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Metabolic flux analysis of the hydrogen production potential in Synechocystis sp. PCC6803
international journal of hydrogen energy 34 (2009) 8828–8838
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Metabolic flux analysis of the hydrogen production potential in Synechocystis sp. PCC6803 E. Navarro a,*, A. Montagud b, P. Ferna´ndez de Co´rdoba b, J.F. Urchueguı´a b a b
Departamento de Lenguajes y Ciencias de la Computacio´n, Campus de Teatrinos, Universidad de Ma´laga, 29071 Ma´laga, Spain Instituto Universitario de Matema´tica Pura y Aplicada, Universidad Polite´cnica de Valencia, Camino de Vera 14, 46022 Valencia, Spain
article info
abstract
Article history:
Hydrogen is a promising energy vector; however, finding methods to produce it from
Received 28 May 2009
renewable sources is essential to allow its wide-scale use. In that line, biological hydrogen
Received in revised form
production, although it is considered as a possible alternative, requires substantial
28 July 2009
improvements to overcome its present low yields. In that direction, genetic manipulation
Accepted 6 August 2009
probably will play a central role and from that point of view metabolic flux analysis (MFA)
Available online 17 September 2009
constitutes an important tool to guide a priori most suitable genetic modifications oriented to a hydrogen yield increase.
Keywords:
In this work MFA has been applied to analyze hydrogen photoproduction of Synechocystis
Metabolism
sp. PCC6803. Flux analysis was carried out based on literature data and several basic fluxes
Hydrogen
were estimated in different growing conditions of the system. From this analysis, an upper
Synechocystis
limit for hydrogen photoproduction has been determined indicating a wide margin for
Photosynthesis
improvement. MFA was also used to find a feasible operating space for hydrogen production, which avoids oxygen inhibition, one of the most important limitations to make hydrogen production cost effective. In addition, a set of biotechnological strategies are proposed that would be consistent with the performed mathematical analysis. ª 2009 Professor T. Nejat Veziroglu. Published by Elsevier Ltd. All rights reserved.
1.
Introduction
Nowadays, the necessity of looking for alternative sources of energy to fossil fuels is a question of major relevance and serious social concern because of their limited reserves and adverse environmental effects. In this respect, the transportation sector represents 2/3 of the net oil consumption [1] and currently there is no reliable solution for a complete replacement by renewable sources. Despite the fact that some biofuels, like biodiesel or ethanol have been proposed as suitable candidates to partially substitute oil, their production potentials are limited [2], and, in spite of massive improvement efforts currently under development to enlarge the spectrum of efficiently usable
biomass from cellulose, other alternatives have to be found in a medium or long term. At this point, hydrogen appears as a very promising candidate to be a prominent future energy vector [3,4], due to its clean, smokeless combustion and high energy density (142 MJ/kg for H2 vs. 42 MJ/kg for oil). At the present time, steam reforming of natural gas is the best established system to produce hydrogen at industrial scale; however it is based on non-renewable energy sources and generates substantial sulphur and CO2 emissions. In fact, most of the H2 producing processes based on fossil fuels release approximately twice as much moles of CO2 than moles of hydrogen. In order to produce hydrogen from renewable and clean sources of energy several alternatives are under study [5,6], and among them biological hydrogen
* Corresponding author. Tel.: þ34 6 7782 2930. E-mail address: [email protected] (E. Navarro). 0360-3199/$ – see front matter ª 2009 Professor T. Nejat Veziroglu. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhydene.2009.08.036
international journal of hydrogen energy 34 (2009) 8828–8838
Table 1 – Hydrogen production rates obtained by different methods, experimental values shown for photolytic methods corresponds mainly with algae, although some relevant works for cyanobacteria can be found in ref. [10]. Process Biophotolysis Indirect biophotolysis Photo-fermentation Dark-fermentation Two stage processes
Production rate
Ref.
0.07 mmol/l per h 0.35 mmol/l per h 145–160 mmol/l per h 77 mmol/l per h 3.37–7.2 mol H2/mol C6
[7,8] [9] Review in ref. [10] [11] [12]
production can be seen as a promising alternative provided several limitations are overcome. Table 1 shows a comparison of experimental values obtained for hydrogen production by different biological processes. The production rate for fermentative processes is much higher than for photolytic processes, nevertheless these values do not take into account the substrate cost and efficiency and from that point of view the balance is not so favourable for that kind of alternative. In fact, theoretically, the maximal energetic efficiency for direct biophotolysis is about 40% [10] compared with a maximum of about 1% for hydrogen production from biomass [13]. Recognizing the important potential of biophotolysis, the National Research Council of US has recommended that the US Department of Energy ‘‘refocus its biobased program on more fundamental research on photosynthetic microbial systems to produce hydrogen from water at high rate and efficiency’’ [13]. Photosynthetic organisms are able to use solar energy to produce their own feed-stock. As it was discovered by Gaffron [14], certain organisms like cyanobacteria and some algae
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possess pathways of hydrogen production-consumption, which play an important role in their metabolism, being coupled to photosynthesis [15] (see Fig. 1). Schematically the process consists of the absorption of photons by the photosystem II (PS II), which energy is used to split the water molecules in protons, oxygen and two electrons that are channeled through the so called ‘‘electron transport chain’’. These electrons are led through several enzymes to the photosystem I (PS I) which directs them back into the cyclic photosynthetic loop. The net result of the process is the production of NADPH as reducing power (where NADPþ acts as a sink for the electrons of PS II) and a proton gradient, which is used to produce ATP. The enzymes involved directly in hydrogen metabolism are [Ni–Fe] hydrogenases, [Fe–Fe] hydrogenases and nitrogenases, where hydrogen is produced as a by-product of nitrogen fixation, see Tamagnini et al. [17] for a review. As a well-known fact, inhibition of hydrogenase activity by the oxygen inherently formed in the photosynthesis is the most important limiting factor for a cost effective production of hydrogen using this mechanism [16]. Several projects [17,18], are dealing with this difficult issue, trying to find or design oxygen-tolerant hydrogenases, expressed in cyanobacteria or algae. A possible way of overcoming this limitation is based on the strategy adopted by certain filamentous cyanobacteria like Nostoc sp. in order to increase their nitrogen fixation efficiency. It consists of isolating certain specialized cells, heterocyst, creating anaerobic environments to activate their nitrogen fixing enzymes and producing hydrogen as byproduct. The main drawback of this approach is its poor efficiency, as nitrogen fixation is energetically expensive, see Lopes et al. [19].
Fig. 1 – Scheme of biophotohydrogen production for different cyanobacteria and algae. In the figure, electrons released through the PSII system are driven trough several steps up to the hydrogenase. In the case of Synechocystis PCC6803, the photosynthetic transport chain share several enzymes with the respiratory transport chain (not represented), this fact allows the possibility of using electrons from glucose or other substrates in order to produce hydrogen. Other reactions which can be sinks of the photosynthetic electrons like the Melher reactions have not been drawn. An extended review of hydrogenase action can be found in ref. [53].
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A further option considered to overtake these limitations is to uncouple hydrogen from oxygen production, as for instance in the system developed by Melis and coworkers described in Melis et al. [20]. In their procedure, sulphur deprived algae are unable to repair the damage produced in their PSII antennas, giving rise to a phase of low activity of the PSII system and, thus, lower oxygen production, up to a level in which it is exceeded by the rate of O2 consumption by the respiration metabolism. In this way an anaerobic environment is temporally set up, hydrogenases are not inhibited and hydrogen is produced during a relatively long period of time. This mechanism relies on the availability of a substrate (for instance succinate, NADH, acetate and the like) to be able to reduce photosynthetic oxygen and provide electrons for hydrogen production. In fact, it was reported that the biomass produced during the active photosynthesis phase is subsequently reconverted to feed the electron transport chain for hydrogen production. This idea is analogous to the strategy of photofermentation, which combines photosynthesis with fermentation processes, in which the residues from fermentation are used to feed photosynthetic bacteria in anaerobic media, resulting in a continuous hydrogen production [21]. A detailed review of these strategies and other with their most important drawbacks can be found in Hallenbeck and Benemann [22]. It is apparent that a deeper understanding of the interactions between biochemical energy, carbon fixation and assimilation pathways through mathematical modelling could be a valuable tool to evaluate maximum theoretical product yield and to help developing improved strategies. One of the best developed conceptual tools in this direction, microbial pathway engineering, has traditionally been applied mainly to industrial processes for biosynthesis of products of high economical value. Accurate quantification of pathway fluxes is hereby an important goal, especially where the aim is to convert as much substrate to a desired metabolic product via strain improvement. There have been several initiatives towards development of metabolic flux models for different organisms in order to gain quantitative information about metabolic physiology of the cultures [23–25], and recently, metabolic flux analysis technique has been reported to be applied to hydrogen production using growth of Escherichia coli [26] and other organisms [27] on glucose has been reported – a brief overview of fermentative hydrogen production is found in Jones [28]. However, up to now this methodology had not been applied to hydrogen photoproduction. Among the different organisms potentially capable of producing hydrogen from sunlight, cyanobacterium Synechocystis sp. PCC6803 possess several interesting features to be of choice as model system in this kind of applications: it can synthesize almost all its requirements from light and CO2, its genome is fully sequenced [29,30], and it allows genetic manipulation through plasmid insertion [31]. In the present contribution we have developed a metabolic model, based on available data, to evaluate the theoretical hydrogen production potential capabilities of Synechocystis sp. PCC6803 from sunlight. Flux balance analysis (FBA) was applied over the obtained network in five cultivation conditions: Autotrophic growth condition depicts the primary biological process of using solar energy for the synthesis of organic molecules from carbon dioxide. Heterotrophic metabolism
allows obtaining energy from glucose in the absence of light. Mixotrophic conditions fall between the above two categories (light and glucose are used concurrently as sources of energy). Finally, two solutions from the metabolic network solutions space with zero net oxygen flow were analyzed, they have been used to situate in an overall scheme different strategies proposed in the literature, providing us with a global vision of the process and suggesting possible improvements.
2. Synechocystis PCC6803 modelling approach 2.1.
Metabolic flux analysis
Quantification of metabolic network fluxes can be achieved via metabolic flux analysis (MFA) (see refs. [24,25] for a more general description). This method uses the stoichiometry of the intracellular metabolic reactions, the extracellular fluxes and the known internal fluxes as input and in return, the metabolic fluxes for the rest of intracellular reactions under pseudo-steady state condition are calculated by means of flux balance analysis (FBA) as established by Edwards et al. [32]. Under this approach [33], a relation is established among the intracellular metabolite concentrations, reaction fluxes and the stoichiometry in a metabolic system by a dynamic mass balance equation given below: dX ¼ An dt
(1)
where A represents the stoichiometric matrix with dimension p q (where p represents the number of metabolites and q the number of reactions). Each stoichiometric matrix element, A(i,j ), symbolizes stoichiometric coefficient of the ith metabolite in the jth reaction, X is the px1 concentration vector of all intracellular metabolites and v represents all metabolic net fluxes or reaction rates with the dimension qx1. Under metabolic steady state conditions, the mass balance equation for metabolites specializes to: A,v ¼ 0
(2)
Then, since the number of reactions q is always larger than the number of metabolites p, the system has a degree of freedom F ¼ q p. Hence, if a system has a number of measured fluxes less than F the system becomes underdetermined by nature. In order to obtain a feasible solution for the intracellular fluxes, an overall optimization criterion on metabolic balances has to be imposed. In such a case, the maximum of a given flux i is determined by imposing Eq. (2) as the constraint in the form given below Maxðvi Þ
s:t A v ¼ 0 at N < vi < N
Where vi is the rate of the ith reaction.
2.2.
Stoichiometric model of Synechocystis PCC6803
The photosynthetic electron transport chain (ptc) and the respiratory electron transport chain (rtc) of Synechocystis sp. PCC6803 are located in the thylakoid membrane of the bacterium. In Synechocystis sp. PCC6803, hydrogen
international journal of hydrogen energy 34 (2009) 8828–8838
photoproduction relies on the photosynthetic system. Ptc is linked to the rest of the metabolism through the central carbon metabolism, which receives the ATP and NADPH produced in the photosynthesis, using it to fix carbon and to obtain energy for the rest of the metabolism. On the other hand, central carbon metabolism produces metabolites, which can feed the rtc with electrons. Rtc shares several enzymes with the photosynthetic system. Thus, any model intended to evaluate the hydrogen production potential of such systems should include, at least, the following pathways: pentose phosphate pathway (or Calvin cycle if the system is working in light conditions), glycolysis and tricarboxylic acid (TCA) cycle. In addition, the effect of ptc and rtc must be taken into account. The general pathway for Synechocystis (KEGG: http://www. genome.jp/kegg/) was used as a basis for construction of its metabolic network. The detailed metabolic network considered in the present analysis is shown in Fig. 2. After careful analysis, the following assumptions, based on the available experimental information, were made in order to develop the final set of stoichiometric equations: (a) Biomass formation equation was formulated based on the macromolecular composition of Synechocystis sp. PCC6803, see refs. [34,35]. (b) A growth and non-growth associated (maintenance) ATP consumption was considered in the reaction network for ATP balance after ref. [35]. (c) Glyoxylate shunt was included in the model as it was identified in isotope labelled glucose feeding experiments following ref. [34]. (d) Due to the absence of α-ketoglutarate dehydrogenase the production of succinyl CoA from a-ketoglutarate has been omitted from TCA cycle [36]. (e) Synechocystis is not known to operate through fermentative pathways [37], and hence production of fermentative products is not considered in the model. (f) The conversion of photons into ATP (photophosphorylation) and NADPH (reducing power) by water oxidation has been reproduced by cyclic and non-cyclic electron transport processes, see refs. [38–40]. (g) The maximum photon uptake rate and CO2 uptake rates were used as measured extracellular fluxes obtained from literature [35]. In addition to these pathways, in order to consider photosynthesis in our model we have used a reduced or effective approach developed in ref. [38], which relates the energy input – in the form of chemical (trough several metabolites) or light energy – with the metabolite output of the organism. Inputs of the model will be the incoming fluxes of photons, CO2 and glucose. Our stoichiometric model comprises 56 compounds and 90 pathway reactions (reversible reactions are considered as different reactions), but not all of them are active in all metabolic conditions. Pseudo-steady state is assumed for 21 intracellular metabolites resulting in a model with six degrees of freedom. The reactions considered to build the metabolic network and construct the stoichiometric matrix used to solve Eq. (2) are shown in Appendix A.
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Optimization was achieved using a commercial dynamic optimization algorithm (ILOG CPLEX’sª mathematical optimization technology). The routine is directed towards maximizing cell growth (biomass), which depicts actual metabolic states of the microorganism and hence gives flux estimation in a wild type strain, and also to evaluate the feasibility of an alternative state, which could point to an increase in hydrogen production. Note that, because of the underdetermined nature of the system, the intracellular fluxes obtained in the present study can be considered as possible solutions in a large solutions space. The obtained values are the basis for our estimation of an upper limit to the hydrogen production capacity of the system.
3.
Model results
This section describes the main results of the flux analysis carried out for the different growth conditions. A summary of the relevant results from the model can be seen in Fig. 3. For the sake of clarity individual fluxes have not been detailed. The flux distributions of central carbon metabolism in Synechocystis under heterotrophic condition are characterized by the following aspects: the oxidative pentose phosphate pathway is the major pathway for glucose catabolism in agreement with results reported in ref. [41]. The high flux through the oxidative part of the pentose pathway is explained by the fact that it provides a large amount of NADPH and biosynthetic precursors. The carbon efficiency of this process d defined as number of carbon atoms inputs (glucose) minus number carbon outputs divided by the carbon input d of this process is approximately of 60%, which means that the rest is released in the form of CO2. A net molar oxygen consumption of 1.5 per mol of glucose is obtained. Under autotrophic condition the reductive pentose phosphate pathway is active and part of the glycolysis flow is in the gluconeogenic direction. The flow through the TCA cycle is reduced by an order of magnitude compared to the heterotrophic one, and the glyoxylate shunt is not active. In this process, as the carbon source is CO2, the efficiency of its fixation can be considered as 1. Regarding the efficiency of light absorption, approximately 4% is used to increase the biomass yield. The ratio PSI/PSII is 0.43. The molar oxygen release per mol of carbon fixed is 1.5. The biomass formation per unit of carbon fixed is 1.45 times larger for autotrophic conditions than for heterotrophic conditions. Flux analysis under mixotrophic conditions involves three independent feeding fluxes to the system: glucose, CO2 and light. The CO2 uptake rate was kept free with an upper bound similar to the one found in autotrophic condition and light absorption efficiency was considered to be similar to the one obtained in autotrophy. The flux topology for mixotrophic growth lies between autotrophic and heterotrophic metabolisms. The reductive pentose phosphate pathway and glycolytic pathway occur simultaneously. Phosphoenol pyruvate (PEP) carboxylase is in the same range as autotrophism and the flow through TCA cycle is in between the values found at previous growth conditions. Assuming that the gaseous CO2 is fixed with an efficiency of 100%, in these conditions the glucose and therefore the total carbon fixation efficiency is
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Fig. 2 – The metabolic network of Synechocystis PCC6803 considered in the present study. Three major pathways have been considered: glycolysis, pentose phosphate (PP) pathway and TCA cycle. Further, the pathways for oxidative phosphorylation and anaplerotic reactions were taken into account. The pathway was further modified for the photosynthetic reactions take place in reaction centers of PSI and PSII and the Calvin cycle.
92% which is considerably higher than without the presence of light. Regarding oxygen emission, it releases 0.45 mol of oxygen is released. The biomass formation per unit of carbon fixed is 2.45, hence larger than at heterotrophic conditions. Hydrogenase inhibition by oxygen is a critical factor in order to obtain a continuous production of hydrogen. FBA can
be used to determine the existence of solutions compatible with a null oxygen net flow. The mixotrophic metabolic network was analyzed with the constraint of a null net oxygen flow. Among this set of solutions, two solutions were studied: the solution of the system with a maximum growth (anoxic maximum) and a solution of the system in which the PSII flow
international journal of hydrogen energy 34 (2009) 8828–8838
Fig. 3 – Ratio of carbon efficiency oxygen evolution TCA cycle and growth for the different metabolic conditions. The carbon efficiency is evaluated according to the ratio between the carbon input and the carbon fixed, the oxygen flow is given by the fraction of oxygen emission related to the carbon fixed (a negative sign means absorption of oxygen) and the TCA and the growth are relative to the heterotrophic growth.
of photons was the same as the flow of Eqs. (A30) and (A40) of the appendix when the system is working in natural mixotrophic conditions (anoxic photoreduction). Flux analysis under anoxic maximum shows the following characteristics: biomass formation in these conditions is higher than heterotrophic and autotrophic conditions. There is a substantial increase in the activity of the TCA cycle (approximately three times the rate of the natural mixotrophic solution). Regarding carbon efficiency, this solution has a 78% of carbon fixation. The last condition analyzed was anoxic photoreduction. That condition shows a flux distribution similar to the heterotrophic condition. This points to the fact that probably the system is using mainly glucose for growth. Carbon fixation efficiency is 67%, which represents the lowest efficiency among all the solutions which use light as an input.
4.
Discussion
According to the results described in the previous section, theoretical hydrogen production potential of the organism has been estimated. From these evaluations, the efficiency of the photosynthetic system (defined as percentage of photons which are absorbed and which energy is used to move electrons through the ptc) is 4% of the total solar radiation. From that percentage, 70% of the photons are used in the PS II. Therefore, assuming a natural irradiation limit of 2100 mE/m2 per s, see ref. [42], and that NADPH produced by photosynthesis can be metabolized completely by hydrogenase, a maximum potential hydrogen production of 103 mmol H2/mmol biomass per h is obtained. This value corresponds to the solution of the optimization problem
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taking into account an objective function, which maximizes at the same time biomass and hydrogen production in mixotrophic conditions. The obtained results show that the system growths in heterotrophic mode and uses all the electrons of the ptc to produce hydrogen. This solution predicts the maximum electron flow potentially deliverable to hydrogenase, without taking into account its conversion efficiency or inhibition factors. The value is quite large compared to results reported from hydrogen production experiments by different methods as can be seen in Table 1. This remarkable result seems to be indicating that there may be a considerable space for improvement in hydrogen photoproduction technologies. Some of the most successful strategies for hydrogen photoproduction, like the one developed by Melis et al. [20] and described in the introduction, can indeed be understood in terms of the simulations performed in the previous section to the metabolic flux solutions with a net null oxygen flux. From the point of view of metabolic balance, the sulphur deprivation approach lies in the fact that the electrons which could reach the [Ni–Fe] hydrogenase are reduced substantially compared to the natural state in order to lower oxygen production of the PS II. This suggests that this strategy can be understood in terms of the anoxic photoreduction conditions. In that frame, the modelling work reveals that this strategy has a poor theoretical efficiency compared with the anoxic maximum as it has a much lower light absorption (energy input). This efficiency penalty could be overcomed by implementing the complementary idea, increasing the respiration level to balance photosynthetic oxygen production. In fact, this strategy would be closer to the implementation of a real solution of the anoxic maximum. Regarding possible ways of implementing this solution in vivo, there are several methods, which organisms use to alter their respiration rates and thus their oxygen consumption. Among them, the expression of membrane proteins like respiratory uncoupling proteins (UCP) [43] is one relevant possible example. UCP is expressed in some eukaryotic cells like yeast, creating pore-like structures in the mitochondrial membrane which modify the gradient potential through the membrane, subsequently causing an increase in the electron flow through the rtc. Recent studies from our group have shown a burst in oxygen consumption in yeast due to UCP [44]. Unfortunately, problems in the functionality of these proteins in bacteria [45] have been reported. Other equivalent strategies, already functional in plants, are based on opening, or enhancing, alternative routes for the electrons of the electron transport chain with oxygen reduction. This is caused by genes such as cytochrome c oxidase (COX), mitochondrion alternative oxidase (AOX) and chloroplast alternative oxidase (PTOX). A detailed review of its action in green algae in order to reduce the oxygen level can be found in ref. [46]. The existence of alternative oxidases present in cyanobacteria like Anabaena variabilis (entry name Ava_1055) and Acaryochloris marina (entry names AM1_0483, AM1_1551 and AM1_A0138) which could be expected to be functional in Synechocystis, should be specially pointed out. Other more sophisticated alternatives which can perform a similar function than UCP in eukaryotic cells consist in the
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reduction of the efficiency of the ATPase which may produce a similar effect to the UCP, solving the problem of expressing an eukaryotic transmembrane protein in cyanobacteria. Summarizing, the reduction of the oxygen level results in the fact that hydrogen is finally produced from electrons coming from respiration which substrate is based mainly in carbohydrates or acetate as the electrons coming from photosynthesis are used to reduce the oxygen created in their production. Producing hydrogen through the ptc coupled to rtc is potentially more efficient than through other fermentative pathways (4 moles of hydrogen per mol of glucose). In addition, considering a two step process in which the biomass produced by light in an aerobic environment (first step) can be used in an anaerobic phase (second step) to produce hydrogen, the system can be considered autonomous. Apart from hydrogenase, the photosynthetic transport chain possesses other electron sinks, which can be of major importance (for example the Calvin cycle, cyclic transport chain or Mehler reactions). Therefore, an alteration of these pathways through genetic engineering, as reported in refs. [47,48], can due to an improvement of the hydrogen production, once oxygen is removed.
5.
Conclusions
Metabolic flux analysis has demonstrated to be a powerful tool to analyze the capabilities of different organisms to produce specific metabolites of interest and to assist in their optimization. We applied this methodology to evaluate the metabolic network of Synechocystis sp. PCC6803 for hydrogen photoproduction, which constitutes the first time this kind of analysis is performed in an autotrophic organism taking into account the electron flow of the photosystem to analyze hydrogen production. In this analysis a metabolic model has been implemented according to which three main growing conditions of the bacteria (heterotrophic, autotrophic and mixotrophic) were studied and the differences among them established in terms of oxygen production and carbon fixation. The obtained results from that analysis in terms of growth are in agreement with the ones reported in literature. The potential hydrogen production of Synechocystis sp. PCC6803 has been evaluated and it was found that potential (or theoretical maximum) hydrogen production is about two orders of magnitude larger than experimental yields reported in literature [49]. This upper limit has been estimated based on the assumptions of no modifications are introduced in the antenna system and all the electrons from the transport chain are redirected to hydrogenase. Therefore, although this is just an upper theoretical limit, it suggests a wide margin for improvement in hydrogen production from biophotolysis in order to make it cost effective. One of the most important limitations of this hydrogen production methodology consists of the inactivation of hydrogenase by oxygen. From the metabolic point of view, it has been shown that there are alternative solutions of the network, which show null net oxygen flow. In this frame, two
solutions with null oxygen flow were analyzed from the metabolic point of view: the anoxic maximum and the anoxic photoreduction. In relation with this analysis, one of the most successful experiments in this field [20] was put in the frame of the anoxic photoreduction solution analyzing its possible limitations. Alternative approaches, closer to the anoxic maximum solution, have been proposed and several ways to implement those solutions in an organism are discussed in the paper. Hopefully, this work will help experimentalists ascertain new strategies and possible approaches based on the present theoretical framework.
Acknowledgements This work has been funded by the European Union through the project of the sixth Framework Program ‘‘Engineered Modular Bacterial Photoproduction of Hydrogen’’ Contract no.: 043340, and seventh Framework Program: ‘‘Targeting environmental pollution with engineered microbial systems a´ la carte’’, Contract no.: 212894, the Spanish Ministerio de Educacion y Ciencia through the program Juan de la Cierva and the FPI grant program of the Generalitat Valenciana. The authors are grateful for the given support.Finally, the authors are thankful to Ph D. F. Villatoro for his thoughtful comments on the paper.
Appendix A List of reactions GLCext þ Hþ /GLC
(A1)
G6P þ ADP4GLC þ ATP
(A2)
F6P4G6P
(A3)
F6P þ ATP4F1; 6P þ ADP
(A4)
GAP4F1; 6P
(A5)
F1; 6P/F6P
(A6)
F6P þ ATP/F1; 6P þ ADP
(A7)
GAP þ NADþ 4NADH þ DHAP
(A8)
DHAP þ ADP43PG þ ATP
(A9)
3PG4PEP
(A10)
PEP þ ADP/PYR þ ATP
(A11)
PYR þ CoA þ NAD/AcCoA þ NADH þ Hþ þ CO2
(A12)
G6P þ NADPþ 4GL6P þ NADPH þ Hþ
(A13)
GL6P þ H2 o/D6PGDL
(A14)
international journal of hydrogen energy 34 (2009) 8828–8838
D6PGDL ¼ NADPþ /Ru5P þ NADPH þ CO2
(A15)
R5P4Ru5P
(A16)
X5P4Ru5P
(A17)
F6P þ GAP4E4P þ X5P
(A18)
GAP þ S7P/E4P þ F6P
(A19)
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1:191 G6P þ 0:133 GAP þ 1:205 3PG þ 1:002 PEP þ 1:197 PYR þ0:715 R5P þ 0:501 E4P þ 2:039 OA þ 1:233 AKG þ3:72 AcCoA þ 0:16 SUCCoA þ 2:82 NAD þ 49:06 NADPH þ53:55 ATP/3:887 CoA þ 53:35 ADP þ 49:06 NADP þ2:82 NADH þ 0:683 FUM þ 0:16 Suc þ BM þ 1:017 CO2 0:882 G6P þ 0:238 GAP þ 1:53 PEP þ 2:64 PYR þ 0:399 R5P þ0:406 E4P þ 1:23 OA þ 1:04 AKG þ 4:64 AcCoA þ 2:82 NAD þ27:22 NADPH þ 39:21 ATP/4:64 CoA þ 39:21 ADP
GAP þ E4P/S7P
(A20)
S7P þ GAP4R5P þ X5P
(A21)
Ru5P þ ATP/R15P þ ADP
(A22)
R15P þ CO2 /23PG
(A23)
AcCoA þ OA/CIT
(A24)
CIT4ICT
(A25)
ICT þ NADPþ /AKG þ NADPH þ CO2
(A26)
SUC þ ATP4SUCCoA þ ADP
(A27)
MAL4FUM
(A28)
OA þ NADH4MAL þ NADþ
(A29)
2SUC þ O2 /2FUM þ 8Hþ
(A30)
ICT/GOX þ SUC
(A31)
GOX þ AcCoA4MAL þ CoA
(A32)
MAL þ NAD4NADH þ PYR þ CO2
(A33)
þ27:22 NADP þ 2:82 NADH þ BM þ 1:835 CO2 1:228 G6P þ 0:208 GAP þ 1:42 PEP þ 2:44 PYR þ 0:382 R5P þ0:376 E4P þ 1:14 OA þ 0:886 AKG þ 3:96 AcCoA þ2:82 NAD þ 29:01 NADPH þ 38:89 ATP/3:96 CoA þ38:89 ADP þ 29:01 NADP þ 2:82 NADH þ BM þ 1:835 CO2
Appendix B. Nomenclature
PYR þ CO2 þ ATP4OA þ ADP
(34)
NADH þ NADPþ þ 2Hþ /NADþ þ NADPH
(A35)
photonex/photon þ photon0
(A36)
4photon þ NADPþ þ H2 O/NADPH þ 6Hþ þ 0:5O2
(A37)
photon0 /2Hþ
(A38)
4Hþ þ ADP/ATP
(A39)
2NADH þ O2 /2NADþ þ 8Hþ
(A40)
CO2 ex/CO2
(A41)
O2 ex/O2
(A42)
PEP þ CO2 /OA
(A43)
Three biomass formation equation [32,33]
GLC G6P F6P F1,6P GAP DHAP 2PGA 3PG PEP PYR AcCoA CoA GL6P D6PGDL R5P Ru5P X5P EP4 S7P R15P OA CIT ICT AKG SUC SUCCoA MAL FUM GOX
glucose glucose 6-phosphate D-fructose 6-phosphate D-fructose 1,6-bisphosphate D-glyceraldehyde 3-phosphate 3-phospho-D-glyceroyl phosphate 3-phospho-D-glycerate 2-phospho-D-glycerate phosphoenolpyruvate pyruvate acetyl-CoA coenzyme A D-gluconate-6-phosphate D-glucono-1,5-lactone 6-phosphate D-ribose 5-phosphate D-ribulose 5-phosphate xylulose 5-phosphate D-erythrose 4-phosphate sedoheptulose 7-phosphate D-ribulose 1,5-bisphosphate oxaloacetate citrate isocitrate a-ketoglutarate succinate succinyl coenzyme A malate fumarate glyoxylate
Appendix C Flux maps for the performed simulations of the different growth conditions: (Figs. 4–8).
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Fig. 4 – Autotrophic flux map. Net flux values are indicated in each arrow, the values are given per mol of CO2 introduced in the system. Linear pathways have been collapsed to obtain a compact representation of the network topology. The dashes arrows are fluxes to biomass formation.
Fig. 5 – Heterotrophic flux map. Net flux values are indicated in each arrow, the values are given per mol of glucose introduced in the system. Linear pathways have been collapsed to obtain a compact representation of the network topology. The dashes arrows are fluxes to biomass formation.
Fig. 6 – Mixotrophic flux map. Net flux values are indicated in each arrow, the values are given per mol of glucose introduced in the system. Linear pathways have been collapsed to obtain a compact representation of the network topology. The dashes arrows are fluxes to biomass formation.
Fig. 7 – Anoxic maximum flux map. Net flux values are indicated in each arrow, the values are given per mol of glucose introduced in the system. Linear pathways have been collapsed to obtain a compact representation of the network topology. The dashes arrows are fluxes to biomass formation.
international journal of hydrogen energy 34 (2009) 8828–8838
Fig. 8 – Anoxic photoreduction flux map. Net flux values are indicated in each arrow, the values are given per mol of glucose introduced in the system. Linear pathways have been collapsed to obtain a compact representation of the network topology. The dashes arrows are fluxes to biomass formation.
references
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[29] Kaneko T, Sato S, Kotani H, Tanaka A, Asamizu E, Nakamura Y, et al. Sequence analysis of the genome of the unicellular Cyanobacterium Synechocystis sp. strain PCC6803. II. Sequence determination of the entire genome and assignment of potential protein-coding regions. DNA Research 1996;3:109–36. [30] Nakamura Y, Kaneko T, Hirosawa M, Miyajima N, Tabata S. CyanoBase, a WWW database containing the complete nucleotide sequence of the genome of Synechocystis sp. strain PCC6803. Nucleic Acids Research 1998;26:63–7. [31] Herrero A, Flores E, editors. The cyanobateria: molecular biology, genetics and evolution. Caister Academic Press; 2008. Chapter 3. [32] Edwards JS, Covert M, Palsson BO. Metabolic modelling microbes: the flux-balance approach. Environmental Microbiology 2002;4:133–40. [33] Edwards JS, Palsson BO. The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proceedings Natural Academy Science 2000;97: 5528–33. [34] Yang C, Huaw Q, Shimizu K. Metabolic flux analysis in Synechocystis using isotope distribution from 13C-labeled glucose. Metabolic Engineering 2002;4:202–16. [35] Shastri AA, Morgan JA. Flux balance analysis of photoautotrophic metabolism. Biotechnology Progress 2005; 21:1617–26. [36] Pearce J, Leach CK, Carr NG. The incomplete tricarboxylic acid cycle in the blue-green algae Anabaena varriabilis. Genetical Microbiology 1969;55:371–8. [37] Stal L, Moezaalar R. Fermentation in cyanobacteria. FEMS Microbiology Review 1997;21:179–211. [38] Rubio FC, Camacho FG, Sevilla JM, Chisti Y, Grima EM. A mechanistic model of photosynthesis in microalgae. Biotechnology and Bioengineering 2003;81(4):459–73. [39] Albertsson PA. A quantitative model of the domain structure of the photosynthetic membrane. Trends Plant Science 2001; 6:349–58.
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Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/he
Metabolic flux analysis of the hydrogen production potential in Synechocystis sp. PCC6803 E. Navarro a,*, A. Montagud b, P. Ferna´ndez de Co´rdoba b, J.F. Urchueguı´a b a b
Departamento de Lenguajes y Ciencias de la Computacio´n, Campus de Teatrinos, Universidad de Ma´laga, 29071 Ma´laga, Spain Instituto Universitario de Matema´tica Pura y Aplicada, Universidad Polite´cnica de Valencia, Camino de Vera 14, 46022 Valencia, Spain
article info
abstract
Article history:
Hydrogen is a promising energy vector; however, finding methods to produce it from
Received 28 May 2009
renewable sources is essential to allow its wide-scale use. In that line, biological hydrogen
Received in revised form
production, although it is considered as a possible alternative, requires substantial
28 July 2009
improvements to overcome its present low yields. In that direction, genetic manipulation
Accepted 6 August 2009
probably will play a central role and from that point of view metabolic flux analysis (MFA)
Available online 17 September 2009
constitutes an important tool to guide a priori most suitable genetic modifications oriented to a hydrogen yield increase.
Keywords:
In this work MFA has been applied to analyze hydrogen photoproduction of Synechocystis
Metabolism
sp. PCC6803. Flux analysis was carried out based on literature data and several basic fluxes
Hydrogen
were estimated in different growing conditions of the system. From this analysis, an upper
Synechocystis
limit for hydrogen photoproduction has been determined indicating a wide margin for
Photosynthesis
improvement. MFA was also used to find a feasible operating space for hydrogen production, which avoids oxygen inhibition, one of the most important limitations to make hydrogen production cost effective. In addition, a set of biotechnological strategies are proposed that would be consistent with the performed mathematical analysis. ª 2009 Professor T. Nejat Veziroglu. Published by Elsevier Ltd. All rights reserved.
1.
Introduction
Nowadays, the necessity of looking for alternative sources of energy to fossil fuels is a question of major relevance and serious social concern because of their limited reserves and adverse environmental effects. In this respect, the transportation sector represents 2/3 of the net oil consumption [1] and currently there is no reliable solution for a complete replacement by renewable sources. Despite the fact that some biofuels, like biodiesel or ethanol have been proposed as suitable candidates to partially substitute oil, their production potentials are limited [2], and, in spite of massive improvement efforts currently under development to enlarge the spectrum of efficiently usable
biomass from cellulose, other alternatives have to be found in a medium or long term. At this point, hydrogen appears as a very promising candidate to be a prominent future energy vector [3,4], due to its clean, smokeless combustion and high energy density (142 MJ/kg for H2 vs. 42 MJ/kg for oil). At the present time, steam reforming of natural gas is the best established system to produce hydrogen at industrial scale; however it is based on non-renewable energy sources and generates substantial sulphur and CO2 emissions. In fact, most of the H2 producing processes based on fossil fuels release approximately twice as much moles of CO2 than moles of hydrogen. In order to produce hydrogen from renewable and clean sources of energy several alternatives are under study [5,6], and among them biological hydrogen
* Corresponding author. Tel.: þ34 6 7782 2930. E-mail address: [email protected] (E. Navarro). 0360-3199/$ – see front matter ª 2009 Professor T. Nejat Veziroglu. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhydene.2009.08.036
international journal of hydrogen energy 34 (2009) 8828–8838
Table 1 – Hydrogen production rates obtained by different methods, experimental values shown for photolytic methods corresponds mainly with algae, although some relevant works for cyanobacteria can be found in ref. [10]. Process Biophotolysis Indirect biophotolysis Photo-fermentation Dark-fermentation Two stage processes
Production rate
Ref.
0.07 mmol/l per h 0.35 mmol/l per h 145–160 mmol/l per h 77 mmol/l per h 3.37–7.2 mol H2/mol C6
[7,8] [9] Review in ref. [10] [11] [12]
production can be seen as a promising alternative provided several limitations are overcome. Table 1 shows a comparison of experimental values obtained for hydrogen production by different biological processes. The production rate for fermentative processes is much higher than for photolytic processes, nevertheless these values do not take into account the substrate cost and efficiency and from that point of view the balance is not so favourable for that kind of alternative. In fact, theoretically, the maximal energetic efficiency for direct biophotolysis is about 40% [10] compared with a maximum of about 1% for hydrogen production from biomass [13]. Recognizing the important potential of biophotolysis, the National Research Council of US has recommended that the US Department of Energy ‘‘refocus its biobased program on more fundamental research on photosynthetic microbial systems to produce hydrogen from water at high rate and efficiency’’ [13]. Photosynthetic organisms are able to use solar energy to produce their own feed-stock. As it was discovered by Gaffron [14], certain organisms like cyanobacteria and some algae
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possess pathways of hydrogen production-consumption, which play an important role in their metabolism, being coupled to photosynthesis [15] (see Fig. 1). Schematically the process consists of the absorption of photons by the photosystem II (PS II), which energy is used to split the water molecules in protons, oxygen and two electrons that are channeled through the so called ‘‘electron transport chain’’. These electrons are led through several enzymes to the photosystem I (PS I) which directs them back into the cyclic photosynthetic loop. The net result of the process is the production of NADPH as reducing power (where NADPþ acts as a sink for the electrons of PS II) and a proton gradient, which is used to produce ATP. The enzymes involved directly in hydrogen metabolism are [Ni–Fe] hydrogenases, [Fe–Fe] hydrogenases and nitrogenases, where hydrogen is produced as a by-product of nitrogen fixation, see Tamagnini et al. [17] for a review. As a well-known fact, inhibition of hydrogenase activity by the oxygen inherently formed in the photosynthesis is the most important limiting factor for a cost effective production of hydrogen using this mechanism [16]. Several projects [17,18], are dealing with this difficult issue, trying to find or design oxygen-tolerant hydrogenases, expressed in cyanobacteria or algae. A possible way of overcoming this limitation is based on the strategy adopted by certain filamentous cyanobacteria like Nostoc sp. in order to increase their nitrogen fixation efficiency. It consists of isolating certain specialized cells, heterocyst, creating anaerobic environments to activate their nitrogen fixing enzymes and producing hydrogen as byproduct. The main drawback of this approach is its poor efficiency, as nitrogen fixation is energetically expensive, see Lopes et al. [19].
Fig. 1 – Scheme of biophotohydrogen production for different cyanobacteria and algae. In the figure, electrons released through the PSII system are driven trough several steps up to the hydrogenase. In the case of Synechocystis PCC6803, the photosynthetic transport chain share several enzymes with the respiratory transport chain (not represented), this fact allows the possibility of using electrons from glucose or other substrates in order to produce hydrogen. Other reactions which can be sinks of the photosynthetic electrons like the Melher reactions have not been drawn. An extended review of hydrogenase action can be found in ref. [53].
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A further option considered to overtake these limitations is to uncouple hydrogen from oxygen production, as for instance in the system developed by Melis and coworkers described in Melis et al. [20]. In their procedure, sulphur deprived algae are unable to repair the damage produced in their PSII antennas, giving rise to a phase of low activity of the PSII system and, thus, lower oxygen production, up to a level in which it is exceeded by the rate of O2 consumption by the respiration metabolism. In this way an anaerobic environment is temporally set up, hydrogenases are not inhibited and hydrogen is produced during a relatively long period of time. This mechanism relies on the availability of a substrate (for instance succinate, NADH, acetate and the like) to be able to reduce photosynthetic oxygen and provide electrons for hydrogen production. In fact, it was reported that the biomass produced during the active photosynthesis phase is subsequently reconverted to feed the electron transport chain for hydrogen production. This idea is analogous to the strategy of photofermentation, which combines photosynthesis with fermentation processes, in which the residues from fermentation are used to feed photosynthetic bacteria in anaerobic media, resulting in a continuous hydrogen production [21]. A detailed review of these strategies and other with their most important drawbacks can be found in Hallenbeck and Benemann [22]. It is apparent that a deeper understanding of the interactions between biochemical energy, carbon fixation and assimilation pathways through mathematical modelling could be a valuable tool to evaluate maximum theoretical product yield and to help developing improved strategies. One of the best developed conceptual tools in this direction, microbial pathway engineering, has traditionally been applied mainly to industrial processes for biosynthesis of products of high economical value. Accurate quantification of pathway fluxes is hereby an important goal, especially where the aim is to convert as much substrate to a desired metabolic product via strain improvement. There have been several initiatives towards development of metabolic flux models for different organisms in order to gain quantitative information about metabolic physiology of the cultures [23–25], and recently, metabolic flux analysis technique has been reported to be applied to hydrogen production using growth of Escherichia coli [26] and other organisms [27] on glucose has been reported – a brief overview of fermentative hydrogen production is found in Jones [28]. However, up to now this methodology had not been applied to hydrogen photoproduction. Among the different organisms potentially capable of producing hydrogen from sunlight, cyanobacterium Synechocystis sp. PCC6803 possess several interesting features to be of choice as model system in this kind of applications: it can synthesize almost all its requirements from light and CO2, its genome is fully sequenced [29,30], and it allows genetic manipulation through plasmid insertion [31]. In the present contribution we have developed a metabolic model, based on available data, to evaluate the theoretical hydrogen production potential capabilities of Synechocystis sp. PCC6803 from sunlight. Flux balance analysis (FBA) was applied over the obtained network in five cultivation conditions: Autotrophic growth condition depicts the primary biological process of using solar energy for the synthesis of organic molecules from carbon dioxide. Heterotrophic metabolism
allows obtaining energy from glucose in the absence of light. Mixotrophic conditions fall between the above two categories (light and glucose are used concurrently as sources of energy). Finally, two solutions from the metabolic network solutions space with zero net oxygen flow were analyzed, they have been used to situate in an overall scheme different strategies proposed in the literature, providing us with a global vision of the process and suggesting possible improvements.
2. Synechocystis PCC6803 modelling approach 2.1.
Metabolic flux analysis
Quantification of metabolic network fluxes can be achieved via metabolic flux analysis (MFA) (see refs. [24,25] for a more general description). This method uses the stoichiometry of the intracellular metabolic reactions, the extracellular fluxes and the known internal fluxes as input and in return, the metabolic fluxes for the rest of intracellular reactions under pseudo-steady state condition are calculated by means of flux balance analysis (FBA) as established by Edwards et al. [32]. Under this approach [33], a relation is established among the intracellular metabolite concentrations, reaction fluxes and the stoichiometry in a metabolic system by a dynamic mass balance equation given below: dX ¼ An dt
(1)
where A represents the stoichiometric matrix with dimension p q (where p represents the number of metabolites and q the number of reactions). Each stoichiometric matrix element, A(i,j ), symbolizes stoichiometric coefficient of the ith metabolite in the jth reaction, X is the px1 concentration vector of all intracellular metabolites and v represents all metabolic net fluxes or reaction rates with the dimension qx1. Under metabolic steady state conditions, the mass balance equation for metabolites specializes to: A,v ¼ 0
(2)
Then, since the number of reactions q is always larger than the number of metabolites p, the system has a degree of freedom F ¼ q p. Hence, if a system has a number of measured fluxes less than F the system becomes underdetermined by nature. In order to obtain a feasible solution for the intracellular fluxes, an overall optimization criterion on metabolic balances has to be imposed. In such a case, the maximum of a given flux i is determined by imposing Eq. (2) as the constraint in the form given below Maxðvi Þ
s:t A v ¼ 0 at N < vi < N
Where vi is the rate of the ith reaction.
2.2.
Stoichiometric model of Synechocystis PCC6803
The photosynthetic electron transport chain (ptc) and the respiratory electron transport chain (rtc) of Synechocystis sp. PCC6803 are located in the thylakoid membrane of the bacterium. In Synechocystis sp. PCC6803, hydrogen
international journal of hydrogen energy 34 (2009) 8828–8838
photoproduction relies on the photosynthetic system. Ptc is linked to the rest of the metabolism through the central carbon metabolism, which receives the ATP and NADPH produced in the photosynthesis, using it to fix carbon and to obtain energy for the rest of the metabolism. On the other hand, central carbon metabolism produces metabolites, which can feed the rtc with electrons. Rtc shares several enzymes with the photosynthetic system. Thus, any model intended to evaluate the hydrogen production potential of such systems should include, at least, the following pathways: pentose phosphate pathway (or Calvin cycle if the system is working in light conditions), glycolysis and tricarboxylic acid (TCA) cycle. In addition, the effect of ptc and rtc must be taken into account. The general pathway for Synechocystis (KEGG: http://www. genome.jp/kegg/) was used as a basis for construction of its metabolic network. The detailed metabolic network considered in the present analysis is shown in Fig. 2. After careful analysis, the following assumptions, based on the available experimental information, were made in order to develop the final set of stoichiometric equations: (a) Biomass formation equation was formulated based on the macromolecular composition of Synechocystis sp. PCC6803, see refs. [34,35]. (b) A growth and non-growth associated (maintenance) ATP consumption was considered in the reaction network for ATP balance after ref. [35]. (c) Glyoxylate shunt was included in the model as it was identified in isotope labelled glucose feeding experiments following ref. [34]. (d) Due to the absence of α-ketoglutarate dehydrogenase the production of succinyl CoA from a-ketoglutarate has been omitted from TCA cycle [36]. (e) Synechocystis is not known to operate through fermentative pathways [37], and hence production of fermentative products is not considered in the model. (f) The conversion of photons into ATP (photophosphorylation) and NADPH (reducing power) by water oxidation has been reproduced by cyclic and non-cyclic electron transport processes, see refs. [38–40]. (g) The maximum photon uptake rate and CO2 uptake rates were used as measured extracellular fluxes obtained from literature [35]. In addition to these pathways, in order to consider photosynthesis in our model we have used a reduced or effective approach developed in ref. [38], which relates the energy input – in the form of chemical (trough several metabolites) or light energy – with the metabolite output of the organism. Inputs of the model will be the incoming fluxes of photons, CO2 and glucose. Our stoichiometric model comprises 56 compounds and 90 pathway reactions (reversible reactions are considered as different reactions), but not all of them are active in all metabolic conditions. Pseudo-steady state is assumed for 21 intracellular metabolites resulting in a model with six degrees of freedom. The reactions considered to build the metabolic network and construct the stoichiometric matrix used to solve Eq. (2) are shown in Appendix A.
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Optimization was achieved using a commercial dynamic optimization algorithm (ILOG CPLEX’sª mathematical optimization technology). The routine is directed towards maximizing cell growth (biomass), which depicts actual metabolic states of the microorganism and hence gives flux estimation in a wild type strain, and also to evaluate the feasibility of an alternative state, which could point to an increase in hydrogen production. Note that, because of the underdetermined nature of the system, the intracellular fluxes obtained in the present study can be considered as possible solutions in a large solutions space. The obtained values are the basis for our estimation of an upper limit to the hydrogen production capacity of the system.
3.
Model results
This section describes the main results of the flux analysis carried out for the different growth conditions. A summary of the relevant results from the model can be seen in Fig. 3. For the sake of clarity individual fluxes have not been detailed. The flux distributions of central carbon metabolism in Synechocystis under heterotrophic condition are characterized by the following aspects: the oxidative pentose phosphate pathway is the major pathway for glucose catabolism in agreement with results reported in ref. [41]. The high flux through the oxidative part of the pentose pathway is explained by the fact that it provides a large amount of NADPH and biosynthetic precursors. The carbon efficiency of this process d defined as number of carbon atoms inputs (glucose) minus number carbon outputs divided by the carbon input d of this process is approximately of 60%, which means that the rest is released in the form of CO2. A net molar oxygen consumption of 1.5 per mol of glucose is obtained. Under autotrophic condition the reductive pentose phosphate pathway is active and part of the glycolysis flow is in the gluconeogenic direction. The flow through the TCA cycle is reduced by an order of magnitude compared to the heterotrophic one, and the glyoxylate shunt is not active. In this process, as the carbon source is CO2, the efficiency of its fixation can be considered as 1. Regarding the efficiency of light absorption, approximately 4% is used to increase the biomass yield. The ratio PSI/PSII is 0.43. The molar oxygen release per mol of carbon fixed is 1.5. The biomass formation per unit of carbon fixed is 1.45 times larger for autotrophic conditions than for heterotrophic conditions. Flux analysis under mixotrophic conditions involves three independent feeding fluxes to the system: glucose, CO2 and light. The CO2 uptake rate was kept free with an upper bound similar to the one found in autotrophic condition and light absorption efficiency was considered to be similar to the one obtained in autotrophy. The flux topology for mixotrophic growth lies between autotrophic and heterotrophic metabolisms. The reductive pentose phosphate pathway and glycolytic pathway occur simultaneously. Phosphoenol pyruvate (PEP) carboxylase is in the same range as autotrophism and the flow through TCA cycle is in between the values found at previous growth conditions. Assuming that the gaseous CO2 is fixed with an efficiency of 100%, in these conditions the glucose and therefore the total carbon fixation efficiency is
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Fig. 2 – The metabolic network of Synechocystis PCC6803 considered in the present study. Three major pathways have been considered: glycolysis, pentose phosphate (PP) pathway and TCA cycle. Further, the pathways for oxidative phosphorylation and anaplerotic reactions were taken into account. The pathway was further modified for the photosynthetic reactions take place in reaction centers of PSI and PSII and the Calvin cycle.
92% which is considerably higher than without the presence of light. Regarding oxygen emission, it releases 0.45 mol of oxygen is released. The biomass formation per unit of carbon fixed is 2.45, hence larger than at heterotrophic conditions. Hydrogenase inhibition by oxygen is a critical factor in order to obtain a continuous production of hydrogen. FBA can
be used to determine the existence of solutions compatible with a null oxygen net flow. The mixotrophic metabolic network was analyzed with the constraint of a null net oxygen flow. Among this set of solutions, two solutions were studied: the solution of the system with a maximum growth (anoxic maximum) and a solution of the system in which the PSII flow
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Fig. 3 – Ratio of carbon efficiency oxygen evolution TCA cycle and growth for the different metabolic conditions. The carbon efficiency is evaluated according to the ratio between the carbon input and the carbon fixed, the oxygen flow is given by the fraction of oxygen emission related to the carbon fixed (a negative sign means absorption of oxygen) and the TCA and the growth are relative to the heterotrophic growth.
of photons was the same as the flow of Eqs. (A30) and (A40) of the appendix when the system is working in natural mixotrophic conditions (anoxic photoreduction). Flux analysis under anoxic maximum shows the following characteristics: biomass formation in these conditions is higher than heterotrophic and autotrophic conditions. There is a substantial increase in the activity of the TCA cycle (approximately three times the rate of the natural mixotrophic solution). Regarding carbon efficiency, this solution has a 78% of carbon fixation. The last condition analyzed was anoxic photoreduction. That condition shows a flux distribution similar to the heterotrophic condition. This points to the fact that probably the system is using mainly glucose for growth. Carbon fixation efficiency is 67%, which represents the lowest efficiency among all the solutions which use light as an input.
4.
Discussion
According to the results described in the previous section, theoretical hydrogen production potential of the organism has been estimated. From these evaluations, the efficiency of the photosynthetic system (defined as percentage of photons which are absorbed and which energy is used to move electrons through the ptc) is 4% of the total solar radiation. From that percentage, 70% of the photons are used in the PS II. Therefore, assuming a natural irradiation limit of 2100 mE/m2 per s, see ref. [42], and that NADPH produced by photosynthesis can be metabolized completely by hydrogenase, a maximum potential hydrogen production of 103 mmol H2/mmol biomass per h is obtained. This value corresponds to the solution of the optimization problem
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taking into account an objective function, which maximizes at the same time biomass and hydrogen production in mixotrophic conditions. The obtained results show that the system growths in heterotrophic mode and uses all the electrons of the ptc to produce hydrogen. This solution predicts the maximum electron flow potentially deliverable to hydrogenase, without taking into account its conversion efficiency or inhibition factors. The value is quite large compared to results reported from hydrogen production experiments by different methods as can be seen in Table 1. This remarkable result seems to be indicating that there may be a considerable space for improvement in hydrogen photoproduction technologies. Some of the most successful strategies for hydrogen photoproduction, like the one developed by Melis et al. [20] and described in the introduction, can indeed be understood in terms of the simulations performed in the previous section to the metabolic flux solutions with a net null oxygen flux. From the point of view of metabolic balance, the sulphur deprivation approach lies in the fact that the electrons which could reach the [Ni–Fe] hydrogenase are reduced substantially compared to the natural state in order to lower oxygen production of the PS II. This suggests that this strategy can be understood in terms of the anoxic photoreduction conditions. In that frame, the modelling work reveals that this strategy has a poor theoretical efficiency compared with the anoxic maximum as it has a much lower light absorption (energy input). This efficiency penalty could be overcomed by implementing the complementary idea, increasing the respiration level to balance photosynthetic oxygen production. In fact, this strategy would be closer to the implementation of a real solution of the anoxic maximum. Regarding possible ways of implementing this solution in vivo, there are several methods, which organisms use to alter their respiration rates and thus their oxygen consumption. Among them, the expression of membrane proteins like respiratory uncoupling proteins (UCP) [43] is one relevant possible example. UCP is expressed in some eukaryotic cells like yeast, creating pore-like structures in the mitochondrial membrane which modify the gradient potential through the membrane, subsequently causing an increase in the electron flow through the rtc. Recent studies from our group have shown a burst in oxygen consumption in yeast due to UCP [44]. Unfortunately, problems in the functionality of these proteins in bacteria [45] have been reported. Other equivalent strategies, already functional in plants, are based on opening, or enhancing, alternative routes for the electrons of the electron transport chain with oxygen reduction. This is caused by genes such as cytochrome c oxidase (COX), mitochondrion alternative oxidase (AOX) and chloroplast alternative oxidase (PTOX). A detailed review of its action in green algae in order to reduce the oxygen level can be found in ref. [46]. The existence of alternative oxidases present in cyanobacteria like Anabaena variabilis (entry name Ava_1055) and Acaryochloris marina (entry names AM1_0483, AM1_1551 and AM1_A0138) which could be expected to be functional in Synechocystis, should be specially pointed out. Other more sophisticated alternatives which can perform a similar function than UCP in eukaryotic cells consist in the
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reduction of the efficiency of the ATPase which may produce a similar effect to the UCP, solving the problem of expressing an eukaryotic transmembrane protein in cyanobacteria. Summarizing, the reduction of the oxygen level results in the fact that hydrogen is finally produced from electrons coming from respiration which substrate is based mainly in carbohydrates or acetate as the electrons coming from photosynthesis are used to reduce the oxygen created in their production. Producing hydrogen through the ptc coupled to rtc is potentially more efficient than through other fermentative pathways (4 moles of hydrogen per mol of glucose). In addition, considering a two step process in which the biomass produced by light in an aerobic environment (first step) can be used in an anaerobic phase (second step) to produce hydrogen, the system can be considered autonomous. Apart from hydrogenase, the photosynthetic transport chain possesses other electron sinks, which can be of major importance (for example the Calvin cycle, cyclic transport chain or Mehler reactions). Therefore, an alteration of these pathways through genetic engineering, as reported in refs. [47,48], can due to an improvement of the hydrogen production, once oxygen is removed.
5.
Conclusions
Metabolic flux analysis has demonstrated to be a powerful tool to analyze the capabilities of different organisms to produce specific metabolites of interest and to assist in their optimization. We applied this methodology to evaluate the metabolic network of Synechocystis sp. PCC6803 for hydrogen photoproduction, which constitutes the first time this kind of analysis is performed in an autotrophic organism taking into account the electron flow of the photosystem to analyze hydrogen production. In this analysis a metabolic model has been implemented according to which three main growing conditions of the bacteria (heterotrophic, autotrophic and mixotrophic) were studied and the differences among them established in terms of oxygen production and carbon fixation. The obtained results from that analysis in terms of growth are in agreement with the ones reported in literature. The potential hydrogen production of Synechocystis sp. PCC6803 has been evaluated and it was found that potential (or theoretical maximum) hydrogen production is about two orders of magnitude larger than experimental yields reported in literature [49]. This upper limit has been estimated based on the assumptions of no modifications are introduced in the antenna system and all the electrons from the transport chain are redirected to hydrogenase. Therefore, although this is just an upper theoretical limit, it suggests a wide margin for improvement in hydrogen production from biophotolysis in order to make it cost effective. One of the most important limitations of this hydrogen production methodology consists of the inactivation of hydrogenase by oxygen. From the metabolic point of view, it has been shown that there are alternative solutions of the network, which show null net oxygen flow. In this frame, two
solutions with null oxygen flow were analyzed from the metabolic point of view: the anoxic maximum and the anoxic photoreduction. In relation with this analysis, one of the most successful experiments in this field [20] was put in the frame of the anoxic photoreduction solution analyzing its possible limitations. Alternative approaches, closer to the anoxic maximum solution, have been proposed and several ways to implement those solutions in an organism are discussed in the paper. Hopefully, this work will help experimentalists ascertain new strategies and possible approaches based on the present theoretical framework.
Acknowledgements This work has been funded by the European Union through the project of the sixth Framework Program ‘‘Engineered Modular Bacterial Photoproduction of Hydrogen’’ Contract no.: 043340, and seventh Framework Program: ‘‘Targeting environmental pollution with engineered microbial systems a´ la carte’’, Contract no.: 212894, the Spanish Ministerio de Educacion y Ciencia through the program Juan de la Cierva and the FPI grant program of the Generalitat Valenciana. The authors are grateful for the given support.Finally, the authors are thankful to Ph D. F. Villatoro for his thoughtful comments on the paper.
Appendix A List of reactions GLCext þ Hþ /GLC
(A1)
G6P þ ADP4GLC þ ATP
(A2)
F6P4G6P
(A3)
F6P þ ATP4F1; 6P þ ADP
(A4)
GAP4F1; 6P
(A5)
F1; 6P/F6P
(A6)
F6P þ ATP/F1; 6P þ ADP
(A7)
GAP þ NADþ 4NADH þ DHAP
(A8)
DHAP þ ADP43PG þ ATP
(A9)
3PG4PEP
(A10)
PEP þ ADP/PYR þ ATP
(A11)
PYR þ CoA þ NAD/AcCoA þ NADH þ Hþ þ CO2
(A12)
G6P þ NADPþ 4GL6P þ NADPH þ Hþ
(A13)
GL6P þ H2 o/D6PGDL
(A14)
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D6PGDL ¼ NADPþ /Ru5P þ NADPH þ CO2
(A15)
R5P4Ru5P
(A16)
X5P4Ru5P
(A17)
F6P þ GAP4E4P þ X5P
(A18)
GAP þ S7P/E4P þ F6P
(A19)
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1:191 G6P þ 0:133 GAP þ 1:205 3PG þ 1:002 PEP þ 1:197 PYR þ0:715 R5P þ 0:501 E4P þ 2:039 OA þ 1:233 AKG þ3:72 AcCoA þ 0:16 SUCCoA þ 2:82 NAD þ 49:06 NADPH þ53:55 ATP/3:887 CoA þ 53:35 ADP þ 49:06 NADP þ2:82 NADH þ 0:683 FUM þ 0:16 Suc þ BM þ 1:017 CO2 0:882 G6P þ 0:238 GAP þ 1:53 PEP þ 2:64 PYR þ 0:399 R5P þ0:406 E4P þ 1:23 OA þ 1:04 AKG þ 4:64 AcCoA þ 2:82 NAD þ27:22 NADPH þ 39:21 ATP/4:64 CoA þ 39:21 ADP
GAP þ E4P/S7P
(A20)
S7P þ GAP4R5P þ X5P
(A21)
Ru5P þ ATP/R15P þ ADP
(A22)
R15P þ CO2 /23PG
(A23)
AcCoA þ OA/CIT
(A24)
CIT4ICT
(A25)
ICT þ NADPþ /AKG þ NADPH þ CO2
(A26)
SUC þ ATP4SUCCoA þ ADP
(A27)
MAL4FUM
(A28)
OA þ NADH4MAL þ NADþ
(A29)
2SUC þ O2 /2FUM þ 8Hþ
(A30)
ICT/GOX þ SUC
(A31)
GOX þ AcCoA4MAL þ CoA
(A32)
MAL þ NAD4NADH þ PYR þ CO2
(A33)
þ27:22 NADP þ 2:82 NADH þ BM þ 1:835 CO2 1:228 G6P þ 0:208 GAP þ 1:42 PEP þ 2:44 PYR þ 0:382 R5P þ0:376 E4P þ 1:14 OA þ 0:886 AKG þ 3:96 AcCoA þ2:82 NAD þ 29:01 NADPH þ 38:89 ATP/3:96 CoA þ38:89 ADP þ 29:01 NADP þ 2:82 NADH þ BM þ 1:835 CO2
Appendix B. Nomenclature
PYR þ CO2 þ ATP4OA þ ADP
(34)
NADH þ NADPþ þ 2Hþ /NADþ þ NADPH
(A35)
photonex/photon þ photon0
(A36)
4photon þ NADPþ þ H2 O/NADPH þ 6Hþ þ 0:5O2
(A37)
photon0 /2Hþ
(A38)
4Hþ þ ADP/ATP
(A39)
2NADH þ O2 /2NADþ þ 8Hþ
(A40)
CO2 ex/CO2
(A41)
O2 ex/O2
(A42)
PEP þ CO2 /OA
(A43)
Three biomass formation equation [32,33]
GLC G6P F6P F1,6P GAP DHAP 2PGA 3PG PEP PYR AcCoA CoA GL6P D6PGDL R5P Ru5P X5P EP4 S7P R15P OA CIT ICT AKG SUC SUCCoA MAL FUM GOX
glucose glucose 6-phosphate D-fructose 6-phosphate D-fructose 1,6-bisphosphate D-glyceraldehyde 3-phosphate 3-phospho-D-glyceroyl phosphate 3-phospho-D-glycerate 2-phospho-D-glycerate phosphoenolpyruvate pyruvate acetyl-CoA coenzyme A D-gluconate-6-phosphate D-glucono-1,5-lactone 6-phosphate D-ribose 5-phosphate D-ribulose 5-phosphate xylulose 5-phosphate D-erythrose 4-phosphate sedoheptulose 7-phosphate D-ribulose 1,5-bisphosphate oxaloacetate citrate isocitrate a-ketoglutarate succinate succinyl coenzyme A malate fumarate glyoxylate
Appendix C Flux maps for the performed simulations of the different growth conditions: (Figs. 4–8).
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Fig. 4 – Autotrophic flux map. Net flux values are indicated in each arrow, the values are given per mol of CO2 introduced in the system. Linear pathways have been collapsed to obtain a compact representation of the network topology. The dashes arrows are fluxes to biomass formation.
Fig. 5 – Heterotrophic flux map. Net flux values are indicated in each arrow, the values are given per mol of glucose introduced in the system. Linear pathways have been collapsed to obtain a compact representation of the network topology. The dashes arrows are fluxes to biomass formation.
Fig. 6 – Mixotrophic flux map. Net flux values are indicated in each arrow, the values are given per mol of glucose introduced in the system. Linear pathways have been collapsed to obtain a compact representation of the network topology. The dashes arrows are fluxes to biomass formation.
Fig. 7 – Anoxic maximum flux map. Net flux values are indicated in each arrow, the values are given per mol of glucose introduced in the system. Linear pathways have been collapsed to obtain a compact representation of the network topology. The dashes arrows are fluxes to biomass formation.
international journal of hydrogen energy 34 (2009) 8828–8838
Fig. 8 – Anoxic photoreduction flux map. Net flux values are indicated in each arrow, the values are given per mol of glucose introduced in the system. Linear pathways have been collapsed to obtain a compact representation of the network topology. The dashes arrows are fluxes to biomass formation.
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