Terra Cognita: Using Earth Observing Systems to Understand Our World

Background: The need for discovery of alternative, renewable, environmentally friendly energy sources and the development of cost-efficient, "clean" methods for their conversion into higher fuels becomes imperative. Ethanol, whose significance as fuel has dramatically increased in the last decade, can be produced from hexoses and pentoses through microbial fermentation. Importantly, plant biomass, if appropriately and effectively decomposed, is a potential inexpensive and highly renewable source of the hexose and pentose mixture. Recently, the engineered (to also catabolize pentoses) anaerobic bacterium Zymomonas mobilis has been widely discussed among the most promising microorganisms for the microbial production of ethanol fuel. However, Z. mobilis genome having been fully sequenced in 2005, there is still a small number of published studies of its in vivo physiology and limited use of the metabolic engineering experimental and computational toolboxes to understand its metabolic pathway interconnectivity and regulation towards the optimization of its hexose and pentose fermentation into ethanol. Results: In this paper, we reconstructed the metabolic network of the engineered Z. mobilis to a level that it could be modelled using the metabolic engineering methodologies. We then used linear programming (LP) analysis and identified the Z. mobilis metabolic boundaries with respect to various biological objectives, these boundaries being determined only by Z. mobilis network's stoichiometric connectivity. This study revealed the essential for bacterial growth reactions and elucidated the association between the metabolic pathways, especially regarding main product and byproduct formation. More specifically, the study indicated that ethanol and biomass production depend directly on anaerobic respiration stoichiometry and activity. Thus, enhanced understanding and improved means for analyzing anaerobic respiration and redox potential in vivo are needed to yield further conclusions for potential genetic targets that may lead to optimized Z. mobilis strains. Conclusion: Applying LP to study the Z. mobilis physiology enabled the identification of the main factors influencing the accomplishment of certain biological objectives due to metabolic network connectivity only. This first-level metabolic analysis model forms the basis for the incorporation of more complex regulatory mechanisms and the formation of more realistic models for the accurate simulation of the in vivo Z. mobilis physiology. Published: 9 March 2007 Microbial Cell Factories 2007, 6:8 doi:10.1186/1475-2859-6-8 Received: 29 December 2006 Accepted: 9 March 2007 This article is available from: http://www.microbialcellfactories.com/content/6/1/8 © 2007 Tsantili et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Page 1 of 23 (page number not for citation purposes) Microbial Cell Factories 2007, 6:8 http://www.microbialcellfactories.com/content/6/1/8 Background In the highly energy-consuming and earth-polluting era of the early 21st century, the need for discovery of alternative, renewable, environmentally friendly energy sources and the development of cost-efficient, environmentally clean methods for their conversion into higher fuels becomes more than imperative. Ethanol's significance as fuel has dramatically increased in the last decade [1] due to characteristics that render it more effective than gasoline in optimized engines [2], with the additional advantage of contributing less to the green house effect than the conventional fuel. Ethanol, among other effective fuels, could be produced from hexoses and pentoses through microbial fermentation [3-8]. Importantly, plant biomass, which constitutes one of the main renewable energy sources on earth, could provide a significant and inexpensive source of the hexose and pentose mixture, if appropriately and effectively depolymerized [2,9-11],. In this context, optimization of the hexose and pentose microbial fermentation into ethanol is of great importance. Metabolic engineering (ME) can significantly contribute towards this end with its experimental and computational toolboxes [12-14]. To-date, Saccharomyces cerevisiae [15-17] and Escherichia coli [18-20] have been the main industrial microorganisms utilized for ethanol production, with Klebsiella oxytopa, Pichia stipitis and pastoris [2,19] being studied as potential candidates. Recently, the anaerobic Zymomonas mobilis is being also discussed among the most promising microorganisms for the microbial conversion of hexoses and pentoses into ethanol fuel due to numerous advantageous characteristics [17]. Its ethanol yield reaches 98% of the theoretical maximum compared to ~90% of S. cerevisiae [17]. Z. mobilis is the only to-date identified bacterium that is toxicologically tolerant to high ethanol concentrations [2,21], requiring thus less intricate and consequently less expensive downstream processing for the removal of ethanol in industrial chemical plants. Moreover, it has (i) low biomass yield [22], biomass competing with ethanol for the available carbon source(s), (ii) high speed of substrate conversion to metabolic products [17], and (iii) comparatively simple glycolytic pathways [21], fact that might prove beneficial for this organism's cell engineering towards the optimization of the ethanol production process. In addition, any disadvantages of the Z. mobilis use for ethanol production in the food and beverage industry, referring mainly to the formation of byproducts modifying food flavor [17], are not applicable in the context of biofuel production. Finally, its wild-type not catabolizing pentose sugars, Z. mobilis engineering [24] resolved the last major obstacle associated with its use for the fermentation of plant biomass [25]. Despite, however, the increasing interest in Z. mobilis, the number of reports in current literature studying its in vivo physiology remains small [22,23,26-29]. This implies a rather limited so far use of the metabolic engineering toolbox for the analysis of the microorganism's metabolic pathway interconnectivity and regulation. The recent publication of Z. mobilis full genome [21] is expected to greatly assist the investigations for the identification of potential genetic modification targets towards optimized Z. mobilis strains. In this context, the main objectives of the presented work, discussed sequentially in the following sections, were (a) to reconstruct the metabolic network of the engineered Z. mobilis using the available resources to a level that it could be modeled according to the existing metabolic engineering methodologies, and (b) to use linear programming (LP) analysis – the first level of metabolic modeling towards the simulation of in vivo physiology [30-37] – for the identification of the microorganism's metabolic boundaries with respect to various biological objectives, as these boundaries are determined only by the stoichiometric connectivity of the network. Results and Discussion A. Reconstruction of the Z. mobilis Metabolic Network The reconstruction of an organism's metabolic network used to be mainly based on the existing knowledge about the metabolic network structure of similar cellular systems, along with any available data regarding in vitro/in vivo enzymatic activity and metabolic output measurements under various genetic backgrounds or environmental conditions [see e.g. [38]]. In the post-genomic era, the available resources are further augmented by the everincreasing knowledge about gene annotation based on high-throughput sequencing [e.g. [33,39,40]] and gene expression analyses [41]. While the availability of the genomic data provides a significant advancement in the process of reconstructing the maximum potentially active metabolic network of a biological system, this remains a non-trivial task that requires the direct involvement of an expert's judgment to decide over the sometimes multiple feasible answers to questions that arise during the process [42]. The reconstructed metabolic network of the engineered Z. mobilis is depicted schematically in Figure 1, while all included reactions are listed in Appendix 1A (in the rest of the text all reactions will be referred by their number in Appendix 1A). The main utilized resources were the available annotation of the recently fully sequenced Z. mobilis genome [21], the public metabolic databases KEGG [43] and EXPASY [44], the Z. mobilis in vivo flux analysis studies [22,23] and biochemistry textbooks [45,46]. Specifically, Z. mobilis utilizes the Entner-Doudoroff (E.D.) and part of the Embden-Meyerhof-Parnas pathway (E.M.P) for the Page 2 of 23 (page number not for citation purposes) Microbial Cell Factories 2007, 6:8 http://www.microbialcellfactories.com/content/6/1/8 catabolism of glucose into pyruvate (reactions 1–11) that leads to the production of 1 mole of ATP, NADPH and NADH. Xylose isomerase, xylulokinase and the full pentose phosphate pathway (PPP) (reactions 12–19) were considered as potentially active to account for the catabolism of pentoses by the engineered strain. Because xylitol has been observed as product of an engineered Z. mobilis strain under a particular set of conditions [22], reaction 39 was also included in the reconstructed network. No α-ketoglutarate dehydrogenase gene has yet been annotated in the Z. mobilis genome, supporting the current hypothesis that the anaerobic Z. mobilis features an incomplete citric acid cycle (TCA) (reactions 28–33) [22,23]. This is in agreement with prior biological knowledge [45], according to which fermenting organisms transform TCA from an oxidative to a reductive pathway. In this case, the two separate parts of the TCA cycle serve in producing the biosynthetic precursors α-ketoglutarate (left branch in Figure 1), oxaloacetate and succinyl-CoA (right branch in Figure 1). The "right" (in figure 1) branch is also connected to the anaerobic respiration, since under anaerobic conditions fumarate could act as electron acceptor and be reduced to succinate through a membrane-bound fumarate reductase enzyme (reaction 32) [45]. According to the currently available genomic and metabolic information, the Z. mobilis oxaloacetate pool is replenished by two anaplerotic reactions catalyzed by the enzymes phosphoenolpyruvate carboxylase (reaction 35) and malate dehydrogenase (reaction 34). Anaerobically growing Z. mobilis can feature a number of fermentation reactions (reactions 20–27). These reactions and their connection with anaerobic respiration are currently considered the determining factor for the Z. mobilis ability to produce ethanol in high yields [17]. Major role in a metabolic network's reconstruction and further modeling plays the selection of the respiration reactions (reactions 41–47). While no clear indication of the activity of the formate dehydrogenase (reaction 40) complex with ubiquinol-cytochrome c reductase (reaction 45) currently exists, reaction 45 was still included in the stoichiometric model [36], formate considered among the potential products of the anaerobic microorganism. An additional assumption, which is not currently backed up by genomic information, is the activity of NAD(P) transhydrogenase (it will be referred as trans in the rest of the text) (reaction 47); including this reaction in the stoichiometric model, NADH and NADPH become equivalent [35]. Finally, the considered amino acid biosynthesis and cumulative biomass formation reactions (reactions 61– 79) were based on the information of Table 1, the latter being populated after appropriately modifying Table 2 in [22]. Among the modifications, methionine biosynthesis, which in [22] was considered as catalyzed by the EC 2.3.1.46 enzyme, was replaced by reaction 71 catalyzed by The Z. mobilis reconstructed metabolic network Figure 1 The Z. mobilis reconstructed metabolic network. The numbers next to the reaction arrows refer to the reaction listing in Appendix 1A. Biomass precursors are circled. GLC XYLOSE