125), which converts ADPGLC into glycogen, is the likely enzymatic bottleneck in glycogen synthesis. In another cyanobacterium Synechococystis 6803, it has been found that these two isoforms are involved in glycogen synthesis with different elongation properties: one is processive and the other one is distributive [43]. Knockout of the corresponding genes in microalgae, denoted glgA-I and glgA-II, resulted in complete elimination of glycogen synthesis and accumulation of 1.8-fold more soluble sugars (osmolytes) [42].

The OPP pathway. The flux through the OPP pathway in cyanobacteria during photoautotrophic growth was previously thought to be minimal, as it results in the direct loss of fixed CO2 and consequently represents a waste of energy resources, both ATP and NADPH. However, our INST-MFA results show that 8% and 17% of photoautotrophically fixed CO2 flux is released through the OPP pathway, for +N and –N conditions, respectively. This is consistent


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with a previous INSTA-MFA study which found that 13% CO2 flux is concurrently released via the OPP pathway under +N conditions in the freshwater cyanobacterium Synechocystis 6803 [15]. Redirection of intermediates from the reductive pentose phosphate (RPP) pathway into the OPP pathway are the source of this flux and may arise from a kinetic bottleneck in RPP. Release of CO2 derived from catabolism of protein-derived amino acids may serve to reduce the cellular osmotic stress by conversion to less osmotically active sugars. The gateway enzyme of the OPP pathway, glucose-6-phosphate dehydrogenase (G6PDH) in the cyanobacterium Aphanocapsa 6714, has been shown to be inhibited in the presence of glucose due to photo-accumulation of an elevated ratio NADPH/NADP+ and from higher intracellular RuBP concentration [44, 45]. Such regulation appears to happen at a posttranslational stage [46]. In Synechococcus 7002, -N deprivation in photoautotrophic conditions strongly induces the expression of four genes in the OPP pathway: G6PDH (zwf, 3.7-fold), a positive regulator of G6PDH (opcA, 2.8-fold), 6-phosphogluconate dehydrogenase (6PGDH) (gnd, 2.9-fold), and transaldolase (tal, 2.4-fold) [47]. Our finding showing a 2.2-fold increase in the relative metabolic flux through the OPP pathway agrees well with this change in transcript levels. In another -N deprivation study of Synechocystis 6803, the in vitro enzymatic activities of two OPP pathway key enzymes, G6PDH and 6PGDH, were shown to increase by 60% and 80%, respectively [48]. It was suggested by the authors that the elevated OPP pathway activity could function to produce additional reducing power for cells to compensate for the loss of NADPH formation during shutdown of photosynthesis under nitrogen deprivation.

Metabolic channeling. Under both +N and –N conditions, 13C-labeling rates of all metabolites downstream of 3PG were slower or similar to those of 3PG, as expected. However, some



downstream metabolites showed larger relative turnover rates than their upstream metabolites (e.g. ADPGLC and 6PG vs. G6P in the +N culture). This feature is commonly attributed to metabolic channeling: the formation of a multienzyme complex that directly converts the initial metabolite into its final product without releasing intermediates [49]. Young et al. proposed metabolic channeling in Synechocystis 6803 for the CBB cycle intermediates F6P, GAP, DHAP and R5P on the basis of

13

C signatures, which were significantly less labeled than their

downstream products S7P, R5P and RUBP [15]. As a result, Young proposed to use “dilution parameters” to represent the fractions of these intermediates that were metabolically inactive and therefore remained unlabeled during the model simulation. Dilution parameters (Supplementary File #2) were used in this work for the same purpose as Young et al. (2011). Similarly, Abernathy et al. have observed in Synechococcus elongatus UTEX 2973 a faster 13C-enrichment in S7P over precursors, which could be contributed by possible channeling of intermediates in the Calvin cycle, as well as channeling of glycolysis intermediates towards citrate in the TCA cycle that exceeds the labeling rate of 3PG [23].

The hGPP pathway induced by nitrogen deprivation. The special hGPP pathway, quantitatively illustrated in Figure 5B, reveals an interesting metabolic switch upon depletion of N. In Synechococcus 7002, GAP/DHAP is converted into either FBP or S17BP by the same set of bifunctional enzymes, fructose-bisphosphate aldolase class I and II (encoded by genes A0010 and A1352). In a transcriptomic study of Synechococcus 7002 the mRNAs of the two genes corresponding to fructose-bisphosphate aldolase classes I and II were doubled under nitrogen deprivation (Table 3) [47]. Interestingly, the gene expression level of transaldolase (TAL), which regenerates E4P and F6P from S7P and GAP, increased by 2.37-fold. These previous findings


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agree well with our fluxomic results showing increases in the absolute fluxes of SBA and TAL by 2-fold and 7.3-fold under -N (Figure 5). Thus, our results suggest that nitrogen availability serves as a trigger to determine the route of glycogen synthesis. Directionality of the FBA reaction is reversed under -N, which might be driven by the much higher activity level of the TAL reaction that requires more S7P as substrates.

The malic cyclic route. In this study, we learned that phosphoenolpyruvate carboxylase (PPC) acts as an integral part of a cyclic route that utilizes four enzymes including malic enzyme (ME) to catalyze interconversion of these metabolites: phosphoenolpyruvate-oxaloacetate-malatepyruvate. PPC catalyzes the irreversible carboxylation of PEP using HCO3- to form OXA [50]. This is the first observation of this novel pathway in cyanobacteria, although it has been reviewed by Sauer and Eickmanns [51]. A crucial role for this route is to synthesize PYR from MAL during photoautotrophic growth.

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C-labeling kinetics of MAL (Figure 4) occurred much

faster than its upstream TCA cycle metabolites (SUCC and AKG), indicating that a large portion of the carbon influx to MAL is contributed by PPC. This portion is quantified in the flux map from INST-MFA (Figure 5). Rapid 13C-labeling kinetics of MAL diminished under -N, which is mainly caused by the 5.2-fold reduced influx rate of MAL through PPC. We observe substantial PPC fluxes under both conditions (71% and 20% of the corresponding RuBiSCO carboxylation activities for +N and –N, respectively). Such high PPC fluxes greatly exceed the 10% RuBiSCO carboxylation activity reported in Young et al. for Synechocystis 6803 [15]. Multiple PPC isoforms are widely distributed in both higher plants and cyanobacteria [52]. Although the malic cycle in cyanobacteria closely resembles the pathway of carbon fixation found in C4 plants [53], it does not directly contribute to net carbon uptake or loss because it involves both uptake and



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release of CO2 by PPC and malic enzymes acting in tandem. A Synechocystis 6803 knockout mutant of malic dehydrogenase grew photoautotrophically 15 times slower than wild type [54]. This growth deficiency was reversed by supplementing PYR in the culture medium, indicating that malic dehydrogenase produces PYR for biosynthesis. The malic cycle bypasses the ATP-generating pyruvate kinase (PK) reaction (PEP + ADP- + H+ PYR + CO2 + ATP) in the final step of conventional glycolysis. Utilization of the malic cycle has been suggested to be associated with a lower pyruvate kinase (PK) activity in the light, due to the ATP generation by efficient photophosphorylation [55]. A recent study of the cyanobacterium Anabaena sp. PCC 7172 has shown that overexpression of the ppc gene encoding PPC increases photosynthetic oxygen evolution rate by 22.5%, demonstrating that this enzyme is a significant contributor to light-saturated photosynthetic electron flow as far upstream as photosystem II in the overexpression strain [56]. This result points directly to the major role of the malic cycle in accelerating utilization of the terminal electron sink (CO2) by removing products of RuBisCO carboxylation. The benefit of the malic cycle in improving the efficiency of RuBisCO carboxylation combined with the efficient ATP generation by phosphorylation, means that PK is completely dispensable in the light among phototrophs.

Another important reaction of the malic cycle is the synthesis of PEP from PYR, eqn [2], which is catalyzed by phosphoenolpyruvate synthase (PPS), encoded by ppsA (A2050): PYR + ATP ↔ PEP + AMP + 2 Pi

[2]

In our studies we found that the PEP pool size of the +N culture was 6-fold larger than that of the –N culture (Figure 3), while the relative metabolic flux rate of PPS was 8-fold larger for the +N culture. PPS purified from the hyperthermohilic archaeon Pyrococcus furiosus was reported to


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exhibit a 4000-fold higher catalytic rate for the forward vs reverse reactions [57]. This energy conserving reaction, generates the higher energy phospho-ester bond in PEP from ATP. Cells need to replenish the PEP pool through PPS to support the active malic cycle. PPS activity from Escherichia coli (E. coli) is down-regulated reversibly by PEP [58]. Consequently, PEP is not only an intermediate needed to synthesize OXA in the malic cycle, but also a phospho-ester energy carrier molecule and a self-regulating signal.

The SSA route. The TCA cycle was once believed to be incomplete in cyanobacteria, since genes encoding 2-oxoglutarate dehydrogenase (2-OGDH), which converts AKG to SUCC-CoA, were shown to be absent [59-61]. Recently, the succinic semialdehyde (SSA) route, which involves 2-oxoglutarate decarboxylase (2-OGDC) and succinic semialdehyde dehydrogenase (SSADH), and converts AKG to SUCC, was identified in the cyanobacterium Synechococcus 7002 [36, 37] and Synechocystis sp.PCC 6803 [62]. The

13

C flux of this pathway was also

identified in Synechocystis sp.PCC 6803 under photomixotrophic conditions by You et al [63]. This route completes the truncated cyanobacterial TCA cycle. However, the physiological importance of this route under photoautotrophic growth remains unclear. Multiple FBA growth simulations of cyanobacteria Synechococcus 7002 and Synechocystis 6803 suggest negligible fluxes ( 200

ACC 0.3 1.2

PDH

CS

ME 32 107

S7P

3PG ENO

CO2

SBA 20 67

DHAP

GAP

X5P

R5P

PRK 48 163

PGM

F6P

GND 3.8 13

Ru5P

PGM 1.0 3.3

SSAL SUCC


GDH 0.5 1.8

GLU

2OGDC 4.0 13

GLN


Page 24 of 31

B) PGL 2.8 29

PGL

6PG E4P

F6P

TAL 37 382

GND

G6P

TAL

PGI

E4P

G1P

ADPGLUC

PGI 3.7 38

Glycogen

S7P

GAP

FBP FBA

RBC (Vc) 16 169 PRK

RBC (Vo) 0.1 1.3

RBC (Vc)

RuBP RBC (Vo)

FBA 29 296

GAP

SBA

GAP 30 313

S7P

E4P

ENO 2.5 26

PEP GOX

PPS PPS

1

10

PYR PCC

PDH 1.6 16

PCC 3.3 34

ACC 0.2 1.9

PDH

ACA

ACC

MaCoA

CS ME

ME 2.9 30

S17BP

3PG ENO

CO2

SBA 41 429

DHAP

GAP

X5P

R5P

PRK 16 170

PGM

F6P

GND 2.8 29

Ru5P

PGM 0.9 9.0

OXA MDH

CS 1.3 13

ICD

MDH 1.8 18

GDH

AKG

MAL FUS 1.2 12

ICD 1.3 13

CIT

2OGDC

FUS

FUM

SSAL

GDH 0.1 1.0

GLU

2OGDC 1.1 11

GLN

SUCC

Figure 5. Flux distribution maps of the: A) +N culture and B) –N culture. Flux values in the left column are absolute metabolic flux rates in µmol g-1 DW min-1, while those in the right column are relative flux rates normalized to 100 units CO2 fixed. Thickness of the solid arrows represents the relative flux of each reaction with the range shown in inset to A). The dashed arrows represent those metabolites that are directly contributing to the process of generating terminal biomass products. The blue arrows in A) show the conventional gluconeogenesis pathway, which is mainly utilized under N repletion. The green intermediates and arrows in B) show the hybrid gluconeogenesis-pentose phosphate (hGPP) pathway, which is mainly utilized under N starvation. Dotted circles around GAP and S7P symbolize these


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metabolites connect gluconeogenesis and pentose phosphate pathways. Only fluxes of important reactions are shown. All reaction fluxes in the map are shown in Supplemental file #2.

Tables Table 1. Photoautotrophic carbon distributions at different gate points. Fraction of total influx are calculated based on the flux values presented in Supplement File #2, and represents the fractions of precursor metabolites funneled into each downstream product. * Relative formation rates of precursor metabolites at +N (left) and –N (right) conditions, respectively. All values shown were normalized to per 100 unit CO2 net fixation. Fraction of Total Influx Gate Metabolite

Reaction

+N

-N

G6P 6PG G6P ADPGLUC 3PG GAP/DHAP 3PG PEP

79 ± 3.0% 20 ± 2.8% 88 ± 2.1% 11 ± 0.7%

76 ± 3.9% 23 ± 4.1% 93 ± 0.3% 7.3 ± 0.6%

Product Metabolite

Net Synthesis Flux Calculation Formula

Net Synthesis Flux +N -N

% Difference to - N

Glycogen

ADPGLU GLYC minus GLYC G1P

G6P (16, 38) 3PG (322, 338)

3.3

9.0

63%

Table 2. Relative carbon fluxes of reactions involved in the malic cyclic route under +N and -N, shown in Figure 5. Unit is µmol g-1 DW min-1. Reaction PYR PEP PEP + CO2 OXA OXA MAL MAL PYR + CO2

Relative Flux Rate +N -N 79 10 115 34 93 18 107 30

Fold-change 7.9 3.4 5.1 3.6

Table 3. Changes in gene expression involved in the gluconeogenesis pathway and the hPGG pathway of Synechococcus 7002. Data are given as the ratio –N/+N and were obtained from Ludwig & Bryant 2012. Gene #

Enzyme Name

Enzyme Full Name


Reaction

-N / + N


A0595

TpiA

Triosephosphate isomerase

A0010

FBA/SBA

Fructose-bisphosphate adlolase class I

A1352

FBA/SBA

Fructose-bisphosphate adlolase class II

A0162

PfkA

A0329

FBP

A1301

GlpX

A0964 A1460

PGI TAL

6-phosphofructosekinase Fructose-1,6-bisphosphatase / Sedoheptulose-1,7-bisphosphatase Fructose-1,6-bisphosphatase / Sedoheptulose-1,7-bisphosphatase Glucose-6-phosphate isomerase Transaldolase

Page 26 of 31

DHAP GAP GAP + DHAP FBP DHAP + E4P SBP GAP + DHAP FBP DHAP + E4P SBP F6P + ATP FBP + ADP FBP + H2O F6P + Pi SBP + H2O S7P + Pi FBP + H2O F6P + Pi SBP + H2O S7P + Pi F6P G6P S7P + GAP E4P + F6P

0.50 2.15 1.79 0.94 1.98 0.13 1.20 2.37

Supporting information Supplementary figures: Figure S1. Pool size stability tests of 12 indicator metabolites in a batch culture Figure S2. Experimentally measured mass isotopomer abundances and INST-MFA model fits under +N and –N • Figure S3. Biomass compositions of WT cultures grown with or without nitrate Supplementary tables: • •

• Table S1. Pool sizes of metabolites in cultures grown with or without nitrate • Table S2. Net flux of reactions in the hGpp pathway. Supplementary file 2: •

Absolute flux and relative flux data of all reactions determined by 13C INST-MFA for the Synechococcus 7002 photobioreactor model under +N and –N.


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Page 30 of 31

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PGL

6PG

E4P

G6P

F6P TAL

PGM

G1P

ADPGLUC

PGI

E4P

F6P

GND

Glycogen

S7P

GAP

FBP FBA

R5P

GAP

Ru5P

PRK

RBC (Vc)

RuBP RBC (Vo)

CO2

SBA

DHAP

GAP

X5P

S17BP

E4P

-N

3PG +N

ENO

PEP

GOX

PPS PCC

PYR PDH

ACA

ACC

MaCoA

CS

OXA

ME

CIT ICD

MDH GDH

AKG

MAL

GLU

2OGDC

FUS

FUM

SSAL SUCC ACS Paragon Plus Environment

GLN

S7P

Rerouting of metabolism into desired cellular products by nutrient stress

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