Examination Committee
Prof. dr. ir. Frank DEVLIEGHERE (Ghent University) Prof. dr. Daniel CHARLIER (Vrije Universiteit Brussel) Prof. dr. ir. Joseph J. HEIJNEN (Technical University Delft) Prof. dr. ir. Patrick VANOOSTVELDT (Ghent University) Prof. dr. ir. Nico BOON (Ghent University) Prof . dr. ir. Wim SOETAERT (Ghent University) em. Prof. dr.ir. Erick VANDAMME (Ghent University)
Supervisors
Prof . dr. ir. Wim SOETAERT em. Prof. dr.ir. Erick VANDAMME Ghent University, Department Biochemical and Microbial Technology, Laboratory for Industrial Microbiology and Biocatalysis
Dean
Prof. dr. ir. Guido VAN HUYLENBROECK
Rector
Prof. dr. Paul VAN CAUWENBERGE
Joeri Beauprez
METABOLIC MODELLING AND ENGINEERING OF ESCHERICHIA COLI FOR SUCCINATE PRODUCTION
Thesis submitted in fulfillment of the requirements For the degree of Doctor (PhD) in Applied Biological Sciences
Dutch translation of the title: Metabolisch modelleren en modificeren van Escherichia coli voor de productie van succinaat
Cover illustrations by Greta Van den Broeck
Please refer to this work as follows: Joeri Beauprez, 2010. Metabolic modelling and engineering of Escherichia coli for succinate production. PhD thesis, Ghent University. Ghent, Belgium. 274 p.
ISBN-number: 978-90-5989-351-1
The author and the promotor give the authorisation to consult and to copy parts of this work for personal use only. Every other use is subject to the copyright laws. Permission to reproduce any material contained in this work should be obtained from the author
This work was funded by the Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT-Vlaanderen) in the framework of the SBO-project 040/25
CONTENTS
Preface iii
Abbreviations vii
Chapter 1 General introduction 1
Chapter 2 Metabolic engineering of succinate biosynthesis in Escherichia coli : identifying gene targets using comparative genomics and stoichiometric network analysis 47
Chapter 3 Influence of C 4-dicarboxylic acid transporters on succinate production 127
Chapter 4 Synergetic effect of the transcriptional regulators ArcA and IclR on the glyoxylate shunt 149
Chapter 5 Metabolic engineering of succinate biosynthesis in Escherichia coli 169
Chapter 6 Conclusions and perspectives 199
References 209
Summary 237
Samenvatting 241
Appendix A1
Acknowledgement – Dank word i
Curriculum vitae vii
2 Preface
Preface
The increased consciousness for environmental issues and the depletion of mineral oil reserves was the cornerstone for this thesis. This new awareness not only led to the search for alternative energy sources but also for alternative chemical or better biochemical processes that are much more environmentally friendly. One of these chemicals that was identified as green is succinic acid. This acid is one of the many chemicals that have great economical potential in a biobased economy and can be produced from renewable resources. In such a biobased economy, energy and chemical building blocks are gained from processes that use sugars or lignocellulotic biomass. Chapter 1 of this thesis describes the goals and preconditions for a biobased economy. Chemical building blocks that show economic potential are reviewed and finally, the state of the art of succinic acid production and its applications are covered. The succinate production processes are evaluated in function of the production hosts with their respective advantages and disadvantages.
Chapter 2 introduces comparative genomics and stoichiometric network analysis as tools for the development of a mutation strategy. In the comparative genomics study, natural succinate producing strains are compared with Escherichia coli in order to discover differences that might relate to succinate production. With stoichiometric network analysis carbon sources and alternative reactions with relation to their effect on succinate yield were evaluated. Finally, an overview of the regulatory levels of the E. coli central carbon metabolism is given, based on database information. The information gathered in this chapter formed the basis for the gene targets that were screened in the following chapters.
Succinate transport is one of the targets that was identified with stoichiometric network analysis in chapter 2. In chapter 3 the effect of C4-dicarboxylic acid transporters on succinate excretion is evaluated. Because the E. coli succinate export protein, DcuC, is primarily active under anaerobic conditions, the native promoter, which is regulated by several transcription factors, was removed and replaced with an artificial promoter. The activity was assessed under aerobic and anaerobic conditions in combination with knock outs of succinate import proteins. Apart from DctA, which is the main succinate importer for Escherichia coli , two other putative importers were examined in this chapter.
The analyses in chapter 2 proved the importance of the glyoxylate route for succinate production. However the route is very tightly regulated. Several genetic regulators are described that can change the carbon flux through this pathway which may have synergetic modes of action. The two main regulators are ArcA and IclR. The phenotypes of knock out strains in these two genes are evaluated in chapter 4 . This study led to a deeper understanding of cellular regulation, but also raised some new interesting questions concerning the interaction between transcription factors.
Not only the glyoxylate route and succinate transport correlated in the elementary flux modes partial least squares analysis of chapter 2, but a set of reactions was identified. Chapter 5 consists of an evaluation of these genetic targets which may affect succinate production and
v Preface byproduct formation. Not only gene knock outs and knock ins were the focus of this chapter, but also the elevation of the allosteric inhibition of for instance citrate synthase and PEP carboxykinase via point mutations were assessed. In addition, the effect of a mutant fumarate nitrate reductase regulating protein (FNR) was determined. As a global transcriptional regulator, this enzyme activates the reductive TCA cycle, which leads directly to succinate.
In chapter 6 , an overall summary is presented, which reviews the conclusions of the 5 previous chapters of this work. The scientific approach of this work is critically assessed and some possible adaptations are proposed. Finally, certain future perspectives are given for succinic acid production. Some new potential research areas are proposed on which this work has not elaborated.
vi
3 Abbreviations 4 5 6 7 8 9 10 11 12 13 14 15
16
Abbreviations
ADP Adenosine diphosphate AMP Adenosine monophosphate asRNA Antisense RNA ATP Adenosine triphosphate BiGG Biochemical, genetic and genomic database BLAST Basic local alignment search tool BRENDA Braunschweig enzyme database cAMP Cyclic AMP CDW Cell dry weight CheBi Chemical entities of biological interest CMR Comprehensive Microbial Resource DDBJ DNA data bank of Japan DHAP Dihydroxyaceton phosphate DIP Database of Interacting Proteins DNA Deoxyribonucleic acid DOE US department of Energy dsRNA Double stranded RNA ECX European Climat Exchange ED Entner-Doudoroff pathway EFM Elementary flux mode EGS External guide sequence EM Elementary mode EMBL European molecular biology laboratory EMP Emden-Meyerhof-Parnas route FNR Fumarate Nitrate Reductase regulating protein GEO Gene Expression Omnibus gTME Global transcriptional machinery engineering HMM Hidden Markov Model IHF Integration Host Factor IMG Integrated Microbial Genomes KEGG Kyoto Encyclopedia of Genes and Genomes KI Knock in KO Knock out LPS Lipopolysaccharides miRNA Micro-RNA mRNA Messenger RNA NAD + Nicotinamide adenine dinucleotide NADH Reduced nicotinamide adenine dinucleotide NADP + Nicotinamide adenine dinucleotide phosphate NADPH Reduced nicotinamide adenine dinucleotide phosphate NDH NADH dehydrogenase NTG N-methyl-N’-nitro-N-nitrosoguanidine P/O Oxidative phosphorylation PANK Pantothenate kinase PBS Polybutylene succinate PBT Polybutyleneterephthalate PCK PEP carboxykinase PDO 1,3-propanediol
ix Abbreviations
PEP Phosphoenolpyruvate PET Polyethyleneterephthalate PLA Poly-lactic acid PLS Partial least squares
PPC PEP carboxylase PPP Pentose phosphate pathway PRESS Predictive residual error sum of squares PRIAM PRofils pour l'identification automatique du métabolisme PSI-BLAST Position specific iterative BLAST PSSM Position-specific score matrices PTS Phosphotransferase system PTT Polytrimethyleneterephthalate PUR Polyurethanes
qp Specific production rate of product p RCSB Research collaboratory for structural bioinformatics RNA Ribonucleic acid RNAi Interference RNA
rp Volumetric production rate of product p RPS-BLAST Reverse psi-blast rRNA Ribosomal RNA siRNA Silencing RNA sRNA Small RNA STRING Search tool for the retrieval of interacting genes/proteins TCA Tricarboxylic acid cycle TIGR’s Tuneable Intergenic Regions Y Yield
x 1 Chapter 1 General introduction
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Polymers I I
I I Pharmacy
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General introduction
CONTENTS
1.1 Ecological, political and economical context 3
1.1 Biochemicals 6
1.1.1 Ethanol 6 1.1.2 Lactic acid 6 1.1.3 1,3- propanediol (PDO) 8 1.1.4 Amino acids 11 1.1.5 Bio-polymers 14
1.2 Succinate 16
1.2.1 Economics 16 1.2.2 Maleic anhydride 18 1.2.3 Applications 19 1.2.4 Microbial succinate production 22
1.3 Conclusions 44
2 General introduction
1.1 Ecological, political and economical context
Chemical industry has always been tightly interwoven with the oil industry. The rising oil prices and the environmental impact of many petrochemical processes are imposing the reconsideration of many of the current chemical technologies. This evolution channels resources towards the development of more economically as well as environmentally sound routes. The whole fabric that interconnects the classical syntheses is thus loosened and increasingly allows the introduction of bio chemical alternatives.
The idea of sustainable development is not new; it has been an issue since the Brundtland report in 1983, Our common future (General Assembly passed Resolution 38/161). Tangible results have flowed from the Brundtland report, for instance the emergence of international agreement's such as the Montreal and Kyoto protocols, and Agenda 21, which further enshrined the concept of environmentally sustainable development (Sneddon et al. , 2006). Though the energy problem and climate change have recently been most prominent in these actions, the concept of sustainability has much more far reaching implications. For one the North-South contrast has been highlighted even more, since the thesis ‘development versus sustainability’ was introduced. While for industrialised countries it is relatively easy to advocate solidarity towards the future generation, developing countries still struggle to meet the needs for the current generation. Lack of technology transfer between North and South and protectionist measures by the North barely give the South the breading room it needs to develop its own solution for the environmental issues the world these days is facing (De Kruijf & Van Vuuren, 1998). These issues, indeed, are grounded in many political, social and economical debates, leading up to different visions on sustainability and its implementation.
As actions are implemented to stimulate the use of sustainable processes, complex interrelations become more and more clear. For instance the introduction of carbon credits and a carbon stock exchange, might lead up to increased food prices. An analysis from 2007 (pre 2008 food crisis) linked the price of carbon with food prices. A carbon price above €16 per ton could make energy crops more favourable on crop land, rather than less productive land (Johansson & Azar, 2007). Data from the European Climate Exchange shows that already from day one the carbon price exceeded €16 per ton, possibly resulting in increased food prices in 2008. This analysis, though, does not take into account the actual oil price, which peaked also mid 2008, parallel to the price of carbon (Figure 1.1). Clearly, in such a dynamic environment it is important to adapt the definition of carbon trading according to the outcome of the policy itself, with input coming from both the users and the producers of greenhouse gasses (Johnson & Heinen, 2004). A comprehensive study on land use to meet future biobased chemical intermediate needs (bio-energy not included) shows for instance clearly some targets for policy and investment. Three scenarios were evaluated, in which low, medium and high (respectively 16 %, 40 % and 83 %) percentages of conventional chemicals would be replaced by white biotechnology chemicals. In Europe this would mean that about 1 million (low) to 38.2 (high) million ha would be needed in 2050 if starch would be used as a
3 Chapter 1 substrate. In the case of lignocellulose, these land areas would drop to 0.4 to 15.6 million ha. In the assumption that there would be a full substitution of organic chemicals, the starch scenario would need 126 million ha and the lignocellulose scenario would need 52 million ha. For reference, the total agricultural area of the EU-27 in 2005 is 192 million ha (FAOSTAT, 2008b; Patel et al. , 2006). Because about 15 % of this total area is now set aside and agricultural yields in central and eastern Europe are still low, by 2050 about 77 million ha would be available (Goldemberg et al. , 2001), making only the lignocellulose scenario feasible in the long run.
ECX CO 2 trading price and oil price 2005-2008 35 160
30 140
120 25
100 20 80 trading price (€/ton) price trading 2 15 60 ($/barrel) price oil
10 ECX CO ECX 40
5 20
0 0 2005 2006 2007 2008 2009
Figure 1.1: The CO 2 trading price according to ECX (European Climate Exchange) (ECX, 2008) in euro per ton (black line) and the oil price according to the EIA (Energy Information Administration) (EIA, 2008) in dollar per barrel (black dotted line) 2005-2008.
The environmental impact of different processes is another sustainability issue. Ever since the industrial revolution, processes have been developed regardless of their eco-toxicity and strain on human health. It was not until the late 1960s that the latter became an issue, when the effects of DDT became clear (Carson, 2002). This resulted in the Stockholm Convention of 2001, were 152 countries ratified the ban or limited use of persistent organic pollutants (Fiedler, 2007; UNEP, 2008). Following this initiative, Europe has started recently with the REACH program, Registration, Evaluation, Authorisation and Restriction of Ch emical substances, which introduces a more strict regulation and not only evaluates the impact of the chemical itself, but also of its production process (Coube, 2008). This approach actually forces chemical industry to rethink and redesign their processes so that they can minimise their environmental impact. It introduces a whole new area of industry to a bio approach: bio chemistry instead of chemistry. In principle every chemical process can have a bio- alternative, though not all of these alternatives are economically feasible. Here an in depth study is necessary to pinpoint where in the existing processes biology can supply valuable
4 General introduction
alternatives. These processes should not only target begin and end-products, but can also aim at the production of intermediate chemicals which can further be processed via well established chemical synthesis routes.
The time and cost to develop a new biotechnological process, is conceived as its main handicap. Comparing with information technology, which has a very short product life cycle (about 6 to 12 months), a biotechnology company in incremental development needs at least one to two years. From discovery to application it can take up to 20 to 30 years to create a mature technology (Hine & Kapeleris, 2006). Such an intensive research program evidently requires a vast amount of capital, extensive skill sets and knowledge; mostly leading up to large collaborative research in which each partner has its own speciality. Though biotechnology pre-dates information technology for several decades, the low expense of IT development, virtual team work and Moore’s law have led to an exponential development in hardware as well as software (Das, 2001; Moore, 1965). The same evolution is now occurring in biotechnology (Gefen & Balaban, 2008). Miniaturization and automation get more and more integrated into biotechnology, think of pipetting robots, gene chips, microtiterplates and many more. Furthermore, the introduction of information technology in biotechnology (bioinformatics) has cut down development time significantly.
Biotechnology in an industrial context – also known as white biotechnology – does not fall under the rigorous regulation of pharma, as its end-products are rarely used in medical applications, but more as an energy source or a chemical building block. Clinical trials, FDA 1 and EMEA 2 approvals take up many development years and large chunks of the invested capital (Ernst & Young, 2007; Guilford-Blake & Strickland, 2008). Industrial biotechnology on the other had bypasses stringent chemical regulations since it uses non toxic renewable resources and water as a solvent.
1 FDA: United States of America Food and Drugs Administration 2 EMEA: European Medicines Agency
5 Chapter 1
1.1 Biochemicals
As stated above, novel technology is needed. The question is: Which technologies need to be developed and which biochemicals will be of use in this context? The US Department of Energy (DoE) and the European Commission have confronted this issue by ordering a study around sustainable production of chemicals from renewable resources (Crank et al. , 2005; Patel et al. , 2006; Werpy et al. , 2004). These reports indicated several chemicals that can be biochemically produced and can be economically viable, stipulating the necessity for further research in white biotechnology. Table 1.1 gives an overview of these chemical building blocks, divided in acids and alcohols and amino acids. Some of the successes are summarised below. 1.1.1 Ethanol Due to the current rise of oil prices and lack of alternatives, up to now bioethanol has gained most attention from this list. It is said to be one of the most mature processes in biotechnology (Wheals et al. , 1999). Although the chemical synthesis route has long been outcompeted by it, bioethanol still cannot compete with classical fossil fuels. About 70 to 80 % of the overall production cost originates from the raw feedstock material, which is still mostly sugar crops (sucrose) or starch crops (grain).
An economical assessment study calculated that in this case a sugar price of about 70 euro per ton 3 would be needed for an economically viable production process (Patel et al. , 2006; Wheals et al. , 1999). Nowadays sucrose and wheat prices are 250 and 120 euro per ton, respectively (early 2008), making it still highly dependent on political willingness to stimulate production and use. The 2008 food crisis has somewhat damped this willingness (Koh & Ghazoul, 2008) and stressed the need for second and third generation technologies. These technologies focus more on waste streams and non-food resources such as paper and lignocellulotic substrates (Cardona & Sánchez, 2007; Murphy & McCarthy, 2005). 1.1.2 Lactic acid Lactic acid production may be called a success story in fermentation technology. Classical chemical synthesis was based on lactonitrile derived from acetaldehyde and hydrogen cyanide (Vijayakumar et al. , 2008), but it results in a racemic DL-lactic acid mixture, which is very difficult to purify. Fermentative routes have overcome this chemical problem.
Commercial lactic acid fermentations reach titers up to 160 g/l with yields higher than 90 %. The rates vary, depending on the substrate and fermentation mode (batch, fed-batch or continuous cell-recycling), but it can reach up to 30 g/l/h (Hofvendahl & Hahn-Hagerdal, 2000; Vijayakumar et al. , 2008).
3 In these calculations no subsidies and carbon credits were included.
6 General introduction
Table 1.1: Overview of different chemical building blocks that potentially can be produced in biotechnological processes, with F, E and C indicating the main production process: fermentative, enzymatic or chemical process, respectively (Leuchtenberger et al. , 2005; Patel et al. , 2006; Werpy et al. , 2004; Xiao et al. , 2007; Zhang et al. , 2007) Type Building block Acids and alcohols C2 Ethanol F Acetic acid C Glyoxylic acid C Oxalic acid C C3 Lactic acid F 3-hydroxypropionic acid C/F Glycerol C/E 1,2-propanediol C/F 1,3-propanediol C/F Propionic acid C Acetone C C4 Fumaric acid F Succinic acid C/F Malic acid C/E Butyric acid C/F 1-butanol C 2,3-butanediol C 1,4-butanediol C Acetoin C/F Aspartic acid F 1,2,4-butanetriol C C5 Itaconic acid F Glutamic acid F C6 Citric acid F Aconic acid F Cis-cis muconic acid F Gluconic acid C/F Kojic acid F Adipic acid C Amino acids L-alanine F L-Glutamine F L-Histidine F L-Hydroxyproline E L-Isoleucine F L-Leucine F L-Proline E L-Serine F L-Valine F L-Arginine F L-Tryptophane F L-Aspartate F L-Phenylalanine F L-Threonine F L-Glutamate F L-Lysine F
7 Chapter 1
The application fields are very versatile, ranging from food industry, over textile and pharmaceuticals to the chemical and polymer industry. Particularly in the polymer industry poly-lactic acid (PLA) has shown to be promising. As a green plastic it was first commercialised in 2002 by a Cargill and Dow Chemicals joint venture. Alhough it can potentially replace many of the established plastics, its price still hampers a full scale introduction (Crank et al. , 2005). Considering the well established fermentation technology behind lactic acid production, the main costs lay, as for ethanol, with the raw materials that are used and with product recovery.
The lactic acid bacteria which are most commonly used, rarely ferment multiple sugar sources and complex substrates such as lignocellulotic substrates (Shanmugam & Ingram, 2008). This has led to further research in alternative micro-organisms such as Rhizopus oryzae , Enterococcus faecalis and Escherichia coli B (Wee et al. , 2006). The latter does not naturally produce lactic acid, but can ferment all sugars derived from lignocellulose. Through a series of genetic engineering steps, this E. coli strain was modified to produce up to 117 g/l at a rate of 2.5 g/l/h (Grabar et al. , 2006). The inhibition of the high acid titer was in this case resolved by means of adaptive evolution and the addition of betaine to the medium.
Another issue that arises with the production of lactic acid is downstream processing. Acids cannot be distilled easily from the fermentation broth like alcohols. Conventionally lactic acid is precipitated as calcium salt and acidified with sulphuric acid, resulting in equimolar amounts of calcium sulphate. This recovery method is expensive, covers about 50 % of the cost, and unfriendly to the environment as it consumes lime and sulphuric acid and also produces a large quantity of gypsum sludge as solid waste (Wasewar, 2005). To reduce this cost, fermentation should be carried out preferably at a pH below the acidity constant of lactic acid, 3.85 (Chotani et al. , 2000). Rhizopus sp., Lactobacillus sp. and Bacillus sp., classically used, cannot cope with these acidic conditions. Yeasts on the other hand, show much better acidophilic properties. For example Kluyveromyces lactis produces up to 104 g/l of lactate with a yield of 1.19 g/g substrate at a pH of 4.5. In this case about 20 % of the lactate existed in the undissociated form. A further reduced pH of 2.8 resulted in 95 % of undissociated acid, but reduced yields and titers significantly to respectively 0.7 g/g and 25 g/l (Porro et al. , 1999). The latter is the result of the uncoupling effects of lactic acid. Undissociated acids can diffuse freely through the membrane and dissociate within the near neutral cytoplasm, hence uncoupling proton transport from cellular processes (Baker-Austin & Dopson, 2007). 1.1.3 1,3- propanediol (PDO) PDO is a component of industrial polyesters such as Dupont's Sorona® and CDP Natureworks® or Shell Chemical's CorterraTM, all PTT (polytrimethylene terephthalic acid) derivatives. Markets for the polyester include thermoplastics, textiles, carpets, and upholstery (Kraus, 2008). Chemical synthesis of PDO is too expensive to compete with diols like 1,2- ethanediol, 1,2-PDO, and 1,4-butanediol, opening development potential for bio-based PDO.
8 General introduction
Several micro-organisms were screened for anaerobic PDO production, such as Klebsiella pneumoniea , Enterobacter agglomerans , Citrobacter freundii , Lactobacillus brevis , Lactobacillus buchneri , Clostridium butyricum and the Clostridium pasteurianum , directly from glycerol (Biebl et al. , 1999). All of these organisms use a reductive route, first by dehydration of glycerol to 3-hydroxypropionaldehyde and then a reduction to PDO. The first reaction is mediated by cobalamine (vitamine B12), which actually limits the overall reaction rate. The enzyme is deactivated during the enzymatic conversion of glycerol by cobalamine. Nature has overcome this inactivation by means of a reactivase enzyme that couples the energy of ATP hydrolysis to the exchange of inactivated cofactor with new coenzyme B12 (Fonseca & Escalante-Semerena, 2001). In order to avoid rate limitations in these reactions, excessive amounts of cobalamine would have to be added to the fermentation broth (O'Brien et al. , 2004). The identification of a cobalamine independent dehydratase and the engineering of a Clostridium acetobutylicum strain circumvented this problem. This strain showed a yield of 0.65 mole/mole glycerol, a titer of 90 g/l and a production rate of 3 g/l/h on a minimal medium. The second reaction is a NADH dependent dehydrogenase, which makes PDO production redox constrained. The maximum theoretical yield calculated for anaerobic fermentations is 0.875 mole/mole glycerol. In this case all acetyl-CoA formed has to enter the Kerbs cycle without oxidative phosphorylation, leading to a highly efficient generation of reduced equivalents. In most cases, acetate is still formed for ATP generation. By allowing this byproduct, a maximum theoretical yield of 0.72 mole/mole glycerol would be obtained (Chen et al. , 2003; Zeng, 1996). However, minor activation of the TCA and oxidative phosphorylation, would result in a similar yield, but more efficient ATP generation. Hence the current search for aerobic and micro-aerobic production of PDO (Hao et al. , 2008).
The above described processes all use glycerol as raw material. The development of novel chemical synthesis routes for PDO by Shell, via the reaction of ethylene oxide with carbon monoxide and hydrogen and Degussa, via the hydrolysis of acrolein followed by catalytic hydrogenation, threatens the economical competitiveness of the biotechnological route (Biebl et al. , 1999). Glycerol has approximately a spot price of €0.8 per kilo, while alternative carbon sources like glucose cost nearly half as much (Table 1.2).
Table 1.2: Prices of some carbon sources that can be used in an industrial biotechnological production process Carbon source Price (€/kg) glucose 0.5 (USDA, 2008) glycerol 0.8 (ICISpricing, 2008) sucrose 0.25 (USDA, 2008) wheat 0.12 (FAOSTAT, 2008a)
9 Chapter 1
Glucose extracellular DAR glycerol-3-phosphate dehydrogenase dhaB1-3 glycerol dehydratase galP Pyruvate dhaBX glycerol dehydratase reactivation factor Glucose intracellular eno Enolase ADP ATP ATP + H2O fbaAB Fructosebisphosphate aldolase ATP PTS pyk pps galP galactose permease glk ATP ADP AMP + PP gapA Glyceraldehyde-3-phosphate dehydrogenase i gldA glycerol dehydrogenase ADP glk glucokinase Glucose-6-P PEP glpK glycerol kinase H2O eno gpmAM, ytjC Phophoglyceratemutase Glycerate-2-P GPP glycerol-3-phosphate phosphatase pgi pfkA,B 6-phophofructokinase gpmM, ytjC, pgi gpmA Phosphogluco-isomerase Fructose-6-P pgi Phosphoglucose isomerase ATP Glycerate-3-P pgk Phophoglyceratekinase pfkA, pfkB ATP pps Phosphoenolpyruvate synthase ADP pgk PTS Phosphotransferase system ADP pyk Pyruvate kinase Fructose-1,6-bis P Glycerate-1,3-bisP tpi Triosephosphate isomerase fbaA, fbaB NADH yqhD NADP dependent glycerol oxidoreductase gapA + Pyruvate NAD Dihydroxyaceton-P Glyceraldehyde-3-P dhaMKL tpiA NADH PEP GPP2 S. cereviae NAD + dihydroxyaceton Glycerol-3-P ADP DAR S. cereviae glpK NADH ATP B12* gldA Glycerol NAD + dhaB1, dhaB2, dhaB3 dhaBX K. pneumoniae K. pneumoniae H2O
3-hydroxypropionaldehyde NADPH B12 yqhD
NADP+
1,3-propanediol
Figure 1.2: Metabolic engineering scheme for the production of 1,3-propanediol in Escherichia coli with glucose as substrate. The symbols , and represent respectively an upregulation, downregulation and elimination of the gene.
Therefore a metabolic engineering approach was developed to produce PDO from glucose with Escherichia coli. Because E. coli does not naturally convert glucose into glycerol and does not synthesise 1,3-propanediol, seven genes were introduces in the genome. Two genes were taken from Saccharomyces cereviseae , DAR, a glycerol-3-phosphate dehydrogenase, and GPP, glycerol-3-phosphate phosphatase and 5 from Klebsiella pneumonia , dhaB1-3,
10 General introduction
glycerol dehydratases, and dhaBX, glycerol dehydratase reactivating factors. The production was further optimised by altering the glucose uptake system and knocking out a glycerol kinase and dehydrogenase to prevent glycerol from re-entering the central carbon metabolism. Finally the conversion of glyceraldehyde-3-phosphate by its dehydrogenase was reduced so more carbon would flow into the newely introduced glycerol synthetic pathway (Emptage et al. , 2004; Nakamura & Whited, 2003). An overview of this strategy is shown in Figure 1.2. This metabolically engineered organism produced 1,3-propanediol at a rate of 3.5 g/L/h, a titer of 135 g/L, and a weight yield of 51 %. Addition of vitamine B12 is still necessary in this case because of the use of B12 dependent glycerol dehydratase. The Clostridium dehydratase, described above, is oxygen sensitive and cannot be used in an aerobic production system (O'Brien et al. , 2004). It should be noted that PTT derived from bio-PDO is not a completely green plastic. The polymer still requires terephthalic acid for its production, resulting in only a small energy and carbon dioxide reductions in comparison with PET. These reductions are solely the effect of biobased PDO (Crank et al. , 2005; Kurian, 2005), suggesting that a biobased alternative for the production of terephthalic acid should be searched for. In nature a quite similar compound can be found in the aromatic amino acid synthesis, such as anthranilate (2-aminobenzoate), L- phenylalanine and L-tyrosine. Deamination of anthranilate would result in benzoic acid, which is an intermediate in the Henkel 2 and the PRP process for the synthesis of terephtalic acid (Chauvel & Lefebre, 1989; Wu, 1980). In these processes, benzoate salts are disproportionated at high temperatures (300 to 490 °C) and high pressures (1.5 to 3 MPa) in a carbon dioxide atmosphere. Biotechnological benzoic acid production could have some challenges in the future, because of its known antimicrobial effects. It is known as an uncoupler of the proton motive force (Warth, 1991); though it is only effective at low pH. In this case high pH fermentations would be preferred to prevent toxicity effects.
1.1.4 Amino acids Amino acids have already a long tradition in industrial biotechnology. Since the discovery of Corynebacterium glutamicum in 1957, a strain capable of producing significant amounts of L- glutamic acid, biotechnology has completely replaced all chemical production routes (Ikeda & Nakagawa, 2003). Nowadays the amino acid market is the second largest fermentation product market after antibiotics (ethanol excluded). It is a continuous growing market (about 7 % per year) and is expected to reach a market volume of over 15 billion euro in 2009 (Leuchtenberger et al. , 2005). The largest share of that market consists of so called feed additives, these are essential amino acids, which are not synthesised by animals and humans and have to be added. Of these additives L-lysine, DL-methionine, L-threonine and L- tryptophane take about 56 % of the market. L-glutamate, L-aspartate and L-phenylalanine from the biggest demand in the food sector as flavour enhancers (monosodium glutamate) and peptide sweeteners (Aspartame). An overview of the global amino acid production is given in Table 1.3.
11 Chapter 1
Table 1.3: Overview of the global amino acid production in ton per year [t/a] (Hermann, 2003; Leuchtenberger et al. , 2005; Patel et al. , 2006) Amino acid x 1000 [t/a] L-Glutamate 1500 L-Lysine 700 L-Threonine 30 L-Aspartate 13 L-Phenylalanine 10 L-Arginine 1.5 L-Alanine 1.2 L-Tryptophane 1.2 L-Glutamine 1 L-Leucine 0.8 L-Proline 0.8 L-Isoleucine 0.55 L-Histidine 0.3 L-Serine 0.3 L-Hydroxyproline 0.1 L-Valine 0.05
At first instance, no genomic information was available for production enhancement. During that time, random mutagenesis methodologies were developed to enhance production or reroute the carbon flow through the metabolism. These techniques were for instance successfully employed to enhance L-lysine production by screening for aminoethyl-cysteine resistant strains, which were treated with NTG 4 (Kumaresan et al. , 1995; Schrumpf et al. , 1992). The strain showed a feedback resistant aspartate kinase. The disadvantage of this method is that undesired mutations will occur and will accumulate without knowing their effect on the strain performance. Currently production strains are being sequenced and compared with their wild type strains. Through comparative genomics tools several mutations have been identified for instance in a high producing lysine Corynebacterium glutamicum strain. The comparison led to the identification of three mutations in three different genes, hom (coding for homoserine dehydrogenase), lysC (coding for aspartokinase) and pyc (coding for pyruvate carboxylase). By reconstructing these mutations in the wild type strain (also known as genome breeding ), the yield and production rate was significantly enhanced (Ohnishi et al. , 2002). These types of studies do not only reveal valuable information about already existing production strains, but they have led already to novel insights about specific genes or other bacteria. For instance a functional comparison between E. coli and C. glutamicum has uncovered the fact that fewer enzymes are involved in the amino acid degradation in the latter. In addition, C. glutamicum seems to have single enzymes for key amino acid biosynthesis routes, while E. coli relies on multiple isoenzymes, which are more tightly regulated (Ikeda & Nakagawa, 2003). Such information is crucial for the development
4 NTG: N-methyl-N’-nitro-N-nitrosoguanidine, exerts its mutational and letal effect by methylation of DNA, which induces mismatches.
12 General introduction
of alternative amino acid production hosts, which might have certain properties that could be advantageous, e.g. access to lignocellulotic substrates or product resistance.
Because of the difficulties to further optimise randomly mutated production strains and the rise of molecular biological tools and genetic information, more rational approaches have been introduced to enhance amino acid production. The biochemistry behind most production routes has long been uncovered. However, the regulation of those routes has yet to be revealed. On the basis of biochemical and genetic information, stoichiometric models can be constructed to calculate theoretical production yields and optimal routes. In the case of amino acid production, NADPH is an essential co-enzyme, which is generated through the pentose phosphate pathway. For instance, a key enzyme for L-lysine production would be glutamate dehydrogenase, which is NADPH dependent in C. glutamicum . By changing the coenzyme dependence from NADPH to NADH, an increase in yield would be expected as the maximal theoretical yield would increase from 75 % to 80 % (Kjeldsen & Nielsen, 2009; Stephanopoulos & Vallino, 1991). A physiological study showed however, that amino acid yield dropped and biomass yield increased. An in silico analysis of this effect showed a complete rearrangement of fluxes in the central carbon metabolism. The key to enhanced production would here be a decrease in TCA flux (Kjeldsen & Nielsen, 2009). Because of the strong correlation between NADPH and L-lysine production, in this case a stoichiometric model has shown to be a useful tool. One of the limitations of this approach is the lack of regulatory information. Gene transcription, translation and enzyme activity are regulated by internally formed products and/or external factors. Such information can be obtained through perturbation studies, C 13 flux analysis, proteomics and transcriptomics (Park & Lee, 2008; Wendisch et al. , 2006).
Consider L-lysine production as an example. The fermentation consists of two phases, a growth phase and a production phase. How the fluxes exactly change and how these changes are regulated is very hard to predict. In this case a combination of transcriptomics, metabolomics and fluxomics exhibited that, although fluxes changed during the phase shift, the transcription of the lysine biosynthesis pathway stayed the same during the whole fermentation. This indicates that this pathway is predominantly controlled at the enzymatic level. The most distinct transcriptional changes were found for enzymes at the entry point of the glycolysis, pentose phosphate pathway (PPP) and the TCA cycle. From this study can be concluded that the supply of NADPH for L-lysine production should be controlled at the transcriptional level, at the entrances of the PPP and the glycolysis. Enhanced flux through the lysine biosynthetic pathway will most probably be achieved either by an increased expression of aspartate aminotransferase and diaminopimelate decarboxylase or by eliminating feedback regulation of intermediate enzymes (Kromer et al. , 2004).
Another example is the identification of a key enzyme in the L-valine production in C. glutamicum by means of proteomics and transcriptomics. Comparative analysis of wild type and valine production strain showed elevated expression of the ilvBN operon and an increase
13 Chapter 1 in IlvB in the proteome profile by extracellular L-valine. IlvB is a subunit of acetohydroxyacid synthase, which seems to control the flux towards isoleucine. High activity leads to limited flux towards isoleucine, thus reducing growth. During valine production, ilvB expression will gradually increase and thus hamper growth. Because of the sensitivity of the system the growth is shut down quite quickly, reducing production rate and yield. In order to increase yields and rates, the expression and translation should thus be regulated, in a two phase system analogous to L-lysine production schemes (Lange et al. , 2003).
These examples show that each level of the cell contains valuable information that can lead to enhanced production or a better understanding of the microbial physiology. For example a certain protein, mRNA or flux has changed, but the linkages between flux, protein and mRNA are still very obscure. In the example of the valine production strain for instance, transcript increases could not be correlated to increased protein levels, indicating that there is much more yet to be uncovered to fully understand the information flow inside the cell. 1.1.5 Bio-polymers Next to the food industry, the polymer industry has created the biggest boost for biochemical production. Lactate, 1,3-propanediol, 1,4-butanediol, polyols, … they all are primarily used in the production of polymers (Table 1.4). However nature does not only provide monomers, but also ready-made polymers, such as polysaccharides and polyhydroxyalkanoates (Crank et al. , 2005). The source of these polymers is not only microbial, but also vegetable. For instance the main sources of starch now are corn and potato. Its application reside mainly in the packaging industry, although novel technologies such as tire fillers tend to become more and more popular (Anonymous, 2001; Fairley, 1998). Addition of starch instead of silica to tires reduces weight and rolling resistance significantly, resulting in a reduction of carbon dioxide output of about 10 grams per kilometre (Morton, 2001).
Table 1.4: Overview of the current most important types of bio-based polymers (Crank et al. , 2005; van Beilen & Poirier, 2008) Type Polymer Monomer derived Lactate Polylactic acid – PLA 1,3-propanediol Polytrimethyleneterephthalate – PTT 1,4-butanediol Polybutyleneterephthalate – PBT Succinate Polybutylene succinate – PBS Polyols Polyurethanes – PUR Bio-caprolactam Nylon6 Adipate Nylon66 Azelaic acid Nylon69 Natural rubber Polyhydroxyalkanoates Polyhydroxybutyrate Polyhydroxyvalerate Polysaccharide Starch Cellulose
14 General introduction
Polyhydroxyalkanoates (PHA’s) can be produced both by green (plant) and white biotechnological processes. Metabolix uses plant biotechnology, for instance switch grass PHA, although the company’s first commercial polymer was from microbial origin (Ondrey, 2008; Seewald, 2008). High PHA concentrations in plants tends to impair growth and development as a result of acetoacetyl-CoA depletion, which limits growth and development hormone production.
Up to now, merely a 40 % concentration has been reached in plants, while bacterial PHA reach 50 to 85 % (for medium chain, C6 to C16, and short chain, C3 to C5 PHA’s, respectively) (Bohmert et al. , 2002; van Beilen & Poirier, 2008). The type of polymer depends on the organism used in the production process, e.g. Pseudomonas putida produces medium chain polymers, while Cupriavidus necator (formally known as Ralstonia eutropha or Alcaligenes eutrophus ) produces polyhydroxybutyrate and copolymers of butyrate and valeriate (Verlinden et al. , 2007). Only Cupriavidus necator and metabolically engineered Escherichia coli are being used as commercial production hosts for polyhydroxyalkanoates with brand names Biopol ® and Nodax ®. Like for 1,3-propanediol, E. coli is not a natural PHA producer. In this case genes from Cupriavidus necator, Alcaligenes latus , Clostridium kluyveri or Pseudomonas aeruginosa were integrated into the genome to create the necessary biochemical routes. By modulating the routes and the substrates, a whole range of PHA’s can be obtained (Li et al. , 2007). These metabolic engineering strategies have led to yields over 80 %, rates higher than 4 g/l/h and cell concentrations near 200 grams per liter.
15 Chapter 1
1.2 Succinate
1.2.1 Economics Succinate as base chemical has first been pointed out by Jain and coworkers in 1989 (Jain et al. , 1989), after which the US Department of Energy marked it as one of the top added value chemicals from renewable resources (Werpy et al. , 2004). Based on the petrochemical analogue, maleic anhydride, they have set the production price at €0.45 per kg. Nowadays, with the increasing oil price (Figure 1.4), this analogue more than tripled in price (Figure 1.3).
Chemical analogous set the target price for bio chemicals; the production price itself is influenced by several factors. One of these factors is the price of renewable resources. An economical assessment was made for succinate based on three different sugar prices, at a constant oil price of $60 per barrel to evaluate the sensitivity of succinate production cost for raw material cost. In this study two cases were looked at, current technology and future technology. As current technology a titer of 80 g/l, production rate of 1.8 g/l/h, yield of 0.88 g/g substrate were assumed, future technology would produce titers of 150 g/l, 15 g/l/h and yields of 1.1 g/g substrate. For raw sugar prices 70, 135 and 200 euro per ton was used.
1.8
1.6
1.4
1.2 €/kg 1.0
0.8
0.6
0.4 2000 2001 2002 2003 2004 2005 2006 2007 2008 Year
Figure 1.3 Maleic anhydride price (€/kg) plotted in time (EIA, 2008; ICISpricing, 2008)
In comparison with technological development, the sensitivity of succinic acid price for sugar price is rather low. Only a small fraction of the price is determined by the substrate price, a fluctuation in sugar price would thus not affect the succinate price gravely. Technological advances, as described above, could reduce succinate production cost about 50 %, keeping in mind that the price of oil would remain constant. In none of the cases the target of €0.45 per kg was reached (Patel et al. , 2006).
16 General introduction
As can be concluded from Figure 1.4, oil prices were not constant the past couple of years. Raw sugar prices seem to float between €100 per ton and €240 per ton (USDA, 2008). Current maleic anhydride price, due to the high oil price is set at about €1.7 per kg (ICISpricing, 2008), which means that even with current technology, succinate production can be economical viable (approx. €1.2 per kg). It should be noted that succinate production is still highly dependent on non-renewable energy, thus future costs will more and more be determined by the price of oil and not the price of sugar (Patel et al. , 2006).
Three important process parameters determine the economical viability of a bioprocess: yield, titer and production rate. The DoE report sets the volumetric production rate at 2.5 g/l/h, although the economical assessment aimed at future rates of 15 g/l/h. A good reference is the industrial production of glutamic acid, which is already quite successful. Here titers of about 150 g/l and volumetric rates of 5 g/l/h yielded economical viable production processes (McKinlay et al. , 2007b). These rates are not easily obtained. Low specific growth and production rates are thus far limiting to reach competitive succinic acid production, since high biomass concentrations are needed to obtain economically viable production rates. In the case of succinate production, yield will have to be balanced against rate.
World oil and raw sugar price
160 600
140 500
120
400 100
80 300
oil price ($/barrel) price oil 60 200 (€/ton) price sugar raw
40
100 20
0 0 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
Figure 1.4 Oil price (€/barrel) and raw sugar (sucrose) price plotted in time(EIA, 2008; USDA, 2008)
The total production cost comprises for about 60 to 80 % of the downstream processing (Song et al. , 2007a; Song & Lee, 2006; Zeikus et al. , 1999). In order to reduce this cost, three issues have to be addressed: byproduct formation, process pH and the process titer (Patel et al. , 2006). Up to now, no known homofermentative succinate production has been reported in the literature. The main byproducts remain acetate, formate, pyruvate, ethanol and lactate, all natural fermentative metabolites. In some cases these byproducts are the result of redox issues (e.g. formate formation), in other cases, not all routes towards the byproduct are known, and
17 Chapter 1 thus cannot be knocked out (for example acetate, which will be elaborately discussed in chapter 5) (Song & Lee, 2006).
Similar to lactic acid production, pH plays an important role in the recovery of succinate. Acid and base costs drop significantly when succinic acid is produced instead of the salt from. Similar to Kluyveromyces lactis lactate production, the yeast, Zygosaccharomyces rouxii was used to produce succinate at low pH (Porro et al. , 1999; Taing & Taing, 2007). Although yields and titers in these cases were still low, the reduced pH route for acid production still has economical potential. One of the main conditions for success, will be a better understanding of microbial acid resistance (Warnecke & Gill, 2005).
Product titer, the third parameter linked to product recovery, relates to the concentration steps during downstream processing. High titers reduce the need for these energy intensive process steps and can thus reduce production cost significantly. Economically viable processes are described to have titers above 100 g/l, concentrations that are rarely obtained in microbial acid production systems, due to the proton uncoupling effects (cfr. § 1.1.2) (Patel et al. , 2006).
In summary, yield relates more to the variable cost of raw feedstock and will be of increasing importance with increasing sugar prices. Production rates and titers relate more to the fixed cost and total investment. Low rates imply larger energy and labour cost; low titers will result in larger capital investment to maintain high plant capacities. Based on these facts Wilke calculated that a yield of 100 % (w/w), a rate of 3g/l/h and titers up to 250 g/l would result in a total production cost of €0.45 per kg (Wilke, 1995, 1999). 1.2.2 Maleic anhydride Maleic anhydride or furan-2,5-dione, was traditionally produced from benzene with a vanadium-molybdene catalyst and oxygen. This process has been optimised since the 1930’s. Typically a conversion of 95 % is obtained and a yield of about 0.70 mole of maleic anhydride per mole of benzene (47 % carbon conversion yield). The major byproducts are carbon monoxide and carbon dioxide (Bielanski & Najbar, 1997). Because of toxicity issues and quite high losses of carbon to CO and CO 2 (53 % of all carbon), n-butane based processes were developed. This process uses V-P-O catalysts (Vanadium Phosphorus Oxide) and yields 0.6 mole maleic anhydride per mole n-butane. Although the molar yield is lower, the total carbon conversion is higher, about 60 % of the butane carbon is converted to maleic anhydride carbon (or 1 gram maleic anhydride per gram of n-butane), with CO and CO 2 accounting for the lost 40 %. In this case the conversion efficiency is lower than the benzene process, only 85 %. This has lead to the development of recycling systems to increase efficiency. By recycling the process off-gas back into the reactor system, the residual 15 % is allowed to react further, but maleic anhydride would also be further oxidised to CO and CO 2. A PETROX unit was added to the process, leading the off-gas of the system into a hydrocarbon selective molecular sieve where the unreacted hydrocarbon is separated from the
18 General introduction
waste and product stream, thus increasing overall yield and conversion efficiency (Chen & Shonnard, 2004; Clarizia et al. , 2003).
The current rise of oil and gas prices has also influenced the development of the already very mature maleic anhydride production processes. Current processes are designed for clean n- butane, but as resources become more and more expensive and scarce. New technologies are being developed to use mixtures of n-Butenes and n-Butane in order to keep maleic anhydride competitive (Dente et al. , 2003). As the price of n-butane has risen to €0.75 per kg (refined) (ICISpricing, 2008), and the yield is 1 g/g maleic anhydride per n-butane, the total resource cost for maleic anhydride has become up to €0.75 per kg. Comparing to glucose as a resource, €0.5 per kg, a yield of 0.68 g/g succinate per glucose would suffice to compete on a resource basis. The total energy consumption of the maleic anhydride process is very high due to the high temperatures and pressures needed, which is also a weakness in comparison with the low temperature and atmospheric pressure bioprocesses that are being developed today. An aspect where the petrochemical process is still better than bio chemistry, is the ease with which its products are recovered with very high purity (Chen & Shonnard, 2004; Patel et al. , 2006). 1.2.3 Applications Nowadays the succinic acid market is still quite modest, about 15000 ton per year worldwide. The market potential is estimated at 270000 ton per year, due to the many applications in a wide variety of economic sectors (McKinlay et al. , 2007b; Willke & Vorlop, 2004).
The four existing succinic acid markets are the detergent/surfactant market, the ion chelator market, food market (e.g. acidulants, flavours or antimicrobials) and the pharmaceutical market (Figure 1.5, upper part). These markets have high added value and do not require very cheap feedstocks. However, commodity chemicals are mostly low cost bulk chemicals (Zeikus et al. , 1999).
Three succinate derivatives with major applications are obtained through hydrogenation routes. These are gamma-butyrolactone (GBL), 1,4-butanediol (BDO) and tetrahydrofuran (THF). BDO has three main branches – polymers, tetrahydrofuran (THF) derivatives and γ- butyrolactone (GBL) derivatives. The butanediol market is now about 1.37 million ton per year and will increase steadily 3 % per year due to the increasing demand in China. The market price in Europe is €1.82 to €1.99 per kg. Currently several production processes are used. BASF uses the Reppe process, which reacts acetylene and formaldehyde, BP/Lurgi Geminox applies the Davy process, which is based on the butane to maleic anhydride process and Mitsubishi Chemical employs a butadiene/acetic acid technology (Anonymous, 2008b; Burridge, 2006). Biobased production routes are not yet commercialised, although chemical routes from succinate already exist. By means of a catalyst, succinic anhydride or maleic acid (both derivatives of succinic acid) can be converted to BDO, although depending on the catalyst this reaction results in mixtures of BDO, GBL,THF and butanol (Cukalovic &
19 Chapter 1
Stevens, 2008). The direct route from renewable feedstocks was not yet available until recently. The company Genomatica describes a novel biosynthetic production route of BDO and will start upscaling in 2009 (Anonymous, 2008a; Burk et al., 2008).
Biophosphors
Plant growth Gorrosion Pharmaceuticals Solvent additives slimuianis inhibitors
Delergents Surtactans
Speciality Additives chemieals
lminodisuccinate 1,4-diaminobutane salt
Succiononitril Succinamide
Sulfosuccinimates
PBS Polybutylene succinate
Alkyl/alkenyl 2-pyrollidone pyrollidone Adipic acid
Tetrahydrofuran Succinimide THF
2-pyrollidone
Fumaric acid Malie acid
Figure 1.5: Overview of applications and products derived from succinic acid (Cukalovic & Stevens, 2008; Delhomme et al., 2009; Song & Lee, 2006; Zeikus et al., 1999)
Polybutylene succinate and polybutylene terephthalate are the main polymers made from BDO. Both of these bioplastics could in future replace PET and PTT, respectively. The key to
20 General introduction
their success will be the reduction of feedstock price and the further development of the polymerization technology (Crank et al. , 2005). Herein lays the opportunity for biotechnology to develop green alternatives for the classical polymer chemistry which is energy intensive. The use of lipases for polyester synthesis has shown some promising results. First applications have already been introduced on an industrial scale. By using a Candida antarctica lipase for polyester polymerization, Baxenden chemicals has shown that energy use can be reduced up to 2000 MW per year and that the use of toxic solvents and acids can almost be eliminated, reducing operation costs significantly (Azim et al. , 2006; Jarworski & Griffiths, 2002). The lion’s share of BDO is used for the production of THF, which is a high performance solvent. Further applications include the production of poly-urenthane elastomers and fibers. As a high added value chemical it is now produced on a global scale of 439000 ton per year (Delhomme et al. , 2009).
γ-butyrolactone can either be produced via a BDO based route, which is a dehydrogenation reaction that utilises a cupper-based catalyst and produces 2 mole of hydrogen gas per mole of GBL, or can be produced through a reduction reaction of maleic anhydride with the consumption of 3 mole hydrogen per mole GBL. Both reaction schemes have also been linked to recycle hydrogen during the reaction, increasing the selectivity for GBL and reducing the production of side products such as THF, butanol and acids (Zhu et al. , 2005). GBL and its derivatives, the pyrrolidones, are mainly used as an organic solvent that can replace methylene chloride, which policy makers try to ban (McKinlay et al. , 2007b). The focus of synthesis optimization for the above described molecules has up to now been the catalyst for vapour or solvent hydrogenation. This is of course the result of a long history of organic solvent based chemistry in this field. Due to the upcoming biotechnological processes, in which water is the principal solvent, novel technologies have to be developed to convert succinic acid, maleic anhydride, maleic acid and many others to the desired end product. The catalysts in this case are quite expensive because most of them belong to the rare metals of group VIII. They all result in mixtures of BDO, GBL and THF, which makes the aquatic environment a disadvantage from a product recovery perspective (Cukalovic & Stevens, 2008; Delhomme et al. , 2009).
A second group of succinic acid derived molecules are the pyrrolidones. Their applications are mainly located in the solvent and polymer industry. N-methyl-pyrrolidone for instance is used as a solvent in hydrocarbon recovery and in many pharmaceutical applications. It is also polymerised, such as its derivative N-vinyl-pyrrolidone, and is also used in de production of semiconductors. BASF is the biggest industrial player in this field. The market is significantly smaller than the one of BDO. About 113000 ton per year is produced, which makes it more a speciality chemical with a selling price in 2008 of about €2.41/kg (Anonymous, 2008c; Reisch, 2008). Ammonium succinate and succinimides are alternative reactants for pyrrolidone production. Through reductive amination, succinic anhydride or maleic anhydride
21 Chapter 1 can be converted in an aqueous environment (Cukalovic & Stevens, 2008; Delhomme et al. , 2009).
Fumarate, malate and itaconate form a third group of potential succinic acid derivatives. The chemical conversion of succinate to these three compounds involves high temperatures and pressures, in some cases in a multistep process. These high energy consuming processes can be avoided by direct fermentative production. All three of these compounds are naturally produced by microorganisms and production systems are being developed for industrial production (Engel et al. , 2008; Moon et al. , 2008; Shekhawat et al. , 2006). The price of the chemical production route will be set by the price of succinic acid itself. In contrast, the price of the microbial route will be set by similar parameters as for succinic acid: rate, titer and yield. The moment these parameters are the same or even better than for the succinate production processes, these acids will become succinic acids major competitors. 1.2.4 Microbial succinate production Because succinic acid is an intermediate of the Krebs cycle and a fermentative end-product, microorganisms lend themselves perfectly as production hosts. The choice of production host is very diverse, although most natural production hosts described in the literature are capnophilic micro-organisms. The non-natural production hosts, on the other hand, are chosen on the basis of their genetic accessibility. Table 1.5 gives a comprehensive overview of the many succinate production processes described in literature. Apart from the strain choice, also medium and fermentation strategy are shown. Mostly batch cultures are employed as a production system, although higher titers and rates are obtained by fed-batch and continuous culture systems. The highest volumetric production rate, 10.4 g/l/h was obtained in a continuous culture of Anaerobiospirillum succiniciproducens with integrated membrane for cell recycling at a dilution rate of 0.98 h -1 (Meynial-Salles et al. , 2008). Titers up to 146 g/l were obtained in a cell recycling fed batch culture of Corynebacterium glutamicum (Okino et al. , 2008). In an Escherichia coli dual phase batch fermentation the highest described yield of 1.2 g/g substrate (in this case sucrose) was obtained (Isar et al. , 2006a). None of the described production systems are homo-fermentative succinate production systems. All of them are performed around neutral pH. Downstream processing remains thus a major issue in microbial production of succinic acid.
The most current used strains are Actinobacillus succinogenes , Anaerobiospirillum succiniciproducens , Mannheimia succiniciproducens and Escherichia coli . Corynebacterium sp. and Bacteroides fragilis have been introduced very recently as succinate platform strains (Isar et al. , 2007; Okino et al. , 2008). The latter is the result of extensive screening for succinate producing microorganisms in bovine rumen (Agarwal et al. , 2005).
All microbial domains are represented in the rumen ecosystem, Bacteria, Archaea and Eucarya (yeasts and fungi). They all have a particular role in a very complex environment where lignocellulotic matter is degraded into volatile organic acids, the ruminants main
22 General introduction
energy source (Deng et al. , 2008). The anaerobic conditions, caused by carbon dioxide, methane and traces of hydrogen production, make sure that anaerobic and facultative anaerobic microorganisms predominantly prevail in the rumen. The feed input and volatile organic acid production create a high buffering capacity and high osmotic pressure that is optimal for the inhabitants but lethal for most organisms foreign to this habitat. These conditions create the unique environment for microbial succinic acid production (Kamra, 2005). Actinobacillus succinogenes , Anaerobiospirillum succiniciproducens , Mannheimia succiniciproducens and Bacteroides fragilis are natural succinate producing strains, which all have been isolated in the rumen. They produce a mixture of volatile organic acids and as capnophiles they can cope with high carbon dioxide partial pressure and use it as a carbon source together with sugars (Song & Lee, 2006). In some cases carbon dioxide is essential for growth and adapted screening methods have to be employed to isolate novel capnophilic strains (Ueda et al. , 2008). Most probably these efforts will lead to many more isolates that efficiently produce succinate.
Optimised fungal succinate production systems are rarely described in the literature, although Fusarium spp, Aspergillus spp and Penicillium simplicissimum are known to excrete the acid (Magnuson & Lasure, 2004). Penicillium simplicissimum succinate and citrate co-excretion was studied under anaerobic and aerobic conditions. A strong increase in succinate excretion was observed when the respiratory chain was inhibited, either by sodium azide or anaerobic conditions. Oddly enough, even though the intracellular concentration of citrate and succinate increased in this case, only succinate excretion increased. Probably the transport mechanisms behind this are different for both acids. Depolarization of the plasma membrane does not inhibit transport of succinic acid in this filamentous fungus, which in contrast is the case in Saccharomyces cereviseae (Duro & Serrano, 1981; Gallmetzer et al. , 2002). Saccharomyces cereviseae succinate production is mostly studied in the context of wine and liquor manufacturing (Arikawa et al. , 1999b). Mutants with elevated succinate productions profiles are used to modify the taste of rice wine (Arikawa et al. , 1999a; Camarasa et al. , 2003). A combined fungal – bacterial two step process with Rhizophus sp. and Enterococcus faecalis showed very high production rates and yield (resp. 2.2 g/l/h and 0.95 g/g glucose).
In the first step the fungus produces fumarate which is then transferred to a second reactor where E. faecalis efficiently converts it to succinic acid. However this innovative system shifts the problem from succinate to fumarate production, which is in this case low in yield (0.5 g/g glucose) and has a low rate (0.2 g/l/h) (Moon et al. , 2003). This process is illustrative for an essential problem that occurs for succinate formation in Eukaryotes, compartmentalization. Succinate has to cross two borders in order to be excreted (mitochondrial membrane and cytoplasma membrane); fumarate on the other hand does not (Engel et al. , 2008). This makes it more favourable to use bacteria instead of fungi or yeasts to produce succinic acid. Yeasts and fungi on the other hand grow at rather acidic pH, which would make downstream processing for those processes more favourable (Porro et al. , 1999).
23 Chapter 1
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b
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1 -
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-1
Time/ D [h/ h 62.8 39-79 78 96 38.5 15 15 30 48 48 46.3 48 65 48 44 20 22.5 38 D=0.4 44.5 40.3 27 19 N.D. 36 D = 0.15h 22 32 D = 0.93h D = 0.20h 10.5 15 ach production host, sorted by the dataof publicat
c 7
63. 17.4 4.2 4.1 33.8 46.4 55.2 60.2 50 64.2 45.5 15.9 43.5 33.2 39.1 34.1 31.6 32.2 34.4 34.3 34.7 19.8 29.6 23.8 83 14.3 15.3
Titer [g/l] 29-39 105.8 33.9 53.2 14.7
.18 1.01 0 0.28 0.3 1.35 0.97 1.15 1.3 0.58 1.19 0.95 0.79 1.93 0.87 2.03 0.77 0.78 1.2 1.8 0.96 3 1.35 0.74 3.3 1.46 succ
[g/l/h] r 1.56 1.36 0.88 1.21 N.D. 10.4 0.97
d
.
succ
q [g/g CDW/h] N.D. N.D. N.D. N.D. N.D. 0.47 0.19 0.3 N.D N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 1.5 0.45 N.D. N.D. N.D. 0.29 0.37 1.1 0.51 1.32 3
.64
Y [g/g glc] 0.94 0.79 0.82 0.87 0.86 0.46 0.5 0.62 0.79 0.94 0.75 0.57 0.81 0.81 0.82 0.79 0.91 0.88 0.85 0.66 0.7 0.99 0.86 0.84 0.91 0 0.97 0.88 0.88 0.71 0.87 0.87
3 3 3 3 3 3
3
3 3 3 3 3 3
3 3 3
3 CO for different succinate production processes for e CO CO CO CO 3 , 2 2 2 2 2 , 2
, 2 CO CO CO CO CO CO 3 CO 2
CO CO CO 2 2 2 2 2 2 , , 2
3 3 2
2 CO 2 2 2 3
3 3 3 , Na , Na , Na , Na / H , Na 2 2 2 2 2 2 , Na , Na , Na , Na , Na , Na Mg , Na , NaHCO , Na , Na , Na 2 2 2 2 2 2 2 CO CO 2 2 2 2 2 2 2 , MgCO 2 , MgCO , MgCO 2 2 , CO Csl, Ye,Csl, , Ye, CO e Glc, ; C; ; Wh,C; Csl, CO Fermentation strategy/Medium an; Ye,Csl, B; Na an; Ye,Csl, B; Na an; B; an; Glc,Csl,B; Ye, Ac, MgCO an; rB; Glc,Csl, Ye, Ac, an; Glc,Def,B; NaHCO an; Glc,Def,B; NaHCO an; Glc B; an; Mo,B; Ye, CO an; F; Mo, Ye, CO an; F; Glc, Ye, CO an; Wh,B; Ye, CO an; Wh B; an; CS, CO B; an; F; CS, CO an; Glc,B; Csl, CO an; Glc,B; Csl, CO an; Glc,B; P, Ye, CO an; 2sC; Glc, Csl, CO an; Glc,B; Csl, CO an; Glc,B; Ye, P, CO an; Glc,B; Ye, P, CO an; Glc,B; Ye, P, CO an; Wh,B; Csl, CO an; F; Wh, CO Csl, an an; Glc/Gl,B; P, Ye, CO an; Wo,B; Csl, CO an; mC; Glc, P, Ye, CO an; mC; Glc, P, Ye, CO an; Gal,B; P, Ye, CO an; Gal/Glc,B; P, Ye, CO
a
8
10 10 - - FZ6 130Z 130Z 130Z 130Z CGMCC1593 CGMCC1593 CGMCC1593 130Z 130Z CGMCC1593 ATCC ATCC 29305 ATCC 53488 ATCC 53488 ATCC 53488 FA FA ATCC 53488 ATCC 53488 ATCC 53488 ATCC 53488 ATCC 53488 ATCC 5348 ATCC 53488 ATCC 53488 ATCC 53488 ATCC 53488 ATCC 53488 130Z FZ53 130Z CGMCC1593 A. succiniciproducens Strain A.. succinogenes
Table 1.5: Overview of the rates yields, and titers
24 General introduction , 2001) , 2005b), , 1999), , 2002), , 1996) , 1998) , 2000) , 2007b), 2008a), et al. , 2006) , 2003b) , 2003b) , 2003b) , 2003a) , 2003a) , 2003a) , 2002a) , 2002) , 2008) , 2008) , 2008) , 2008) , 2005c), 2005b), 2005d), 2005a), et al. et al. et al. et al. et al. et et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. (Millard (Stols & Donnelly, 1997) (Gokarn (Nghiem (Gokarn (Hong Lee, & 2001) (Chatterjee (Vemuri (Hong Lee, & 2002) (Pan (Hong Lee, & 2004) (Lin (Lin (Lin (Lin (Sanchez Reference (Song (Oh (Oh (Oh (Song (Lee (Oh (Lee (Lee (Lee (Lee (Lee (Lee (Lee
b
Byproducts Fo, La, Ac Fo, La, Ac Fo, Ac Fo, La, Ac Fo, La, Ac Fo, La, Ac Fo, La, Ac Py, Ma, Ac, La Fo, La, Ac Py, Ac, La Py, Ac, La Py, Ac, La Py, Ac, La Fo, La, Ac Fo, Ac, Et, La Ac, Et Fo, La Ac Fo, Ac, Et, La Ma, Ac, Fo, La, Et Fo, Ac, Et, La Ac, Et Ma, Ac, Et Py Ac, Et Py, Ac Py, Ac Py, Ac Py, Ac Fo, Ac
1 1 1 1 1 1 1 1 1 ------] -1
Time/ D [h/ h
7.5 11 D=0.1 h D=0.7 h 12 D=0.2 h D=0.4 h 30 6 D=0.1 h D=0.3 h D=0.1 h D=0.3 h 6 18 40 10 99 9.8 120 35 76 75 34 120 49 59 59 D=0.1 h 95
c
Titer [g/l]
14 13.5 12 5 11.7 9.5 8 52.4 10.5 12.9 5.2 10.7 3.5 10.1 10.7 12.8 4.2 51 1.5 9.4 6.1 99.2 10 11 7 5 58.3 8.3 7 40
succ [g/l/h] r 1.87 1.22 1.00 3.90 1.17 1.40 3.19 1.80 1.75 1.29 1.56 1.07 1.05 1.67 0.59 0.32 0.43 0.52 0.14 0.08 0.61 1.30 0.13 0.32 0.06 0.16 0.72 0.14 0.70 0.42
d
succ q [g/g CDW/h]
0.54 0.37 0.45 1.77 0.58 1.80 6.38 0.72 0.52 0.64 0.78 0.53 0.52 0.53 N.D. N.D. 0.48 N.D. 0.17 N.D. N.D. 0.13 N.D. N.D. N.D. N.D. 0.09 0.05 0.20 0.21 Y [g/g glc] 0.70 0.72 0.69 0.60 0.56 0.60 0.55 0.76 0.59 0.71 0.29 0.28 0.10 0.54 0.29 0.64 0.42 0.54 0.15 0.47 0.61 1.10 1.10 0.73 0.35 0.45 0.62 0.71 0.59 1.06
3
2 3
3 3
3 3 2 2
, CO
CO 3 3 3
3 2 2 2 3 , MgCO 3 3
2 2
2 CO CO 2 2 2 2 2 , MgCO
CO CO 2 2 2 / / H , Na / H 2 / / H 2 2 2 2
2 2 , NaHCO 2
, Na 2 2 2 2 2 , Na 2 2 Fermentation strategy/Medium
d; Glc,B; Ye, T, CO ae; Glc,B; Ye, T, NaHCO an; GlcC; (9 g/l), Ye, CO an; Glc,B; Ye, T, CO an; Glc,B; Ye, T, Na ae; Glc,C; Ye, T, NaHCO an; F; Glc, Ye, T, NaHCO d; Glc,B; Ye, T, CO d; Glc,B; Ye, T, CO d; Glc,B; Ye, CO an; Glc,B; P, Ye, CO d; Glc,B; Csl, CO an; Glc,B; Ye, CO d; So,B; Ye, T, CO an; GlcC; (18 g/l), Ye, CO an; Glc,B; Def, CO an; Glc,B; Ye, T, CO an; Wo,C; Ye, CO an; Wo,C; Ye, CO an; F; Glc, Ye, CO an; Glc,B; Ye, T, MgCO an; Wh,B; Ye,Csl, CO an; Wh,C; Ye,Csl, CO an; Wh,C; Ye,Csl, CO an; Wo,B; Ye, CO an; GlcC; (9 g/l), Ye, CO an; GlcC; (18 g/l), Ye, CO ae; Glc,B; Ye, T, Na d; F; Glc, Ye, T, CO ae; F; Glc, Ye, T, NaHCO
pepc pepc* pyc pyc MBEL55E MBEL55E MBEL55E MBEL55E MBEL55E MBEL55E MBEL55E LPK7 MBEL55E LPK7 LPK7 LPK7 LPK7 LPK7 JCL1208pPC201 NZN111pMEE1 MG1655/pUC18 AFP111 JCL1242- NZN111pTrcML AFP400 AFP111- NZN111pTrcML SS373 NZN111pTrcMLFu HL27615k HL27659k - HL27659k SBS550MG HL51276k - Strain E. coli Table 1.5 continued M. succiniciproducens
25 Chapter 1 , , d d D: : ontinuous Symbols ; ; , 2007) , 2007) , 2007) , 2007) , 2005a) , 2008b), , 2008a), , 2008a), , 2008a), o: Pretreated woo , 2006a), 2006b), , 2005) , 2008) et al. et al. et al. et , 2007b), , 2006a) , 2007), , 2006b), 2007), , 2005), et al. et al. et et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. et al. Glc: glucose, Gl: glycerol, Sucr: (Okino (Okino (Isar (Isar Reference (Lee (Jantama (Jantama (Jantama (Agarwal (Jantama (San (Andersson (Andersson (Andersson (Wu (Sanchez (Wang (Wang (Isar
b Medium: Fo: Formic acid, La: lactic acid, Ac: acetic acid
Byproducts Fo, Ac, Et, La Fo, Ac Ac, Et, La Ac, Et, La N.D. Fo, Ac Py Py Py Py, Ac N.D. Ma, Ac, La Ma, Ac, Py Ma, Ac, La, Py Ma, Ac, Py Ac, La Ac ,Ma, La, Py N.D. N.D. atch, rF: fed-batch with cell recycling , C: c Byproducts: ] -1 tover hydrolysate, Ac: acetate, Def: Defined medium
Time/ D [h/ h 80 24 16 20 30 95 30 30 30 40 30 120 96 120 93 6 46 30 24
c casamino acids, Csl: corn steep liquor, Wh: Whey, W
e presencee of more byproducts urce indicated); Titer [g/l]
tegrated membrane for cell recycling; 2.1 16 13 12 24 40 38 30 23 28 26 87 79 73 83 23 146 12.5 20
succ [g/l/h] r 0.03 0.65 0.79 0.59 0.81 0.42 1.27 1.01 0.78 0.70 0.87 0.90 0.82 0.61 0.88 3.8 3.17 0.42 0.83
d
succ cro-aerobic; B: batch, rB: repeated-batch, F: fed-b q [g/g CDW/h]
N.D. N.D. N.D. 1.2 N.D. N.D. N.D. N.D. N.D. 0.2 N.D. N.D. N.D. N.D. 0.4 0.13 0.06 N.D. N.D.
e
Y [g/g glc] 0.9 0.8 1.1 0.9 0.19 0.92 0.62 0.57 0.2 0.9 0.8 0.8 1.2 1.1 0.8 0.7 0.5 0.7 0.52
2 2
992 2 2 3 3 3
2 2 2 2 2 2 2 ar content
o: cane molasse, Whe: wheat hydrolysate, CS: corn s 2 , CO 3 , CO 3 3 , CO , CO : specific production rate, Y: yield ( S=carbon so 3 3 ome the cases balance doesnot close,indicating th fic production rate could notbe calculated (N.D.) , P: peptone, T: tryptone, Ye: yeast extract, Cas: , CO , CO , CO , CO , CO , CO , CO ol 3 3 3 3 3 3 3 , CO ent dilutionent rates), mC: continuous culture with in succ on sourceconcentration , MgCO 3 CO CO 2 2 2
, MgCO 2 2 2 2 Fermentation strategy/Medium
d; Glc,B; Csl, CO d; Glc,B; Ye, T, MgCO d; Sucr,B; Ye, T, CO an; Glc,B; Ye, P, Na an; Glc,B; def, NaHCO d; Glc,B; Ye, T, MgCO d; F; Glc, Ac, NaHCO an; F; Glc, Ye, NaHCO an; Glc,B; def, NaHCO an; Glc,B; def, NaHCO an; Glc,B; def, NaHCO d; Xyl,B; CO Csl, d; Fru,B; CO Csl, d; Mo,B; CO Csl, an; F; Glc, Ye, T, NaHCO an; Glc,B; Ye, T, NaHCO µae; rF; Glc, Ye, Cas, NaHCO an; Glc,B; Ye, P, Na µae; rF; Glc, Ye, Cas, NaHCO an: anaerobic, ae: aerobic, d: dual phase; µae: mi
pyc : volumetric production rate, q -pCRA717 succ ldhA ∆ MTCC1045 MTCC1045 W3110GFA SBS110MG- TUQ19/pQZ6 TUQ2/pQZ6/pQZ5 W3110 SBS550 pHL314 AFP184 AFP184 AFP184 NZN111 W3110 KJ060 KJ073 KJ060 KJ122 R R
Strain C. glutamicum In most cases no carbonbalance was reported, in s Most studies only reportvalues, OD thus the speci ATCC 29305 has been redeposited as ATCC 53488 in 1 some of the titers are duelow to low initial carb Approximated value based on an average molasse sug Fermentation strategy: Table 1.5 continued (2) culture, 2sC: two stage continous culture (2differ sucrose, Fru: fructose Gal: galactose, So: sorbitol hydrolysate (extraction of inhibitory compounds), M dilution rate, r Py: pyruvate, Ma: malate,propionate, Pr: Et: ethan a b c d e E. coli Bacteroides fragalis
26 General introduction
1.2.4.1 Theoretical yields The maximum theoretical yield can be calculated in different ways. The yield can either be calculated on the basis of the total amount of carbon in the substrate, e.g. for glucose this is 6, or it can be calculated on the basis of the amount of electrons in the substrate, e.g. for glucose this is 24. Out of one mole of glucose carbon, 1.5 mole of succinate can be produced (0.98 g/g). To complete then the stoichiometry, oxygen will have to be added and water will be formed:
C6H12 O6 + 0.75 O 2 → 1.5 C 4H6O4 + 1.5 H 2O Succinic acid counts 14 electrons, based on its degree of reduction, thus 24 glucose electrons would result in 1.71 mole of succinate (1.12 g/g):
C6H12 O6 + 0.86 CO 2 → 1.714 C 4H6O4 + 0.86 H 2O Here the stoichiometric equation is completed with carbon dioxide and water. Additional reductive power (under the form of hydrogen) would result in a 2 mole per mole glucose succinate yield (1.31 g/g):
C6H12 O6 + 2 CO 2 + 2 H 2 → 2 C 4H6O4 + 2 H 2O In the case of glycerol as substrate, no additional hydrogen would be needed, because glycerol is more reduced than glucose (1.3 g/g):
C3H8O3 + CO 2 → C 4H6O4 + H 2O These are purely chemical balances, independent from the biochemistry of a certain microorganism. A biochemical network is constrained by its cofactors, such as ATP, ADP, NAD(P) +, NAD(P)H, and does not have one route towards the end product succinate, but will excrete a wide variety of byproducts under the right conditions. The trick would then be to eliminate all byproducts through genetic modification, in the hope yield increases. However, with each genetic modification there are consequences. A cells metabolism is tightly regulated on all 4 levels and all levels interact with each other. These interactions, called the central dogma in molecular biology (Figure 1.6), make the optimization of a microbial production process much more complicated. A whole systems approach will thus be essential to rationally design microbial metabolisms for industrial production (Hong, 2007; Lee et al. , 2002b; McKinlay et al. , 2007b).
Figure 1.6 The Central Dogma in Molecular Biology (Crick, 1970)
The overview in Table 1.5 also shows all obtained yields in g/g substrate. In few instances the maximal theoretical yield is obtained, only in one example the theoretical yield has been reached, 1.21 g/g sucrose. In order to reach such yields, carbon dioxide addition is essential.
27 Chapter 1
In principle a yield should be calculated on the total amount of carbon put into the system, and then its maximal yield can only be equal to 1 c-mole per c-mole substrate. Some of the yields on glucose or sucrose are of course higher due to carbon dioxide input, but also due to the complex medium components added. Table 1.6 shows a short overview of some recalculated E. coli fermentation yields. Reported yields were corrected for the total amount of metabolisable carbon that has been added to the culture via complex medium components. In some cases this carbon fraction is rather low or non-existent (e.g. when defined media are used) in other cases this fraction is high and should not be ignored. In the case of strain SBS550MG for instance a long biomass accumulation phase preceded the actual production phase. During that biomass production phase large quantities of yeast extract and tryptone were used. The production phase eventually lead to a very high yields of 1.07 c-mole/c-mole glucose, but by incorporating the biomass production phase in the yield calculation, the yield drops to 0.25 (Sanchez et al. , 2005b). It is indicatory though for the influence of complex medium components on succinate production yields as such.
In a similar way the effect of carbon dioxide and carbonate can be evaluated. In this case it can be concluded that the amount of carbonate and carbon dioxide added to the medium will not contribute significantly to the acquired yield. The carbon loss through biomass synthesis compensates most of the carbon fixation routes for succinate production. Carbon dioxide and carbonate as medium or environmental components function as activators of carbon fixation routes rather than as substrate for production.
Table 1.6: Overview of selected recalculated yields for E. coli succinate production processes, Adapted 1 includes the total amount of carbon that originates from complex medium components, Adapted 2 includes the amount of carbon dioxide and carbonate added to the medium. Strain published yield a Adapted 1 d Adapted 2 Reference c-mole/c-mole c-mole/c-mole c-mole/c-mole AFP111- pyc 1.12 0.93 0.83 (Vemuri et al. , 2002) AFP184 0.48 0.38 0.38 (Andersson et al. , 2007) HL27659k - pepc 0.57 0.41 0.41 (Lin et al. , 2005b) HL51276k - pepC 0.73 0.35 0.34 (Lin et al. , 2005d) JCL1208pPC201 0.29 0.22 0.22 (Millard et al. , 1996) KJ060 c 0.94 0.88 0.84 (Jantama et al. , 2008a) KJ060 c 1.07 0.75 0.72 (Jantama et al. , 2008a) KJ073 c 0.80 0.80 0.77 (Jantama et al. , 2008a) NZ111pTrcML 0.65 0.39 0.38 (Hong & Lee, 2001) SBS110MG - pyc 0.83 0.48 0.47 (Sanchez et al. , 2005a) SBS550MG b 1.07 0.25 0.24 (Sanchez et al. , 2005b) SBS880MG - pyc 0.63 0.22 0.22 (San et al. , 2007a) W3110 1.17 0.96 0.93 (Isar et al. , 2006a) a This yield is described as c-mole succinate per c-mole sugar source b Calculations include the biomass production step that preceded the bioconversion c In these cases a defined medium was used; the fact that the recalculated yields are different from the reported yields is in this case due to discrepancies between the raw data and the reported yields. d The composition of complex medium components were obtained from BD TM and are averages from different batches.
28 General introduction
1.2.4.2 Actinobacillus succinogenes Actinobacillus succinogenes is a facultative anaerobic, capnophilic, mesophilic, pleomorphic, Gram-negative rod. It is described to have a ‘Morse code’ morphology which is quite characteristic for the genus Actinobacillus . Its full taxonomy is represented in Table 1.7. Actinobacillus as a genus is member of the Pasteurellaceae family, which also contains the genera of Pasteurella , Haemophilus , Mannheimia , Gallibacterium , Lonepinella and Phocoenobacter . The G+C number of bases for Actinobacillus succinogenes is estimated to be 44.92 % and 90.31 % of the DNA bases are part of the coding sequence. A total of 2115 genes have been identified of which 1768 have a predicted function. In total only 404 genes have been connected to a KEGG pathway, which means a lot of research is still needed to fully understand the biochemistry of this organism (Markowitz et al. , 2008).
Table 1.7: Taxonomy of Actinobacillus succinogenes (Garrity et al. , 2004) Taxonomy Domain Bacteria Phylum Proteobacteria Class Gamma-proteobacteria Order Pasteuralles Family Pasteurellaceae Genus Actinobacilus Species Actinobacillus succinogenes
The genus Actinobacillus sensu stricto is, based on 16S rRNA sequencing (Figure 1.7), comprised of the following species: A. lignieresii , A. pleuropneumoniae , A. ureae , A. hominis , A. equuli , A. suis ., A. arthritidis and H. parahaemolyticus. A. capsulatus does not seem to cluster with true actinobacilli, the different isolates of this species seem to form a cluster on their own (Garrity et al. , 2004). Actinobacillus succinogenes is located in a completely different cluster, the one of Bisgaard taxon 10 and H. somnus (not included in Figure 1.7 because the complete 16S rRNA sequence was not available). Phenotypically these strains show some differences, e.g. H. somnus and Bisgaard taxon 10 lack catalase activity, which are the basis of their phenotypical distinction (Guettler et al. , 1999).
Many of the members of the Pastereurellaceae are capnophilic, thus either carbon dioxide is needed for growth or simulates growth. The latter is the case for Actinobacillus succinogenes , growth is stimulated and succinate production is increased by addition of carbon dioxide (Van der Werf et al. , 1997). A key enzyme for these organisms is phosphoenolpyruvate carboxykinase (PEPCK), which converts phosphoenolpyruvate with carbon dioxide and ADP into oxaloacetate and ATP. The increase in growth rate is thus linked to the increase in substrate level phosphorylation by this reaction. The difference with non-capnophilic bacteria PEPCK, such as E. coli PEPCK, can be found in the kinetic properties of the enzyme.
29 Chapter 1 actinomy testudinis salpingitidis Actinobacillus rella a llus enes yensis ~ s Act semi steur 17 10 inarum Pa subsp. stomati~ mairii aegyptius anaaa l gal Taxon Taxon pararallinar pittmaniae pneumotropica avium a aerogenes 16 canis muris lnfluenzae Actinobacillus inobacillus multocida multocida haemoglobinophilus paracuniculus Ac Pasteurella Elsgaard Bis~aard Pasteurella Taxon haemolyticus Haemophilus quentini succiniciproducens granulomatis ruminalis varigena Pasteurella lus Past:F>urF>ll haemolytica Haemophilus indolicus Haemophilus Pasteurella Pasteurella bettyae Pasteurella Pasteurella ophi pleurop Haemophilus lus 1 rarasuis Pasteurella Pasteurella Bisgaard Haemophilus Actinobacillus nol Act Haem i Mannheimia Mannheimia Haemophilus Haemophilus pleur Mannheimia HaemoJ Mannheimia Mannheimia Act Actinobc: Pasteurella Actinobaci Pasteurell Actlnobaci Actinobacil Haemophilus Actinobacilh Haemophilus Actinobacillu ll.rrinnhr 30 General introduction In E. coli PEPCK functions as a part of gluconeogenesis, which means that the enzyme has a high affinity for ATP and oxaloacetate, but a low affinity for ADP and carbon dioxide or carbonate (Brenda-database, 2005). The introduction of an A. succinogenes PEPCK in E. coli was described by Kim et al. (2004b) and resulted in an 6.5 fold increase of succinate productions in anaerobic conditions CO 2-rich conditions, proving its difference in function. The environment in which A. succinogenes lives has also led to the selection of some interesting properties apart from carbon fixation. Ruminants digest large amounts of lignocellulotic material, which contains next to glucose, also pentoses such as xylose. Phenotype characterization tests have shown that it can ferment D-glucose, D-fructose, D- galactose, L-arabinose, D-xylose, D-mannitol, D-sorbitol, amygdalin, arbutin, aesculin, salicin, cellobiose, lactose, gentiobiose, D-arabitol, sucrose, glycerol and gluconate (Garrity et al. , 2004; Guettler et al. , 1999; Van der Werf et al. , 1997). These properties allow fermentation of cane molasses, whey and wheat hydrolysates that are much cheaper carbon sources than refined sugar and glucose (Du et al. , 2008; Liu et al. , 2008b; Wan et al. , 2008). A disadvantage of the same environment is the richness of different substrates. A lot of vitamins and amino acids are abundant in the rumen, which resulted in the loss of biosynthetic routes. Cysteine, glutamate and methionine are required for growth on a minimal medium, next to a whole series of vitamins (McKinlay et al. , 2005). The fact that glutamate is an essential amino acid and alfa-ketoglutarate can be used as a substitute indicates that two essential genes from the TCA cycle are missing or inactive during growth on glucose, isocitrate dehydrogenase and alfa-ketoglutarate dehydrogenase. 13 C flux analysis of the Actinobacillus succinogenes metabolism should clarify the question raised above, how does the carbon flow through the metabolism and which genes are active. Such a study combined with comparative genomics can be a power tool to identify metabolic engineering targets. For instance the glyoxylate shunt has not been found in Pasteurellaceae yet. When grown on [1-13 C]glucose, oxaloacetate would show, with an active a glyoxylate route, a fraction with double-labelling, one from acetyl-CoA and one from malate. However, double-labelled oxaloacetate could not be significantly detected in isotopomer experiments (McKinlay et al. , 2007a). In a similar way the split ratios of the pentose phosphate pathway, Entner-Doudoroff pathway and glycolysis were determined. Enzymatic assays have shown all three pathways are present in the metabolism, though not all of these pathways seem to be significantly active. About 5 % of the carbon of glucose is measured to flow through the pentose phosphate route (PPP), no significant amounts of carbon flow through the Entner- Doudoroff pathway (ED) and 95 % flows through the glycolysis. The 5 percent flux via the PPP accounts for only 20 % of the total needed NADPH in the cell, thus alternative reactions have to supply the rest. This gap is filled by transhydrogenases. This is in contrast to E. coli which has a split ratio of 64-29-7 % for the EMP, PPP and ED pathways, respectively. Here the PPP accounts for 50 % of the needed NADPH and isocitrate dehydrogenase accounts for the rest (Nanchen et al. , 2006). One of the enzymes responsible for transhydrogenation in Actinobacillus succinogenes could be malic enzyme. Combined with the malate 31 Chapter 1 dehydrogenase, that yields a NAD + for every oxaloacetate conversion to malate, malic enzyme converts malate to pyruvate with the formation of one NADPH. This enzyme system forms also the shunt between the C 4 and C 3 pathways and thus between succinate formation and byproduct formation. The flux towards pyruvate is thus the result of three enzyme systems, the malate dehydrogenase – malic enzyme system, the PTS system and the pyruvate kinase system. Through pyruvate oxidation, the major (by)-products of Actinobacillus succinogenes fermentations are formed: formate, acetate and ethanol (McKinlay et al. , 2007a). These reactions form extra reductive power under the form of NADH (except for ethanol), which can increase the flux through the reductive TCA branch towards succinate. Their formation is modulated by the presence of carbonate and carbon dioxide in the medium (McKinlay et al. , 2005). Decarboxylation of oxaloacetate decreases with increased carbonate. This is most probably the effect of a reversed activity of malic enzyme and a decreased activity of oxaloacetate decarboxylation enzymes. Phosphoenolpyruvate carboxykinase does not experience any changed flux with an increased carbonate concentration. The addition of extra reductive power by means of hydrogen reduces the fumarate and acetate flux and increases succinate and ethanol formation. Hydrogen activates fumarate reductase and reduces formate dehydrogenase and pyruvate dehydrogenase activity (McKinlay & Vieille, 2008). Clearly the above described experiments point at some interesting metabolic engineering targets. Logically the genes coding for ethanol dehydrogenase, acetate kinase and formate dehydrogenase should be knocked out to prevent byproduct formation. A formate dehydrogenase mutant A. succinogenes strain FZ6, showed increased pyruvate production instead of succinate production (Guettler et al. , 1995; Park et al. , 1999). This indicates that byproduct formation is the result of a problem that occurs more upstream in the metabolism, namely at the phosphoenolpyruvate-pyruvate-oxaloacetate node. The flux towards pyruvate should be controlled as such that it allows enough pyruvate as a biosynthetic precursor, which means a perfect fine-tuning of this node (Sauer & Eikmanns, 2005). Every engineering strategy of course requires the essential genetic tools to modify the organism accordingly. Not many genetic engineering technologies have been developed yet for Pasteurellaceae . Only recently a shuttle vector has been constructed for Actinobacillus succinogenes which allows the over expression of exogenous genes (Kim et al. , 2004a). The availability of genomic information will very likely speed up the access to more advanced genetic tools for gene expression finetuning, which are already available for Escherichia coli (De Mey et al. , 2007b). 1.2.4.3 Mannheimia succiniproducens Mannheimia succiniciproducens is a facultative anaerobic, capnophilic, mesophilic, Gram- negative rod. Its full taxonomy is represented in Table 1.8. Similar to Actinobacillus succinogenes , M. succiniciproducens is a member of the Pasteurellaceae family. The G+C content is estimated to be 42.5 % and 89.9 % of the DNA bases are part of the coding sequence. Gene prediction and annotation identified 2384 open reading frames (ORF’s) of 32 General introduction which 1471 were assigned a putative function. Of the remaining ORF’s, 360 genes showed high similarity with hypothetical proteins of unknown function and 553 genes could not be identified (Hong et al. , 2004). The Genus Mannheimia was previously classified as Pasteurella haemolytica , 16S rRNA analysis and DNA-DNA hybridization studies showed significant differences with the Pasteurella genus (Angen et al. , 1999). Figure 1.7 indicates the Mannheimia cluster separate from the other Pasteurellaceae . Phenotypical, the genera can be distinguished by growth on mannitol and threhalose, all members of the Mannheimia genus are threhalose negative and mannitol positive (Lee et al. , 2002a). Table 1.8: Taxonomy of Mannheimia succiniciproducens (Garrity et al. , 2004) Taxonomy Domain Bacteria Phylum Proteobacteria Class Gamma-proteobacteria Order Pasteuralles Family Pasteurellaceae Genus Mannheimia Species Mannheimia succiniproducens As indicated in Table 1.5, Mannheimia succiniciproducens ferments a wide varaiety of substrates and hydrolysates. This strain is able to utilise mannitol, arabitol, fructose, xylose, sucrose, maltose and lactose as efficiently as glucose. Xylitol, inositol, sorbitol, glycerol, xylan and cellulose cannot be metabolised (Lee et al. , 2002a). A disadvantage of this strain are the many auxotrophies it exhibits. Cysteine and methionine are essential amino acids and nicotinic acid, pantothenate, coenzyme A, pyridoxine and thiamine are essential vitamins (Song et al. , 2008b). Glutamine is in contrast to Actinobacillus succinogenes , not required. M. succiniciproducens has a complete TCA cycle and can efficiently grow in aerobic as well as in anaerobic conditions. Such a metabolism requires stringent regulatory systems. This has been elaborately studied in Escherichia coli . On a genomic/transcriptomic level, the whole aerobic/anaerobic metabolism of Escherichia coli is regulated by a two component signal transduction system, ArcAB and an oxygen sensitive system FNR. The FNR protein (Fumarate Nitrate Reductase regulating protein) is directly controlled by oxygen through an oxygen sensitive iron-sulphur cluster which reacts with oxygen. As a result of this reaction, the dimer protein, FNR, is oxidised to a monomer. Further oxidations leads to the complete loss of the iron sulphur cluster, resulting in the formation of apoFNR (Georgellis et al. , 1998; Schmitz et al. , 2004; Tran et al. , 2000; Unden et al. , 2002). The E. coli ArcAB system is a dual transcriptional regulator that autophosphorylates under anoxic conditions (Georgellis et al. , 1999; Kwon et al. , 2000). In a first step ArcB, the sensor kinase, is phosphorylated at histidine-292, which is further transferred to aspartate-576, histidine-717 of the membrane protein. Finally the phosphor is transferred to aspartate-54 of ArcA, which then can actively bind specific DNA sequences. 33 Chapter 1 Under aerobiosis, this system is inhibited and reversed into a phosphorylase mode. This is the result of the oxidation by quinine electron carriers of two redox active cystein residues that participate in a disulphide bridge in the ArcB protein (Malpica et al. , 2004). Apart from the general sensitivity to the redox state of the cell, the ArcAB system also can be modulated by the fermentative products that are formed during anoxic growth such as lactate, acetate and pyruvate (Rodriguez et al. , 2004). In comparison to E. coli , Mannheimia succiniciproducens also possesses a FNR and ArcAB system. The FNR protein was annotated from the genome with high identity (82 %) but has not yet been physiologically investigated (Hong et al. , 2004). The ArcAB system has been characterised recently (Jung et al. , 2008). Sequence analysis showed that, although high homology exists between the ArcB of E. coli and M. succiniciproducens (and the Pasteurellaceae ), the Mannheimia succiniciproducens ArcB lacks almost an entire linker region, corresponding to residues 93 to 271, in which the two regulatory cysteines should be present. The absence of these regulatory residues has made the protein insensitive to quinones and fermentative byproducts (lactate, acetate and pyruvate), which could imply that in the case of M. succiniciproducens , the mode of operation of ArcAB is quite different from the E. coli mode and thus the redox state of the cell is controlled in a different way (Jung et al. , 2008). From a metabolic engineering perspective, regulatory modification strategies, such as applied for E. coli (see §1.2.4.5), would not be easily transferred. The genome sequence has led to a big leap forward in the development of genetic engineering tools for this succinate producing strain. With the construction of stable vectors for rumen bacteria, over expression of certain genes became possible for these bacteria (Jang et al. , 2007; Kim et al. , 2004a). Such a vector is also essential for the development of gene knock out strategies. Looking at the Datsenko and Wanner methodology (2000), for E. coli , multiple of these vectors are necessary to develop a fast and specific knock out system. Alternatively a single vector could be developed that requires a less efficient double cross-over of the plasmid with genomic DNA. The group of Sang Yup Lee constructed a plasmid, especially for these purposes (Lee et al. , 2006). In order to be able to identify the gene deletion, for each knock out an antibiotic resistance gene is introduced into the genome at the location of the target gene. This does limit the amount of modifications, because of the limited amount of antibiotic markers that exists. Hence these genes have to be eliminated from the strain after modification. In order to do so, two particular DNA sequences are added flanking the resistance gene. These sequences are recognised by specific exogenous recombinases that excise the genetic marker. The recombinase has to be introduced in a temperature sensitive plasmid, so it can be removed from the mutant strain after excision. Such a temperature sensitive plasmid is stably maintained at low temperatures, e.g. 30 °C, and will fail to replicate at elevated temperatures, e.g. 42 °C. This property is mainly determined by the origin of replication (Ori) of the plasmid. A point mutation in the Ori of pMVSC1, a Mannheimia varigena plasmid, resulted in temperature sensitivity and thus an excellent plasmid for a markerless gene knock-out system (Kim et al. , 2008). 34 General introduction The developed genetic tools have already resulted in several Mannheimia succiniciproducens mutant strains. Within these strains byproduct formation was targeted. The target genes were ldhA , pflB , pta and ackA , coding for lactate dehydrogenase, pyruvate-formate lyase, acetylphosphotransferase, and acetate kinase, respectively. The optimal succinate production route for Mannheimia succiniciproducens involves then PEP carboxykinase as well as malic enzyme (Figure 1.8), both will fix one carbon dioxide with the formation of 1 ATP. Such a route was envisaged in a strain designated LPK7, eliminating acetate, lactate and formate formation. A fed-batch culture resulted eventually in the formation of 52.43 g/l succinate with acetate, malate and pyruvate as byproducts. The yield reached 68 % of the maximal theoretical yield of 1.12 g/g (Table 1.5), although the proposed route in Figure 1.8 only would allow a maximal theoretical yield of 0.66g/g due to the lack of sufficient reducing equivalents (Lee et al. , 2006). Here two notes should be made. Firstly, Figure 1.8 is a simplification of the metabolism and does not consider the oxidative pentose phosphate route, which yields two NAD(P)H’s instead of one NADH formed in the glycolysis (this will be discussed in detail in Chapter 2); secondly, the medium composition should be evaluated in detail. The medium contains on the one hand yeast extract, which can introduce extra reduced equivalents, but more importantly, it contains disodium sulphide. Conversion of 1 mole of sulphide to 1 mole sulphate yields 3 mole NADPH and 1 mole reduced ferridoxine. This effect has also been shown with an in silico analysis of the Mannheimia succiniciproducens metabolism. The calculated flux towards succinate increased about 30 % in a hydrogen – carbon dioxide environment (Hong et al. , 2004). Metabolic flux analysis of different culture conditions also showed that glycolysis is the major source for reduced equivalents, about 85 % of the total, which leads to the lower yields that were observed and the accumulation of malate and pyruvate as byproducts. It also showed the necessity of carbon dioxide and/or carbonate as an environmental driving force for production and substrate. A dissolved carbon dioxide concentration of 141 mM increased succinate yield with 1.5 times in comparison with 8.7 mM. This is the result of the affinity constants of PEP carboxykinase and malic enzyme for carbon dioxide and carbonate (Hong, 2007; Song et al. , 2007b). The latter, malic enzyme, was overexpressed in strain LPK7 in an attempt to reduce pyruvate and malate production (Lee et al. , 2008c). In this case malate excretion was about 37 % reduced but pyruvate excretion increased. This formed pyruvate was finally metabolised by Mannheimia succiniciproducens after glucose depletion with the formation of acetate (Lee et al. , 2008d). The added time to the fermentation was about 10 hours, which decreased the productivity about 30 %. An efficient cofactor manipulation strategy is thus necessary to improve yield, reduce byproduct formation and increase production rate. 35 Chapter 1 Figure 1.8: The proposed metabolic route to succinate in Mannheimia succiniciproducens (Lee et al. , 2006), with pck : PEP carboxykinase, pykA : pyruvate kinase A, pykF : pyruvate kinase F, ptsG : subunit of the phosphotransferase system, ldhA : lactate dehydrogenase, pflB and tdcE : pyruvate-formate lyase, poxB : pyruvate oxidase, adhCEP : subunits of alcohol dehydrogenases, acs : acetyl-CoA synthase, pta : acetylphosphotransferase, ackA : acetate kinase, maeA and maeB : malic enzyme, mdh : malate dehydrogenas, fumABC : fumarase A, B and C, frdABCD : subunits of fumarate reductase. 1.2.4.4 Anaerobiospirillum succiniproducens Anaerobiospirillum succiniproducens is a strictly anaerobic, capnophilic, mesophilic, pleomorphic, Gram-negative spiral rod. Its full taxonomy is represented in Table 1.9. Anaerobiospirillum as a genus is member of the Succinovibrionaceae family, which also contains the genera of Succinivibrio , Ruminobacter and Succinimonas . The G+C number of bases for Actinobacillus succinogenes is estimated to be 42 to 44 %. Further genomic information is not available yet (Stackebrandt & Hespell, 2006). 36 General introduction Table 1.9: Taxonomy of Anaerobiospirillum succiniciproducens (Garrity et al. , 2004; Stackebrandt & Hespell, 2006) Taxonomy Domain Bacteria Phylum Proteobacteria Class Gamma-proteobacteria Order Aeromonadales Family Succinovibrionaceae Genus Anaerobiospirillum Species Anaerobiospirillum succiniciproducens Succinivibrio , Ruminobacter and Succinimonas are found in the rumen of ruminants, Anaerobiospirillum sp. have been isolated from the stomach, ilea, ceca and colon of beagle dogs. Depending on the geographical location, the genus is also found in humans and other mammals (Rifkin & Opdyke, 1981). It can cause sepsis in medically impaired people and several cases of bacteraemia have been reported in human immunodeficiency virus – positive individuals (Tee et al. , 1998). Healthy individuals the strains have no adverse effect, making it an opportunistic pathogen. Morphologically Anaerobiospirillum succiniciproducens and Anaerobiospirillum thomasii are very similar; they are very closely related base on 16S rRNA analysis (Figure 1.9). Phenotypically, these strains are quite different. A. thomasii can only ferment aldonitol, galactose, glucose and maltose while A. succiniciproducens ferments glycerol, fructose, glucose, lactose, maltose, sucrose, raffinose, inulin, galactosides and glucosides (Davis et al. , 1976; Garrity et al. , 2004; Lee et al. , 2001). The broad spectrum of carbon sources it ferments has allowed succinate production of several complex sugar mixtures originating from for instance wood hydrolysates and whey (Lee et al. , 2003b; Samuelov et al. , 1999). No information on specific auxotrophies is available yet. For the isolation of Anaerobiospirillum succiniciproducens yeast extract and cysteine are required (Stackebrandt & Hespell, 2006), but for optimal growth a combination of different complex medium components is necessary. The amino acid and vitamin of these medium components is quite different, indicating that the strain is auxotrophic for multiple amino acids and vitamins (Lee et al. , 1999b). In most cases only yeast extract in combination with peptone is used and result in high yields and production rates. Corn steep liquor is also frequently used, although quite high concentrations are needed to supplement for the auxotrophic phenotype (Table 1.5). A. succiniciproducens is a typical capnophilic micro-organism that needs high carbon dioxide for efficient growth, which is not a requirement for A. thomasii . CO 2 supply affects the production of succinic acid significantly, increasing concentrations resulted in increasing succinate yield and reduced lactate and ethanol concentrations, indicating a preference for PEP carboxykinase and the reductive TCA pathway over NADH regeneration via lactate and ethanol dehydrogenase (Samuelov et al. , 1991). 37 Chapter 1 Figure 1.9: Phylogenetic tree of the Succinovibrionaceae . The tree was constructed based on complete 16S rRNA sequences of the different organisms. These sequences were obtained from NCBI (NCBI, 2009) and were processed by M-coffee, which combines multiple sequence alignments methods with T-Coffee (Moretti et al., 2007; Wallace et al. , 2006). The phylogenetic distance between the strains is given by the line underneath the figure. However, the yields do not approach the theoretical maximum due to the lack of sufficient reduced equivalents and due to the repression of the pentose phosphate route by carbon dioxide. 6-phopsphogluconate dehydrogenase, the second oxidative step in the oxidative pentose phosphate undergoes product inhibition at high carbon dioxide concentrations, which 38 General introduction diverts the carbon flux from the pentose phosphate route to the glycolysis (Toews et al. , 1976). This altered flux is typical for capnophilic organisms grown in a CO 2 rich environment (McKinlay & Vieille, 2008). Addition of hydrogen gas to the sparging gas, increases yields and production rates significantly. The increase in yield is logically explained by the increase in NADH in the cell. Increased productivity would seem peculiar at first sight, but can be assigned to an increased activity of PEP carboxykinase. Each mole of succinate formed results in a mole of ATP formed (Laivenieks et al. , 1997), which makes the enzyme indispensible for growth coupled succinate production. 1.2.4.5 Escherichia coli Molecular biologists all time favourite, E. coli , was discovered in 1885 by Theodor Escherich. Because of its small doubling time and its abundance in nature, it is perfect to use as a reference organism. Additionally, genetic engineering tools are widely available and quite simple to apply, which makes it very attractive to develop production processes with such a strain (Datsenko & Wanner, 2000; Neidhardt & Curtis, 1996). A wild type E. coli does not produce anything that has significant industrial importance. In aerobic conditions, at a maximal growth rate, it mainly produces acetate as a kind of overflow metabolite. Anaerobically, it will produce a mixture of formate, acetate, lactate and ethanol. Some small amounts of succinic acid can also be detected in that case (Clark, 1989; De Mey et al. , 2007a; Wolfe, 2005). However, through metabolic engineering economically viable production processes can be developed, e.g. 1,3-propanediol production. Even completely foreign molecules, such as artemisinin, can be produced with E. coli , by introducing complete biosynthetic pathways (Zeng et al. , 2008). Succinate seems to be a very challenging molecule to produce with E. coli . The route to succinate is quite complex and is regulated on all cellular levels. Optimization strategies have thus far focussed on two main pathways, the PEP-pyruvate-oxaloacetate node and the TCA/glyoxylate route. The PEP-pyruvate-oxaloacetate node (Figure 1.10) forms the cellular roundabout that distributes carbon towards the different biomass precursors, such as amino acids and fatty acids, and the energy metabolism (anaplerotic, catabolic and gluconeogenic route). The three metabolites can be directly converted into each other via 6 enzymes, PEP carboxylase, PEP carboxykinase, pyruvate kinase, PEP synthase, the phosphotransferase system and oxaloacetate decarboxylase, also known as 2-keto-3-deoxygluconate 6-phosphate aldolase. The triangle is further expanded with malic enzyme, pyruvate dehydrogenase, malate dehydrogenase, citrate synthase and the acetate pathways (Sauer & Eikmanns, 2005). A first complication of the triangle is the PTS system which bounds the glucose uptake to the conversion of PEP into pyruvate (Postma et al. , 1993). By doing so, for each glucose molecule metabolised, only one PEP molecule can be converted into succinate. Pyruvate could then be further converted into acetyl-CoA, lactate, acetate or formate, depending on the growth conditions. In principle pyruvate could be phosphorylated by PEP synthase, but here 39 Chapter 1 the rigorous control of the node starts to play. All gluconeogenic reactions, PEP carboxykinase, PEP synthase and malic enzyme, are transcriptional regulated by the cAMP dependent global regulators, to prevent futile cycling. PEP synthase is controlled by Cra, which activates transcription in glucose depleted conditions (Geerse et al. , 1989; Saier & Ramseier, 1996). For this problem multiple solutions have been proposed. Figure 1.10: The PEP-pyruvate-oxaloacetate node in E. coli . With mdh : malate dehydrogenase, mae : malic enzyme, ppc : PEP carboxylase, pck : PEP carboxykinase, eda : oxaloacetate decarboxylase, pyk : pyruvate kinase, pps : PEP synthase, pdh : pyruvate dehydrogenase, gltA : citrate synthase, poxB : pyruvate oxidase, ackA : acetate kinase, pta : phosphotransacetylase, citDEF : citrate lyase, PTS: phosphotransferase system (Sauer & Eikmanns, 2005). The PTS can be knocked out to avoid the simultaneous conversion of PEP to pyruvate with glucose uptake. In this case an alternative glucose uptake system should exist. For E. coli the alternative is the galactose permease – glucokinase system. This system is however, not very efficient and reduces the growth rate significantly in E. coli but increases succinate yield (Chatterjee et al. , 2001). To reduce the effect on the growth rate, the PTS alternative galactose permease, glucokinase can be overexpressed (De Anda et al. , 2006; Flores et al. , 2007). Not only the wild type growth rate can nearly be regained, but also byproduct formation, e.g. acetate and lactate formation, is reduced. The expression of these genes was done on plasmids with IPTG induction, which means expression fine tuning is very hard to do and metabolic strain will occur very easily. Such a metabolic burden is very hard to assess and in a large scale production context this kind of expression is not desirable (Wang et al. , 2006b). Alternatively PEP carboxylase (PPC) and PEP carboxykinase (PCK) were overexpressed to draw PEP away from the PTS system, aiming for permease/kinase system to take over. 40 General introduction Overexpression of PPC resulted in a similar phenotype as the PTS knock out, growth reduced when the expression was to steep. Finetuning of the expression led to a system in which no adverse growth effects were observed anymore but increase in succinate yield was achieved. This would mean a perfect split between the PEP-oxaloacetate conversion and the PEP- pyruvate conversion. Here a second control level has to be considered, enzymatic control through allosteric inhibition (Fujita et al. , 1984; Wohl & Markus, 1972). Malate, aspartate and to some extent, succinate and citrate bind at the lysine residues lys491, lys620, lys650 and lys773, which reduces the enzyme activity (Yano & Izui, 1997). In order to resolve this problem, mutant PEP carboxylase from Sorghum vulgare was introduced (Lin et al. , 2004a, 2005b). When in addition pantothenate kinase (PANK) is overexpressed combined with PPC an increase in succinate yields was observed. PANK increases the CoA and acetyl-CoA levels in the cell, which in turn acts as an activator of PPC. A similar result was obtained with the coexpression of Rhizobium etli pyruvate carboxylase and PANK (Izui et al. , 2004; Lin et al. , 2004b). Overexpression of endogenous PCK did not show an increase in succinate production, nor any adverse effects on growth rate (Millard et al. , 1996). Because the split between pyruvate and oxaloacetate is highly dependent on the PTS system, genes were introduced to convert pyruvate to oxaloacetate or malate. Malic enzyme takes care of the latter reaction, although in gluconeogenic conditions. Its kinetics is so that it has a high affinity for oxaloacetate and a low affinity for pyruvate and carbon dioxide. Overexpression of malic enzyme has to be combined with addition of carbonate or carbon dioxide to the environment in order to have an increased succinate formation (Hong & Lee, 2001; Stols & Donnelly, 1997). A similar effect has been achieved by introducing Lactococcus lactis or Rhizobium etli pyruvate carboxylase into E. coli . In contrast to malic enzyme, this reaction does not yield any NAD(P) +, but has to be combined with malate dehydrogenase to form malate. The enzyme is strongly activated by acetyl-CoA and thus crucial for the desired effect on succinate yield. Acetyl-CoA formation, however, competes for pyruvate with pyruvate carboxylase. This means that here the crucial step will also be the fine tuning of gene expression (Lin et al. , 2004b; Sanchez et al. , 2005a). Further down in the metabolism, carbon enters the tricarboxylic acid cycle and the glyoxylate bypass. The mode of operation of these routes depends strongly on environmental conditions. For instance glucose and oxygen modulate the different reactions on various cellular levels (Alexeeva et al. , 2000; Clark, 1989; Flores et al. , 2005). In a succinate production context choices have to be made which route is optimal towards succinate and how to achieve that. The classical TCA cycle depicted in Figure 1.11 could yield three different routes. First, the oxidative TCA cycle, which results in two NAD(P)H and the loss of two carbon dioxide molecules; second, the glyoxylate route, which yields succinate directly from isocitrate; and last, the reductive TCA cycle, which consumes 2 NADH for each molecule of succinate, but assimilates one carbon dioxide. The oxidative TCA is active under aerobic conditions, the reductive TCA under anaerobic conditions and the glyoxylate route is activated when glucose is absent and for instance acetate is the main carbon source for growth (Alexeeva et al. , 2002; 41 Chapter 1 Wolfe, 2005). Aerobic conditions are considered to be most favourable for production rate, ATP is produced most efficiently through oxidative phosphorylation, though NADH is drawn away from the reductive TCA cycle. In order to compensate that, the glyoxylate route has to be opened. This route is primarily controlled by IclR. IclR opens the bypass when fermentative products, such as lactate and acetate are solely present in the environment, to avoid carbon loss through the oxidative branch of the TCA. The net carbon assimilation would otherwise be nil, because for each acetyl-CoA that enters the TCA, two carbon dioxide molecules would be lost (Lin et al. , 2004a, 2005c). By introducing glyoxylate bypass activity and knocking out succinate dehydrogenase, isocitrate dehydrogenase, and byproduct formation next to Rhizobium etli PPC overexpression, the theoretical yield was nearly achieved (Lin et al. , 2005d). This strategy however has disadvantages. The constructed strain is auxotrophic for alfa-ketoglutarate due to the isocitrate knock out, and needs thus complex medium components such as tryptone and yeast extract for optimal growth (Lin et al. , 2005b). Recalculations of the yields, including the carbon from those complex medium components also shows that the yield was artificially increased (§1.2.4.1). In a similar way an anaerobic strain was constructed with additional byproduct gene knock outs and the overexpression of pyruvate carboxylase (Sanchez et al. , 2005a, b, 2006). Figure 1.11: The TCA cycle in E. coli . With mdh : malate dehydrogenase, gltA : citrate synthase, acn: aconitase, icd : isocitrate dehydrogenase, sucAB : alfa-ketoglutarate dehydrogenase, lpd : lipoamide dehydrogenase, sucCD : succinly-CoA synthase, sdhABCD : succinate dehydrogenase, fumA : fumarase, aceA : isocitrate lyase, aceB : malate synthase. 42 General introduction Recently novel knock out strategies have shown increased yields and titers in an alternative E. coli strain, E. coli C (Jantama et al. , 2007; Wiman et al. , 1970). This strain has by nature an active glyoxylate route in the presence of glucose (Sauer & Eikmanns, 2005) and does not need extensive TCA cycle modifications. The strain is mainly modified in byproduct formation pathways, such as ethanol, acetate, lactate and formate. In order to increase the titers above 58.3 g/l, betaine, a natural osmoprotectant was added to the medium. The highest titer, yield and rate obtained with this strain were 86.5 g/l, 0.83 g/g, and 0.9 g/l/h, respectively (Jantama et al. , 2008a; Jantama et al. , 2008b), which is thus far the best E. coli production process described. The main byproducts are still pyruvate, malate and acetate, which points at a redox deficiency in the cell. 43 Chapter 1 1.3 Conclusions This introductory chapter has covered multiple aspects concerning bio succinate and bio chemicals in general. It is clear that biotechnological research these days happens in a very dynamic and complex socio-economic and political environment, with multiple pressure groups. It yields sometimes contradictory goals and will need in depth study, not only on a scientific level, but also on an economical and political level. Issues such as rationalised land use, the environmental impact of biobased production processes and the distribution of resources between food, feed, bio-energy and biochemicals still need to be resolved. Answers on these issues are important to implement future focus points in the development of novel technologies. Is there more need to focus on upstream processing, the use of lignocellulotic substrates, or can agriculture handle the demand of human and animal consumption next to the feedstock demand for chemical and energy production processes? How should the transition of a petrochemical based value chain to a bio chemical based value chain best be organised? The introduction of novel biotechnological processes will slowly dismantle certain chemical industry branches and down the line replace them. Because this is a slow evolution, both branches will have to coexist for a certain time, which could lead to both unforeseen economical and technological problems. For instance, pure biobased polymers, for instance PLA and PHA, cannot cover the technological needs of today’s society but partially biobased polymers, for instance PTT and PBT can help this transition. The development of biotechnological processes as such clearly obeys Moore’s law of technological development. The number of novel processes grows exponentially, which is illustrated by the many examples given in this chapter. Almost daily novel insights are published or ideas are patented. The way problems have been approached has also evolved from random screening (combinatorial) approach to a more rational approach. This transition resulted in a higher success rate and more robust technology but requires very detailed knowledge of the organism’s biochemical processes. Crick’s central dogma explains that the phenotype of an individual organism is regulated on 4 different levels all of which require a certain amount of specialization for thorough understanding. These biochemical processes are no longer black boxes but have become a lot of different shades of grey which are the starting point. Grey always leads to a number of questions that have to be answered and in most cases multiple answers can be correct. Some of these questions will be introduced in Chapter 2. Strain choice, mutation strategies and process development will be covered there. The past two decades the development of a biotechnological succinate production process has drawn a lot of attention. Table 1.5 reviews these processes in detail. Although some of the process performances are very promising, many problems have been uncovered over the years. Natural succinate producing strains cope with a number of auxotrophies, which increase substrate requirements and price. Furthermore, yields seem to be reaching maximal theoretical yield in such a complex medium, but only when sugar carbon is taken into account. The production rates are rarely reaching the by the US department of energy set 44 General introduction target of 2.5 g/l/h. In order to avoid the first problem, auxotrophy, an alternative strategy with E. coli was tried. E. coli can easily grow on a minimal medium and many known genetic tools exist to alter its metabolism. Extensive engineering has led to very nice increases in succinate yield, but also required again complex medium components. Only recently a strain was developed which does not require auxotrophy complementation. This strain showed similar yields but also showed – as is the case for all succinate production processes – a large number of byproducts. From this literature study can be concluded that production rate and byproduct formation should be the points of attention. Novel ways to increase the production rate should be developed (Chapter 3) and potential byproduct formation pathways should be investigated (Chapter 5). A more thorough knowledge of the control mechanisms in the cell will also be crucial to identify the right targets for further process optimization (Chapter 4). 45 2 Chapter 2 Metabolic engineering of succinate biosynthesis in Escherichia coli: Identifying gene targets using comparative genomics and stoichiometric network analysis ad hE Chapter 2 CONTENTS 2.1 Introduction 49 2.1.1 Considerations for biotechnological process development 49 2.1.2 Escherichia coli 65 2.1.3 Biological databases 66 2.1.4 Stoichiometric network analysis 72 2.2 Materials and Methods 75 2.2.1 Comparative genomics 75 2.2.2 Metabolic model 75 2.2.3 Partial Least Squares 75 2.3 Results and discussion 77 2.3.1 Comparative genomics of E. coli and natural succinate producers 77 2.3.2 Stoichiometric network analysis 86 2.3.3 Genetic and enzymatic regulation 118 2.4 Conclusions 123 48 Metabolic engineering of succinate biosynthesis in Escherichia coli 2.1 Introduction 2.1.1 Considerations for biotechnological process development The development of a novel biotechnological process requires several strategic considerations (Table 2.1). These considerations are closely related to one and other and start in most cases with the choice of a microbial strain or in the case of biocatalytic processes, an enzyme. In this paragraph the issues concerning strain choice will be addressed and weighed. Table 2.1: Issues that need to be addressed to optimise industrial biotechnological processes (Van Hoek et al. , 2003) Issue Strain selection Substrate versatility Production characteristics Byproduct characteristics Robustness Cell viability Physiological characteristics Genetic accessibility Metabolic and cellular engineering Improvement of strain characteristics Introduction/deletion of functions Fermentation Culture and media development Optimising cultivation parameters Cell retention and recycling Downstream processing Introduction of downstream unit operations within a fermentation process A first consideration is whether to screen for natural producing microorganisms or to apply well known, easy to modify microorganisms. A natural producing strain has the advantage that process optimisation does not have to start from scratch; however, genetic tools are rarely available and thus need to be developed. If the strain is closely related to well known organisms, this might be an easy task, if this is not the case, a long development process could be ahead. Furthermore, the available information on novel screened organisms will be limited. This means that to obtain easy access to the strains genetic makeup, the genome will have to be sequenced. Nature rarely foresees homofermentative microorganisms, thus quite some byproduct routes will have to be knocked out to obtain an optimal process. Genomic knowledge is indispensable for such an optimisation. A screening process also rarely considers the substrate versatility and auxotrophy of a strain. This process might thus result in the isolation of auxotrophic microorganisms that cannot grow on cheap minimal media. In addition, the access to all carbon sources that will be supplied in the final process could be limited. When aiming at a bulk production process, substrate versatility and limited auxotrophy will be essential, strains growing only on very rich and complex media are in that case less attractive than strains that require a cheap minimal medium. For a high added value production process the cost of the medium will be of lesser importance (Aristidou & Penttilä, 49 Chapter 2 2000; Kent, 2007; McNeil & Harvey, 2008). On the other hand, natural producing strains will have obtained over time resistance to the compound they produce. Such a property is not guaranteed in a non-natural producing strain. Overproduction of certain compounds can be inhibitory or even lethal for the microorganism. In that case resistance has to be created, which is not always an easy task. For instance antibiotic resistance is easy to introduce, but acid resistance is more complex. All depends on the complexity of the regulatory network behind the resistant phenotype (Parekh et al. , 2000; Van Hoek et al. , 2003) Only a handful of strains are widely used in industrial biotechnological applications. Within the eukaryotes, Saccharomyces cerevisiae and Aspergillus niger seem to be industries favourites. Escherichia coli , Bacillus subtilis , Corynebacterium glutamicum and lactic acid bacteria are the favoured prokaryotes (Kern et al. , 2007; Leuchtenberger et al. , 2005). Each of these organisms have their specific strengths and weaknesses and are mostly used for specific production processes. Because of their importance for industry and science their genetics have been extensively probed and a wide range of genetic tools have been developed. The extent of the genetic accessibility is highly dependent on the envisaged process. For instance the conversion of glycerol to 1,3-propanediol does not need a very extensive genetic engineering (Biebl et al. , 1999). In this case gene knock out and knock in tools are probably sufficient to reach production. In contrast, terpenoid biosynthesis will require a more elaborate toolbox (Martin et al. , 2003). 2.1.1.1 Deletion of cellular functions Nowadays nearly every cellular level is accessible through genetic engineering. Table 2.2 gives an overview of the genetic toolboxes that have been developed. These tools are basically developed to address one of the issues reviewed in Table 2.1. Production characteristics and byproduct formation imply deletion of certain cellular functions, which is knocking out genes. This type of engineering is one of the first that was applied for metabolic engineering purposes. Originally fluxes and byproducts were altered through random mutations and selection, but this implied laborious screening and unidentifiable secondary mutations. To avoid secondary mutations, Tn5-based mini-transposon technology can be used. Although these transposons have the property to be functional in a wide range of organisms, it still has several drawbacks. The insertions are still random, which means that elaborate screening is necessary to identify the insertions. Second, the integrated transposable elements contain an antibiotic resistance marker which prevents reuse of the markers for sequential mutation rounds. In addition, the obtained strain will be considered as a GMO due to the residual marker, which is not the case for the former strategy (Curic et al. , 1999; Schweizer, 2003). In order to avoid random mutations, more site specific techniques, based on homologous recombination were developed. 50 Metabolic engineering of succinate biosynthesis in Escherichia coli Table 2.2: Overview of the genetic toolboxes used for the development of biotechnological processes. TIGR’s:Tunable InterGenic Regions, gTME: global Transcriptional Machinery Engineering Cellular level Tool References Genome Gene knock out/knock in (Ayres et al. , 1993; Cherepanov & Wackernagel, 1995; Datsenko & Wanner, 2000; Hoang et al. , 1998; Kristensen et al. , 1995; Sauer, 1987; Schweizer, 2003; Sternberg et al. , 1981) Promoter libraries (Alper et al. , 2005a; De Mey et al. , 2007d; Jensen & Hammer, 1998a; Miksch et al. , 2005) Genome shuffling (Hou, 2009; John et al. , 2008; Patnaik et al. , 2002) Transcriptome TIGR’s (Pfleger et al. , 2006; Smolke & Keasling, 2002) gTME (Alper & Stephanopoulos, 2007; Klein-Marcuschamer & Stephanopoulos, 2008) gene silencing (Hebert et al. , 2008; Rasmussen et al. , 2007) Proteome Ribosome engineering (Ochi, 2007) Directed evolution (Johannes & Zhao, 2006; Tao et al. , 2005) Gene shuffeling (Conant & Wagner, 2005) Chaperonne engineering (Nishihara et al. , 2000; Wynn et al. , 2000) Site specific gene deletion Initially genes were disrupted through homologous recombination as shown in Figure 2.1. However this strategy leaves a selection marker behind, which means with every gene disruption a new marker has to be used. Such a marker can also obscure the phenotype of the gene disruption. Many bacterial genes are organised in open reading frames which are cotranscribed. Insertional, frameshift, nonsense, or antisense disruption of an ORF within an operon can affect upstream and downstream gene expression in addition to the gene targeted for inactivation (Link et al. , 1997). These problems are avoided when the marker is removed after gene deletion. This can be obtained for instance by a second cross over event. Moreover, in E. coli , this system requires a polA , recBC , sbcB , or recD deficient genetic background that leads to the inability of maintaining ColE1 plasmids. This property is necessary to select for the strains in which the plasmid has recombined with the chromosome. Wild type strains cannot be modified with these types of plasmids (Balbás et al. , 1996; Balbas & Gosset, 2001). 51 Chapter 2 For these strains alternative plasmids which replicate under well defined conditions were developed (Hamilton et al. , 1989). Figure 2.1: Homologous recombination strategies for gene disruption in microorganisms based on single and double cross over, the plasmid contains one (left) or two (right) homologous region(s) that can recombine with a sequence within the target gene. Via this recombination the homologous region in the target gene is replaced by a marker gene. In the case of a single homologous region (left), a large part of the plasmid is also integrated into the chromosome (at the location of the target gene), in the case of a two homologous regions, only the marker gene that is located on the plasmid is integrated into the chromosome, at the location of the target gene (Balbás et al. , 1996; Balbas & Gosset, 2001; Mills, 2001). Figure 2.2 shows the strategy of double cross over with a temperature sensitive plasmid. In a first step the wild type strain is transformed with a temperature sensitive plasmid. This plasmid cannot replicate at high temperatures (so called non-permissive conditions). When incubated at those temperatures in the presence of a selective pressure only the strains in which the plasmid has integrated in the chromosome, through homologous recombination, can survive. In a second step the recombinant strain is grown at the plasmids so called permissive temperature. During that incubation a second recombination can occur that allows either the plasmid to excise itself via homologous recombination with or without the removal of the target gene. Eventually a mixture of mutant strains and wild type strains will be obtained that have to be screened by PCR (Hamilton et al. , 1989; Link et al. , 1997). This system is however very inefficient, it requires strains with high transformation and recombination efficiencies and every mutation cycle requires an extensive screening to differentiate the mutants from the wild types when the marker is excised out of the 52 Metabolic engineering of succinate biosynthesis in Escherichia coli chromosome. In some cases the plasmid stays stable within the strain, requiring another non- permissive screening growth cycle to eliminate the plasmid from the strain (Link et al. , 1997). Markerless gene deletion/replacement ori Marker gene T sensitive plasmid Homologous region H1 Homologous region H2 Variable region chromosome H1 H2 Target gene Growth at non – permissive temperature for plasmid DNA replication H1 H2 H1 H2 chromosome Variable region Marker gene Target gene H1 H2 H1 H2 chromosome chromosome H1 H2 H1 H2 Variable region Marker gene Variable region Marker gene H1 H2 H1 H2 chromosome chromosome Target gene Variable region Figure 2.2: Markerless gene deletion strategy with a temperature sensitive plasmid that is non-permissive (cannot replicated) at higher temperatures (e.g. 44°C) and permissive at lower temperatures (e.g. 30°C). In a first step a single recombination event takes place at the non-permissive temperature, which selects for the recombination event. In a second step the strains are grown at the permissive temperature and a second recombination can occur. This can result in either a recombination event that reinstates the original gene or that deletes the gene. The variable region marked between the two homologous regions on the plasmid can represent an insertion, deletion, single-base change, etc…. The dashed line represents the plasmid DNA, the full line represents chromosomal DNA (Hamilton et al. , 1989; Link et al. , 1997). These difficulties can be avoided by using the four well-characterised site-specific recombination systems: the ParA-res system of the broad-host-range plasmid RP4, the TnpR- res system of a class II (Tn 3-like) transposon, Tn 1000 , the Cre-loxP system of bacteriophage P1, and the Flp-FRT system of Saccharomyces cerevisiae (Ayres et al. , 1993; Camilli et al. , 1994; Cherepanov & Wackernagel, 1995; Kristensen et al. , 1995). Each site-specific recombinase (ParA, TnpR, Cre, or Flp) has resolvase activity and efficiently mediates 53 Chapter 2 intramolecular recombination between the two directly oriented copies of the recombination (loxP, FRT or res ) site, leading to a precise excision of the intervening DNA fragment (Tsuda, 1998). ParA and TnpR belong to the serine recombinase family, while Flp and Cre belong to the tyrosine family according to the amino acid residues in their DNA binding and cleavage site. Their biological function is to resolve multimeric forms of circular plasmids and chromosomes that origin from homologous recombination. This activity increases for instance the segregational stability of plasmids. The plasmid ColE1 for example uses the host encoded Xer recombinase which is highly conserved amongst bacteria and archaea (Hallet et al. , 2004). These recombinases carry out their site specific recombination activity stepwise. First, one pair of DNA strands is exchanged to form a so called Holliday junction intermediate. In a second step this junction is converted to the recombinant product by exchange of the second strand of DNA (Van Duyne, 2001). How these systems have been applied for gene deletion is shown in Figure 2.3. The strategies used with ParA and TnpR are based on a one plasmid system on which the recombinase, the recognition sequences and the selection marker are present. The recombinase can be placed as such that it either stays integrated in the chromosome or is removed after res sequence recombination. In order to avoid immediate recombination between the res sequences, an inducible promoter is added in front of the recombinase gene. For TnpR, this can be its natural promoter which is induced at low iron concentrations, Par can for instance be induced by IPTG (Camilli et al. , 1994; Kristensen et al. , 1995). The Flp-FRT system and the Cre-loxP system are the most broadly used systems for genetic modifications. Both are not restrictive in their host range and functioning in bacteria, yeasts as well as plants and mammalian cells (Barrett et al. , 2008; Erler et al. , 2006; Hu et al. , 2006; Leibig et al. , 2008; Mallo, 2006; Stephan et al. , 2004; Suzuki et al. , 2007; Vergunst & Hooykaas, 1999). The strategy used for both systems is exactly the same. First, the organism is transformed with a plasmid with the homologous sequences from the target gene, the recombinase recognition sites and the marker gene. After recombination of the plasmid with the chromosome a second plasmid is introduced to remove the marker from the chromosome. Both plasmids are then cured from the strain by incubation at higher temperatures. Recombinase mediated excision of the marker gene leaves a scar behind in de chromosome because a single recognition sequence is always left behind. Such a scar might raise potential polarity problems, either by the introduction of transcriptional terminators or translational terminators. However tests show that polarity problems do not occur after excision (Merlin et al. , 2002). The sites do not even show significant effects when they are located between the promoter and its gene (Ellermeier et al. , 2002). All the above mentioned systems make use of plasmids that recombine with the chromosome to either disrupt or delete a certain gene. The reason for that is mainly that many strains possess intracellular exonucleases that break down foreign linear DNA. However the use of linear DNA speeds up the creation of new mutants, because laborious plasmid construction is avoided. 54 Metabolic engineering of succinate biosynthesis in Escherichia coli H2 regron 2 H • gene • res npR gene -Par/T ·Marker Homalogous 2 ' ~ ker H - r --- - Ma gene/\ inductian 1 P d i p ~- ~ H2 gene i Marker r - . ~ 1 1 1 o ! · asm es es l .. ;. 7 r r . res p ParfTnpR arget P ft Par-res T • H2 H1 H1 • - TnpR-res • res 1 ParfTnpR ( • H 1 H - 1 H mo reg1on osome chrornosome logeus - -cl1romosome -chrom -chromoso oma H H2 recombinase 2 Cre region gene er k gene oiogeus er Mar Hom H2 • \ i P -Mark H2 - • -- lox ·· ·~ ' gene gene r l H2 gene r - 1 1 V Of get · · r laxP loxP plasmld Marke ~ 1 Cre-/ox Marke Ta H1 H - - • 1 ~ H -- • H11oxP H1 me region hramosome hromoso chromosome chromosomo - - -c -c Homologoos H2 Flipase 2 region gene gene Markef Hamologous H2 • i\ Marker H2 - • FRT gene gene gene H2 - 1 ~ 1 V on T R arker plasmld · · FRT F Marker M 1 Target Flp-FRT H1 H - - • FRT 1 H ~ - • H1 H e e region som mosom chromosome -chro -chromosome -chromo - Homoiogeus Figure 2.3: Overview of site-specific recombinase strategies for gene deletion. The plasmids used in these strategies are curable either by temperature of selective pressure. In the case of TnpR-res and Par-res the induction of the Par/TnpR promoter is IPTG, Fe2+, or temperature based that does not show leak expression (Ayres et al., 1993; Camilli et al., 1994; Cherepanov & Wackernagel, 1995; Kristensen et al., 1995). 55 Chapter 2 Simply by performing a PCR with 35 nucleotide homology extended primers the insertion sequence can be constructed. To avoid the intracellular degradation of the linear DNA a much more efficient recombination system has to be applied. Such recombinases can be found in bacteriophages that employ their own homologous recombination systems. The λ red recombinase is such a recombinase that enhances recombination over the native recombination system. First the organism is transformed with a plasmid that contains this recombinase, which allows the use of linear DNA instead of plasmids. Once the integration is complete the mutant strain can be cured from the λ red helper plasmid. In a second phase the selection marker gene is excised in a similar way as described above (Datsenko & Wanner, 2000). The site specific recombinase systems can only excise DNA fragments when the recognition sites are oriented in the same direction, when they are inverted, the sequence in between these two sequences will invert due to recombination. This type of activity can be used to switch genes on and off at will via an inducible recombinase activity (Schweizer, 2003). Interference RNA The above described techniques all interfere at the genomic level by directly eliminating gene functions either through insertion or complete deletion. However gene functions can also be eliminated at the transcriptomic level. Nature also foresees interference RNA, next to gene expression modulations through transcription factors. These are small non-coding RNA’s that interact with specific mRNA sequences resulting in either blockage or the degradation of the target RNA. These types of mechanisms are present in all three kingdoms of life – though might differ slightly in eukaryotic and prokaryotic organisms. The eukaryotic RNAi systems come in two basic varieties, first, siRNAs which are produced from viral or transposon dsRNAs which protect the host from the respective agents via perfect base-pairing with their mRNAs, and second, miRNA that regulates translation of endogenous genes via base- pairing, either perfect (plants) or imperfect (animals) (Zamore & Haley, 2005). The application in gene studies and the true mechanisms behind these interference RNAs have not been uncovered till the late 1990’s when it was shown that double stranded RNA is more efficient to silence certain genes instead of single stranded sense or anti-sense RNA (Fire et al. , 1998). The RNAi pathway in eukaryotic cells is composed out of two steps. First, a trigger, either dsRNA or miRNA, is processed into siRNA by the RNAseIII enzymes named Drosha and Dicer (with dsRNA binding partners Pasha/DGCR8 and Loqs/TRBP, respectively). In the second step, the siRNAs are loaded into the RNA induced silencing complex (RISC). In this complex the siRNA is unwound and prepared for base pairing with the target mRNA. If this hybridisation event is perfect (no base pair mismatches), the mRNA is targeted for degradation; when mismatches occur and hairpins arise, the target gene function is mostly silenced through inhibition of translation (Hammond, 2005; Tomari & Zamore, 2005). Figure 2.4 depicts a schematic representation of these steps. 56 Metabolic engineering of succinate biosynthesis in Escherichia coli Figure 2.4: A schematic representation of the mode of action of RNAi in eukaryotic cells. In the first step the miRNA is processed by Drosha with binding partners Pasha/DGCR8 (in the nucleus) and Dicer with binding partner protein, Loqs/TRBP (in the cytoplasm). Finally a kinase phosphorylates the miRNA in RNAi which is processed into RNA induced silencing complex (RISC) via a helicase. Perfect hybridisation leads to mRNA degradation, hairpin formation leads to inhibition of translation (Agrawal et al. , 2003; Hammond, 2005; Zamore & Haley, 2005). Prokaryotes have analogous mechanisms to the eukaryotic miRNA system, regulation of bacterial gene expression by small antisense RNAs. There are about four classes of antisense RNAs in bacteria each with their own mode of action (Figure 2.5)(Brantl, 2002). First there is the antisense RNA that targets the mRNA for degradation by dsRNA specific RNases. A second type hinders translation either by binding at the ribosome binding site or somewhere inside the mRNA structure. The latter might be less efficient due to possible helicase activity of some ribosomes, which helps the ribosomes with the translation of mRNAs with extensive secondary structures. External guide sequences or EGS are the third type of asRNAs in prokaryotes. These small RNAs hybridise their target and mimic a natural occurring cleavage site of RNase P. This RNase is normally involved in the maturing of the 5’ ends of tRNAs and is not sequence specific but requires the RNA to fulfil certain properties. 57 Chapter 2 Figure 2.5: Different modes of action of antisense RNA in prokaryotes. 1. asRNA targeting of mRNA for dsRNA specific RNase, 2. asRNA interference of translation, 3. EGS or external guide sequence targeting for RNase P and 4. catalytic RNA/DNA (Brantl, 2002; Rasmussen et al. , 2007) The EGS must contain an unpaired CCA sequence; the mRNA must contain a guanosine 3’ from the cleavage site and, a pyrimidine 5’ from the cleavage site (Lawrence et al. , 1987). The forth type is called catalytic antisense RNA or DNA. Two conserved motives play here an important role, the hammerhead motif and the hairpin motif. The asRNA, here ribozyme, hybridises with the mRNA to form such motifs. They form helices with the target mRNA and dissolve the RNA into two strands, one of them directing the in trans cleavage of the other (Rasmussen et al. , 2007). The target RNA secondary structure plays an important role in the application of RNAi. The structure is in essence unchangeable and has to be invaded by the antisense molecule. It is therefore likely that the target RNA will determine the degree of inhibition that can be reached. Modifications to the antisense RNA can be beneficial for inhibition, for instance the target recognition sites can be varied to identify better target binding (Engdahl et al. , 1997). By simply changing the length of the target recognition sites, hybridisation rates can even change by a 100 fold (Rittner et al. , 1993). The only method to evaluate the degree of inhibition by a newly created asRNA is by screening. However this can be a tremendous task. Screening methodologies have to be developed and a library of asRNA has to be constructed. Secondary structure predictions can help with the creation of these libraries. These can be determined with computer assisted free energy minimisation algorithms (Mfold) (Markham & Zuker, 2005) or structure sampling algorithms (Sfold) (Ding et al. , 2004) to identify accessible regions (Figure 2.6). It is important though to keep in mind that these are models that predict in vitro secondary RNA structures. For instance, these algorithms cannot take the RNA-protein interactions into account, which might change loop, joint sequence, or bulge location in the structure and render the predictions useless. This also makes it a more laborious technique as a metabolic engineering tool because of the screening in comparison to the site directed gene deletion techniques that were mentioned above. 58 Metabolic engineering of succinate biosynthesis in Escherichia coli Figure 2.6: Secondary structures that are favorable for antisense RNA to hybridise to. These regions in the target RNA are defined as joint sequences, external and internal loops and, bulges (Rasmussen et al. , 2007). Because RNAi technology is still immature and the time to result is still quite steep, applications in metabolic engineering are not as wide spread as site specific gene deletions. Nevertheless there are already some reports of successful gene silencing and productivity improvements. For instance, the RNase E knock down in E. coli resulted in increased RNA levels in the cytoplasm without a significant effect on the growth rate and as a result an increased potential for recombinant protein production (Kemmer & Neubauer, 2006). Another example can be found in the production of acetone and butanol by Clostridium acetobutylicum . By introducing asRNAs into the strain, the acetone-butanol production ratio could be modulated to a process that either favours one or the other (Tummala et al. , 2003). RNAi technology can also be applied to increase the process stability. Bacterial based biotechnological processes, especially involving E. coli , can be bacteriophage sensitive. By introducing asRNA that either degrades the phage RNA or tags phage RNA for degradation, into the industrial strain, processes tend to become much more stable and less susceptible for lyses (Sturino & Klaenhammer, 2006). Riboswitches The expression of antisense RNA itself is controlled by the expression of the antisense RNA gene, which is in turn determined by its promoter and the transcription factors that bind to it. Hence, there is no causal effect between an environmental change and the inhibitory effect of asRNA due to an intermediate transcriptional step. An alternative control system that circumvents transcription but is also based on antisense RNA principles is called riboswitches. These are small RNAs or elements in the untranslated region of the target mRNA that are composed of two regions, the aptamer that binds small molecules and a modulator region that modifies for instance translation of the downstream gene (Avihoo et al. , 2007; Lubertozzi & Keasling, 2009). The riboswitch RNA can switch between two states by binding a ligand. This changes the secondary structure of the RNA which either activates or deactivates a certain cellular process. Figure 2.7 gives an overview of the known mechanisms of riboswitches (Bauer & Suess, 2006). In eukaryotes they can also induce gene splicing next to translational modulation (Cheah et al. , 2007). The application and construction of riboswitches is however much more complicated in comparison with RNAi. The two parts of the riboswitch need to be developed in synergy with each other, the ligand and the target mRNA. This results in a tedious in silico and in vitro screening process (Avihoo et al. , 2007). 59 Chapter 2 0 .. .. ;u .. - ~ (/)"' - ~ I I AUG I AUG mRNA ...... - ~ - ~ mRNA RBs~lA=UG~_ mRN I AUG / x [f] .... -~ - ~ mRNA RBS IAUG mRN AUG / x ...... -~ ---RBS_l'IA=UG~_ / Figure 2.7:The different modes of action of riboswitches. A. Riboswitch located in the UTR of the target mRNA. In the presence of a ligand “L” a secondary structure forms, hindering the ribosomes to bind the RBS. B. Riboswitch located in the UTR which forms a secondary RNA structure upstream from the RBS in the presence of the ligand, avoiding secondary structure formation near the RBS. C. Small RNA (asRNA) that functions as a Riboswitch, either in the presence or the absence of a ligand, the small RNA binds upstream from the RBS avoiding secondary structure formation at the RBS. D. Small RNA (asRNA) that hybridises at the RBS sequence in absence or presence of a ligand (Bauer & Suess, 2006). 60 Metabolic engineering of succinate biosynthesis in Escherichia coli 2.1.1.2 Introduction and overexpression of genes Metabolic engineering and process optimisation not only requires gene deletions or silencing but also the introduction of new functions. One of the simplest methods for the introduction of a new function is the use of a expression vector, usually a plasmid. Plasmids can exist autonomously in the host but the plasmid and the host have to obey certain rules. First, the number of different plasmids that can be transformed or conjugated into an organism is limited. Plasmids are classified in incompatibility groups which cannot exist stably together. This incompatibility is a manifestation of relatedness: the sharing of common elements involved in plasmid replication control or equipartitioning (Couturier et al. , 1988). Second, the host range. The most stable and successful plasmids with the broadest host range are those with the best inheritance system, the lowest fitness cost (lowest metabolic burden) and the highest replication rate (De Gelder et al. , 2008; Sorensen et al. , 2005). These latter properties not only determine the plasmid’s stability but also the copy number, which is an important feature that is useful for gene dosage in metabolic engineering strategies (Keasling, 1999). As described previously plasmids are also involved in many gene deletion tools. However plasmid stability is not always ensured for every strain. For instance a newly isolated organism may stably maintain a plasmid, but this plasmid might not be curable at increased temperatures, an essential characteristic for many gene knock out strategies. Recent advances in error prone PCR and adaptive evolution however have allowed researchers to broaden the range, change the copy number or curability of plasmids (De Gelder et al. , 2008; Kim et al. , 2004a; Tao et al. , 2005). Most plasmids are inducible expression systems which means that the expression of the heterologous gene that has been introduced on this vector is controlled by an inducible promoter. Examples of these types of promoters are the IPTG/lactose induced P lac , L- arabinose induced P BAD , the tryptophane starvation promoter P trp and temperature induced λ pL promoter. IPTG inducible hybrid promoters P tac and P trc are probably the most widely used promoters, however their use in large scale production is limited due to the toxicity of IPTG and its cost (Markides, 1996). In many cases, the use of inducible promoters is not desirable because the new function has to be constitutively present in the cell for optimal production. However the isolation and screening of different natural promoters from different organisms to allow gene fine-tuning is very laborious. Hence the development of promoter libraries. Two strategies have been proposed for the construction of such a library. First, the promoter region of the organism can be studied, for instance prokaryotes have a very distinct promoter consensus sequence, the -35 (TTGACA) to -10 (TATAAT) box. The regions flanking these sequences and the distance between these boxes determines the strength of the promoter and the affinity of the RNA polymerase sigma factors (for example the 17 base pairs distance that is found in sigma 70 promoters). By varying the nucleotides in these flanking regions promoters can be constructed of different strengths (De Mey et al. , 2007d; Jensen & Hammer, 1998b; Miksch et al. , 2005). An alternative method does not require the in vitro synthesis of different oligos as described in the previous reports, but uses error prone PCR to vary the sequence of a known constitutive promoter (Alper et al. , 2005a). In principle the same result 61 Chapter 2 is achieved with both methods. The correlation between strength and sequence was determined and can consequently be applied to fine-tune gene expression (De Mey et al. , 2007d). As a tool for gene introduction, plasmids are very useful but still limited to a certain number of genes. When whole pathways have to be introduced with multiple genes, chromosomal editing might be more feasible than combining multiple plasmids with different promoters. The same techniques can be applied in this case as for gene deletions. The sole difference is that instead of removing the whole recombined plasmid DNA from the chromosome with the lox , FRT or res sites, a new gene or promoter is left behind in the chromosome. For the introduction of a constitutive promoter in front of a homologous gene multiple strategies are possible. i) The promoter with an artificial RBS can be introduced in front of the native promoter, ii) the promoter together with an artificial RBS can replace the native promoter and RBS and iii) the artificial promoter can replace the native promoter, but leaving the native RBS intact. An extensive analysis on this matter has shown that only the third option actually alters gene expression and enzyme activity. An artificial RBS probably reduces the mRNA stability and therefore counters the effect of a stronger artificial promoter (De Mey, 2007a). Nature however rarely uses multiple promoters for genes that assemble the units of an enzyme or a metabolic route. Prokaryotes group their genes in operons and eukaryotes in gene clusters. This way the cell can coordinate its gene expression more tightly with the appropriate stoichiometric needs (White, 2006). A single promoter controls the expression of the operon, but the true regulation lays then with post transcriptional processes such as transcription termination, mRNA stability and translation initiation, processes that have been mentioned above extensively concerning antisense RNA technology. In an operon, the intergenic regions play an important role in post-transcriptional control processes. By varying these regions, the secondary structures and sequence, tuneable intergenic regions (TIGRs) that determine the post-transcriptional control could be constructed. In order to achieve this, a library of multiple intergenic gene sequences was constructed and assayed. In this library RNA stabilising loops and RNase sensitive sequences can be found that either enhanced translation of decreased it (Pfleger et al. , 2006). This library was applied for the production of isoprenoid in E. coli and allowed an optimisation of the different enzyme activities in the pathway (Pitera et al. , 2007). Eukaryotic cells however do not allow polycistronic transcription. This was solved by using internal ribosomal entry sequences (IRES) from eukaryotic viruses and host stress response pathways (Martin et al. , 2006). This technique was applied in yeast to express two reporter genes and modulate their activity (Masek et al. , 2007). Transferring genes from one organism to another is not always straightforward. When considering the different steps in the biogenesis of a protein, some bottlenecks can be identified. For instance, when a eukaryotic gene is transferred to a prokaryotic organism the gene organisation of an eukaryote has to be taken into account. Eukaryotic genes are 62 Metabolic engineering of succinate biosynthesis in Escherichia coli composed of introns and exons which are spliced out, while prokaryotes do not posses such a mechanisms. Therefore, eukaryotic genes have to be cloned from mRNA level and not from DNA. In addition, codon usage between organisms can differ. Therefore a gene that is transferred between organisms has to be adapted to the appropriate codon usages of the host organism (Supek & Vlahovicek, 2004). Initially the optimisation of these two matters was quite laborious. First the gene has to be identified, then it had to be picked up and finally many point mutations had to be introduced to optimise codon usage. Nowadays, high throughput sequencing and de novo gene synthesis significantly reduced the time to result and cost (Czar et al. , 2009; Rothberg & Leamon, 2008). However the adaptation of the gene and the mRNA to the host organism does not always guarantee a functioning protein. During translation a protein goes through a complex folding process which involves chaperones that help the formation of its tertiary and quaternary structure. The protein can also require post translational modifications to be functional, such as glycosylations (Gräslund et al. , 2008). By eliminating and adding gene functions in an organism, a production process can be optimised. However, these pathways are natural occurring biochemical routes that are not always optimal for the syntheses of the target compound. Some of the enzymes involved in the synthesis of the target molecule may actually have alternate primary functions or may even be none existent. If there is an enzyme that performs a reaction that is similar to the envisaged reaction, an enzyme can be created through evolution to perform that particular reaction (Johannes & Zhao, 2006). For this purpose directed evolution and gene shuffling technologies were created to evolve and screen for new enzymes with different functions and/or properties (Schmidt-Dannert, 2001). Enhanced production of glucosamine and N- acetylglucosamine via an enhanced glucosamine synthase by Escherichia coli and the production of doramectin, a new antihelmetic drug, by Streptomyces avermitilis are examples of the application of these technologies (Deng et al. , 2005; Stutzman-Engwall et al. , 2005). 2.1.1.3 Global phenotype adaptation A strain such as E. coli can in theory produce any molecule. Its limitations lay then with the robustness of the process. Production of antimicrobial agents, for instance, with an E. coli strain would not be advisable, unless it is resistant to the endproduct and resistance does not result in degradation of the endproduct. In some cases none of the known microorganisms are resistant to the envisaged product. Then a screening process is necessary, which would mean screening for a completely new microorganism and which brings along a whole set of new problems which have been covered above. Alternatively, a resistant strain can be created from a known microorganism. In some cases this is easy. Consider again antibiotic production for which resistance is a well described phenomenon. For such a production, resistance can be transferred from one organism to another – something that is often used in molecular biology. For organic acid production however it is much more complicated to create resistant strains. In that case resistance cannot be assigned to a single gene or protein, but to a whole network of complex signal and control systems. Here evolution could help a hand. Multiple options are 63 Chapter 2 then available. First, slow adaptation of the wild type strain to the desired condition can be applied, e.g. low pH or high ethanol concentration. This strategy might take many generations, but results in a very robust strain (Yomano et al. , 1998). The adaptation can be performed in either batch cultures or chemostat cultures, depending on the goal of the evolution experiment. The idea is that over the many generation, classical Monod kinetics do not apply anymore, which considers µ max and K S as constants (equation 2.1; with µ, the apparent growth rate; µ max , the maximal growth rate; S, the substrate concentration; K S, substrate affinity; and ρ, the constant maintenance respiration rate). S µ = µmax × - ρ 2.1 KS +S The change in maximal growth rate and substrate affinity of an organism is determined by flexibility constants δµ and δK respectively. These rates are determined by the selective pressure applied and the mutation rate of the studied organism. A selection for increasing µ can be explained with the concept of µ max - and K S- type of strategists, which postulates that sequential batch cultures select for the fastest growing strains while chemostat cultures at low dilution rates select for the cells with the highest affinity for the limiting substrate. Both strategies can be combined by slowly increasing growth rate in a chemostat to rates near µ max (Wick et al. , 2002; Wirtz, 2002). Random mutagenesis of strains with mutagen agents or rays (UV, radioactive) is an alternative for natural evolution as described above. The screening principles are the same for both methods (Du et al. , 2007). By applying mutagen agens, the flexibility constants of the strains are increased, which means the change rate in maximal growth rate and affinity constant increases. However, long term exposure to mutagens could lead to lethal phenotypes. Hence, it is crucial to optimise dosage/exposure time meticulously. To avoid these ill defined random mutations in the genome, more target specific random mutations can be applied. For instance one of the RNA polymerase sigma factors can be targeted to change the transcriptome, so called global transcription machinery engineering (gTME). Mostly if there is already some resistance it is a matter of changing the transcriptome in such a way that tolerance increases. Randomly mutating a sigma factor with error prone PCR changes the affinity for the different RNA polymerases for their promoters and changes gene expression. By doing so randomly, possible transcript combinations can be screened and the optimal combination that is best adapted to the new condition can be selected (Alper & Stephanopoulos, 2007). This type of engineering was first applied in plant systems to induce flavonoid gene expression and anthocyanin accumulation in transgenic plants (Broun, 2004). In yeasts and bacteria mainly resistance systems have been targeted through the modification of global transcription regulators (Alper & Stephanopoulos, 2007; Lee et al. , 2008). In a similar way ribosomal proteins and rRNA can be targeted. This type of engineering, ribosome engineering, targets the cell at the translational level. This technique has been applied to improve the antibiotic producing properties of Streptomyces sp. and for enzyme 64 Metabolic engineering of succinate biosynthesis in Escherichia coli overproduction in Bacillus subtilis . The exact mechanisms behind these improvements are not fully understood yet, but presumably the adapted phenotypes are the result of the activation of the stringent response, which enhances protein synthesis (Ochi, 2007; Ochi et al. , 2004; Santos & Stephanopoulos, 2008). The previously described techniques all lead to a pool of interesting new phenotypes that might be subtle improvements, but combined with others might lead to synergetic effects (Gong et al. , 2009; Warner et al. , 2009). Whole genome shuffling allows the generation of combinatorial libraries that might lead to these synergetic effects. In essence protoplasts are made from the different strains and fused into novel strains. These strains can then be assayed for interesting properties either by an enrichment culture as described above or a high throughput assay that looks at a particular cellular property. In this way Lactobacillus strains which had undergone already multiple rounds of classical strain improvements (e.g. with chemical mutagens) were fused to select a new low pH adapted strain. After 5 rounds of protoplast fusion the threshold of a pH of 3.8 was reached (the pK a of lactic acid) (Patnaik et al. , 2002). Lactobacillus delbrueckii performance was enhanced analogously, in a first round the strain was adapted to a reduced pH, in a second round the genome of the pH adapted strain was shuffled with Bacillus amyloliquefaciens , an organism that efficiently metabolises starch. This resulted in a novel Lactobacillus that could convert waste starch at low pH into lactic acid (John et al. , 2008). Also production can be optimised through genome shuffling. Enhanced tylosine, a complex polyketide antibiotic, production was obtained by breeding classically improved S. fradiae strains (Zhang et al. , 2002). 2.1.2 Escherichia coli A question still left unanswered is, which strain is the best to use in a particular production process, for instance succinic acid production? E. coli certainly cannot be ignored because of its long history in research. Over the years many different laboratories all over the world have selected and modified different E. coli strains. In the earlier days those modifications were mostly carried out randomly, through UV or chemical mutagenesis. This has lead to a kind of family tree of E. coli with E. coli K12 as ancestor (Bachmann, 1996). The wild type E. coli K12 is F + and λ+, which means that it is able to perform conjugation and use the p L promoter. It does not contain any antibiotic resistance-genes nor is it auxotrophic for any compound. Direct descendants are MG1655 and W3110. Both W3110 and MG1655 can be seen as ‘nearly wild type’ strains. They both are F -, λ- and contain a 1 bp nonsense frame shift in the phosphorolytic ribonuclease gene (RNase PH). W3110 differs from MG1655 in an inversion of rrnD and rrnE , genes that code for 16S ribosomal RNA, which is necessary for efficient translation. It is reported that due to this inversion W3110 exhibits a slower growth rate. Because of the rph -1 mutation a decreased transcription of pyrE occurs. This implicates that both strains need pyrimidine or uracil for optimal growth (Jensen, 1993). 65 Chapter 2 Two alternative ancestors are E. coli C and B. E. coli C lacks host restriction and modification activity and is a nonsupressing host strain used in the complementation tests with the amber mutants of bacteriophage λ. It is very phage sensitive which could be problematic in an industrial context, because a single contamination could lyse every cell in the production process (Bertani & Weigle, 1956; Sambrook et al. , 2001; Sibley & Raleigh, 2004). Only recently this strain has been adapted for succinate production (Jantama et al. , 2008a; Jantama et al. , 2008b). The second strain, E. coli B has previously been reported as a production strain, for instance for the production of glutathione (Li et al. , 2004; Murata & Kimura, 1990). In patent literature this strain is described for the production of phenylalanine because it has a very efficient aspartate transaminase (Wood & Carlton, 1987). A similar process has been described for the production of L-tryptophane with E. coli B10, one of its descendants (Bang et al. , 1983). Comparison with E. coli K12 has shown that many biochemical processes show subtle differences in their regulation, e.g. the regulation of the glyoxylate shunt is seemingly different in both strains (Sauer & Eikmanns, 2005). The choice of E. coli as a production host may be considered as safe because of its long historical background in research. However, any choice would be as good as E. coli as long as the necessary genetic tools and basic genomic information are available. The paragraphs above show that any pathway can be introduced into the genome, any enzyme function can in principle be adapted to the needs of the process and any resistance can be created either by simple adaptation of genomic breeding with more resistant organisms. In the end, the strain that is the result of all these manipulations will probably be classified as a completely new organism if it were to be isolated and characterised from nature. With the vast progress that has been made in the field of synthetic biology and the search for minimal cells, the issue of strain choice will likely disappear and be replaced by libraries of genes and interesting strain properties that can be recombined into a newly synthesised microorganism (Czar et al. , 2009; Forster & Church, 2006). The downside of this evolution is that it creates not only potential advances but also implies certain threats. The ease with which the Spanish flue virus and poliovirus could be constructed via these techniques does raise concerns on how to proceed with the further development (Cello et al. , 2002; Tumpey et al. , 2005). 2.1.3 Biological databases In principle any organism can be modified to produce any molecule, as long as the biosynthetic route exists and genetic tools for the strain of choice exist. The key is thus to find the biosynthetic routes towards the product and the genes that are involved in that route. Biological databases are crucial for this search. 2.1.3.1 A brief history In 1981 the first biological database was developed on magnetic band by Dayhoff and coworkers. This database was the predescessor of the Protein Information Resource (PIR) and was until then only available as a book (Ellis & Attwood, 2001; Martinez, 1984). Few 66 Metabolic engineering of succinate biosynthesis in Escherichia coli years later Genbank, EMBL and DDBJ were founded, which, to date, are still the main resources for nucleic acid sequence information. Since 1988 these three resources are fully synchronised (Hingamp et al. , 1999). Information exchange was until then still limited by the speed of postal services because all data had to be physically transferred from one place to the other. This was changed by the introduction of the World Wide Web into day to day computing (Lucky, 2000). From that point onward information was shared through web pages linked together with hypertext. The linking system is roughly based on a classification system developed for the integration of bibliographic, image, and textual databases in the early nineteen hundreds (Rayward, 1994). These technological leaps resulted in a proliferative growing information stream in which biological databases are imbedded. The journal Nucleic Acid Research presents every year a database issue that gives an overview of all databases available (Brazas et al. , 2008; Galperin & Cochrane, 2009). In order to structuralise the massive amount of information, the databases are classified in different groups of which some are shown in Table 2.3. 2.1.3.2 A metabolic engineering perspective Classification is roughly based on the central dogma (Figure 2.8). The first group of databases and bioinformatics tools is the taxonomy, phenotype databases and phylogenetic tools. They all concern strain classification and identification and from a metabolic engineering’s point of view they are important in strain selection and for the development of genetic tools. Cell Reaction network Genome operon organisation enzyme translation transcription Figure 2.8 The central dogma of molecular biology. An organisms phenotype is determined by its DNA, RNA, proteins and the reactions/functions these proteins perform. Each of these levels are tightly regulated, either by the other level or by autoregulation. 67 Chapter 2 Table 2.3: Overview of some biological databases (Brazas et al. , 2008; Durot et al. , 2009; Williams et al. , 2005) DNA sequence and genome annotation databases DDBJ http://www.ddbj.nig.ac.jp/ CMR http://cmr.jcvi.org/ DCODE www.dcode.org EMBL http://www.ebi.ac.uk/embl/ GenBank http://www.ncbi.nlm.nih.gov/Genbank/ Genome Browser http://genome.ucsc.edu/ IMG http://img.jgi.doe.gov/ Integr8 http://www.ebi.ac.uk/integr8/ Map viewer http://www.ncbi.nlm.nih.gov/mapview SEED http://seed-viewer.theseed.org/ Protein and enzyme databases BRENDA http://www.brenda-enzymes.info/ ENZYME http://www.expasy.ch/enzyme/ IntEnz http://www.ebi.ac.uk/intenz Prolinks http://prolinks.mbi.ucla.edu/ RCSB http://www.rcsb.org/pdb/home/home.do STRING http://string.embl.de/ TransportDB http://www.membranetransport.org/ UniProt /swissprot/PIR http://www.ebi.ac.uk/uniprot/ Metabolic databases CheBI http://www.ebi.ac.uk/chebi/ BioCyc http://www.biocyc.org/ KEGG http://www.genome.jp/kegg/ LipidMaps http://www.lipidmaps.org/ Manet http://www.manet.uiuc.edu/index.php Pubchem http://pubchem.ncbi.nlm.nih.gov/ Reactome http://www.reactome.org/ UM-BBD http://umbbd.msi.umn.edu/ UniPathway http://www.grenoble.prabi.fr/obiwarehouse/unipathway/ Experimental data repositories IntAct http://www.ebi.ac.uk/intact/ Array Express http://www.ebi.ac.uk/aerep/ ASAP http://asap.ahabs.wisc.edu/ BIND http://bind.ca DIP http://dip.doe-mbi.ucla.edu/ E. coli multi-omics http://ecoli.iab.keio.ac.jp/ GEO http://www.ncbi.nlm.nih.gov/geo/ LIGAND http://genome.ad.jp/ligand PubMed http://www.pubmed.org/ RegulonDB http://regulondb.ccg.unam.mx/ Systomonas http://www.systomonas.de/ Metabolic model repositories BiGG http://bigg.ucsd.edu/ BioModels http://www.ebi.ac.uk/biomodels/ JWS Online - Model Database http://jjj.biochem.sun.ac.za/database/ Database links Bioinformatics links directory http://bioinformatics.ca/links_directory/ 68 Metabolic engineering of succinate biosynthesis in Escherichia coli Many genetic tools developed for a certain strain are based on the tools for closely related organisms. In order to find these related organisms, phylogenetic trees can be constructed using for instance ClustalW or T-Coffee (Wallace et al. , 2006; Whelan, 2007). Such an approach led to the construction of genetic tools for Mannheimia succiniciproducens and Actinobacillus succinogenes based on the tools for Haemophilus sp., a phylogenetic close relative (Jang et al. , 2007; Kim et al. , 2008). Furthermore taxonomy is closely related to the phenotype of a certain organism, which was originally the basis for microbial classification. This information can help with the development of media through the identification of auxotrophies and the identification of strain related toxicities. Technologies such as Phenotype Arrays™ are in this context very useful for screening phenotype properties of certain strains and can be linked to other types of information, for instance genomic information (Barry, 2009). Nucleotide sequence information is gathered in three main databases, Genbank, EMBL and DDBJ. With the advances in sequencing technology (e.g. the development of 454 sequencing technology), the amount of sequences and genomes available increases exponentially (Rothberg & Leamon, 2008). These large amounts of data have opened the door to new ways of discovery, for instance by comparative genomics or functional genomics, genomes can be annotated very fast and new functions can be uncovered (Markowitz et al. , 2008a; Overbeek et al. , 2005). In medicine 454 sequencing technology and comparative genomics have helped to uncover antibiotic resistance mechanisms in Mycobacterium turbeculosis and the identification of the first tuberculosis specific drug candidate (Andries et al. , 2005). Furthermore, sequence data analysis has given valuable insight in the organisation of operons and transcriptional regulation networks. Databases such as RegulonDB and Ecocyc connect this type of information with experimental gene expression data to validate predictions and incorporated it in a broader dataset of genomic and transcriptomic information (Gama-Castro et al. , 2008; Keseler et al. , 2009). Because in many cases not one, but a whole set of genes has to be targeted in an engineering strategy this information is crucial for so called transcription factor engineering. Recently the effect of small RNA’s on transcription has gained more and more attention. These tiny RNA’s ( 21-26 nucleotides) induce gene silencing through homologous sequence interactions. They are marked by many different names: short interfering (si) RNA’s , small temporal (st) RNA’s, heterochromatic siRNA’s, tiny noncoding RNA’s and microRNA’s (miRNAs) (Finnegan & Matzke, 2003). Their cellular functions vary from regulating mRNA stability and translation to the modulation of RNA polymerase activity and the tagging of proteins for degradation (Massé et al. , 2003). They form a whole set of alternative regulatory networks on the transcriptional and translational level. Similar tools to the once used for transcription factor site identification, have been used to identify in silico small RNA sequences in many genomes. This information is summarised in RNA databases, such as Rfam (Gardner et al. , 2009) and sRNAmap (Huang et al. , 2009). Knowledge about the operation modes of these RNA’s made the development of interference RNA possible. 69 Chapter 2 As mentioned above, this type of RNA can be used to silence genes (cfr. §2.1.1.1). It allows the identification of gene-function relations in a high throughput manner in eukaryotic cells, for instance in mammalian cell cultures (Agrawal et al. , 2003; Raab & Stephanopoulos, 2004). In parallel to small RNA regulation, translation is also regulated through secondary structure formation in the messenger RNA. A well known example of such a regulatory system is called attenuation in the tryptophane biosynthetic route. Depending on the speed of translation due to tryptophane availability a different mRNA secondary structure is formed which either interrupts transcription or allows advancement (Hartl & Jones, 2000). Secondary structures influence ribosome and RNA polymerase binding as well as RNA stability (Hackermüller et al. , 2005). RNA structures are summarised on the RNA STRAND database, a database that predicts secondary structures and curates them with experimental data (Andronescu et al. , 2008). Based on those structures the RNA’s are classified and deposited in Rfam (Gardner et al. , 2009). This information can also be applied in engineering strategies and has been proven to be essential in the expression of the mevalonate pathways in E. coli . Via the alteration of the secondary structure, the expression of this route could be increased a 100 fold (Pfleger et al. , 2006; Smolke & Keasling, 2002). The third informational level of the cell comprises the proteome. Proteins can have numerous functions and show complex structures. Sequence and function information is mainly deposited in the Uniprot database, structural information can be found in the RCSB database (Berman et al. , 2003; The UniProt, 2009). All proteins are further classified in families according to their sequence which can be found in the Pfam database (Finn et al. , 2008). Next to these main databases there are many specialised databases that focus on certain cellular functions or locations. The enzyme databases ENZYME and BRENDA for instance provide information on classification, sequence, and many enzyme properties such as inhibition, optima, substrates and products (Chang et al. , 2009). These databases link to published data that can provide for instance regulatory or kinetic information. For instance, in the case of citrate synthase, the first enzyme of the TCA cycle, information is provided concerning the allosteric inhibitors, NADH and alfa-ketoglutarate. This is linked to the original data that provides insight in the inhibition mechanisms and it indicates the amino acid residues in the enzyme that are responsible for the allosteric inhibition effects (Francois et al. , 2006). Through point mutations and protein engineering the allosteric regulation can then be altered and therefore altering metabolic fluxes. Analogously, novel non-natural biosynthetic routes can be introduced. Information about the active site, provided by the databases can be applied on the alteration of substrate specificity. Because in most cases the effect of a certain mutation on the activity or substrate specificity is not known, evolutionary approaches are used rather than rational design. By means of error prone PCR, active sites of proteins can be altered randomly and novel functionalities can be screened (Johannes & Zhao, 2006). 70 Metabolic engineering of succinate biosynthesis in Escherichia coli Proteins can be divided in two groups, hydrophilic or lipophilic proteins. Enzymes are mostly soluble proteins (when not linked to a membrane), while transport proteins are insoluble. With the Kyte and Doolittle algorithm (Kyte & Doolittle, 1982) proteins can be classified according to the hydropathy of the amino acid sequence. The more hydrophobic amino acid residues a certain protein contains, the more chance there is that this protein is membrane bound. The location of such a protein is determined by a signal peptide, which can be determined with the signalP or targetP tool (Dyrløv Bendtsen et al. , 2004; Emanuelsson et al. , 2007). These signals are recognised by a certain chaperone system that folds the protein and integrates it in the membrane. In general these chaperones form an essential regulation mechanism in protein synthesis, they control which proteins will be folded correctly and which will be marked for degradation (Makrides, 1996). In the case of heterologous protein expression, chaperones may have to be co-expressed in order to avoid formation of inclusion bodies and fast degradation (Kolaj et al. , 2009). More specifically for membrane embedded proteins, the most widely used biogenesis system is called the Sec translocation system. About 40 % of these proteins tend to transport reactions, of which the majority are influx (Daley et al. , 2005). Sequence and substrate specificity of transport proteins are summarised in the transport classification database (Saier et al. , 2006). Enzymes perform reactions that form reaction networks and finally the whole biochemistry of the cell. Each organism has its specific metabolism that has to be considered when optimising a certain process. This is summarised in so called metabolic databases. The largest databases are biocyc and KEGG (Kanehisa et al. , 2008; Paley & Karp, 2006). They are based on genomic information which is annotated and combined with the many databases mentioned above. Once each gene is assigned a function, a metabolic network is reconstructed based on the classically known biochemical pathways. These processes are all automated and can lead to errors. In order to avoid incorrect interpretation of the data, Biocyc has introduced a kind of curation system that indicates the level of validation of the data (Keseler et al. , 2009). Because the metabolome is the lowest level of information in the cell, linked to all levels above (genome, transcriptome and proteome) these type of databases include many links to the other so called ‘omes’ and allow data integration. This type of integration is seen within one organism. Different kinds of tools and databases focus on the comparison of functions and genomes. SEED and IMG for instance allow the comparison of metabolic en genomic networks between different microorganisms (Markowitz et al. , 2008b; Overbeek et al. , 2005). These algorithms also help with so called gap filling. This is the identification of missing functions from pathways via comparative genomics (Osterman & Overbeek, 2003). 2.1.3.3 Applied on the development of a metabolic engineering strategy When a certain molecule is envisaged for production the first question is, can it be biochemically synthesised? When the molecule is a known biochemical, metabolome databases can be consulted, but in the case of more obscure molecules enzyme databases have to be checked first to ensure the molecule can be synthesised. If not, enzyme substrates that are analogous to the target molecule will have to be screened and activity of these enzymes 71 Chapter 2 toward this molecule have to be tested. These enzymes should be integrated into a biochemical route that is present in a(n) (micro)-organism or can be adopted by a(n) (micro)- organism. Once enzymes from biochemical routes towards the target molecule are identified, all possible pathways from substrate to product have to be considered to find the optimal route. Nowadays the metabolism of a certain organism is central in the optimisation of these routes (Alper et al. , 2005b; Kromer et al. , 2006; Lee et al. , 2007; Lee et al. , 2002; Park et al. , 2007; Raman et al. , 2005). In principle all biochemical networks of all known organisms can be considered to find the optimum. For instance the Calvin Benson cycle and the Bifidobacterium shunt are rarely included in metabolic models if they are not present in the organism of interest, while they might have some interesting properties (e.g. carbon dioxide assimilation or the direct synthesis of glyceraldehyde-3-phosphate from fructose-6- phosphate). Natural producing organisms might have interesting gene sets that can be introduced in non-natural producing strains with interesting properties. Hence, the need for a comparative and functional genomics studies. Finally, stoichiometric network analysis will point the direction of the optimal carbon flow towards a certain molecule, without considering any biomass formation. Regulatory effects that come with the envisaged genetic modifications cannot be predicted in such a way yet. These effects will have to be checked with regulatory knowledge present in the different databases mentioned above. 2.1.4 Stoichiometric network analysis Look before you leap is the message behind stoichiometric network analysis. For a simple molecule two steps removed from the substrate, the optimal route is quite obvious. For instance the production of PDO from glycerol described in Chapter 1 consists of 2 reactions, a glycerol dehydratase and a glycerol oxidoreductase. When the molecule is entangled in reactions further down the metabolism, things tend to get more complicated. The cells’ redox state and energy household have to be balanced. To cope with these challenges, stoichiometric network analysis tools were developed. These tools are based on the premise that for every metabolite a mass balance can be written and the structure of a biochemical network does not change unless evolutionary time scales are considered. The change of a certain metabolite concentration x i in time is dependent on the flux vj from and towards that metabolite with certain stoichiometry s ij and the net transport reaction b i (equation 2.2). dx i = ∑ vj s ij -b i 2.2 dt j For a metabolic network equation 2.2 can be written in matrix from: dX 0 = S×V- 2.3 dt b where S is a matrix that contains the stoichiometric coefficients of all reactions included in the network, V is the vector of metabolite fluxes and b is the vector representing transport 72 Metabolic engineering of succinate biosynthesis in Escherichia coli fluxes. Because the changes in internal metabolite concentrations are kept low in the cell, pseudo steady state conditions can be assumed (equation 2.4). 0 ≅ S×V 2.4 b On the basis of this last equation, several stoichiometric network analysis principles have been developed to find optimal routes towards a certain biochemical, e.g. elementary flux modes, extreme pathways (Llaneras & Picó, 2008; Maertens, 2008). These are based on convex analysis, which takes the stoichiometric constrains and the irreversibility of certain reactions into account. 2.1.4.1 Elementary flux modes Elementary modes are defined as flux vectors that satisfy steady state, that is thermodynamically feasible and that is unique. This definition leads to three properties: 1. There is a unique set of elementary flux modes for a given network 2. Each elementary mode is genetically independent and cannot be decomposed. This means each elementary mode represents a stoichiometrically and thermodynamically feasible route to the conversion of substrates into products, which cannot be decomposed into simpler routes. 3. Elementary flux modes are the set of all routes through the network consistent with the second property This means that the elementary flux modes span a solution region by a convex region in which all possible steady state flux distributions are non negative linear combinations of the elementary flux modes. Within these modes, the route with the highest yield towards a certain metabolite can be searched. This results in the evaluation of the metabolic potential of a certain metabolic network (Papin et al. , 2004; Schuster et al. , 2002). 2.1.4.2 Extreme pathways An extreme pathway differs from an elementary flux mode in the property that no extreme pathway can be constructed as a non-negative linear combination of extreme pathways (see Figure 2.9, here EM 4 is e non-negative linear combination of EM 2 and 3, hence this EM cannot be considered as an extreme pathway and 1, 2 and 3 can). This means that extreme pathways form the edge of the convex polyhedral cones (Wagner & Urbanczik, 2005). The algorithm as such treats reversible and irreversible reactions differently internally. While elementary flux mode analysis accounts for the reaction directions with a series of rules in the corresponding calculations, extreme pathways separate reversible reaction in a forward and a reverse reaction. Figure 2.9 shows the elementary modes and extreme pathways for a simple reaction network. The extreme pathways are fewer in number than the elementary flux modes because elementary mode 4 is a non-negative linear combination of 2 and 3. The extreme 73 Chapter 2 pathways are not a set of all genetically independent routes through a metabolic network, but the edges of the convex solution space of a biochemical network (Llaneras & Picó, 2008; Maertens, 2008; Papin et al., 2004). c v,~ ~ ExP2 B~ ~A V1 pathways ExP3 Extreme ~A B~ ExP1 /, c Vel 8~ network v, c Vel ~/, Metabolic 8~ c ~A Vel c ~ EM4 EM2 Vel ------"!1 ~ A ~A EM2 modes + flux EM3 = EM4 Elementary B~ 8~ V1 EM3 EM1 /, c v,~ ~A Figure 2.9: The elementary modes and extreme pathways of a simplified metabolic network. Note that elementary mode 4 is a non-negative linear combination of elementary mode 2 and 3, which implies that EM1, 2 and 3 are also extreme pathways but EM4 is not (Llaneras & Picó, 2008; Maertens, 2008). 74 Metabolic engineering of succinate biosynthesis in Escherichia coli 2.2 Materials and Methods 2.2.1 Comparative genomics The four genomes of Escherichia coli MG1655, Wolinella succinogenes DSM 1740, Actinobacillus succinogenes 130Z and Mannheimia suciniciproducens MBEL55E (Baar et al. , 2003; Blattner et al. , 1997; Hong et al. , 2004) were compared with the integrated microbial genomes (IMG) system (Markowitz et al. , 2008a; Markowitz et al. , 2008b). This system uses RPS-BLAST against the PRIAM 1 database with a cut-off E-value of 10 -10 , a percent identity along the alignment of 45 % and minimal alignment fraction over PSSM consensus sequence of 70 %. The reference E.C. numbers are collected through RefSeq 2. Further, functional roles are defined by their association with functional classifications, including COG functional categories, TIGR role categories and the KEGG pathway collection. In the case of a missing function, PRIAM, ortholog and homolog definitions were used to screen for candidate genes in the genome that could fulfil that particular function (Claudel-Renard et al. , 2003). 2.2.2 Metabolic model The metabolic network model of Lequeux et al. (2005) was used and expanded. It includes glycolysis, with glucose transport by the PTS system, the pentose phosphate pathway, the Krebs cycle, and overflow metabolism. For each amino acid and nucleotide the anabolic reaction was included. Biosynthesis of LPS, lipid A, peptidoglycane, and the lipid bilayer are incorporated as well. The P/O ratio was set to 1.33 (Majewski & Domach, 1990; Varma & Palsson, 1993). An overview of the reactions in the model is given in the appendix, sections A.1 and A.2. The elementary flux modes of the stoichiometric E. coli model were calculated by using Metatool 5 (von Kamp & Schuster, 2006). 2.2.3 Partial Least Squares Partial Least Squares (PLS) regression has been performed in the software package R (2006). This generalisation of multiple linear regression is able to analyse data with strongly collinear and numerous independent variables as is the case for the elementary flux modes under study. Partial least squares regression is a statistical method that links a matrix of independent variables X with a matrix of dependent variables Y, the flux ratios and the succinate yield, respectively. The objective in PLS modelling, similar to principal component analysis is to find a few new variables, Latent Variables (LV), in such a way that the dependent Y variables can be predicted as good as possible. Therefore, the multivariate spaces of X and Y are transformed to new matrices of lower dimensionality that are correlated to each other. This 1 PRIAM is a method for automated enzyme detection in a fully sequenced genome, based on all sequences available in the ENZYME database. It relies on sets of position-specific score matrices (PSSMs) automatically tailored for each ENZYME entry 2 The Reference Sequence (RefSeq) collection aims to provide a comprehensive, integrated, non-redundant, well-annotated set of sequences, including genomic DNA, transcripts, and proteins. 75 Chapter 2 reduction of dimensionality is accomplished by principal component analysis like decompositions that are slightly tilted to achieve maximum correlation between the latent variables of X and Y (Wold et al., 2001). Prior to data analysis, the data was appropriately pre-treated. Several pre-treatment methods were tested and finally auto-scaling was retained. 76 Metabolic engineering of succinate biosynthesis in Escherichia coli 2.3 Results and discussion 2.3.1 Comparative genomics of E. coli and natural succinate producers 2.3.1.1 The Krebs cycle or TCA cycle Except for pyruvate carboxylase, Escherichia coli consists of the complete reaction network presented in Figure 2.10 and Table 2.4. The natural succinate producing strains deviate quite a bit from the commonly known reaction scheme of the TCA cycle; the glyoxylate route does not appear to be present in any of these strains. Wolinella succinogenes is not able to convert alfa-ketoglutarate to succinyl-CoA. It does not possess dihydrolipoyl dehydrogenase, an enzyme that is also crucial for the pyruvate dehydrogenase complex. Isocitrate dehydrogenase also uses NADP + instead of NAD + as coenzyme, which has implications for the flux through that reaction. Fumarate hydratase, is not present as such in Wolinella succinogenes , it is replaced by an aspartate ammonium lyase, which may function as fumarate hydratase, or could be a part of an alternative pathway towards succinate. Aspartate aminotransferase can supply aspartate from oxaloacetate. As an alternative for pyruvate dehydrogenase, W. succinogenes can rely on a pyruvate carboxylase, to feed the TCA cycle with oxaloacetate, next to PEP carboxylase. The more energy efficient PEP carboxykinase reaction does not seem to be present in this strain. This reaction is only present in E. coli , A. succinogenes and M. succiniciproducens . The difference between the E. coli and the two other PEP carboxykinases is the affinity for carbon dioxide and PEP (Kim et al. , 2004b; Kwon et al. , 2008). A. succinogenes differs from E. coli in three TCA enzymes. It does not possess a citrate synthase, but relies on the citrate lyase enzyme complex (E.C: 2.8.3.10, 4.1.3.34 and 4.1.3.6). Aconitate hydratase was not detected in the initial functional profiling, although, more thorough analysis revealed that an ortholog, aconitate hydratase 2 (or B) is present (73.32 % identity, E-value = 0 and bitscore = 1273), which probably can also function as a 3- isopropylmalate dehydratase (EC: 4.2.1.33, PRIAM: 25.87 % identity, E-value = 2 x 10 -44 and bitscore = 173). The next reaction towards alfa-ketoglutarate is predicted as a tartrate dehydrogenase by IMG (E.C: 1.1.1.93, PRIAM: 33.61 % identity, E-value = 7 x 10 -56 , bitscore = 221), or a 3-isopropylmalate dehydrogenase (EC: 1.1.1.85, PRIAM: 22.88 % identity, E-value = 4 x 10 -37 , bitscore = 148). Physiological tests have shown that the latter reaction is not present in A. succinogenes , which has led to an auxotrophy for glutamate or alfa-ketoglutarate (McKinlay et al. , 2005). In the case of M. succiniciproducens , similar to A. succinogenes , there is also an alternative aconitate hydratase and isocitrate dehydrogenase present. An ortholog was found for isocitrate dehydrogenase with 73.14 % identity, an E-value equal to 0 and a bitscore of 619. The aconitate hydratase is of the aconitase B group which contains in the active centre an iron sulphur cluster that is sensitive to oxygen, which means that no aerobic TCA activity is possible for both M. succiniciproducens and A. succinogenes (Tang et al. , 2005). The next step in the TCA, isocitrate dehydrogenase, which was not recognised as such, but ortholog 77 Chapter 2 and PRIAM analyses lead to the conclusion that a yet to be annotated gene is present that has a high identity with the E. coli NADP + dependent isocitrate dehydrogenase (73 % identity, E- value = 0, bitscore = 619). This would mean that Mannheimia succiniciproducens has a fully functional TCA cycle, unlike the other natural succinate producing microorganisms. The strain does lack a glyoxylate route, similar to the other strains. This means, growth on acetate and aerobic succinate production is not possible. Figure 2.10: The reactions of the TCA cycle and glyoxylate route and their enzyme classification numbers. Table 2.4 gives summarizes the presence of these enzymes in Actinobacillus succinogenes 130Z (ACT), Bacteroides fragilis NCTC 9343 (BAC), Corynebacterium glutamicum R (COR), Escherichia coli MG1655 (ECO), Mannheimia suciniciproducens MBEL55E (MAN), and Wolinella succinogenes DSM 1740 (WOL). Two alternative strains described as succinate producing strains, Corynebacterium glutamicum R and Bacteroides fragilis show quite some differences with the other strains. They both seem to lack succinate dehydrogenase, fumarate reductase, alfa-ketoglutarate dehydrogenase and the glyoxylate pathway. Furthermore B. fragilis lacks citrate synthase and C. glutamicum R seems not to dispose of a malate dehydrogenase. Except for the alfa- ketoglutarate dehydrogenase enzyme and for B. fragilis , the glyoxylate route, all these genes could be identified via an ortholog and PRIAM analysis. In the case of Corynebacterium glutamicum multiple strains have already been sequenced and annotated. A comparison between these strains ( Corynebacterium glutamicum Bielefield and Corynebacterium 78 Metabolic engineering of succinate biosynthesis in Escherichia coli glutamicum Kitasato) shows that both of these strains do have an alfa-ketoglutarate dehydrogenase (Yukawa et al. , 2007). This may indicate that the reduced activity of this dehydrogenase in Corynebacterium is crucial for enhanced succinate production. Table 2.4: The different enzymes that play a role in the TCA cycle and glyoxylate route (see Figure 2.10) and their presence in Actinobacillus succinogenes 130Z (ACT), Bacteroides fragilis NCTC 9343 (BAC), Corynebacterium glutamicum R (COR), Escherichia coli MG1655 (ECO), Mannheimia suciniciproducens MBEL55E (MAN), and Wolinella succinogenes DSM 1740 (WOL). The numbers in the table indicates the number of enzymes that were found in each of the microorganisms. 0 indicates that within the boundaries of the IMG algorithm no enzymes with that particular function could be found. When the number is higher than 1, multiple enzymes with high identity were found with a high probability that the enzymes perform the indicated function. Enzyme class Enzyme name ACT BAC COR ECO MAN WOL EC:1.1.1.37 Malate dehydrogenase 2 1 0 2 1 1 EC:1.1.1.41 Isocitrate dehydrogenase (NAD +) 0 0 0 1 0 0 EC:1.1.1.42 Isocitrate dehydrogenase (NADP +) 0 1 1 0 0 1 EC:1.2.4.2 Oxoglutarate dehydrogenase (succinyl-transferring) 1 0 0 1 1 0 EC:1.2.7.3 2-oxoglutarate synthase 0 0 0 0 0 0 EC:1.3.1.6 Fumarate reductase (NADH) 0 0 0 1 0 0 EC:1.3.5.1 Succinate dehydrogenase (ubiquinone) 0 0 0 3 0 1 EC:1.3.99.1 Succinate dehydrogenase 2 0 0 6 1 4 EC:1.8.1.4 Dihydrolipoyl dehydrogenase 1 2 1 1 1 0 EC:2.3.1.61 Dihydrolipoyllysine-residue succinyltransferase 2 0 0 2 1 0 EC:2.3.3.1 Citrate (Si)-synthase 0 0 1 1 1 1 EC:2.3.3.8 ATP citrate synthase 0 0 0 0 0 0 EC:2.3.3.9 Malate synthase 0 0 0 2 0 0 EC:2.8.3.10 Citrate CoA-transferase 1 0 0 1 0 0 EC:3.1.2.3 Succinyl-CoA hydrolase 0 0 0 0 0 0 EC:4.1.1.31 Phosphoenolpyruvate carboxylase 0 0 0 1 0 1 EC:4.1.1.32 Phosphoenolpyruvate carboxykinase (GTP) 0 0 1 0 0 0 EC:4.1.1.49 Phosphoenolpyruvate carboxykinase (ATP) 1 1 0 1 1 0 EC:4.1.3.1 Isocitrate lyase 0 0 0 1 0 0 EC:4.1.3.34 Citryl-CoA lyase 1 0 0 1 0 0 EC:4.1.3.6 Citrate (pro-3S)-lyase 1 0 0 2 0 0 EC:4.2.1.2 Fumarate hydratase 1 1 1 3 1 0 EC:4.2.1.3 Aconitate hydratase 0 1 1 2 0 1 EC:6.2.1.18 Citrate-CoA ligase 0 0 0 0 0 0 EC:6.2.1.4 Succinate-CoA ligase (GDP-forming) 0 0 0 0 0 0 EC:6.2.1.5 Succinate-CoA ligase (ADP-forming) 2 2 2 2 2 2 EC:6.4.1.1 Pyruvate carboxylase 0 1 0 0 0 1 2.3.1.2 Glycolysis, Pentose phosphate pathway and Entner Doudoroff route Before entering the TCA cycle, sugars are processed through the pentose phosphate, glycolysis or the Entner Doudoroff pathway. These routes are not always present in microorganisms; depending on the evolutionary pressure, for instance, enzyme activity of phosphofructokinase can be lost, which means the carbon flow has to be rerouted through either the Enter Doudoroff route or the Pentose phosphate pathway (Baart, 2008). An overview of these three routes in the five microorganisms considered is shown in Figure 2.11 and Table 2.5. 79 Chapter 2 es es the Escherichia R (COR), Corynebacterium glutamicum me classification numbers. Table 2.5 gives summariz DSM 1740 (WOL). NCTC 9343 (BAC), succinogenes Wolinella Bacteroides fragilis 130Z (ACT), pathway and Entner Doudoroff route with their enzy MBEL55E (MAN), and succinogenes suciniciproducens Actinobacillus Mannheimia MG1655 (ECO), coli presence of these enzymes in Figure 2.11: The reactions of the pentose phosphate 80 Metabolic engineering of succinate biosynthesis in Escherichia coli Table 2.5: The different enzymes that play a role in the pentose phosphate pathway, the glycolysis and the Entner Doudoroff route and their presence in Corynebacterium glutamicum R (COR), Bacteroides fragilis NCTC 9343 (BAC), Escherichia coli MG1655 (ECO), Wolinella succinogenes DSM 1740 (WOL), Actinobacillus succinogenes 130Z (ACT) and Mannheimia suciniciproducens MBEL55E (MAN). The numbers in the table indicates the number of enzymes that were found in each of the microorganisms. 0 indicates that within the boundaries of the IMG algorithm no enzymes with that particular function could be found. When the number is higher than 1, multiple enzymes with high identity were found with a high probability that the enzymes perform the indicated function. Enzyme class Enzyme name ACT BAC COR ECO MAN WOL EC:1.1.1.44 Phosphogluconate dehydrogenase 1 1 1 1 1 0 (decarboxylating) EC:1.1.1.49 Glucose-6-phosphate dehydrogenase 1 1 1 1 1 0 EC:1.2.1.12 Glyceraldehyde-3-phosphate dehydrogenase 1 1 1 1 1 2 (phosphorylating) EC:2.2.1.1 Transketolase 0 1 0 2 0 0 EC:2.2.1.2 Transaldolase 1 1 0 2 0 0 EC:2.7.1.1 Hexokinase 0 0 0 0 0 0 EC:2.7.1.11 6-phosphofructokinase 1 2 0 2 1 0 EC:2.7.1.2 Glucokinase 0 0 0 1 0 0 EC:2.7.1.4 Fructokinase 1 1 0 1 1 0 EC:2.7.1.40 Pyruvate kinase 1 1 1 2 1 0 EC:2.7.1.5 Rhamnulokinase 0 1 0 1 1 0 EC:2.7.1.51 L-fuculokinase 0 0 0 1 0 0 EC:2.7.1.52 Fucokinase 0 0 0 0 0 0 EC:2.7.1.69 Protein-N(pi)-phosphohistidine-sugar 5 0 0 23 1 0 phosphotransferase EC:2.7.1.7 Mannokinase 0 0 0 0 0 0 EC:2.7.2.3 Phosphoglycerate kinase 1 1 1 1 1 1 EC:2.7.9.2 Pyruvate, water dikinase 0 0 0 1 0 1 EC:3.1.1.31 6-phosphogluconolactonase 0 1 0 1 0 0 EC:3.1.3.11 Fructose-bisphosphatase 1 0 0 2 1 1 EC:4.1.2.13 Fructose-bisphosphate aldolase 1 2 0 2 0 0 EC:4.1.2.14 2-dehydro-3-deoxy-phosphogluconate aldolase 3 0 0 1 1 0 EC:4.1.2.9 Phosphoketolase 0 0 0 0 0 0 EC:4.2.1.11 Phosphopyruvate hydratase 1 1 1 1 1 1 EC:4.2.1.12 Phosphogluconate dehydratase 0 0 0 1 0 0 EC:5.1.3.1 Ribulose-phosphate 3-epimerase 1 1 1 2 1 1 EC:5.3.1.1 Triose-phosphate isomerase 2 1 1 1 1 1 EC:5.3.1.6 Ribose-5-phosphate isomerase 1 1 0 2 1 0 EC:5.3.1.9 Glucose-6-phosphate isomerase 1 1 1 1 1 0 EC:5.4.2.1 Phosphoglycerate mutase 1 2 1 3 1 1 The Entner Doudoroff route (ED) is predominantly described in Gram negative bacteria, although it is present in all three phylogenetic domains, even in the Archaea . Evolutionary it predates the glycolysis and it is described as an essential metabolic pathway for gastrointestinal microorganisms. The sugars and organic acid rich environment of the gastrointestinal tract provided the selective pressure for the ED route. The emergence of the glycolysis or Emden-Meyerhof-Parnas pathway (EMP) would be the result of the selection of a more energy efficient metabolic route. In Archaea the EMP pathway reactions are described to function in reverse as the gluconeogenic route. These reactions have probably formed the basis for the glycolysis as it is known today (Peekhaus & Conway, 1998; Romano & Conway, 1996). Within the five organisms considered in this study, the ED route only occurs in 81 Chapter 2 Escherichia coli , which is the only gastrointestinal microorganism. However the second enzyme of this route recurs also in A. succinogenes and M. succiniciproducens . 2-dehydro-3- deoxy-phosphogluconate aldolase might function in these organisms as oxaloacetate decarboxylase. In both cases the enzyme belongs to the same family of the E. coli 2-dehydro-3-deoxy- phosphogluconate aldolase, class I aldolase family. This family of enzymes generally shows both aldolase and decarboxylase activity (Patil & Dekker, 1992). The extra decarboxylation reaction from oxaloacetate to pyruvate might have consequences on the carbon flow from and towards the TCA, which has to be considered in the engineering strategy. The third route, the pentose phosphate pathway, is commonly divided in the oxidative and non-oxidative part. Phosphogluconate dehydrogenase, glucose-6-phosphate dehydrogenase and 6-phosphogluconolactonase form the oxidative pentose phosphate route. The first two enzymes supply the cell with NADPH, an essential coenzyme for biosynthesis. In M. succiniciproducens , C. glutamicum R and W. succinogenes the route seems to be incomplete. M. succiniciproducens and C. glutamicum R lack 6-phosphogluconolactonase but for both the enzyme could be annotated with an ortholog search. The C. glutamicum R enzyme shows 99.7 % identity with the C. glutamicum Bielefeld enzyme (E-value = 10 -137 , bitscore = 457) and M. succiniciproducens shows 34.48 % identity with the B. fragilis enzyme and has a 96.12 % identity with the Pfam domain for the 6-phosphogluconolactonase family (E-value = 4.8 x 10 -7, HMM score = 760). For W. succinogenes however the three enzymes could not be identified in the genome. This strain might use its transaldolases and transketolases to form the pentoses from EMP intermediates, necessary for many biosynthetic routes. The non-oxidative part of the pentose phosphate pathway consists of 4 enzymes, ribose-5- phosphate isomerase, ribulose-phosphate 3-epimerase, transketolase and transaldolase. Not all functions were assigned with the standard settings of the IMG tool. In the case of C. glutamicum R again the Bielefeld strain enzymes showed high identity with the R strain enzymes. The transketolases of A. succinogenes , M. succiniciproducens and W. succinogenes showed approximately a 36 % identity with the C. glutamicum Bielefeld enzyme and showed approximately 27 % identity with the Pfam transketolase binding domain. The low sequence identity is the result of a separate cluster of transketolases, which also includes Vibrio sp.. With Vibrio cholerea the sequence identity is approximately 72 %. In addition, the transaldolases of M. succiniciproducens and W. succinogenes show high identity with the Pfam transaldolase family (94 %). Finally, W. succinogenes ribose-5-phosphate isomerase was identified as an ortholog of the Bacteroides fragilis enzyme and showed 98 % identity with the ribose/galactose enzyme family in Pfam. 82 Metabolic engineering of succinate biosynthesis in Escherichia coli An alternative for the pentose phosphate pathway that has not been considered yet is the Bifidobacterium shunt, typical for Bifidobacteria . The shunt differs from the PPP in two enzymes, a fructose-6-phosphate phosphoketolase and a xylulose-6-phosphate phospho- ketolase. These enzymes replace the oxidative part of the PPP and yield acetylphosphate, which is then further converted into acetate (Fandi et al. , 2001; Wolin et al. , 1998). The terminal electron acceptor for this type of organism is pyruvate, which is converted into lactic acid. The second enzyme of the shunt, xylulose-6-phosphate phosphoketolase can also be found in heterolactic acid bacteria. These bacteria have maintained the oxidative pentose phosphate pathway but have lost phosphofructokinase and fructosebisphosphate aldolase, which means they lack full EMP activity (Wisselink et al. , 2002). An interesting feature of the pentose phosphate pathway enzymes in these bacteria, which is shared by some of the acetic acid bacteria, is the coenzyme specificity. In contrast to other organisms, phosphogluconate dehydrogenase and glucose-6-phosphate dehydrogenase preferably use NAD + in stead of NADP + as coenzyme. Probably this preference is the result of the coenzyme specificities of the terminal electron acceptor reactions that take care of coenzyme regeneration (Anderson et al. , 1997; Murphy et al. , 1987b; Ohara et al. , 2004; Toews et al. , 1976; Tonouchi et al. , 2003). 2.3.1.3 Byproduct routes Table 1.5 summarises the main byproducts formed in the different succinate production processes. These byproducts are ethanol, pyruvate, lactate, formate and acetate. In Error! Reference source not found. and Table 2.6 the reactions to each of these compounds are summarised and their presence in each of the organisms is shown in the accompanying table. First, the ethanol formation reactions are considered. Corynebacterium glutamicum R, Wolinella succiniciproducens and Bacteroides fragilis do not seem to have any pathway towards ethanol. Alcohol dehydrogenases are very common in microorganisms, actually organisms in general. For instance W. succiniciproducens and B. fragilis have, according to Pfam, an iron containing alcohol dehydrogenase, which might convert acetaldehyde into ethanol (95.72 % identity, E-value = 1.3 x 10 -27 , HMM score = 89 and 96.09 % identity, E-value = 0, HMM score = 541, respectively). Corynebacterium glutamicum R possesses an ortholog to the Escherichia coli alcohol dehydrogenase with 35 % identity (E-value = 2 x 10 -51 , bitscore = 205), and is recognised by Pfam as a zinc binding dehydrogenase with GroES like domain (37.43 % identity, E-value = 10 -40 , HMM score = 132 and 35 % identity, E-value = 2 x 10 -38 , HMM score = 125, respectively). This reaction does require the formation of acetaldehyde in the cell. In both W. succiniciproducens and B. fragilis none of the acetaldehyde forming enzymes could be detected with the available tools, which might indicate that they are not able to form ethanol as byproduct. 83 Chapter 2 Figure 2.12: The most common byproduct formation reactions found in succinate production processes. Table 2.5 gives summarizes the presence of these enzymes in Actinobacillus succinogenes 130Z (ACT), Bacteroides fragilis NCTC 9343 (BAC), Corynebacterium glutamicum R (COR), Escherichia coli MG1655 (ECO), Mannheimia suciniciproducens MBEL55E (MAN), and Wolinella succinogenes DSM 1740 (WOL). All microorganisms considered except W. succiniciproducens produce lactic acid from pyruvate. The latter has a malate dehydrogenase that shows 30 % identity with the L-lactate dehydrogenase of C. glutamicum R (E-value = 8 x 10 -37 , bitscore = 156). Only few reports validate lactate dehydrogenase activity for malate dehydrogenases, which are mostly substrate specific (Chang et al. , 2009; Chapman et al. , 1999). These malate dehydrogenases are called lactate dehydrogenase like malate dehydrogenases and show a glutamine residue at position 102 (Madern, 2002). This is also the case for the lactate dehydrogenase like malate dehydrogenase from W. succiniciproducens . The most common byproduct in fermentations in general is acetate (De Mey et al. , 2007b). Multiple reactions can be responsible for acetate formation, mostly involving acetyl-CoA or pyruvate as substrates. Some reports however also describe citrate lyase as an acetate formation route (cfr. Figure 2.10). Except for W. succiniciproducens all succinate producing strains have multiple routes towards acetate, either via the acetylphosphate transferase – acetate kinase route, the pyruvate oxidase route, the acetyl-CoA route or the acetaldehyde route. The main route towards acetate, which is also targeted in most metabolic engineering strategies, is the acetylphosphate transferase – acetate kinase route. This route could be identified in M. succiniciproducens or C. glutamicum R, by comparison to the A. succinogenes and the C. glutamicum Bielefeld pathways with 86 % (E-value = 0 and bitscore = 1207) and 99.72 % identity (E-value = 0 and bitscore = 895), respectively. Pyruvate 84 Metabolic engineering of succinate biosynthesis in Escherichia coli oxidation, the second major route towards acetate is not present in M. succiniciproducens and A. succinogenes. Table 2.6: The different enzymes that play a role byproduct formation and their presence in Actinobacillus succinogenes 130Z (ACT), ), Bacteroides fragilis NCTC 9343 (BAC), Corynebacterium glutamicum R (COR Escherichia coli MG1655 (ECO), Mannheimia suciniciproducens MBEL55E (MAN), and Wolinella succinogenes DSM 1740 (WOL). The numbers in the table indicates the number of enzymes that were found in each of the microorganisms. 0 indicates that within the boundaries of the IMG algorithm no enzymes with that particular function could be found. When the number is higher than 1, multiple enzymes with high identity were found with a high probability that the enzymes perform the indicated function. Enzyme class Enzyme name ACT BAC COR ECO MAN WOL EC:1.1.1.1 Alcohol dehydrogenase 1 0 0 4 1 0 EC:1.1.1.27 L-lactate dehydrogenase 0 0 1 0 0 0 EC:1.1.1.28 D-lactate dehydrogenase 1 1 0 2 1 0 EC:1.1.1.37 Malate dehydrogenase 2 1 0 2 1 1 Malate dehydrogenase: oxaloacetate 0 0 0 1 0 0 EC:1.1.1.38 decarboxylating Malate dehydrogenase 0 0 0 1 0 0 EC:1.1.1.39 (decarboxylating) EC:1.1.1.40 Malate dehydrogenase: oxaloacetate 1 1 0 1 0 0 decarboxylating (NADP +) EC:1.1.1.82 Malate dehydrogenase (NADP +) 0 0 1 0 0 0 EC:1.1.2.3 L-lactate dehydrogenase (cytochrome) 0 0 0 1 0 0 EC:1.1.2.4 D-lactate dehydrogenase (cytochrome) 0 0 0 0 0 0 EC:1.1.2.5 D-lactate dehydrogenase (cytochrome c-553) 0 0 0 0 0 0 EC:1.2.1.3 Aldehyde dehydrogenase (NAD +) 1 0 1 1 1 0 EC:1.2.1.4 Aldehyde dehydrogenase (NADP +) 0 0 0 0 0 0 EC:1.2.1.5 Aldehyde dehydrogenase (NAD(P) +) 0 0 0 0 0 0 EC:1.2.1.51 Pyruvate dehydrogenase (NADP +) 0 0 0 0 0 0 EC:1.2.2.2 Pyruvate dehydrogenase (cytochrome) 0 1 1 1 0 0 EC:1.2.3.3 Pyruvate oxidase 0 0 0 0 0 0 EC:1.2.3.6 Pyruvate oxidase (CoA-acetylating) 0 0 0 0 0 0 EC:1.2.4.1 Pyruvate dehydrogenase (acetyl-transferring) 1 0 1 1 1 0 EC:1.2.7.1 Pyruvate synthase 0 0 0 0 0 1 EC:1.8.1.4 Dihydrolipoyl dehydrogenase 1 2 1 1 1 0 EC:2.3.1.2 Imidazole N-acetyltransferase 0 0 0 0 0 0 EC:2.3.1.54 Formate C-acetyltransferase 1 2 0 3 0 0 EC:2.3.1.8 Phosphate acetyltransferase 1 2 0 1 0 0 EC:2.7.1.40 Pyruvate kinase 1 1 1 2 1 0 Protein-N(pi)-phosphohistidine-sugar 5 0 0 23 1 0 EC:2.7.1.69 phosphotransferase EC:2.7.2.1 Acetate kinase 1 1 1 2 1 0 EC:2.7.9.2 Pyruvate, water dikinase 0 0 0 1 0 1 EC:4.1.1.1 Pyruvate decarboxylase 0 0 0 0 0 0 EC:4.1.1.3 Oxaloacetate decarboxylase 2 1 0 0 2 0 EC:4.1.1.31 Phosphoenolpyruvate carboxylase 0 0 0 1 0 1 EC:4.1.1.32 Phosphoenolpyruvate carboxykinase (GTP) 0 0 1 0 0 0 EC:4.1.1.49 Phosphoenolpyruvate carboxykinase (ATP) 1 1 0 1 1 0 EC:6.2.1.1 Acetate-CoA ligase 0 0 0 1 0 1 EC:6.2.1.13 Acetate-CoA ligase (ADP-forming) 0 0 0 0 0 0 EC:6.4.1.1 Pyruvate carboxylase 0 1 0 0 0 1 These pathways can also function as acetate assimilation routes, and therefore allow growth on acetate. In the case of high acetate concentrations, the acetylphosphate transferase – acetate 85 Chapter 2 kinase route is preferred, when acetate is low in concentration, the acetyl-CoA ligase or acetyl-CoA synthetase route is preferred. Because this reaction involves the formation of pyrophosphate during acetate assimilation, it is considered as unlikely that the reverse reaction will occur. Intracellular pyrophosphate concentrations are kept low via pyrophosphatases. This reduces the substrate availability for acetyl-CoA ligase and limits the reaction rate towards acetate via this enzymatic reaction (Kukko & Heinonen, 1982; Starai & Escalante-Semerena, 2004). Acetyl-CoA formation reactions also differ in the different strains that were considered. Some strains mainly use the pyruvate dehydrogenase route, others the pyruvate formate lyase route. The exception, W. succiniciproducens uses the pyruvate synthase route, which has the same function as pyruvate dehydrogenase, but has a much simpler quaternary structure that does not contain lipoamide dehydrogenase (Yang et al. , 1985). This enzyme is common in Archaea and the genus Clostridium and has a very broad pH stability range, which is typical for enzymes of acidophilic microorganisms (Blamey & Adams, 1993; Bush & Sauer, 1977; Chabriere et al. , 2001). W. succiniciproducens , a member of the Helicobacteriaceae family, might have retained acidophilic properties for some crucial enzymes, like its family member Helicobacter pylori (Garrity et al. , 2004; Ha et al. , 2001). The third pathway, the pyruvate formate lyase route leads to the formation of formate but is only active in anaerobic conditions (Knappe et al. , 1974; Knappe & Sawers, 1990). The enzyme activity is based on a free radical reaction, sensitive to oxygen and needs a reactivase after exposure to oxygen (Sawers & Watson, 1998; Wagner et al. , 1992). Formate as byproduct is thus only possible in anaerobic and micro-aerobic conditions and is an excellent indicator for the aeration efficiency during an aerobic microbial culture. 2.3.2 Stoichiometric network analysis 2.3.2.1 Entner Doudoroff pathway As discussed in the introduction, stoichiometric network analysis lends itself perfectly in the search for the optimal pathway towards a certain metabolite, in this case succinate. In this section a selection of metabolic models are analysed and discussed with different carbon sources and reactions. First a standard metabolic model of Lequeux et al. (2008), given in appendix A1, has been used and expanded with the Entner Doudoroff (ED) route, a route that is in many cases ignored in stoichiometric network analysis because of its low flux ratio in comparison to the pentose phosphate pathway and the glycolysis (Nanchen et al. , 2006). The Entner Doudoroff pathway bypasses glycolysis and the pentose phosphate pathway till pyruvate and glyceraldehyde-3-phosphate, which implies that it influences the redox state of the cell. Because the availability of reduced equivalents affects succinate yield, the presence or absence of the Entner Doudoroff route may therefore change this yield. An elementary flux mode analysis of this stoichiometric model under aerobic conditions (oxygen is a substrate in this model) is shown in Figure 2.13. 86 Metabolic engineering of succinate biosynthesis in Escherichia coli A PLS model was used to analyse the influence of each reaction present in the model on the succinate yield. Table 2.7 presents the X and Y variability and cumulative X and Y variability explained by the latent variables of the model. The X variables here are the elementary flux modes, the Y variables are the succinate yields of each of the elementary modes. 6 latent variables explain already 99.99 % of the Y variability and about 87.94 % of the X variability, which was found sufficient to proceed with the analysis. Entner Doudoroff Figure 2.13: The elementary flux modes of the stoichiometric E coli with glucose and oxygen as a substrate. The modes are shown in function of their biomass yield (Y X,S ) – succinate yield (Ysuccinate, S ) relation (in mole per mole glucose) . The maximal theoretical yield for this model is 1.5 mole succinate per mole glucose. Table 2.7: The X and Y variability and the cumulative X and Y variability explained by the latent variables of the PLS model fitted on the elementary flux mode data and the corresponding succinate yield data. Latent variable X variability (%) Cumulative X Y variability (%) Cumulative Y (LV) variability (%) variability (%) 1 63.52 63.52 10.82 10.82 2 10.62 74.13 39.47 50.29 3 4.05 78.18 44.11 94.39 4 3.23 81.41 4.89 99.28 5 3.89 85.3 0.6 99.88 6 2.64 87.94 0.12 99.99 7 1.79 89.72 0 100 8 1.51 91.23 0 100 9 1.48 92.71 0 100 10 1.93 94.64 0 100 11 1.27 95.91 0 100 12 1.21 97.12 0 100 87 Chapter 2 The predictive power of the model is determined based on the Nash–Sutcliffe model efficiency coefficient (Nash & Sutcliffe, 1970). This efficiency coefficient can range from −∞ to 1. An efficiency of 1 corresponds to a perfect fit of the model to the observed data. When the model efficiency equals 0, the model predictions are as accurate as the mean of the observed data. Efficiencies less than zero imply that the observed mean is a better predictor than the model. Basically this means that the closer the model efficiency approaches 1, the more accurate this model is. Here, a PLS model with 5 latent variables reached a model efficiency of 1. Entner Doudoroff Entner Doudoroff Figure 2.14: Two dimensional and three dimensional score plot of the first two and three latent variables, respectively. The colours indicate the succinate yield of these scores (low = red; intermediate = black and high = green) 88 Metabolic engineering of succinate biosynthesis in Escherichia coli The scores of the model of two and three latent variables are shown in Figure 2.14. These plots indicate how the samples, the elementary flux modes, relate to each other and how the latent variables explain their variability. Because there is one response variable (succinate yield), the score plots will tell something about how the EFMs are grouped based on the succinate yield. Figure 2.15 shows the PLS coefficients of a model with 6 latent variables based on data that originates from the elementary flux modes that were constructed with a stoichiometric model of E. coli , including the Entner Doudoroff pathway. The reactions are shown as numbers for clarity, the corresponding reactions can be found in the appendix, section A.4. The reaction loading weights for each of the latent variables are given in Figure 2.16. Entner Doudoroff 0.5 0.4 0.3 0.2 0.1 PLS PLS coeffincients 0.0 -0.1 -0.2 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 reaction Figure 2.15: The PLS coefficients of the PLS model based on 6 LV’s for the EFMs of a metabolic network with the Entner Doudoroff pathway included and glucose as substrate. To keep the figure clear the reactions in the abscissa are represented as numbers. The corresponding reactions are given in appendix section A.4. These values indicate how a reaction in a single latent variable weighs on the PLS regression coefficient and should be taken into consideration when the PLS coefficient of a reaction is small. Positive coefficients point at a positive correlation between the reaction and succinate yield; negative coefficients indicate a negative correlation. In an engineering context, the activity of the positively correlating reactions will have to be stimulated and the activity of the negatively correlating reactions reduced. The most strongly positive correlated reaction with succinate yield is succinate export (reaction 105), which is logical, because the yield is based on the exported succinate concentration. The reactions of the TCA cycle, aconitase (reaction 114) and citrate synthase (reaction 22) also correlate positive with succinate yield. However, the coefficients of isocitrate dehydrogenase (reaction 121) and alfa-ketoglutarate dehydrogenase (reaction 6) are zero, which means they should have neither a positive nor negative effect on the succinate yield. The glyoxylate shunt (reactions 55 and 64) on the other hand affects the succinate yield positively. Glycolysis (reactions 41, 74, 117, 124, and 129) 89 Chapter 2 shows a slightly positive correlation with the succinate yield. Looking at the loading weights, all latent variables show positive values for the glycolysis reactions except the first latent variable (Figure 2.16). More peculiar is the positive correlation of respiration (reaction 88) with succinate yield. Because succinate is a redox dependent product, it requires NADH for its formation; one might expect that any reaction that oxidises NADH would negatively correlate with succinate. This image is however skewed by the loading weights of LV 4 and 5, which are positive. Furthermore, the slight positive correlation of ATP hydrolysis (reaction 12) with succinate yield indicates at an excess of ATP during succinate formation under aerobic conditions. The type of cofactor is also an important issue for succinate production. The conversion of FADH 2 to NADH (reaction 127) shows a strong negative effect on succinate yield and the conversion of NADPH to NADH (reaction 139) is slightly positive. FADH 2 and NADH are the coenzymes that are involved in the reductive TCA route that leads to succinate; hence, their correlations indicate the importance of this route (reactions 128, 138, and 155). Fumarate reductase uses FADH 2 to convert fumarate to succinate. This reaction is however included in the model as a reaction from succinate to fumarate, which is the classical aerobic representation of the reaction (reaction 155). This leads to a negative correlation of the reaction with succinate yield although the reverse reaction would be positive. Fumarase (reaction 128) undergoes a similar effect, the reaction is implemented from fumarate to malate, which leads to a negative correlation, however the reaction can be reversed, which would lead to a positive effect on the succinate yield. All byproduct formation reactions (reactions 106-109, 113, 125, 126, and 136) correlate negatively with succinate production. However there is one exception, formic acid seems to indicate a slight positive effect on succinate formation (reaction 75). The reason for this may lay with pyruvate formate lyase. Lactate dehydrogenase, acetate kinase, ethanol dehydrogenase all lead carbon away from acetyl-CoA or pyruvate. Pyruvate formate lyase on the other hand yields an acetyl-CoA for each formate. This reaction is an alternative for pyruvate dehydrogenase which yields carbon dioxide and acetyl-CoA. The model output for all enzymes revolving around pyruvate and PEP behaves in the same way. Reactions yielding pyruvate or PEP correlate negatively with succinate production (malic enzyme (reaction 87) and pyruvate carboxykinase (reaction 72)), while reactions away from pyruvate and PEP towards the Krebs cycle correlate positive (PEP carboxylase (reaction 71), citrate synthase (reaction 22)). The coefficients for the pentose phosphate reactions (reaction 44, 76, 144, and 145), glucose- 6-phosphate dehydrogenase, lactonase and 6-phosphogluconase dehydrogenase are all negative, indicating at a negative effect of these reactions on the succinate yield. Instead of yielding NADH, as in glycolysis, in E. coli the pentose phosphate pathway reduces NADP + to NADPH. In addition the pathway also forms for each mole of glucose a carbon dioxide, which is not the case in the glycolysis. 90 Metabolic engineering of succinate biosynthesis in Escherichia coli LV1 LV2 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 loading weights -0.2 -0.2 -0.4 -0.4 -0.6 -0.6 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160 reaction reaction LV3 LV4 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 loading weights -0.2 -0.2 -0.4 -0.4 -0.6 -0.6 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160 reaction reaction LV5 LV6 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 loading weights -0.2 -0.2 -0.4 -0.4 -0.6 -0.6 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160 reaction reaction Figure 2.16: Latent variable loading weights for a PLS model of the EFMs of a metabolic network with the Entner Doudoroff pathway included and glucose as substrate. For clarity the reactions in the abscissa are represented as numbers. The corresponding reactions are given in appendix section A.4. 91 Chapter 2 The Entner-Doudoroff (ED) (reaction 156) actually bypasses the formation of carbon dioxide in the pentose phosphate pathway, which should imply that the ED route is favourable for succinate formation. However the ED route correlates negatively with succinate yield. The cause for this observation has to be sought in the ATP yield of each of the pathways. Glycolysis till pyruvate yields two mole NADH and 3 mole ATP for each mole of glucose. The ED route also yields two reduced equivalents (NADPH and NADH) but yields only 2 mole of ATP. Furthermore it bypasses PEP, which is needed for glucose uptake. Although glucose uptake does not correlate with succinate production (glucose uptake is present in every elementary mode), the direct formation of pyruvate will require PEP synthase activity to supply the cell with sufficient PEP. In principle, the ED route creates a parallel reaction to pyruvate kinase that converts PEP to pyruvate and ATP and correlates negatively, though without ATP formation. Based on the information given by the elementary flux modes an optimal pathway towards succinate can be constructed that leads to a yield of 1.5 mole per mole (Figure 2.17). The proposed reaction scheme includes the glycolysis, the TCA cycle and the glyoxylate route. It also implies which reaction should be reduced or knocked out. The proposed route starts at glucose which is phosphorylated by the PTS system. This means that for each mole of glucose, one mole of PEP has to be converted to pyruvate (Postma et al. , 1993). Such a system limits the possibilities from an engineering perspective, however in this case the PTS system will not hamper succinate production. Glucose is split into two glyceraldehyde-3- phosphates of which one can react in the PTS system and the other can be carboxylated by PEP carboxykinase. Half of the yielded oxaloacetate reacts further with acetyl-CoA to citrate; the other half follows the reductive TCA to succinate. In a similar way the molecules of acetyl-CoA are split up between citrate synthase and malate synthase, feeding the glyoxylate route to form succinate and malate. During succinate formation within this set of reactions, two mole NADH are formed in the glycolysis and one mole is formed by pyruvate dehydrogenase for each mole of glucose. However, only 1.5 mole of NADH is needed to form succinate via the reductive TCA pathway. This means that the excess of NADH has to be respired in the oxidative phosphorylation, which was indicated by the PLS model. With a P/O ratio of 1.33, the 1.5 mole of NADH yields 2 ATP. In this reaction network the glycolysis also yields one mole ATP per mole glucose. Overall 3 mole of ATP are formed. The excess of ATP explains also the positive correlation of ATP hydrolysis with the succinate yield. A reaction network that uses PEP carboxykinase instead of PEP carboxylase would have even a larger excess. With each PEP converted to oxaloacetate, an additional ATP is formed. This means that for each mole of glucose, 4 ATP will be formed. The cell however will not waste this energy purely on maintenance as might be suggested by the PLS model, but will use it to form biomass. With the knowledge of the maximal theoretical biomass yield on ATP and on glucose (0.45 c-mole/mole ATP and 0.63 c-mole/c-mole glucose (Kayser et al. , 2005)) and with the assumption of a no maintenance, a succinate yield can be calculated that takes into account the formation of biomass. With the formation of one c-mole of succinate 1 mole of ATP can be formed. 92 Metabolic engineering of succinate biosynthesis in Escherichia coli \ Isocitratc H20 0,5 ""'0,5 acn Citratc A NAD CoA, NADH ace CoA, C02. 5 , 0 Glucose Pyruvatc Glyoxylat Il20 pdh -T Acetyi-CoA 0,5 AcetYI-CoA 5 , 11,5 NAD PTS 1 Succinatc Succinate Malate mdhÁ~ADTI Fumarate ~ '----~Oxaloacetate dABC~ PI!:P r 2H > f kA Tl,~,J~0 I pc Ihü Menaquinon-~ Glucosc-6-P '\: 2 cno ATP Menaquinul 0,CO)' ADP 2 H ~ FADII2 pgi NAD gpm Fructose-6-P FAD' H+ -c- Clycerate-3-P~Glyceratc-2-P< ATP NADII .) pfkB ATP 1 "\? 2 P: Clyceraldchyde-3-P Figure 2.17: The optimal route for succinate production in aerobic conditions based on the EFM analysis output. The maximal theoretical yield for this scheme is 1.5 mole per mole glucose. The genes and their gene products are described in the appendix, section A.3. 93 Chapter 2 Taking into account a 0.45 c-mole/mole ATP yield, about 0.45 c-mole biomass can be produced from this ATP. However, no glucose has then been accounted to biomass formation, and with a theoretical yield of 0.63 c-mole/c-mole glucose this should be 0.71 c-mole glucose. The total amount of glucose input is 1.71 c-mole and the true succinate is 0.58 c-mole/ c-mole glucose or 0.875 mole/mole. 2.3.2.2 Alternative carbon sources Sucrose The choice of carbon source can influence the intrinsic potential of a metabolic network for succinate production. They can either be more reduced, for instance glycerol or they can yield more energy per carbon, for instance sucrose. E. coli K12 generally does not utilize sucrose as a carbon source, it has to be engineered to do so (Tsunekawa et al. , 1992). The choice of sucrose utilisation systems is quite diverse. First, a PTS system can be used, that forms sucrose-6-phosphate which is hydrolysed with a sucrase to glucose-6-phosphate and fructose (Meadow et al. , 1990). Second, a sucrose reductase converts sucrose to dehydro - glucosylfructofuranoside, which is in turn hydrolysed to fructose and 3-hydroglucose. The 3- hydroglucose is then reduced to glucose (Schuerman et al. , 1997). The third system uses sucrose invertase to convert sucrose to glucose and fructose and the final system applies a sucrose phosphorylase to hydrolyse sucrose into glucose-1-phosphate and fructose (Ali & Haq, 2007; Reid & Abratt, 2005). Sucrose Figure 2.18: The elementary flux modes of the stoichiometric E coli with sucrose (indicated as S) and oxygen as a substrate. The modes are shown in function of their biomass yield (Y X,S ) – succinate yield (Ysuccinate, S) relation (in mole per mole sucrose). The maximal theoretical yield for this model is 3.43 mole succinate per mole sucrose. The latter, sucrose phosphorylase is the only enzyme that converts the energy of the glucosidic bond into a phosphorylated sugar, which yields an additional ATP. This makes this 94 Metabolic engineering of succinate biosynthesis in Escherichia coli particular enzyme interesting to evaluate within the stoichiometric model. Similar to the previous analysis the EFMs were calculated and plotted in function of their biomass and succinate yield relation (Figure 2.18). The maximal theoretical for succinate on sucrose is 3.43 mole per mole sucrose. Note that in order to obtain this yield carbon dioxide will have to be fixed. Table 2.8: The X and Y variability and the cumulative X and Y variability explained by the latent variables of the PLS model fitted on the elementary flux mode data of a sucrose stoichiometric model and the corresponding succinate yield data. Latent variable X variability (%) Cumulative X Y variability (%) Cumulative Y (LV) variability (%) variability (%) 1 66.82 66.82 15.61 15.61 2 4.36 71.19 74.33 89.94 3 9.45 80.63 8.95 98.89 4 4.58 85.21 1 99.89 5 2.17 87.38 0.1 99.99 6 1.75 89.12 0.01 100 7 1.52 90.65 0 100 8 2.05 92.7 0 100 9 1.42 94.12 0 100 10 1.2 95.32 0 100 11 1.79 97.11 0 100 12 1.46 98.57 0 100 Sucrose 0.5 0.4 0.3 0.2 0.1 PLS coeffincients PLS 0.0 -0.1 -0.2 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 reaction Figure 2.19: The PLS coefficients of the PLS model based on 6 LV’s for the EFMs of a metabolic model with sucrose as substrate. To keep the figure clear the reactions in the abscissa are represented as numbers. Their corresponding reactions are given in appendix section A.4. 95 Chapter 2 LV1 LV2 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 loadingweights -0.2 -0.2 -0.4 -0.4 -0.6 -0.6 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160 reaction reaction LV3 LV4 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 loading weights -0.2 -0.2 -0.4 -0.4 -0.6 -0.6 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160 reaction reaction LV5 LV6 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 loading weights -0.2 -0.2 -0.4 -0.4 -0.6 -0.6 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160 reaction reaction Figure 2.20: The PLS model latent variable loading weights of the EFM’s of a metabolic network with sucrose as substrate. For clarity the reactions in the abscissa are represented as numbers. Their corresponding reactions are given in appendix section A.4. 96 Metabolic engineering of succinate biosynthesis in Escherichia coli The reactions loading weights for each of the latent variables are given in Figure 2.20. The overall pattern of the PLS coefficients is quite similar to the pattern of the coefficients from the Enter Doudoroff model. However there are some intriguing differences. ATP hydrolysis (reaction 12) and respiration (reaction 88) do not correlate with succinate production anymore when sucrose is applied as a substrate, which means that there is no excess of reducing equivalents nor ATP. It should be noted that in this model, also carbon dioxide can be used as a substrate, which was not the case for the model in which the Entner Doudoroff pathway was introduced. This led to the fact that carbon is not limiting anymore to obtain the maximal theoretical yield and all electrons can be employed for succinate production. Hence the effect on the respiration and ATP hydrolysis. In contrast to the previous model, the electron transport chain will not have to regenerate NADH into NAD + with formation of ATP via ATP synthase because there is no excess of electrons anymore. Further, the TCA cycle enzymes, isocitrate dehydrogenase (reaction 121), alfa-ketoglutarate dehydrogenase (reaction 6) and succinyl-CoA synthase (reaction 148) correlate strongly with succinate production, apart from the reductive TCA (reactions 128, 155, and 138) enzymes and the glyoxylate pathway (reactions 55 and 64). Moreover, the conversion of NADPH to NADH seems not to be needed (reaction 139). Apparently the extra NADH and NADPH from the oxidative branch of the TCA suffice to supplement the reductive TCA enzymes. The use of the full TCA cycle also reduced the need for acetyl-CoA, which can be deduced from the negative correlation of the formate excretion reaction and pyruvate formate lyase (reaction 75). From these observations an optimal pathway for succinate production with sucrose as a substrate could be deduced (Figure 2.21). As predicted by the PLS, the glyoxylate route plays a much smaller role than in the previous model (§ 2.3.2.1). While in the model in paragraph 2.3.2.1 about half the carbon followed the glyoxylate route and half the reductive TCA cycle, in the sucrose case, the ratio reductive TCA: glyoxylate route: oxidative TCA is 9:1:1. The three routes together with glycolysis balance nicely the NADH formation and consumption. However, also in this optimal route ATP hydrolysis is required. Within the scheme presented here, there is a net gain of 9 ATP. This means that for every succinate 0.5 ATP is formed. With PEP carboxykinase instead of PEP carboxylase, this would be 1.5 ATP per succinate. If sucrase instead of sucrose phosphorylase would be introduced, each sucrose molecule would require two ATP for the formation of fructose-6-phosphate and glucose-6-phosphate instead of one. In this network, the net ATP yield per succinate is then 0.2. The choice of reactions will depend on the growth rate of the created strain. A strain that produces ATP more easily will grow faster than a strain that barely produces any ATP. However, excessive ATP production will result in a fast growing, high biomass yielding strain but with a low succinate yield. The difficulty is to find the balance between both. 97 Chapter 2 + + 2 e t a NADP CO CoA, NAD r e t a t icd a u r l t i g sucAB, lpd c o 4 o t s e 2 A I k o O 2 e C n a t H c l + a , H a NADPH y r D 2 t A D n i i o A A c C , NADH, A C c N i 2 N e , u , c 2 S A a CO o O A t C C t l a 4 2 g e a 6 l t ADP+P A y a o A x v 2 o o C u y r P O l C sucCD l 2 y y h l 1 t G e d P H y e s t p e 7 c o + e s 2 r c o A e c c t P ATP, CoA A H u a u T e l t e D S t + t 2 e A 4 a G A c a D 2 n P a n N 6 i l i A S B c a c A e c N c P a o c u x a u 8 D C e S e m 0 S 1 t t O A 7 g 7 2 a a l p r h a 0 a d 2 M D m m 2 A u C P 8 2 F P B m n 6 e u o A E s + f n e O d i o P H s 2 r t u c 2 f o c q H p c a + O u p n l 2 u r e l o H n F i M G 2 u i q o O 8 a P n n 2 C e e , 2 M O H 2 D P H i A P g 7 2 F p T e t A k a a 7 r e m c 0 P y 2 l D + G A D P A 6 m N 8 p e 2 g s o t P c u 3 r + e F D t a + A r F H e c + y l P H T G D B A P k A f T p N 4 A , 1 k 8 A g 2 k f p P p P D D A A P s i b 3 , 1 P e s t i a b r e 6 , c 1 y l e H s G D o t A c A u N p 8 r a 2 F g + P D n A P o t N 3 e a c b e a f 4 d y 1 y x h o e r 4 d d 1 l y a h r i i e p t c D y l G Figure 2.21 The optimal route for succinate production based on the EFM analysis with sucrose and carbon dioxide as substrate. The maximal theoretical yield for this scheme is 3.42 mole per mole sucrose. The genes and their gene products are described in the appendix, section A.3. 98 Metabolic engineering of succinate biosynthesis in Escherichia coli Another factor to consider is succinate export (Janausch et al. , 2002; Zientz et al. , 1999). While in the model succinate export is considered as a neutral reaction that does not affect the cells ATP and NADH household, in reality this is not the case. With each succinate exported, two protons are imported into the cell. This inflow of protons has to be countered by a proton pump. Under aerobic conditions the proton gradient is maintained by the electron transport chain and ATP synthase uses this gradient for ATP synthesis. In the case of the proposed sucrose metabolism no electron transport chain (no respiration) is present. Then ATP synthase takes on the function of ATPase to maintain the membrane proton gradient (Kasimoglu et al. , 1996). Stoichiometric studies of this activity have shown that the ATPase translocates 1.9 protons for each ATP hydrolysis (Perlin et al. , 1986). Hence, 1.05 ATP extra is needed per succinate, which implies that a network with sucrase and PEP carboxylase will not supply sufficient ATP but PEP carboxykinase will. The Enter Doudoroff network can be reconsidered analogously. With each succinate, 1.5 NADH is produced in excess; however this NADH will translocate 6.5 protons across the membrane, taking into account the NDH I/NDH II and cytochrome bd /cytochrome bo ratios of E. coli (Bogachev et al. , 1996; Puustinen et al. , 1991). Two of these protons are used for succinate export, thus 4.5 protons are left for ATP synthesis. Accounting for a stoichiometry of 3 protons per ATP, this results in 1.5 ATP (Kashket, 1982). Together the substrate level phosphorylation of the glycolysis this results in 2.5 mole ATP per mole succinate. For each c- mole of succinate formed, therefore ATP is available for 0.28 c-mole of biomass from 0.44 c- mole of glucose (based on the same maximal theoretical yields as previously). The yield of succinate from the network is then 0.69 c-mole succinate per c-mole glucose or 1.04 mole per mole. Glycerol Glycerol can be catabolised by organisms in two ways. Glycerol can either be phosphorylated to glycerol-3-phosphate by glycerol kinase which is then oxidised by glycerol-3-phosphate dehydrogenase to DHAP or glycerol can first be oxidised to dihydroxyaceton by glycerol dehydrogenase and then phosphorylated to DHAP by dihydroxyaceton kinase (Chen et al. , 2003; Dharmadi et al. , 2006). In comparison to sucrose and glucose, glycerol is much more reduced (degree of reduction of 4.66). This additional reductive power will then shift the optimal route quite a bit. Figure 2.22 depicts the biomass and succinate yields of the elementary flux modes of an E. coli stoichiometric network with glycerol as substrate. The predicted maximal theoretical yield is 1 mole succinate per mole of glycerol. Striking is the number of EFMs that were created based on the glycerol model. In comparison to glucose and sucrose this number is much lower because glycerol enters the glycolysis at glyceraldehyde-3-phosphate, which eliminates the pentose phosphate pathway and glycolysis as parallel pathways. The EFMs and yields were fed into a PLS model of which the X and Y variability and cumulative X and Y variability explained by the models with a different number of latent variables are shown in Table 2.9. A model with 6 latent variables explains 99 Chapter 2 already 100 % of the Y variability and about 89 % of the X variability, which was found sufficient to proceed with the analysis. A PLS model with 5 latent variables reached the maximal efficiency of 1. Glycerol Figure 2.22: A plot of the elementary flux modes of a stoichiometric network E coli with glycerol as substrate. The elementary modes are shown in function of their biomass yield (Y X,S ) – succinate yield (Ysuccinate, S ) relation (in mole per mole glycerol). The maximal theoretical yield for this model is 1 mole succinate per mole glycerol. Table 2.9: The X and Y variability and the cumulative X and Y variability explained by the latent variables of the PLS model fitted on the elementary flux mode data of a glycerol stoichiometric model and the corresponding succinate yield data. Latent variable X variability (%) Cumulative X Y variability (%) Cumulative Y (LV) variability (%) variability (%) 1 67.99 67.99 26.43 26.43 2 6.88 74.87 42.35 68.78 3 6.71 81.58 19.99 88.77 4 3.59 85.17 10.84 99.61 5 1.92 87.09 0.37 99.98 6 1.85 88.94 0.02 100 7 1.58 90.51 0 100 8 1.84 92.35 0 100 9 1.62 93.97 0 100 10 1.15 95.12 0 100 11 1.97 97.09 0 100 12 1.56 98.65 0 100 The PLS model coefficients for 6 latent variables are given in Figure 2.23. The reactions loading weights for each of the latent variables are given in Figure 2.24. All reactions are shown as numbers that are explained in appendix, section A.4. 100 Metabolic engineering of succinate biosynthesis in Escherichia coli The reactions that correlate negatively with succinate formation in this model are analogous to the previous two models (§ 2.3.2.1 and 2.3.2.2 – sucrose ). Byproduct formation (reactions 75, 106-109, 113, 125, 126, and 136), malic enzyme (reaction 87), PEP carboxykinase (reaction 72) and the conversion of FADH 2 to NADH (reaction 127) all show a negative correlation. However a significant difference with the model described in paragraph 2.3.2.1 is the negative correlation of respiration (reaction 88). Similar to the model with sucrose as a substrate, carbon dioxide can be used as a substrate too, which results in the full use of the electrons that glycerol holds. Regeneration of NADH via the electron transport chain would thus lead to a reduced yield. The optimal pathway for succinate production from glycerol is therefore anaerobic. This observation is corroborated by the reduced correlations of aconitase (reaction 114), citrate synthase (reaction 22) and the glyoxylate pathway (reactions 55 and 64) enzymes with succinate yield. In a PLS model with less LVs these reactions indicate even a negative correlation (Figure 2.24). Figure 2.24 illustrates the optimal route based on the EFMs with glycerol as substrate. The redox state is nicely balanced with the formation of two NADH in the glycolysis and their consumption in the reductive TCA. The use of the reductive pathway allows the fixation of one carbon dioxide per consumed glycerol, which leads to a maximal theoretical yield of 1 mole per mole glycerol. In the scheme the energy household is also balanced with the consumption of one ATP for glycerol uptake and the formation of one ATP with glycerate-3-phosphate. However, accounting for the proton:succinate antiport described above (§2.3.2.2 – sucrose ), an extra 1.05 ATP per mole of succinate will be needed (with an ATPase efficiency of 1.9 protons per ATP). Hence, the energy inefficient PEP carboxylase has to be replaced with the more energy efficient PEP carboxykinase from A. succinogenes . Glycerol 0.5 0.4 0.3 0.2 0.1 PLS coeffincients PLS 0.0 -0.1 -0.2 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 reaction Figure 2.23: The PLS coefficients of the PLS model with 6 LV’s for the EFMs of a stoichiometric network with glycerol as substrate. To keep the figure clear the reactions in the abscissa are represented as numbers. Their corresponding reactions are given in appendix section A.4. 101 Chapter 2 LV1 LV2 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 loading weights -0.2 -0.2 -0.4 -0.4 -0.6 -0.6 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160 reaction reaction LV3 LV4 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 loading weights -0.2 -0.2 -0.4 -0.4 -0.6 -0.6 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160 reaction reaction LV5 LV6 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 loading weights -0.2 -0.2 -0.4 -0.4 -0.6 -0.6 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160 reaction reaction Figure 2.24: Latent variable loading weights for a PLS model of a the metabolic network with glycerol as substrate. For clarity the reactions in the abscissa are represented as numbers. Their corresponding reactions are given in appendix section A.4 102 Metabolic engineering of succinate biosynthesis in Escherichia coli Even with the additional ATP formed per succinate through PEP carboxykinase, the balance does not close. Furthermore, no ATP will be produced for biomass formation, which is essential to obtain an adequate production rate. The glyoxylate pathway can serve here as a solution to this problem. For each glyoxylate pathway cycle, three PEP molecules are needed, (from three glycerol molecules). One will enter the TCA via PEP carboxykinase, yielding an ATP and the other two will enter via pyruvate and acetyl-CoA yielding two extra ATP’s and NADH’s. The malate formed in the glyoxylate route will be converted via fumarate to succinate with the oxidation of one NADH. Net, this route yields 1 ATP and 2.33 NADH that can be converted to 3 ATP (P/O ratio of 1.33) which totals in 4 ATP per consumed glycerol and a succinate yield of 0.66 mole per mole. Because for each succinate molecule formed through the reductive pathway 0.05 ATP is lacking (1.05 ATP needed for succinate transport and only 1 ATP formed via PEP carboxykinase), 80 succinate molecules can be formed for each time the glyoxylate pathway is used (when biomass formation is disregarded). Figure 2.25: The optimal route for succinate production based on the EFM analysis output with glycerol as substrate. The maximal theoretical yield for this scheme is 1 mole per mole glycerol. The genes and their gene products are described in the appendix, section A.3 2.3.2.3 Alternative reactions to the pentose phosphate and glycolysis The TCA and the glyoxylate route are always considered in the context of succinate production; however alternatives for glycolysis and the pentose phosphate are rarely regarded. Natural succinate producing organisms and engineered organisms do not posses pathways such as the Calvin cycle, the Bifidobacterium shunt or the pentose phosphate enzymes of heterolactic acid bacteria. Nevertheless these metabolic paths can have some interesting features that may be beneficial for succinate production. This section analyses these pathways in an E. coli metabolic network and evaluates their potential. Calvin cycle The Calvin Benson cycle is usually present in plants and algae, but also in purple bacteria, cyanobactria, most chemolithotrophic bacteria and some Archaea. In order to complete the cycle in E. coli only phosphoribulokinase and ribulose bisphosphate carboxylase, also known as RubisCO, have to be introduced. Phosphoribulokinase converts ribulose-5-phosphate, a pentose phosphate intermediate, into ribulose-1,5-bisphosphate. Next, RubisCO carboxylates ribulose-1,5-bisphosphate and forms two 3-phosphoglycerates. However, RubisCO accepts oxygen as well as carbon dioxide as substrate. The oxygenation activity of RubisCO converts ribulose-1,5-bisphosphate into 2-phosphoglycolate and 3-phosphoglycerate. Depending on the partial pressure of these gasses the enzyme either performs the carboxylation reaction or the 103 Chapter 2 oxygenation reaction (Terzaghi et al. , 1986). The carboxylation product enters the glycolysis whereas, the oxygenation product, 2-phosphoglycolate will be degraded first by 2- phosphoglycolate phosphatase into glycolate and then by glycolate oxidase into glyoxylate (Lyngstadaas et al. , 1999; Pellicer et al. , 1999). In a bioreactor the activity of the enzyme will be dependent on the aeration. An anaerobic or micro-aerobic reactor system through which high concentrations of carbon dioxide is flushed will induce surely the carboxylation reaction; a fully aerobic reactor will result in oxygenation. This implies that the carboxylation activity is linked to an anaerobic or fermentative metabolism and the oxygenation reaction to the aerobic metabolism. Figure 2.26 shows the biomass and succinate yields that can be achieved based on the elementary flux mode analysis of the E. coli stoichiometric network with the Calvin cycle enzymes added. It should be noted that only the carboxylation activity of RubisCO was considered for this analysis. With the extra parallel pathway that the cycle introduces into the model, the number of EFMs increased significantly. This also led to a steep increase in calculation time. The maximal theoretical yield for this model is 1.71 mole succinate per mole glucose, which is the result of carbon dioxide fixation, either via PEP carboxylase or RubisCO. Noteworthy are the low biomass yield, succinate yield combinations that can be observed in Figure 2.26. This may be the result of the extra parallel pathway in the system that is introduced by the two Calvin cycle. Calvin cycle Figure 2.26: The elementary flux modes of the stoichiometric network of E coli with the Calvin cycle reactions phosphoribulokinase and ribulose bisphosphate carboxylase added. The modes are shown in function of their biomass yield (Y X,S ) – succinate yield (Ysuccinate, S ) relation (in mole per mole glucose). The maximal theoretical yield for this model is 1.71 mole succinate per mole glucose 104 Metabolic engineering of succinate biosynthesis in Escherichia coli The EFMs and yields were further analysed with the PLS technique of which the X and Y variability and cumulative X and Y variability explained by the PLS models are shown in Table 2.10. A model with 7 latent variables explains already 100 % of the Y variability and about 86.88 % of the X variability. 5 latent variables suffice to reach a model efficiency of 1. Table 2.10: The X and Y variability and the cumulative X and Y variability explained by the latent variables of the PLS model fitted on the elementary flux mode data of the stoichiometric model of E coli with the Calvin cycle reactions phosphoribulokinase and ribulose bisphosphate carboxylase added and the corresponding succinate yield data. Latent variable X variability (%) Cumulative X Y variability (%) Cumulative Y (LV) variability (%) variability (%) 1 57.54 57.54 14.5 14.5 2 5.68 63.22 66.17 80.66 3 7.21 70.44 15.63 96.29 4 8.22 78.66 2.56 98.85 5 2.19 80.85 1.01 99.86 6 4.4 85.25 0.1 99.96 7 1.63 86.88 0.03 100 8 1.9 88.78 0 100 9 1.93 90.71 0 100 10 1.83 92.54 0 100 11 1.86 94.4 0 100 12 1.17 95.57 0 100 The coefficients of the PLS model with 7 latent variables are given in Figure 2.27. The corresponding reactions to the numbers shown in this figure are summarised in appendix section A.4. The reactions loading weights for each of the latent variables are given in Figure 2.28. As for all previous models, the byproducts (reactions 75, 106-109, 113, 125, 126, 136) correlate negatively with succinate yield and succinate export (reaction 105) correlates positively. New is the negative correlation of ATP hydrolysis (reaction 12) with succinate production. Additionally, respiration (reaction 88) correlates positive. This is likely the result of an extra ATP consuming reaction in the network. The introduction of the Calvin cycle in E. coli is thus optimally combined with the more energy efficient PEP carboxykinase of A. succinogenes . This reaction should supply the network with enough ATP to export succinate and form some biomass. Alfa-ketoglutarate dehydrogenase (reaction 6), isocitrate dehydrogenase (reaction 121) and succinyl-CoA synthase (reaction 148) display a positive correlation, which is similar to the sucrose model. For this reaction network too a certain distribution between the reductive TCA, the glyoxylate route and the oxidative TCA will form the optimal route towards succinate. The Calvin cycle enzymes (reactions 156 and 157) however tap their substrate from the pentose phosphate route (reactions 44, 76, 144, 145, 149, 151, and 152) which correlates negatively with succinate formation. On the other hand, the transketolases (reactions 151, 152) transaldolases (reaction 149) from the pentose phosphate are reversible reactions, which can supply ribulose-5-phosphate starting from glyceraldehyde-3-phosphate and fructose-6- phosphate. Two fructose-6-phosphate molecules and one glyceraldehyde-3-phosphate can 105 Chapter 2 form via the transketolases, transaldolases, isomerases and epimerases of the pentose phosphate 3 ribulose-5-phosphates. This eventually will be carboxylated with 3 carbon dioxides into 6 3-phosphoglycerates. Calvin cycle 0.5 0.4 0.3 0.2 0.1 PLS coeffincients PLS 0.0 -0.1 -0.2 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 reaction Figure 2.27: The PLS coefficients of the PLS model with 6 LV’s for the EFMs of a network with the Calvin cycle reactions phosphoribulokinase and ribulose bisphosphate carboxylase added. To keep the figure clear the reactions in the abscissa are represented as numbers. Their corresponding reactions are given in appendix section A.4. Figure 2.29 represents the schematic overview of the optimal succinate production pathway with the Calvin cycle enzymes added to the metabolic network. In comparison with the basic network in paragraph 2.3.2.1, here the system can reach a maximal theoretical yield without the replacement of the PTS system with the galactose permease/glucokinase system or the use of PEP synthase. The glucose uptake flux can be balanced with the PEP to pyruvate flux. The reversed pentose phosphate pathway:glycolysis ratio, which is the ratio between the Calvin pathway and glycolysis is 4:3. This ratio however reduces the substrate level phosphorylation in glycolysis significantly and increases ATP hydrolysis via phosphoribulokinase. In a network were only PEP carboxylase catalyses the reaction from PEP to oxaloacetate, the ATP requirements cannot be met. For 12 succinate molecules, phosphofructokinase consumes 3 ATP and phosphoribulokinase consumes 6 ATP. Phosphoglycerate kinase only generates 4 ATP and succinyl-CoA synthase 1 ATP per 12 succinate molecules. This system would therefore lack 0.33 ATP per succinate (4 per 12 succinate molecules). With PEP carboxykinase, which generates an additional 9 ATP per 12 succinate molecules, the system has 0.41 ATP per succinate in excess. However, this does not take the ATP requirements of succinate export into account as described before. 106 Metabolic engineering of succinate biosynthesis in Escherichia coli LV1 LV2 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 loadingweights -0.2 -0.2 -0.4 -0.4 -0.6 -0.6 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160 reaction reaction LV3 LV4 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 loading weights -0.2 -0.2 -0.4 -0.4 -0.6 -0.6 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160 reaction reaction LV5 LV6 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 loadingweights -0.2 -0.2 -0.4 -0.4 -0.6 -0.6 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160 reaction reaction Figure 2.28: Latent variable loading weights of a PLS model of the EFMs of a metabolic network with the Calvin cycle reactions phosphoribulokinase and ribulose bisphosphate carboxylase added. For clarity the reactions in the abscissa are represented as numbers. Their corresponding reactions are given in appendix section A.4. 107 Chapter 2 Alternatively the ratio between the Calvin route and glycolysis can be reduced, allowing more substrate level phosphorylation. With a ratio 2:5 the ATP requirements can be met but the PTS system will not be balanced anymore. For each 7 molecules of PEP converted to pyruvate, 2 will have to be phosphorylated back with pyruvate kinase to fulfil the carbon and redox balance. The latter network will produce 0.83 ATP in excess, without taking succinate transport into account. With the energy requirements of succinate transport, the network still lacks 0.21 ATP per succinate. The metabolic route presented in Figure 2.29 assumes the activation of both an aerobic and anaerobic metabolism. Because of the energy requirements, respiration will be necessary for ATP generation. However the carboxylation reaction requires an anaerobic environment. This implies that either both the oxidative TCA and glyoxylate route are activated under anaerobic conditions or that only the reductive TCA is used toward succinate. The latter requires much more reduced equivalents and is hampered by the PTS system that converts PEP to pyruvate. Only the pentose phosphate route (PPP) can supply enough reduced equivalents to meet the needs of the reductive TCA. For each glucose molecule passing through the PPP, two NADPH are formed and further down the glycolysis, another two NADH is yielded. In this metabolic route, glucose-6-phosphate first passes through the pentose phosphate and then enters the glycolysis at fructose-6-phosphate and glyceraldehyde-3-phosphate. The split ratio between the pentose phosphate and the Calvin cycle is in this case 7:1. Overall, the three routes will then yield 3.42 NADH per glucose, which meets the requirements for a yield of 1.71 mole succinate per mole glucose. With each succinate formed, PEP carboxykinase will form one ATP, which nearly meets the ATP demand of succinate export. To solve the PTS problem under anaerobic conditions, in a previous report, pyruvate carboxylase of Lactococcus lactis was suggested (Sanchez et al. , 2005). Pyruvate carboxylase converts pyruvate into oxaloacetate with carbon dioxide and the hydrolysis of one ATP. The net energy balance of the enzyme activity is the same as for PEP carboxylase. Although this enzyme facilitates glucose uptake via the PTS system, it will not meet the energy needs of the anaerobic production scheme. Furthermore, the low ATP yield will reduce the growth rate of the strain under these conditions, therefore hampering the production rate. Under aerobic conditions, pyruvate carboxylase is more useful, because it will aid the PTS system by pulling away pyruvate. The additional ATP that is generated by PEP carboxykinase is not really a necessity because respiration and substrate level phosphorylation supply plenty of ATP for growth and succinate export (Lin et al. , 2005). Instead of ATP hydrolysis as proposed above to temper biomass formation, the Calvin cycle presents an opportunity to convert the excess ATP into an increased succinate yield. The extent in which this route should be used will be determined by the growth rate of the strain and the yields. The flux ratio of the Calvin route and glycolysis will then also determine the flux ratios between the reductive and oxidative TCA and the glyoxylate shunt. 108 Metabolic engineering of succinate biosynthesis in Escherichia coli + + 2 e t NADP a CO r CoA, NADCoA, e t a t icd a u r l t 1 i g sucAB, lpd c o 4 t o s e A 1 I k o O 2 e C n a t H c l a , a NADPH y r 3 t A n i i o H c C , NADH, A C c i D 2 e + u c A S D a CO N A , A t 2 t N l a 4 , O g a l ADP + P ADP + A C A y o o A x 1 C o o C y 7 O l C sucCD l 2 y l t G H y e t 3 c e + h 3 c A d e t ATP, CoA ATP, A p H a e t e D e t + 3 t e t 2 a A c a a D 1 n a v n N i 9 i P A o B c u l c A e E c r N c a o c y u P x a u 2 C ) e 5 S e P S P t t * O 8 O ( a T a PTS l C r k A h a , a c 8 d p O M D m 2 m A u C 7 8 H o F B m P e 6 n n s u o A 1 e + O f 6 n o O d 2 i H 2 c r u e 2 f H u q s H l a + o n l c G e o n u M i l u P P q G a 2 3 n e 6 e e 1 M t t a a r r m e e c p c y g y l l i 7 G G g p D sedoheptulose 7 P 2 H D P A 3 F tktA, tktB tktA, 2 D ribose 5 P e 6 d 2 y h P e 8 O, O, CO T d 2 l + A rpiA, alsI rpiA, a k H D r g 4 e p A c 2 y N P l RubisCO D G A + 2 D + A 4 phosphoribulokinase F H P + 6 6 rpe H e P P s D talA, talBtalA, o T D ribulose 5 P D D erythrose 4 P D xylulose 5 P t A 2 A c A N D ribulose 1,5 bisP u r P F P 3 s i P e b B T d k f 3 A y , p h 1 4 , tktA, tktB e 3 e A d t l k P f a a r p r A D + e e p A c D H c P a y y g s D A l l i N A G b G a , b i N f 6 , P 1 i 3 p e t s 3 o t P c u n r o t F e c a y x o r d y h i D Figure 2.29: The optimal route for succinate production based on the EFM analysis of a stoichiometric network with the Calvin cycle enzymes added. The maximal theoretical yield for this scheme is 1.71 mole per mole glucose. The genes and their gene products are described in the appendix, section A.3 109 Chapter 2 The Bifidobacterium shunt The Bifidobacterium shunt is a unique set of reactions that produces primarily acetate and lactate in Bifidobacteria . Two mole of glucose are converted via fructose-6-phosphate phosphoketolase and xylulose-5-phosphate ketolase into 3 mole of acetate and 2 mole of lactate. The overall ATP yield for this reaction set is 5 mole (De Vries et al. , 1967; Ventura et al. , 2004). Fructose-6-phosphate phosphoketolase (reaction 156) produces one acetyl- phosphate and one erythrose-5-phosphate from fructose-6-phosphate and xylulose-5- phosphate ketolase yields one acetyl-phosphate and one glyceraldehyde-3-phosphate from xylulose-5-phosphate. The reaction numbers in the network are given below. Fructose-6-phosphate→ acetyl-phosphate + e rythrose-5-phosphate (reaction 156) Xylulose-5-phosphate→ acetyl-phosphate + g lyceraldehyde-3-phosphate (reaction 157) The remaining reactions of the shunt are the same as the reactions in the non-oxidative branch of the pentose phosphate pathway. Figure 2.30 shows yields generated by the elementary flux mode analysis of the E. coli stoichiometric model with the Bifidobacterium shunt reactions added. The maximal theoretical yield calculated for this model is 1.5 mole succinate per mole glucose. Bifidobacterium shunt Figure 2.30: The elementary flux modes of the stoichiometric model of E coli with the Bifidobacterium shunt reactions added. The modes are shown in function of their biomass yield (Y X,S ) – succinate yield (Ysuccinate, S ) relation (in mole per mole glucose). The maximal theoretical yield for this model is 1.5 mole succinate per mole glucose 110 Metabolic engineering of succinate biosynthesis in Escherichia coli The EFMs and yields were processed with a PLS model of which the X and Y variability and cumulative X and Y variability explained by the latent variables are presented in Table 2.11. A model with 6 latent variables explains 100 % of the Y variability and about 86.94 % of the X variability. A model with 5 latent variables reached maximum efficiency. Table 2.11: The X and Y variability and the cumulative X and Y variability explained by the latent variables of the PLS model fitted on the elementary flux mode data of a stoichiometric model with the Bifidobacterium shunt reactions and the corresponding succinate yield data. Latent variable X variability (%) Cumulative X Y variability (%) Cumulative Y (LV) variability (%) variability (%) 1 63.39 63.39 10.65 10.65 2 11.56 74.96 39.95 50.6 3 4.18 79.13 46.22 96.82 4 3.18 82.31 2.79 99.61 5 2.43 84.74 0.34 99.95 6 2.21 86.94 0.04 100 7 1.82 88.76 0 100 8 1.54 90.3 0 100 9 1.27 91.58 0 100 10 2 93.58 0 100 11 1.05 94.62 0 100 12 1.27 95.89 0 100 The coefficients of a PLS with 6 latent variables are given section A.5.5 of the appendix of this work and Figure 2.31. The reactions, shown as numbers, can be found in the appendix section A.4. The reactions loading weights for each of the latent variables are given in Figure 2.32. Bifidobacterium shunt 0.5 0.4 0.3 0.2 0.1 PLS coeffincients PLS 0.0 -0.1 -0.2 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 reaction Figure 2.31: The PLS coefficients of the PLS model based on 6 LV’s for the EFMs of a model with the Bifidobacterium shunt reactions added. To keep the figure clear the reactions in the abscissa are represented as numbers. Their corresponding reactions are given in appendix section A.4. 111 Chapter 2 LV 1 LV 2 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 loadingweights -0.2 -0.2 -0.4 -0.4 -0.6 -0.6 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160 reaction reaction LV 3 LV 4 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 loading weights -0.2 -0.2 -0.4 -0.4 -0.6 -0.6 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160 reaction reaction LV 5 LV 6 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 loadingweights -0.2 -0.2 -0.4 -0.4 -0.6 -0.6 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160 reaction reaction Figure 2.32: Latent variable loading weights for the metabolic model with the Bifidobacterium shunt reactions. For clarity the reactions in the abscissa are represented as numbers. Their corresponding reactions are given in appendix section A.4. 112 Metabolic engineering of succinate biosynthesis in Escherichia coli The maximal yield obtained with these additional reactions is the same as the yield that was obtained with the Entner Douderoff model. However, when the Bifidobacterium ketolases are used, either the oxidative TCA or the glyoxylate pathway has to be utilised. Their activity yields acetyl-phosphate, which is further converted to acetate by acetate kinase. The acetate can be considered as a byproduct, however, E. coli can recuperate the acetate via acetyl-CoA synthase (Starai & Escalante-Semerena, 2004). These reactions were not considered as such in the metabolic model but lumped as one, which led to the negative correlations of acetate kinase with succinate yield. None of the oxidative TCA enzymes (reactions 6, 121, and 148) correlate with succinate, which indicates that the optimal flux to succinate should flow through the glyoxylate pathway (reactions 55 and 64). The direct formation of acetyl-CoA from acetyl-phosphate, bypasses pyruvate dehydrogenase and thus the loss of carbon dioxide but also the formation of one NADH. The correlation of respiration (reaction 88) with succinate production indicates an excess of NADH formation, which leads further to ATP formation, which explains the correlation of ATP hydrolysis (reaction 12). Therefore, pyruvate dehydrogenase activity is not very important for NADH formation, but may be essential for the synthesis of additional acetyl-CoA. The positive correlation of pyruvate formate lyase (reaction 75) may indicate a high need of acetyl-CoA due to the use of the glyoxylate route. Both Bifidobacterium shunt enzymes do not seem to correlate with succinate production. The PLS model even exhibits a slight negative coefficient for Xylulose-5-phosphate ketolase (reaction 157). The maximal theoretical yield that was obtained for the network probably does not involve either of the enzymes but follows the network as described in Figure 2.17. Figure 2.33 schematises a possible route with fructose-6-phosphate ketolase. Note that glucose uptake is not taken care of by the PTS system but by the galactose permease- glucokinase system. This system allows that all oxaloacetate is converted to succinate via the glyoxylate pathway. As a result, the NADH and ATP yield per succinate is higher than the classical network that was described before. In this case 1 NADH and 2 ATP per succinate are formed. For each three succinate molecules formed there is then sufficient NADH for the synthesis of two more succinate molecule via the reductive TCA from an extra glucose molecule. This will result in an increased yield of 1.66 mole succinate per mole glucose. A similar result can be obtained via the oxidative branch of the pentose phosphate combined with xylose-5-phosphate ketolase. This combination of pathways is used by heterofermentative lactic acid bacteria. These bacteria catabolise glucose into lactate, ethanol, acetate and mannitol (Wisselink et al. , 2002). Both the PLS model and the stoichiometric network analysis proof that Bifidobacteria and lactic acid bacteria in general do not posses an efficient metabolism for succinate production. Their maximal theoretical yield that can be obtained is lower than that of E. coli . In light of their metabolic potential, with only partial glycolysis, pentose phosphate pathway or TCA, quite some strain engineering will have to be performed (Kleerebezem et al. , 2003; Schell et 113 Chapter 2 al., 2002). Nevertheless, the potential of lactic acid bacteria may not lie with their metabolic capacities, but rather with their acidophilic character. D-ribose-5-P 0.5 tktA,:;k_HtB15_ ' ---=rp-,~Ai-=,'--a-ls-~-' D-ribulose-5-P ATP Glucose Glyceraldehyde-3-P 0.5 D 0.5 2'1 glk . o(;Î;,: -xylulose-5-P rpe 2 ADP~ Glucose-6-P ~ F~ru;tose-6-P 0 5 D-sedoheptulose-7-P · ta!A, ta!B F D-erythrose-4-p tktA, tktB Acetyl-P ructose-6-P ketolase P~~: :;ls~-6-P ADP ackA 1.5 ADP :::1 ATP Fructose-1,6-bis p Dlhyd FAD+ NADH NADH j Glycerate-1,3-bisP NAD- ;1 ADP ~ P"k ATPA ATP, CoA Glycerate-3-P FADH, a es 1.5 31 gpm AMP Glycerate-2-P H,O :1eno ATP ADP 1 Pyruvate c VY1.5') PEP CoA, NAD+:j H,O, co,:ct 1.5 dh ADP - P, - ______.'.:c:'"o'2,2·~N~AD~ HA P 1.5 ppc cetyl-CoA 1 Oxaloacetate H20 1.5 5 A,H20 Citrate Malate C A aceB ~ H20 ° n ,1- L5 5 1 Fom•n« ~Yl''"":, ""''"'" Menaquinol + zH+ ~ 1_5 frdABCr)~ Menaquinon-8 Succinate 31 Succinate Figure 2.33: A possible optimal route for succinate production in aerobic conditions based on the EFM analysis with the Bifidobacterium shunt reactions. The maximal theoretical yield for this scheme is 1.5 mole per mole glucose. The genes and their gene products are described in the appendix, section A.3. 114 Metabolic engineering of succinate biosynthesis in Escherichia coli The pentose phosphate pathway of heterofermentative lactic acid bacteria The cofactor preference of the E. coli oxidative pentose phosphate pathway enzymes is quite strict. These enzymes only accept NADP(H) as coenzyme, which are mainly involved in biosynthetic pathways. Transhydrogenases can convert NADPH into NADH which is more involved in the central carbon metabolism. Alternatively NAD + can be derived through the dephosphorylation of NADP +. This comes with the loss of one ATP, because NADP+ originally phosphorylated by a kinase which uses ATP. Continuous conversion of both coenzymes will then form a futile cycle in the cell (Fukuda et al. , 2007; Shigeyuki et al. , 2001). There are two types of transhydrogenases in E. coli , a soluble and a membrane-bound proton-translocating pyridine nucleotide transhydrogenases. The primary metabolic role of the soluble transhydrogenases is to reoxidise NADPH which leads to an increased NADH in the cell. Its activity has proven to be essential for growth of a glucose-6-phosphate isomerase knock out strain (Fabrizio et al. , 2001; Sauer et al. , 2004). All previous PLS models have shown that transhydrogenases activity correlates positive with succinate formation. The pentose phosphate pathway enzymes (reactions 44, 76, 144 and 145) however correlate negatively. The introduction of two alternative pentose phosphate pathway enzymes that prefer NAD(H) as coenzyme probably will not change its PLS coefficients much, but they will change the redox household an may therefore affect several other reactions. Heterolactic acid bacteria such as Leuconostoc sp. use these types of glucose-6-phosphate dehydrogenases and 6-phosphogluconate dehydrogenases (Bondar & Mead, 1974; Murphy et al. , 1987a; Ohara et al. , 2004). Figure 2.34 presents the elementary flux modes of a metabolic network with these two reactions introduced. Heterolactic acid bacteria Figure 2.34: The elementary flux modes of the stoichiometric model of E coli with the alternative pentose phosphate reactions added. The modes are shown in function of their biomass yield (Y X,S ) – succinate yield (Ysuccinate, S ) relation (in mole per mole glucose). The maximal theoretical yield for this model is 1.5 mole succinate per mole glucose 115 Chapter 2 The NAD +-dependent dehydrogenases do not change the maximal theoretical yield calculated from the model because in essence the reaction network is the same as the Entner Doudoroff network. The correlations of the reactions with succinate yield were calculated with a PLS model of which variability explained by the latent variables is shown in Table 2.12. A model with 6 latent variables was selected for this analysis, because it explained 100 % of the Y variability and 88.95 % of the X variability. Models with 5 latent variables or more indicated a model efficiency of 1, which determined as described in paragraph 2.3.2.1. The coefficients of a PLS model with 6 latent variables are given in Figure 2.35. The reactions are shown as numbers in this figure and their corresponding reactions can be found in the appendix section A.4. The reactions loading weights for each of the latent variables are given in Figure 2.36. Table 2.12: The X and Y variability and the cumulative X and Y variability explained by the PLS model with a different number of latent variables, fitted on the elementary flux mode data of a stoichiometric network and the corresponding succinate yield data. Latent variable X variability (%) Cumulative X Y variability (%) Cumulative Y (LV) variability (%) variability (%) 1 66.6 66.6 13.1 13.1 2 5.85 72.44 74.55 87.66 3 8.64 81.08 11.84 99.49 4 1.85 82.93 0.47 99.96 5 3.92 86.85 0.03 99.99 6 2.1 88.95 0.01 100 7 1.63 90.58 0 100 8 1.83 92.42 0 100 9 1.55 93.97 0 100 10 1.51 95.48 0 100 11 1.68 97.16 0 100 12 1.46 98.62 0 100 PPP heterofermentative lactic acid bacteria 0.5 0.4 0.3 0.2 0.1 PLS coeffincients PLS 0.0 -0.1 -0.2 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 reaction Figure 2.35: The PLS coefficients of the PLS model based on 6 LV’s for the EFMs of a network with the alternative pentose phosphate pathway reactions added. To keep the figure clear the reactions in the abscissa are represented as numbers. Their corresponding reactions are given in appendix section A.4. 116 Metabolic engineering of succinate biosynthesis in Escherichia coli LV 1 LV 2 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 loading weights -0.2 -0.2 -0.4 -0.4 -0.6 -0.6 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160 reaction reaction LV 3 LV 4 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 loading weights -0.2 -0.2 -0.4 -0.4 -0.6 -0.6 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160 reaction reaction LV 5 LV 6 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 loading weights -0.2 -0.2 -0.4 -0.4 -0.6 -0.6 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160 reaction reaction Figure 2.36: Latent variable loading weights for the metabolic model with the heterolactic acid bacteria pentose phosphate pathway. For clarity the reactions in the abscissa are represented as numbers. Their corresponding reactions are given in appendix section A.4. 117 Chapter 2 The most obvious difference between both is the disappearance of transhydrogenation correlation with succinate production when NAD +-dependent pentose phosphate dehydrogenases are introduced into the model. In addition, the oxidative branch of the TCA cycle correlates with succinate production. This result is similar to the models in paragraph 2.3.2.2 with the Calvin cycle enzymes and paragraph 2.3.2.3 with sucrose as substrate. Obviously, the oxidative TCA will function as a NADPH source through isocitrate dehydrogenase activity. In some of the elementary modes in which biomass is formed, there is a demand for NADPH. If some pentose phosphate flux would be allowed within the optimal network presented before (Figure 2.17), this could supply with the needed NADPH. In this case, this pentose phosphate flux would not resolve this problem. Therefore alternative reactions will become more important in the PLS model, such as isocitrate dehydrogenase, alfa-ketoglutarate dehydrogenase and succinyl-CoA synthase. In all the PLS models presented, the pentose phosphate correlates negatively with succinate production. This route is however a parallel pathway with the glycolysis, thus in principle it should not make much difference. It even supplies net more reduced equivalents per glucose (3.66) than the glycolysis (2). Reductive power is thus not an issue, but carbon is. With every molecule that passes 6-phosphogluconate dehydrogenase, one carbon is lost through carbon dioxide. This loss cannot completely be compensated by PEP carboxylase or PEP carboxykinase (Figure 2.37), which leads to a reduced yield of 1.66 mole per mole glucose. Figure 2.37 proposes a solution for this loss of carbon. The maximal theoretical yield of succinate (1.71) requires 3.42 NADH per glucose. The pentose phosphate pathway produces 3.66, which leaves about 0.24 NADH unoxidised. 6 glucose molecules will then supply sufficient reductive power to reduce one glucose to succinate via the glycolysis. This 6 to 1 flux ratio between the PPP and glycolysis results then in the maximal theoretical yield of 1.71 mole per mole glucose. This route could not be derived from any of the glucose based models that were presented in this paragraph although it results in the perfect conversion of glucose to succinate. There is no clear reason for this observation, although a hypothesis may be that because the pentose phosphate also correlates strongly with biomass formation, the PLS regression is skewed. The loss of carbon dioxide in the pathway may also influence the regression. Combine this with other decarboxylation reactions and the succinate yield will drop much quicker with the pentose phosphate than without (but with glycolysis). However, the fact that this pathway could not be identified indicates that even though models are a good guide, still a lot of human inference is needed. 2.3.3 Genetic and enzymatic regulation Stoichiometric network analysis uses, as the name suggests, only the reaction stoichiometry is used to search for the optimal route. Once this route is identified, the other cellular levels will also come into play when a genetic engineering strategy has to be determined. Here the possible influence of these cellular controls are summarized, in chapter 5, these will be further evaluated in the context of succinate production. 118 Metabolic engineering of succinate biosynthesis in Escherichia coli 6 6 7 6 phosphogluconate D Glucono δ lactone 6 P D Glucose 6 P D Glucose + pgl zwf glk NAD H2O NADH NAD + ADP ATP 6 gnd NADH, CO 2 D ribulose 5 P rpe rpiA, alsI 4 2 D xylulose 5 P D ribose 5 P Glyceraldehyde 3 P 2 tktA, tktB pgi tktA, tktB 2 Glyceraldehyde 3 P D sedoheptulose 7 P 2 talA, talB 1 Fructose 6 P D erythrose 4 P Fructose 6 P ATP 2 3 ATP 2 ADP fbaA, fbaB ADP Fructose 1,6 bis P 5 fba Glyceraldehyde 3 P Dihydroxyaceton P gapA tpiA 5 + Pi, NAD 12 NADH Glycerate 1,3 bisP ADP 12 pgk ATP Glycerate 3 P gpmM, ytjC, 12 gpmA Glycerate 2 P 12 eno H2O PEP ADP, CO 2 12 pckA ATP OAA NADH 12 mdh NAD + Malate H2O 12 fumABCD Fumarate MQH2 12 frd MQ Succinate Figure 2.37: The possible optimal route for succinate production under fully anaerobic conditions without PTS activity. The genes and their gene products are described in the appendix, section A.3. 119 Chapter 2 First transcription factors will have to be taken into account. Modifications to the metabolism will only be effective if the cell does not counter their effect on the genetic level. The genes that are considered in the optimal production route should also be active in the projected production conditions. For instance the reductive TCA enzymes will be repressed in aerobic conditions and the glyoxylate pathway is not active in a glucose rich environment (Perrenoud & Sauer, 2005; Shalel-Levanon et al. , 2005). Knowledge of those regulatory systems is here essential. Table 2.14 reviews all the genes involved in the optimal pathways that were presented above and their transcription factors. ArcA, FNR, FruR (also known as Cra), and CRP are the most abundant regulators. Together they affect almost the whole pathway. The TCA cycle and glyoxylate pathway are mainly affected by ArcA and FNR. They activate the reductive TCA (mainly FNR) and repress the oxidative TCA and glyoxylate route (mainly ArcA). CRP upregulates almost the whole glycolysis, which lends itself perfectly within the proposed schemes. FruR may counter the CRP activity on the glycolysis genes, but also the ArcA activity on the TCA genes. This regulator will thus have an ambiguous role. The presence of a certain enzyme activity is not only regulated by gene expression, but also by its environment. The cell produces activators and repressors to modulate their activity. By altering the intracellular fluxes, intracellular metabolite concentrations will change. A rate limiting enzyme can cause accumulation of certain metabolites which in turn can cause feedback inhibition. A comprehensive summary of metabolic regulators and their effect on the enzymes in the production routes is given in Table 2.13. Especially TCA cycle intermediates may be of concern because these are the metabolites that may accumulate during succinate production. 120 Metabolic engineering of succinate biosynthesis in Escherichia coli Table 2.13: Overview of the enzymes that are involved in the optimal succinate production routes with their respective metabolic modulators. The genes and their gene products are described in the appendix, section A.3 (Chang et al. , 2009; Keseler et al. , 2009). Genes E.C. Activator/inhibitor e diphosphate bisphosphate - - phosphate 5' phosphate - - - 3 CoA 6 1,6 - - - - CoA - SH + ketoglutarate - - phosphogluconat phosphoglycerate i - - AMP ADP ATP P NAD NADH GTP Guanosine 6 Fructose Fructose DHAP 3 Glycerol PEP Pyruvate Lactate Acetyl CoA Citrate Aconitate Alfa Succinyl Glyoxylate Glycolate Succinate Malate Oxaloacetate Aspartate Fatty acids Magnesium (+II) Calcium (+II) pgi 5.3.1.9 – pfkA 2.7.1.11 + + + + – pfkB 2.7.1.11 – – – glpX 3.1.3.11 – – + fbaA 4.1.2.13 fbaB 4.1.2.13 – + + + tpi 5.3.1.1 gapA 1.2.1.12 + + pgk 2.7.2.3 + + gpmA 5.4.2.1 gpmM 5.4.2.1 eno 4.2.1.11 + pykA 2.7.1.40 + + pykF 2.7.1.40 – + – + + pps 2.7.9.2 – – – + pdh 1.2.4.1 – – – – mdh 1.1.1.37 lpdA 1.8.1.4 ± ppc 4.1.1.31 + + + – – – + gltA 2.3.3.1 – – – – – acnA 4.2.1.3 acnB 4.2.1.3 icd 1.1.1.42 – – – sucAB 1.2.4.2 + + – sucCD 6.2.1.5 – – – – – – sdhCDAB 1.3.5.1 frdABCD 1.3.1.6 fumA 4.2.1.2 fumB 4.2.1.2 fumC 4.2.1.2 – aceA 4.1.3.1 – – – – – aceB 2.3.3.9 – 121 Chapter 2 Table 2.14: Overview of the operons that are involved in the optimal succinate production routes with their respective transcription factors. The genes and their gene products are described in the appendix, section A.3 (Gama-Castro et al. , 2008; Keseler et al. , 2009). Operon Transcription factor GlpR Fur FNR PdhR KdggR PhoB GntR GadE IHF FlhDC Hns Fis Fis ArcA DgsA CRP SoxS SoxR SgrS FruR GalR GalS NagC NarL DcuR MarA Rob IclR ptsG – – – + + – glk – galP + – – – pgi + pfkAB – glpX + – fbaA + – fbaB tpi gapA + – pgk + – gpmA – gpmM eno – pykA pykF – pps + aceEF – + ± – lpdA + – – ± – ppc pck + maeA maeB edd – – eda – – – – cyoABCD – + – – – + cydAB + + – cydCD + + + pntAB sthA gltA – + + – mdh – + – acnA – + + – acnB – – + – icd – + sucABCD ± + + – – sdhCDAB ± + + – frdABCD + – + fumA – + – fumB – + + + + – + fumC – + + + + aceBAK – – + + – dcuC + – dctA – + + 122 Metabolic engineering of succinate biosynthesis in Escherichia coli 2.4 Conclusions The goal of an industrial production process is quite simple: it has to be as cheap as possible and as simple as possible. The translation of this goal into a well thought out development strategy can be more challenging. Originally, an enzyme or microorganism was screened to meet the process requirements and then randomly changed to enhance its productive properties. With the recent developments in genetic engineering, this approach has changed into a more rational design. Furthermore, screening of organisms that produce a certain target molecule has changed into engineering of organisms for production. Nowadays, gene knock outs and knock ins are common practices. In approximately one to two weeks a mutant can be created, and with high throughput techniques, this time can even be shortened to a couple of days of work for a whole library of mutant strains. But even these techniques may become outdated soon, with the fast development of synthetic biology and the creation of minimal microorganisms. However, these organisms may not meet essential process requirements, for instance low pH resistance. To overcome this hurdle, some old tricks have recently been refurnished. Random mutation has been replaced with genome breeding, global transcription factor engineering and ribosome engineering to adapt organisms to the sometimes harsh production environment requirements. Most likely, the future process development will hence be composed of two steps. First the development of an organism that optimally grows in the process environment that is required and second, the (synthetic) development of the optimal pathway towards the target molecule. Gathering information is one of the most crucial tasks within process development. Through the years a lot of experimental and genetic data has been gathered which can be useful. These datasets have been classified in databases based on the cellular level they relate to. The difficulty is to brows through this vast amount of data and to combine datasets to extract useful information. The ever growing databases do not allow manual browsing anymore, but require data mining tools that aid with the recombination and interpretation of datasets. Comparative and functional genomics are two examples that have been applied here to analyse natural succinate production strains with E. coli and Corynebacterium glutamicum . Natural succinate producing strains differ on the genomic level in many reactions from E. coli. Recurring differences are the lack of glyoxylate route enzymes, isocitrate dehydrogenase and differences in the affinity of PEP carboxykinase. An interesting observation is the fact that Corynebacterium glutamicum R lacks alfa-ketoglutarate dehydrogenase instead of an oxygen sensitive aconitase or the absence of an isocitrate dehydrogenase like A. succinogenes and M. succiniciproducens . The latter two lead to glutamine auxotrophies which result in increased substrate cost. Succinyl-CoA, the product of alfa-ketoglutarate dehydrogenase, can be formed by alternative reactions via succinate. These alternative reactions avoid lysine and methionine 3 auxotrophy problems in a Corynebacterium glutamicum R production system. 3 Succinyl-CoA is an intermediate of the methionine and lysine biosynthetic pathway (Keseler et al., 2009) 123 Chapter 2 Furthermore this means that modifications of the oxidative branch of the TCA lead to auxotrophies, but not necessarily lead to enhanced succinate production. Byproducts are for every strain a problem. Nearly all of them form ethanol, lactate, acetate or formate in certain conditions. These are the primary targets for genetic engineering. The odd one out is Wolinella succinogenes . Based on the genome information available and the tools that have been used, no byproduct formation reactions were found. However, W. succinogenes ’ inability to grow on carbohydrates should be taken into account. This strain converts fumarate into succinate, but needs fumarate or nitrate as terminal electron acceptors and shows growth on formic acid or carbon dioxide and hydrogen (Garrity et al. , 2004). Hence the strain has never been considered for succinate production. It might be in future, when genetic tools become available for Helicobacter pylori , a near family member of W. succinogenes to try to engineer it so that it can assimilate carbohydrates. This strain might have some interesting properties that all other strains lack. In none of the natural succinate producing strains, the Entner Doudoroff route is present, which might mean that the route is obsolete for succinate production. It might even have an adverse effect because of the oxaloacetate decarboxylase activity of 2-dehydro-3-deoxy- phosphogluconate. This route is rarely considered in E. coli engineering strategies, although it has some interesting properties. It bypasses for instance the strongly allosterically regulated phosphofructokinase (Auzat et al. , 1995) and yields the same amount of reduced equivalents. Stoichiometric network analysis could give in this case more insight. This type of analysis can also provide more insight into the split between glycolysis and the pentose phosphate pathway which is present in all considered microorganisms except W. succinogenes , which lacks the oxidative pentose phosphate enzymes. In a given environment, either aerobic or anaerobic, one or the other pathway might be favourable due to their net NAD(P)H yield. Alternative routes, for instance the Bifidobacterium shunt or the heterolactic acid pathway should also be considered in this case. The comparative and functional genomics supply insights in the difference between organisms, but does not result in a mutation strategy. Metabolic pathways are all linked by cofactors which have to be balanced in order to result into a viable organism. Stoichiometric network analysis facilitates the exploration of metabolic networks. Elementary flux modes already indicate what the yield potential is of a certain reaction network, but because of the number of modes that can be created, it is difficult to analyse which reactions are important for production. Here the elementary flux modes were processed with partial least square regression, to correlate the individual reactions with the succinate yield. In general such a study is organism oriented, which means the metabolism of the production host is modelled and analysed. However, the genetic tools that are nowadays available have changed the meaning of “the metabolism of an organism”. Reactions can be removed or added easily, therefore a stoichiometric network analysis should not be organism oriented anymore but “reactome” oriented, which includes all known biochemical reactions. Unfortunately, 124 Metabolic engineering of succinate biosynthesis in Escherichia coli analysing all possible reactions at once still runs into computational problems, hence some potentially interesting pathways were chosen in this study that have not been considered before for succinate production. The EFM-PLS models validated the observation made in the comparative genomics study that the Entner Doudoroff route is adverse for succinate production. With respect to the oxidative TCA, both methods produced a different result. The oxidative TCA was not present in any of the succinate producing microorganisms, but the PLS model indicated in some instances a positive correlation. The natural succinate producing microorganisms can thus only reach the maximal theoretical yield of 1.71 via a pentose phosphate route:glycolysis ratio of 6:1. C13 labelling experiments of Actinobacillus succinogenes showed that only a small fraction (5 %) of the carbon flux through the pentose phosphate, which indicates that the maximal yield that this strain can obtain is around 1.5 mole succinate per mole glucose unless an external source of reductive compounds is added to the growth medium (McKinlay et al. , 2007). The full use of the pentose phosphate route for succinate production would also require very strong transhydrogenase activity because of the coenzyme specificity of the oxidative reactions or the coenzyme specificity has to be altered by introducing enzymes from heterofermentative lactic acid bacteria. The optimal pathways show nicely that, aerobically there is a competition for NADH between respiration and succinate production. The reductive TCA has to be activated in such a way that its affinity for NADH is higher than that of the electron transport chain. The excess of ATP can be dealt with via the introduction of the Calvin cycle, which allows full PTS activity. Without the Calvin pathway, glucose uptake and the conversion of PEP to pyruvate is unbalanced, which indicates that the PTS system has to be replaced by the glucokinase- galactose permease system. However, a completely carbon and redox balanced network with the Calvin cycle reactions is not balanced with the energy requirements of succinate transport. The Calvin cycle is useful as an ATP drain in an aerobic environment, which may influence biomass yield. In all pathways that have been presented the ratio between the reductive TCA, glyoxylate pathway and oxidative TCA is very important. In the sucrose case, a ratio of 18:2:2 (reductive TCA:glyoxylate route:oxidative TCA) was required to reach the maximal theoretical yield. These pathways are very tightly regulated, which means not only genes will have to be knock out or introduced to create an optimal flux ratio, but also the transcription factors will have to be considered. Previous studies reveal that FNR, ArcA, CRP and IclR play an important role in the regulation of the TCA (Nanchen et al. , 2008; Perrenoud & Sauer, 2005; Shalel- Levanon et al. , 2005). These are most likely the transcription factors that should be targeted for engineering. However, synergetic effects between regulators are rarely studied and may lead to unexpected results (cfr. Chapter 4). 125 Chapter 2 Maybe the most obvious target that was identified by the EFM-PLS model was succinate transport. Transport correlated strong with succinate production in every experiment. The importance of the transport reaction is further confirmed by the enzyme inhibition data of some of the TCA enzymes. Fumarase, PEP carboxylase and succinyl-CoA synthase all undergo allosteric inhibition by succinate (Table 2.13). Withdrawing succinate from the cell therefore helps avoiding feedback inhibition. Chapter 3 elaborates more on the matter of transport. 126 3 Chapter 3 Influence of C4-dicarboxylic acid transporters on succinate production ~ - Chapter 3 CONTENTS 3.1 Introduction 129 3.2 Materials and methods 131 3.2.1 Strains 131 3.2.2 Media 131 3.2.3 Cultivation conditions 132 3.2.4 Sampling methodology 132 3.2.5 Analytical methods 133 3.2.6 Genetic methods 133 3.2.7 Calculation methods 137 3.3 Results and discussion 138 3.4 Conclusions 148 128 Succinate Transport 3.1 Introduction Current engineering strategies for the production of value added bio chemicals are mainly focussed on pushing carbon towards a target molecule. The effect of transport, thus pulling the product out of the cell, is rarely considered. However, from an industrial biotechnological perspective the efficient excretion of an end-product is important. It reduces byproduct formation, and thus will lead to more easy to purify end-products. Additionally, feedback inhibition of the pathways towards the product will be lowered, which logically induces higher production rates. Both process parameters, product purity and production rate, have previously been referred to as key parameters for biotechnological production processes next to production yield (McKinlay et al. , 2007; Patel et al. , 2006; Werpy et al. , 2004). They are crucial for their economic feasibility. As a top 12 added value chemical, C4-dicarboxylic acids and in particular succinate lends itself excellent as a proof of concept (Werpy et al. , 2004). Most environments are substrate limiting for micro-organisms, which has resulted in very efficient substrate uptake systems (Postma et al. , 1993). However, survival is rarely linked to the excretion of a certain primary metabolite. There was never evolutionary pressure which has selected for optimised export mechanisms, but rather led to a wide variety of transport mechanisms (Paulsen et al. , 2000; 1998). Specifically for C4-dicarboxylic acid transport, the protein families are very diverse. The transporters can be grouped in 7 superfamilies (MFS, Dcu, DAACS, CSS, DASS, DcuC and AEC), which all have a different mode of action (Saier et al. , 2006). To change the excretion of an end-product, the expression of a membrane transporter has to be altered. These proteins form only a small fraction of the bacterial proteome. About 737 proteins in E. coli out of the 4288 predicted genes (Figure 3.1) code for integral membrane proteins of which only 7 % code for efflux transport (Daley et al. , 2005; Luirink et al. , 2005). In contrast to the hydrophilic cytoplasmic proteins, the structure of an integral membrane protein is organised as such that the lipophilic residues are placed towards the protein surface, while the hydrophilic residues are oriented inside. This hydrophobic and sometimes amphiphilic nature makes it very difficult to study these proteins, hence the limited amount of information available on membrane protein structures and biochemistry (White, 2004). Herein lays the challenge to identify the right membrane proteins that allows enhanced efflux of for instance succinate. In Escherichia coli C4-dicarboxylic acid transporters can, as in most facultative anaerobic micro-organisms, be subdivided in aerobic and anaerobic transporter. While the DctA family (Dicarboxylate transport system) is mainly operational under an aerobic environment, the DcuAB and DcuC families ( Dicarboxylate uptake systems) are operational under anaerobic conditions (Janausch et al. , 2002). Their function is closely related to the E. coli metabolism under these conditions. 129 Chapter 3 Transport/efflux 7% Metabolic 7% Transport/influx Biogenesis 33% 6% Signalling Cytoplasm Membrane 737 5% 3551 Lipid 4% Unknown Channels 36% < 1% Flagellar < 1% Figure 3.1: Overview of the functional categorisation of the E. coli membrane proteome (Daley et al. , 2005) Anaerobically, fumarate will function as terminal electron acceptor, thus C4-dicarboxylic acids such as fumarate and malate will be interesting metabolic intermediates for E. coli , while succinate is an end-product and will thus be preferably excreted (Clark, 1989). Transport in this environment will mainly focus on the import of fumarate, malate and other pathway intermediates and the export of succinate. Aerobically on the other hand, succinate is a crucial intermediate in the Krebs-cycle. It would thus be unfavourable for the cell to excrete succinate. In this case the cell is provided with a rather efficient succinate (C4-dicarboxylic acid) uptake system (DctA), which keeps the extracellular concentration low. It is also known that not only the DctA family, but a yet to be discovered carrier ensures the cell of succinate uptake (Davies et al. , 1999). Enhancing succinate excretion would evidently mean changing the expression scheme of these transporters. However, succinate excretion will only work when succinate is supplied to the transporters. In an anaerobic environment succinate is supplied by nature, in an aerobic environment the cell will have to be altered in such a way that it produces succinate. Therefore the TCA cycle has to be modified in such a way that succinate can accumulate. By knocking out succinate dehydrogenase, succinate will no longer be oxidised efficiently to fumarate and will start to pile up in the cell. Such a mutant strain lends itself perfectly as a background genotype to evaluate alternations in succinate transport systems. 130 Succinate Transport 3.2 Materials and methods 3.2.1 Strains Escherichia coli MG1655 [ λ−, F –, rph–1] was obtained from the Coli Genetic Stock Center (CGSC). The different strains were preserved in a (1:1) glycerol:LB growth medium solution. A summary of the strains used in this work is given in Table 3.1. Table 3.1: Strains used in this chapter are derived from the wild type strain MG1655. ∆FNR-pro37-dcuC indicates the complete replacement of the natural dcuC promoter with an artificial promoter. In the case of DcuC, there is also a regulation sequence present, an FNR binding site, which was also removed. Strain MG1655 MG1655 ∆dct A∆yfb S MG1655 ∆dct A∆sstT MG1655 ∆dct A∆ydjN MG1655 ∆dct A∆ybh I MG1655 ∆dct A∆yhj E MG1655 ∆dct A∆ydf J MG1655 ∆dct A∆ttdT MG1655 ∆dctA∆cit T MG1655 ∆sdhAB MG1655 ∆sdhAB ∆dctA MG1655 ∆FNR-pro8-dcuC MG1655 ∆FNR-pro37-dcuC MG1655 ∆FNR-pro55-dcuC MG1655 ∆sdhAB ∆FNR-pro37-dcuC MG1655 ∆sdhAB ∆FNR-pro37-dcuC ∆dctA MG1655 ∆sdhAB ∆FNR-pro37-dcuC ∆dctA MG1655 ∆sdhAB ∆FNR-pro37-dcuC ∆dctA ∆ybh I MG1655 ∆sdhAB ∆FNR-pro37-dcuC ∆dctA ∆ydjN MG1655 ∆sdhAB ∆FNR-pro37-dcuC ∆dctA ∆ybh I∆ydjN 3.2.2 Media The Luria Broth (LB) medium consisted of 1 % tryptone peptone (Difco, Erembodegem, Belgium), 0.5 % yeast extract (Difco) and 0.5 % sodium chloride (VWR, Leuven, Belgium). Shake flask medium contained 2 g/l NH 4Cl, 5 g/l (NH 4)2SO 4, 2.993 g/l KH 2PO 4, 7.315 g/l K2HPO 4, 8.372 g/l MOPS, 0.5 g/l NaCl, 0.5 g/l MgSO 4·7H 2O, 16.5 g/l glucose·H 2O, 1 ml/l vitamin solution, 100 l/l molybdate solution, and 1 ml/l selenium solution. The medium was set to a pH of 7 with 1M KOH. Vitamin solution consisted of 3.6 g/l FeCl 2 · 4H 2O, 5 g/l CaCl 2 · 2H 2O, 1.3 g/l MnCl 2 · 2H 2O, 0.38 g/l CuCl 2 · 2H 2O, 0.5 g/l CoCl 2 · 6H 2O, 0.94 g/l ZnCl 2, 0.0311 g/l H 3BO 4, 0.4 g/l 131 Chapter 3 Na 2EDTA· 2H 2O and 1.01 g/l thiamine · HCl. The molybdate solution contained 0.967 g/l Na 2MoO 4 · 2H 2O. The selenium solution contained 42 g/l SeO 2. The minimal medium for fermentations contained 6.75 g/l NH 4Cl, 1.25 g/l (NH 4)2SO 4, 1.15 g/l KH 2PO 4, 0.5 g/l NaCl, 0.5 g/l MgSO 4·7H 2O, 16.5 g/l glucose·H 2O, 1 ml/l vitamin solution, 100 l/l molybdate solution, and 1 ml/l selenium solution with the same composition as described above. 3.2.3 Cultivation conditions A preculture, from a single colony on a LB-plate, in 5 ml LB medium was incubated during 8 hours at 37 °C on an orbital shaker at 200 rpm. From this culture, 2 ml was transferred to 100 ml minimal medium in a 500 ml shake flask and incubated for 16 hours at 37 °C on an orbital shaker at 200 rpm. 4 % inoculum was used in a 2 l Biostat B Plus culture vessel with 1.5 l working volume (Sartorius Stedim Biotech, Melsungen, Germany). The culture conditions were: 37 °C, stirring at 800 rpm, and a gas flow rate of 1.5 l/min. Aerobic conditions were maintained by sparging with air, anaerobic conditions were obtained by flushing the culture with a mixture of 3 % CO 2 and 97 % of N 2. The pH was maintained at 7 with 0.5 M H 2SO4 and 4 M KOH. The exhaust gas was cooled down to 4 °C by an exhaust cooler (Frigomix 1000, Sartorius Stedim Biotech, Melsungen, Germany). 10 % solution of silicone antifoaming agent (BDH 331512K, VWR Int Ltd., Poole, England) was added when foaming raised during the fermentation (approximately 10 µl). The off-gas was measured with an EL3020 off-gas analyser (ABB Automation GmbH, 60488 Frankfurt am Main, Germany). All data was logged with the Sartorius MFCS/win v3.0 system (Sartorius Stedim Biotech, Melsungen, Germany). All strains were cultivated at least twice and the given standard deviations on yields and rates are based on at least 10 data points taken during the repeated experiments. 3.2.4 Sampling methodology The bioreactor contains in its interior a harvest pipe (BD Spinal Needle, 1.2x152 mm (BDMedical Systems, Franklin Lakes, NJ - USA) connected to a reactor port, linked outside to a Masterflex-14 tubing (Cole-Parmer, Antwerpen, Belgium) followed by a harvest port with a septum for sampling. The other side of this harvest port is connected back to the reactor vessel with a Masterflex-16 tubing. This system is referred to as rapid sampling loop. During sampling, reactor broth is pumped around in the sampling loop. It has been estimated that, at a flow rate of 150 ml/min, the reactor broth needs 0.04 s to reach the harvest port and 3.2 s to re-enter the reactor. At a pO2 level of 50 %, there is around 3 mg/l of oxygen in the liquid at 37 °C. The pO2 level should never drop below 20 % to avoid micro-aerobic conditions. Thus 1.8 mg/l of oxygen may be consumed during transit through the harvesting loop. Assuming an oxygen uptake rate of 0.4 g oxygen/g biomass/h (the maximal oxygen uptake rate found at µ max ), this gives for 5 g/l biomass, an oxygen uptake rate of 2 g/l/h or 0.56 mg/l/s, which multiplied by 3.2 s (residence time in the loop) gives 1.8 mg/l oxygen consumption. 132 Succinate Transport In order to quench the metabolism of cells during the sampling, reactor broth was sucked through the harvest port in a syringe filled with 62 g stainless steel beads precooled at −20 °C, to cool down 5 ml broth immediately to 4 °C. Sampling was immediately followed by cold centrifugation (15000 g, 5 min, 4 °C). During the batch experiments, a sample for OD 600nm and extracellular measurements was taken each hour using the rapid sampling loop and the cold stainless bead sampling method. When exponential growth was reached, the sampling frequency was increased to every 20 to 30 minutes. 3.2.5 Analytical methods Cell density of the culture was frequently monitored by measuring optical density at 600 nm (Uvikom 922 spectrophotometer, BRS, Brussel, Belgium). Cell dry weight was obtained by centrifugation (15 min, 5000 g, GSA rotor, Sorvall RC–5B, Goffin Meyvis, Kapellen, Belgium) of 20 g reactor broth in pre-dried and weighted falcons. The pellets were subsequently washed once with 20 ml physiological solution (9 g/l NaCl) and dried at 70 °C to a constant weight. To be able to convert OD 600nm measurements to biomass concentrations, a correlation curve of the OD 600nm to the biomass concentration was made. The concentrations of glucose and organic acids were determined on a Varian Prostar HPLC system (Varian, Sint-Katelijne-Waver, Belgium), using an Aminex HPX-87H column (Bio- Rad, Eke, Belgium) heated at 65 °C, equipped with a 1 cm precolumn, using 5 mM H2SO4 (0.6 ml/min) as mobile phase. A dual-wave UV-VIS (210 nm and 265 nm) detector (Varian Prostar 325) and a differential refractive index detector (Merck LaChrom L-7490, Merck, Leuven, Belgium) was used for peak detection. By dividing the absorptions of the peaks in both 265 and 210 nm, the peaks could be identified. The division results in a constant value, typical for a certain compound (formula of Beer-Lambert). 3.2.6 Genetic methods All knock outs and knock ins were constructed by and obtained from dr. M.R. Foulquié Moreno, Prof dr. D. Charlier and Prof. dr. R. Cunin (the research group of genetics and microbiology at the Free University of Brusselsl). The methods used for mutant construction is described below. Plasmids were maintained in the host E. coli DH5 α (F-, φ80d lacZ M15, (lacZYA- argF )U169, deoR , recA 1, endA 1, hsdR 17(rk -, mk +), phoA , supE 44, λ-, thi -1, gyrA 96, relA 1). Plasmids. pKD46 (Red helper plasmid, Ampicillin resistance), pKD3 (contains an FRT- flanked chloramphenicol resistance ( cat ) gene), pKD4 (contains an FRT-flanked kanamycin resistance ( kan ) gene), and pCP20 (expresses FLP recombinase activity) plasmids were obtained from Prof. Dr. J-P Hernalsteens (Vrije Universiteit Brussel, Belgium). The plasmid pBluescript (Fermentas, St. Leon-Rot, Germany) was used to construct the derivates of pKD3 and pKD4 with a promoter library, or with alleles carrying a point mutation. Mutations. The mutations consisted in gene disruption (knock-out, KO), replacement of an endogenous promoter by an artificial promoter (knock-in, KI) (De Mey et al. , 2007), Figure 133 Chapter 3 3.2 and Figure 3.3, respectively. They were introduced using the concept of Datsenko and Wanner (2000). The primers for the mutation strategies are described in Table 3.2. Transformants carrying a Red helper plasmid were grown in 10 ml LB media with ampicillin (100 mg/l) and L-arabinose (10 mM) at 30 °C to an OD 600nm of 0.6. The cells were made electrocompetent by washing them with 50 ml of ice-cold water, a first time, and with 1 ml ice-cold water, a second time. Then, the cells were resuspended in 50 µl of ice-cold water. Electroporation was done with 50 µl of cells and 10–100 ng of linear double-stranded-DNA product by using a Gene Pulser TM (BioRad) (600 , 25 µFD, and 250 volts). Table 3.2: Overview of the primers used for the mutation strategies Primer name sequence Fw-dctA -P1 caggggtcaattatgcgcaaacacccgcactcggggaagggagtgcgggcataagtgatgaga gtgtaggctggagctgcttc Rv-dctA -P2 caggtaccccataaccttacaagacctgtggttttactaaaggacaccct catatgaatatcctccttag Fw-yfbS -P1 Gagtctgcgtcgcatcaggcaataagcgccggatgcgacatcaggctcttg gtgtaggctggagctgcttc Rv-yfbS -P2 gggcttttttatggcagaatcaagtcatccccctcaattaacaaggataagtt catatgaatatcctccttag Fw-sstT -P1 atgcgtcgacagaacgcaccagggatgtgcgacaacacaatgaaaggatcgaaaa gtgtaggctggagctgcttc Rv-sstT -P2 gaattgatccgtttaaagttgagaaaaccccttccgccgtagacgaaaggggttaaacaa catatgaatatcctccttag Fw-ydjN -P1 gcgccaacactatgactgctacgcagtgatagaaataataagatcaggagaacgggg gtgtaggctggagctgcttc Rv-ydjN -P2 ggaacatttaaaaagtaaggcaacggcccctatacaaaacggaccgttgccagcataagaa catatgaatatcctccttag Fw-ybhI -P1 aaggacaatcaataaaggacttctgtatgagtcatacagaaagaacaggattttaa gtgtaggctggagctgcttc Rv-ybhI -P2 gactggggtgagcgaacgcagacgcagcacatgcaacttgaagtatgacgagaatat catatgaatatcctccttag Fw-yhjE -P1 cacttttgctgtgcgtaatatggctattcgttagccaaaaaataagaaaagatt gtgtaggctggagctgcttc Rv-yhjE -P2 tatattgtatggaaattcaggcctgataagcgtagcgcatcaggcttttcactctta catatgaatatcctccttag Fw-ydfJ -P1 atgttttgttatatgaataaaaatcccctctccggtaagagaagggattaagggt gtgtaggctggagctgcttc Rv-ydfJ -P2 aaaacctagaaaacgccaaggaaaccacaggatgggaaaaaacacctgtgaatt catatgaatatcctccttag Fw-ttdT -P1 tcaaataaccctcccggagaggctcacccctctcctttttcgcaggcataacacg gtgtaggctggagctgcttc Rv-ttdT -P2 gctgcgtaaaactattgggtgccggagagcaatttccggcaccgtcctcac catatgaatatcctccttag Fw-citT -P1 aggtgggcttacctggcgggcgtgatgatttattcagcgtttggcgaacgta gtgtaggctggagctgcttc Rv-citT -P2 taattatttaagcacttgataaatttggaaatattaattttcggagaacccgt catatgaatatcctccttag Fw-sdhAB -P1 cactggtggtttacgtgatttatggattcgttgtggtgtggggtgtgtgagtgtaggctggagctgcttc Rv-sdhAB -P2 tccggcactggttgcctgatgcgacgcttgcgcgtcttatcaggcctacggtcatatgaatatcctccttag Rv-dcuC -P2 gctacaattaacataaccttaatagacccaacataaagaataatctgaatagccatatgaatatcctccttag Fw-dcuC -pro37 tgcccaataaattggcgatgaatgctgattaaaatcaagaaaaactgccataaaaatgacatataccacatgga PCR amplify FRT-flanked resistance gene (pKD3, pKD4) FRT FRT H1 Antibiotic resistance +1 P1 (cat , kan ) H2 P2 H1 H2 Gene A Gene B Transform strain expressing λ Red recombinase (pKD46) H1 H2 Gene A Gene B Gene C FRT FRT H1 Antibiotic resistance promoter Select antibiotic-resistant transformants (cat , kan ) H2 P1 pr FRT FRT H1 H2 Antibiotic resistance Gene A Gene C (cat , kan ) FRT FRT Antibiotic resistance Gene A Gene B (cat , kan ) promoter Eliminate resistance cassette using a FLT expression plasmid (pCP20) FRT FRT Gene A Gene B Gene A Gene C promoter Figure 3.2: Gene knock out strategy (Datsenko & Wanner, 2000) (left) and Gene knock in strategy (right) 134 Succinate Transport 4 8 , t I n e e n H n e i c i l c l l l m n i m n i c a a c g a i t i t r B a p s p s i r i f s m s m e I e e A A r t r H t m I o a c H B S - m I u n I m a I o r K i d B t t - o n c I i i I r t - r I H s t c d s u n p t e i n d R H o p e p r n o i b c i p l + l n i 1 r n c c a i 6 t t o i p s 9 c S t i 2 a B s n m p y s g e i o R i A i r r Chl: 4030 4030 bp Chl: Kan: 4493 bp 4493 Kan: t r L r e a e t e g e e c i t o t i i u L v s s l m s i o l T T r e B R R P e F F u l p l a d r l u o t B Z c S i a 9 c 9 C n n 8 a 1 p l e 6 M 7 h , , I p I I H I m e d a m c r n i a n o l a B H t h s i c / e s e c e n r n i a e c t c y s i n s m a t a e 7 r s n 8 i l a s 4 o e 1 K c r i , I n I I n e e d o n h i i t n p c i a y H m g i a 4 m r L 7 a P o 8 l n e 4 h a t 4 4 i 1 7 K C 7 s P P , 8 : : 8 I I 4 g e e 4 3 4 I t t e 1 i i n 1 t e d i D D i t s s , i , n I e s K i K I m I t s I i i g g I e p p e I r H t T t s n n d i i T d i i P s n R s n i T R 4 i m m F i i T F H T R l r P r H o F R R P P e c l t F i F i a I l l n s I r 3 I o o e u g d c c t h i i t n t n a n p i i c n n D n o n N u e e i H m 7 4 m e e t o i R d h h 3 i - a 6 0 r c c t I i o r p m p K C P 2 8 n r r P a o t g R l 3 2 P a m m p g e e e s t a i o t h p : : a a c c r i s e c L n r r f i c 4 3 / s n n / o o o I s R E i l l a I a D D e t t g t I e h h 4 r n K K a s s n d i i i c c i / p p / g c n s s i i e e e m y e t e i L D i H r r n n r i i - m s I P c c a e y y T R t K n e i t o i R a m s m c s F a a p K T E l n n T 3 a R a a r R P F K K u F l t 4 e l 3 t a 3 a i , r I a P s P r N r u a R t t u 4 e e g e t t o t 4 a 4 e t , i n i a I c i o , 8 b s N s I E N R , m m I g i R g o r S o n o H n c r i i P B c E P m m m R E i i a r r B P P n o i t a g i m L P 2 F P G e t n i 4 r i s 8 c y , e g I n m t i H o I m m e i o H r a n P B m t n a n m o S i r B 4 e t B e , - c t o I m I 4 i 4 r R o g 8 t R r R 8 R S a p , S s o r o 1 m , I r r B I C b f e c c e B o p P e t H r I t P R H E R E 5 R 4 o p e 4 o m H 8 t , m i , I m a 3 m I a s m + o 5 R B o a R r B r g o o B P p n c - i c I 5 E E m R i K r o 6 6 c P R E 1 V i k r i o V 4 p P R T i r o Figure 3.3: Construction of promoter delivery system for gene overexpression (De Mey et al. , 2007) 135 Chapter 3 After electroporation, cells were added to 1 ml LB media incubated 1 h at 37 °C, and finally spread onto LB-agar containing 25 mg/l of chloramphenicol or 50 mg/l of kanamycin to select antibiotic resistant transformants. The selected mutants were verified by PCR with primers upstream and downstream of the modified region and were grown in LB-agar at 42 °C for the loss of the helper plasmid. The mutants were tested for ampicillin sensitivity Linear double-stranded-DNA. The linear ds-DNA amplicons were obtained by PCR using pKD3, pKD4 and their derivates as template. The primers used had a part of the sequence complementary to the template and another part complementary to the side on the chromosomal DNA where the recombination has to take place. For the KO, the region of homology was designed 50-nt upstream and 50-nt downstream of the start and stop codon of the gene of interest. For the KI, the transcriptional starting point (+1) had to be respected. PCR products were PCR-purified, digested with Dpn I, repurified from an agarose gel, and suspended in elution buffer (5 mM Tris, pH 8.0). Table 3.3: Overview of the control primers used to check knock outs and knock ins Primer name sequence Fw-dctA -out tgcccgttgtcggtcagtc Rv-dctA -out atcttcgaagagagttacctgg Fw-yfbS -out aggagatggactacttcatgga Rv-yfbS -out tttggtcctccacagtctgg Fw-sstT -out agcatttttcgcttcccgaag Rv-sstT -out gggtgattcacaacttctggg Fw-ydjN -out ataccttgcagatccagtttcatcg Rv-ydjN -out gtatgaggttttaagcttctcg Fw-ybhI -out aggtcaggatgccacgacg Rv-ybhI-out agatatctgcgcaccgtg Fw-yhjE -out ttcatagaagaaaaatcactggc Rv-yhjE -out attgccggatgtctgacatcc Fw-ydjN -out ataccttgcagatccagtttcatcg Rv-ydjN -out gtatgaggttttaagcttctcg Fw-ybhI -out aggtcaggatgccacgacg Rv-ybhI -out agatatctgcgcaccgtg Fw-ydfJ -out Aatgaacaattcttggagccagg Rv-ydfJ -out tatctgttggggagtttttttgg Fw-ttdT -out tgattgtctctattgatacccac Rv-ttdT -out cggaatggtgcggtttaaagc Fw-citT -out gcagaagagaagggaagcagag Rv-citT -out ttatctccggcagttcgacag Fw-sdhAB -out catgtggcaggtgttgaccg Rv-sdhAB -out2 tcctgcatttctttgttacgcct Fw-DcuC -out cgagctacacccacaataacc Rv-DcuC -out tcaggctgttgccaggtttgt Elimination of the antibiotic resistance gene. The selected mutants (chloramphenicol or kanamycin resistant) were transformed with pCP20 plasmid, which is an ampicillin and 136 Succinate Transport chloramphenicol resistant plasmid that shows temperature-sensitive replication and thermal induction of FLP synthesis. The ampicillin-resistant transformants were selected at 30 °C, after which a few were colony purified in LB at 42 °C and then tested for loss of all antibiotic resistance and of the FLP helper plasmid. The gene knock outs and knock ins are checked with control primers. These primers are given in Table 3.1. RT-qPCR mRNA was extracted with the RNeasy kit (Qiagen,Venlo, The Netherlands). RNA quality was checked with a nanodrop ND-1000 spectrophotometer (Nanodrop technologies, Wilmingto, USA). The ratios 260:280 (nm) and 260:230 (nm) were between 1.8 and 2. cDNA was synthesised with random primers with the RevertAid™ H minus first strand cDNA synthesis kit (Fermentas, St. Leon-Rot, Germany). Finally, the expression of DcuC with different promoters was analysed with the qPCR Core kit for SYBR Green (Eurogentec, Seraing, Belgium) with a Bio-Rad iCycler (Bio-rad, Eke, Belgium). The primers used for gene quantification are: Fw-dcuC -qPCR: agccacttccagaccagaga and Rv-dcuC -qPCR: ccgatcatcggtgtactgat. 3.2.7 Calculation methods The yields were calculated on at least ten data points within the exponential phase of the batch fermentation. Each sample resulted in a substrate concentration S and a product concentration P. By plotting P in function of S, a regression line can be fitted. The slope of this regression is the yield of product P on substrate S. The dilution effect due to base and antifoam addition was assumed as negligible for these calculations because the total amount of fluid added to the reactor was less than 1% of the total volume of the reactor. On each of the slopes a standard deviation was calculated which was used in the error propagation to obtain an error on the averaged yields of the duplicate fermentations. The maximal growth rate was obtained by plotting the Neperian logarithm of the optical density in function of time. A linear model was fitted on this plot. The slope of this regression is the maximal growth rate µ max . Maximal specific production rates were calculated by first determining the biomass specific yields of all products P and substrates S. These calculations are similar to the yield calculations described above (with the same assumptions concerning dilution effects). A regression curve was fitted on the substrate and product concentrations in function of the biomass concentration plots. The obtained slope was multiplied by the maximal growth rate to obtain the maximal specific production rates and substrate uptake rates. On each of the slopes and on the µ max a standard deviation was calculated which was used in the error propagation to obtain an error on the averaged specific production rates and substrate uptake rates of the duplicate fermentations. 137 Chapter 3 3.3 Results and discussion The choice of promoter strength, when using an artificial promoter is crucial when expressing membrane proteins. Excessive expression leads to the formation of inclusion bodies, which results in stress-responses. Therefore 3 different promoter strengths were selected from a promoter library (De Mey et al. , 2007). The so called P8, P37, and P55 promoters are ranked from weak to strong, respectively (with relative expression to the wild type of 11.4, 24 and 90.2). Increased acetate production rates were found when evaluating the strain with dcuC constitutively expressed with promoter P55 in comparison with the other two promoters. Moreover, inclusion bodies were observed at the cellular poles of the dcuC-P55 strain (data not shown). This has led to the conclusion that P55 is too strong for the expression of the membrane protein and a weaker promoter, e.g. P37 should be used. The effect of the transporters was tested in an sdhAB KO strain, which produced already some succinate (Figure 3.4). Neither an enhanced production rate nor a higher yield could be observed in strains in which solely DctA or DcuC activity was altered. The combination of altered import ( ∆dctA ) and export ( ∆FNR-pro37-dcuC ) increased the specific production rate with about 55% and the yield with approximately 53% (Figure 3.4). 0.20 0.20 A B 0.18 0.18 0.16 0.16 0.14 0.14 0.12 0.12 0.10 0.10 0.08 0.08 (c-mole/c-mole glucose) (c-mole/c-mole 0.06 biomass/h) (c-mole/c-mole 0.06 succinate succinate succinate Y 0.04 0.04 q 0.02 0.02 0.00 0.00 sdhAB sdhAB sdhABdctA sdhABdctA sdhAB dcuC sdhABdcuC sdhAB dctA dcuC sdhAB dctA dcuC Figure 3.4: Different succinate yields (A) and specific succinate production rates (B) of E. coli MG1655 strains with modified C4-dicarboxylic acid transport. sdhAB: KO of sdhAB , dcuC: overexpression of dcuC with promoter 37, dctA: KO of dctA . 138 Succinate Transport Further investigation of the dctA single knock out showed that this strain grows faster on succinate than the wild type strain (MG1655) (Figure 3.5, p value = 0.0098). On glucose, pyruvate, and the mixture of glucose and pyruvate the strains seemed to grow equally fast. Both strains seem to grow differently on the glucose – succinate mixture but this can only be corroborated with 90% confidence, a t-test with 95% confidence implies that both strains grow equally fast on this mixture. Only slight growth could be detected on fumarate and no growth could be detected on malate, which is similar to the results obtained by Davies et al . (1999). Average growth rate 0.6 p value = 0.0807 p value = 0.4215 0.5 p value = 0.1841 0.4 ) -1 (h 0.3 max max µ 0.2 p value = 0.8605 p value = 0.0098 0.1 0.0 WT dctA WT dctA WT dctA WT dctA WT dctA glucose glucose glucose succinate pyruvate + + pyruvate succinate Figure 3.5: Average growth rate of the wild type MG1655 strain and the dctA knock out strain on different carbon sources. The total amount of carbon is the same in each of the experiments (set to 0.5 c- mole/l). The p-value was obtained from a Student t-test with 95% confidence interval (n = 6). These results indicate that there are multiple succinate importers present in E. coli . In order to screen for these importers, some bio-informatics tools were used. As mentioned in the introduction, there are multiple transporter families classified in the transport classification database with succinate importers as members. Representatives of these families were used to PSI-BLAST (Altschul et al. , 1997) against the E. coli MG1655 genome, a type of BLAST that is much more sensitive to weak but biologically relevant sequence similarities. The DASS family search resulted in the identification of three possible E. coli genes (E- values < 6.00E-06): yfbS (putative membrane protein probably a transporter), citT (a citrate: succinate antiporter), and ttdT (a tartrate:succinate antiporters. The E. coli DAACS representative was DctA , which was used to BLAST for similar sequences in E. coli . This resulted in 2 potential importer genes: sstT and ydjN (E-values < 9e-15) . In literature next to 139 Chapter 3 these two genes ybhI , ttdT, and citT are also mentioned (Davies et al. , 1999). Finally, a potential succinate transporter in the major facilitator superfamily was found in Pseudomonas putida. In total there are 66 members in E. coli of this family, mostly sugar transporters (Pao et al. , 1998) and mostly not annotated. A BLAST search with the PcaT transporter of P. putida resulted in kgtP , proP , shiA , yhjE, and ydfJ annotated as a alpha-ketoglutarate transporter, a proline/glycine betaine transporter, a shikimate transporter, a predicted transporter, and a predicted transporter (E-values > 4e-42), respectively. Based on these results, sstT , ydjN , ttdT , citT, ybhI, yfbS, yhjE and ydfJ were knocked out in combination with dctA. These mutant strains were grown on succinic acid (Table 3.4). Table 3.4: Results of aerobic growth of different potential transporter gene knock outs on succinate. + = growth, - = no growth, ++ = strong growth Strain Growth ∆dct A∆yfb S + ∆dct A∆sstT + ∆dct A∆ydjN - ∆dct A∆ybh I - ∆dct A∆yhj E ++ ∆dct A∆ydf J + ∆dct A∆ttdT + ∆dct A∆cit T + Because a knock out of either ybhI or ydjN combined with dctA resulted in a phenotype that did not allow growth on succinate, both of these gene knock outs were combined with the knock-outs described in Figure 3.4. Results of batch fermentations of these strains are shown in Figure 3.6. Succinate yield reduced when either ybhI or ydjN were knocked out (Figure 3.6 A), but the specific production rate increased (Figure 3.6 B). Peculiar is the fact that at the point of glucose depletion, none of the acids, produced during the fermentation could be consumed anymore by the strains with ybhI and ydjN knocked out. This fact might indicate another function of these genes than import activity. They might operate as chaperones for the integration of certain transport proteins, or they could code for a proton pump, which is needed to counter influx of protons during growth on acids. However, when sdhAB was knocked out with or without either of these two putative genes, growth on acids such as lactate, pyruvate and acetate was not different (data not shown). Reduced or inhibited growth on acids for strains with ybhI and ydjN knocked out thus only seems to occur after growth on glucose. Functional interaction profiling by means of STRING 8 (Jensen et al. , 2009) showed coexpression of YdjN with the genes cysJ and cysI which form the final step in the sulphur assimilation and cysP , a sulphate uptake system (medium confidence of 0.4 was used). YdjN showed strong cooccurrence with SstT, a transporter with high similarity to DctA. The main function of SstT is described as a serine/sodium symporter (Kim et al. , 2002). These three 140 Succinate Transport correlations might indicate that YdjN is involved in the cysteine biosynthesis process. The fact that a sstT knock out has no influence on succinate grown cells indicates that both proteins do not need to interact to be functional. YdjN might thus be an independent transport molecule. YbhI functional interaction profiling results in the identification of 8 putative membrane proteins, which are coexpressed with ybhI (high confidence of 0.7 was used). No clear indication of a correlation with a certain cellular process could be obtained with either high or medium confidence scores in this case. 0.20 0.30 AB 0.18 0.25 0.16 0.14 0.20 0.12 0.10 0.15 0.08 (c-mole/c-mole glucose) (c-mole/c-mole (c-mole/c-mole biomass/h) (c-mole/c-mole 0.10 0.06 succinate succinate Y 0.04 q 0.05 0.02 0.00 0.00 sdhAB sdhAB sdhABdctA sdhABdctA sdhABdcuC sdhABdcuC sdhAB dcuCybhI sdhABdcuC ybhI sdhAB dcuC dctA sdhAB dctA dcuC sdhABdcuCydjN sdhAB dcuCydjN sdhAB dctA dcuCybhI sdhAB dcuC dctA ybhI sdhAB dcuCydjN dctA sdhAB dctA dcuCydjN sdhAB dctA dcuC ybhIydjN sdhABdctA dcuCybhI ydjN Figure 3.6: Different succinate yields (A) and specific succinate production rates (B) of E. coli MG1655 strains with modified C4-dicarboxylic acid transport. sdhAB: KO of sdhAB , dcuC: overexpression of dcuC with promoter 37, dctA: KO of dctA ., ybhI: KO of ybhI and ydjN: KO of ydjN . Both putative proteins, YbhI and YdjN, have a high occurrence in the Enterobacteriaceae family. YdjN is also identified based on a sequence analysis in Bacillus sp. and Staphylococcus sp. The natural succinate producing strains Mannheimia succiniciproducens and Actinobacillus succinogenes have a gene that has a high similarity with ybhI (E-value = 2 x 10 -72 and identity of 32% and E-value = 0 and identity of 50%, respectively), but no highly similar gene could be detected with ydjN . 141 Chapter 3 Increased specific production rates are a direct consequence of increased growth rates and decreased biomass yields. When both dcuC is overexpressed and dctA is knocked out, the growth rate decreased significantly (Figure 3.7 A), while this is not the case for single modifications in either the exporter or the importer. Additional mutations in ybhI and ydjN restored the growth rate approximately to the base strain ∆sdhAB value. Biomass yield decreased about 25 to 45% in comparison with the base strain yield for all strains except ∆sdhAB ∆dctA and ∆sdhAB ∆FNR-pro37-dcuC (Figure 3.7 B). The strains with the putative transporter genes knocked out showed a slightly increased biomass yield in comparison with ∆sdhAB ∆dctA ∆FNR-pro37-dcuC , which affected succinate yield as can be seen in Figure 3.6 A. The addition of ∆ydjN to strain ∆sdhAB ∆dctA ∆FNR-pro37-dcuC showed the highest increase in biomass yield. This increase was not compensated by an extra increase in growth rate and thus reduced specific succinate production rate (Figure 3.6 B). 0.7 0.7 AB 0.6 0.6 0.5 0.5 ) 0.4 0.4 -1 (h max µ 0.3 0.3 (c-mole/c-mole glucose) (c-mole/c-mole 0.2 0.2 biomass Y 0.1 0.1 0.0 0.0 sdhAB sdhAB sdhAB dctA sdhAB dctA sdhAB dcuC sdhAB dcuC sdhAB dcuC ybhI sdhAB dcuC ybhI sdhAB dctA dctA sdhABdcuC dctA sdhABdcuC sdhAB dcuC ydjN sdhAB dcuC ydjN sdhAB dctA dctA sdhABdcuC ybhI dctA sdhABdcuC ybhI sdhAB dctA dctA sdhABdcuC ydjN dctA sdhABdcuC ydjN sdhAB dctA dctA sdhABdcuC ybhI ydjN dctA sdhABdcuC ybhI ydjN Figure 3.7 Different maximal growth rates (A) and biomass yields (B) of E. coli MG1655 strains with modified C4-dicarboxylic acid transport. The same abbreviations were used as in Figure 3.6 Anaerobically the effect of the dcuC overexpression/ dctA knock out system is not as distinct as under aerobic conditions (Figure 3.8). Only a slight increase in succinate yield and specific 142 Succinate Transport production rate was detected when DcuC was overexpressed. When dctA was additionally knocked out, the increase in yield and rate disappeared. The expression of dctA is mediated by the DcuRS, ArcAB and the CRP system (Davies et al. , 1999). Abundant glucose and/or succinate in the environment will activate the gene, while absence of oxygen represses gene expression. In the case of a batch culture in which high glucose and succinate concentrations are present (which is the case for a succinate producing strain), the dctA gene will thus be activated. Anaerobic conditions in the reactor will lead to a reduced expression. 0.20 0.40 AB 0.18 0.35 0.16 0.30 0.14 0.12 0.25 0.10 0.20 0.08 (c-mole/c-mole glucose) (c-mole/c-mole 0.15 0.06 biomass/h) (c-mole/c-mole succinate 0.10 Y 0.04 succinate q 0.02 0.05 0.00 0.00 sdhAB sdhAB sdhABdctA sdhABdctA sdhABdcuC sdhAB dcuC sdhAB dcuC dctA sdhAB dctA dcuC sdhABdcuC dctA ybhIydjN sdhABdctA dcuC ybhI ydjN Figure 3.8: Different yields (A) and specific succinate production rates (B) of E. coli MG1655 strains with modified C4-dicarboxylic acid transport grown under anaerobic conditions The same abbreviations were used as in Figure 3.6 DcuC is normally expressed under anaerobic conditions (Zientz et al. , 1999), which might be the reason for the small difference between the dcuC overexpression mutant and the base strain. The addition of a dctA gene deletion, would logically lead to an increase in rate and yield in both the strains with and without dcuC . However, in this case an adverse effect was observed. The question rises here if DctA might have a different function in aerobic and anaerobic conditions. In the anaerobic case it might be employed as an export system, rather 143 Chapter 3 than an import system for succinate. In addition the two putative genes ydjN and ybhI did not show an observable effect on the yield or specific production rate. This indicates that they function mainly under aerobic conditions, not anaerobic. Different transporters might thus play a role in anaerobic succinate transport. Two C4-dicarboxylic acid systems that were not considered up to now, because of their clear link to anaerobic metabolism, are DcuA and DcuB. Both catalyse C4-dicarboxylate antiport (Six et al. , 1994). They could on the other hand take over functions for C4-dicarboxylate uptake when DctA is knocked out (Janausch et al. , 2001). Yet, their activity requires fumarate as well as succinate, because of their antiport properties. The mutations do not affect the biomass yield nor the growth rate as distinct as under aerobic conditions. Enhanced transport for production purposes does not only increase yields and/or specific production rates, but also changes the internal carbon fluxes and byproduct formation. Figure 3.9 demonstrates the byproduct yields for the different strains tested under aerobic (A) and anaerobic (B) conditions. In both cases high yields of acetate were observed. This is the primary byproduct for every E. coli strain without a modified acetate pathway (Clark, 1989). The total amount of byproduct decreased under aerobic conditions with the introduction of DcuC. This decrease was not maintained when dctA was additionally knocked out. In that case pyruvate was produced in significant amounts. The putative succinate transporter gene knock outs ∆ybhI and ∆ydjN reduced pyruvate production, which indicates either an increased carbon flux through PEP carboxylase or citrate synthase. Because there is no direct increase of succinate yield in those strains, most probable the latter route towards the Krebs cycle is enhanced, which is actually a combination between PEP carboxylase and citrate synthase. The reason for the enhanced PEP carboxylase and citrate synthase activity could be a decrease in the concentration of the Krebs cycle intermediates that allosterically inhibit the enzymes. For instance, malate, succinate, fumarate and, oxaloacetate inhibit PEP carboxylase; alfa- ketoglutarate and NADH inhibit citrate synthase (Pereira et al. , 1994; Yano & Izui, 1997). The fact that with the large increase in pyruvate no increased lactate production was observed indicates that lactate dehydrogenase is outcompeted for NADH to reduce pyruvate. Under anaerobic conditions, the main byproduct next to acetate is pyruvate in the ∆sdhAB base strain. A change in C4-dicarboxylic acid transport seems to change the internal carbon fluxes. However, no additional metabolites were detected that can explain the reduction in pyruvate yield unless it is converted into formic acid. The yield of formic acid was not included in Figure 3.9 due to the dynamic pattern in its concentration during the fermentation. This metabolite was measured with a slightly different HPLC protocol. The column temperature was set at 30 °C instead of 65 °C to obtain a better separation between formic acid and fumaric acid. This allowed a more accurate quantification. 144 Succinate Transport Figure 3.9: Byproduct yields under aerobic (A) and anaerobic conditions (B) of E. coli MG1655 strains with modified C4-dicarboxylic acid transport. The same abbreviations were used as in Figure 3.6 All anaerobic fermentations show a similar pattern, which does not occur in the wild type E. coli MG1655 strain (Figure 3.10). First formic acid is formed to be consumed again during the mid exponential phase. The moment the glucose concentration drops below about 4 g/l, formic acid accumulations occurs again. Pyruvate – formate lyase is the main enzyme responsible for the formation of formic acid in E. coli . This reaction yields one formate and one acetyl-CoA molecule from one pyruvate and is coded by the genes pflB and tdcE . The enzyme activity is controlled by an activating enzyme which is oxygen sensitive (Heßlinger et al. , 1998; Knappe & Sawers, 1990; Rödel et al. , 1988). If there were changes in the activity of either the activating enzyme or pyruvate – formate lyase itself, then pyruvate and acetate should show similar patterns to formic acid, which is not the case. Furthermore, pyruvate formation is reduced significantly when C4-dicarboxylic acid transport is altered. 145 Chapter 3 WT ∆sdhAB 12 7 18 1.0 16 10 6 14 0.8 5 8 12 0.6 4 10 6 8 3 0.4 formate (g/l) formate formate (g/l) formate glucose (g/l) glucose(g/l) 4 6 2 4 2 0.2 1 2 0 0 0 0.0 0 1 2 3 4 5 6 7 8 0 2 4 6 8 10 t (h) t (h) ∆sdhAB ∆FNR-pro37- dcuC ∆sdhAB ∆dctA 18 1.0 18 1.0 16 16 14 0.8 14 0.8 12 12 0.6 0.6 10 10 8 8 0.4 0.4 glucose (g/l) glucose (g/l) (g/l) formate 6 (g/l) formate 6 4 0.2 4 0.2 2 2 0 0.0 0 0.0 0 2 4 6 8 10 12 0 2 4 6 8 10 t(h) t (h) ∆sdhAB ∆dctA ∆ybhI ∆ydjN ∆FNR-pro37- dcuC ∆sdhAB ∆dctA ∆FNR-pro37- dcuC 18 1.0 18 1.0 16 16 14 0.8 14 0.8 12 12 0.6 0.6 10 10 8 8 0.4 0.4 glucose (g/l) formate (g/l) formate formate (g/l) formate 6 glucose (g/l) 6 4 0.2 4 0.2 2 2 0 0.0 0 0.0 0 2 4 6 8 10 12 0 2 4 6 8 10 t (h) t (h) formate glucose Figure 3.10: Evolution of formic acid concentration during the fermentation of the strains tested under anaerobic conditions. The same abbreviations were used as in Figure 3.6 The concentration changes thus have to be assigned to formate degradation reactions. The enzymes responsible for these reactions are formate dehydrogenase and formate hydrogen lyase. The former enzyme yields NADH, the latter yields hydrogen. The fact that other redox related reactions, such as ethanol and lactate formation, were not influenced during the fermentation, they do not show similar patterns (pattern not shown), might indicate that formate hydrogen lyase is responsible for the consumption of formic acid mid exponential phase. The hydrogen formed in this reaction immediately flushes away with the off-gas and will not influence the redox state of the cell. The genetic regulation of formate hydrogen lyase 146 Succinate Transport does, however, not supply an explanation for the observed dynamics. This operon is activated by Fhl, FNR, IHF and ModE. NarL is a transcriptional repressor, active in the presence of nitrate or nitrite in the environment (Keseler et al., 2009). In order to fully grasp these dynamics further investigation of the transcriptome and proteome will be necessary. 147 Chapter 3 3.4 Conclusions Changing the transport systems of a certain biochemical intermediates can affect the production rate and yield. In this work we have shown that by overexpressing export and knocking out importers we could increase the succinate yield and specific production rates more than 50 %. However this revealed alternative transporters under aerobic conditions. A screening of 8 different genes has led to the identification of two potential C4-dicarboxylic acid transporters, YbhI and YdjN. Mutations in both genes showed an increase in specific succinate rate but did not increase succinate yield. These results indicate that both of the identified proteins are linked to succinate transport. In addition both knock outs show only an observable phenotype under aerobic conditions, pointing at a regulatory mechanism that stimulates or represses their activity in an aerobic or anaerobic environment, respectively. The exact mode of action however has yet to be uncovered. Changing the two main succinate transporters, DcuC and DctA, resulted in a decreased biomass yield and growth rate. Additional modifications in YbhI and YdjN resulted in an increase in biomass yield and more importantly in an increased growth rate. The former led to reduced succinate yields, the latter led to enhanced specific production rates. The reason for these phenotypes is unclear. Reduced feedback inhibition by succinate could result in increased growth rates and increases in flux towards biomass. The results also show various changes in byproduct formation. More particularly under anaerobic conditions, the changes in C4-dicarboxylic acid transport influence the pyruvate yield significantly. In comparison with the base strain, no pyruvate could be detected anymore when this type of transport was altered. This effect was not observed as such under aerobic conditions, where pyruvate was formed only when DctA was knocked out and DcuC was overexpressed. This effect was reduced by additionally knocking out YbhI or YdjN. Internal fluxes are thus influenced significantly by these different C4 dicarboxylic acid transporters. In the case of formic acid, the dynamics of the extracellular concentrations is somewhat unexpected. As far as known, there are no previous reports that show fluctuating formic acid concentrations during anaerobic E. coli batch cultures linked to a succinate dehydrogenase knock out. The timescale in which the fluctuation occurs allows changes in the transcriptome, proteome and metabolome. The presented data indicated that formate hydrogen lyase might be responsible for the dynamic formate concentration because during the formate assimilation phase, none of the redox dependent reactions (and metabolites) were influenced. This enzyme complex originates from the hycB and fdhF operons that show multiple transcription factor binding sites. During the exponential phase of the culture, expression could be modulated/activated by one of the transcription factors leading to the alteration of enzyme activities in the cell. 148 4 Chapter 4 Synergetic effect of the transcriptional regulators ArcA and IclR on the glyoxylate shunt Chapter 4 CONTENTS 4.1 Introduction 151 4.2 Materials and methods 153 4.3 Results and discussion 155 4.3.1 Product yields of the four strains in batch and chemostat cultures 155 4.3.2 Malate synthase and isocitrate lyase 160 4.3.3 Glycogen and threhalose measurements 162 4.4 Conclusions 166 150 Synergetic effect of the transcriptional regulators ArcA and IclR 4.1 Introduction Each revolution of the Krebs cycle leads to the oxidation of citrate to oxaloacetate with the formation of two carbon dioxide molecules. In order to regenerate citrate, oxaloacetate condenses with acetyl-CoA. When acetate is the main carbon source, this would mean that no carbon can be steered to biosynthetic pathways. Nature solved this problem with the introduction of the glyoxylate route. This route links the TCA cycle with the anaplerotic reactions and gluconeogenesis through isocitrate lyase and malate synthase (Kornberg, 1966; Sunnarborg et al. , 1990). Phylogenetic analysis has shown that the route is quite common in all domains of living organisms and shows high protein structure conservation (Kim et al. , 2006; Serrano & Bonete, 2001). Escherichia coli ’s operon organisation however is quite unique for the Enterobacteriaceae . The aceBAK operon consists of 3 genes, aceB , aceA and aceK . AceB is malate synthase, AceA, isocitrate lyase and AceK is isocitrate dehydrogenase kinase. The latter has a regulatory function. Phosphorylation of isocitrate dehydrogenase reduces the enzymes activity and thus reduces carbon flux via the oxidative branch of the TCA cycle (Jensen et al. , 2009; Rittinger et al. , 1996). Apart from an interaction on the proteome level, there are several transcriptional regulators that control the glyoxylate route. Figure 4.1 shows the organisation of the aceBAK operon with its different regulators. Figure 4.1: Organisation of the aceBAK operon with the different transcriptional regulators. ArcA, IclR and CRP are transcriptional repressors, FruR and IHF are activators (Keseler et al. , 2009). There are about five transcriptional regulators that bind the aceBAK regulon; IclR, CRP and ArcA repress transcription, IHF and Cra enhance expression. Depending on the environment, different regulators will affect transcription e.g. ArcA regulates the aerobic/anaerobic metabolism, and CRP and Cra regulate carbohydrate metabolism. Generally ArcA is considered to be active under anaerobic conditions. Transcriptome analysis showed that the effect of an arcA knock out in fully aerobic conditions is negligible (Shalel-Levanon et al. , 2005a, b), however 13-C flux analysis showed that an ArcA knock out increased the flux through the TCA cycle in an aerobic batch culture (Perrenoud & Sauer, 2005), in chemostat it does not change the flux significantly. These differences occur in a similar way for crp knock out mutants. In batch cultures this knock out seems to have minor effects on the fluxes and transcription, in chemostat CRP is described as the most dominant regulator (Nanchen et al. , 2008; Zhang et al. , 2005). The third repressor, IclR, should show more activity in glucose abundant environments in comparison with glucose deprived environments because it has to bind either to pyruvate or PEP to be active and intracellular metabolite concentrations in chemostat cultures are very low (Buchholz et al. , 2001; Yamamoto & Ishihama, 2003). 151 Chapter 4 Cra is a dual transcriptional regulator that plays an important role in the regulation of E. coli ’s carbohydrate metabolism (Ramseier, 1996) . It is considered to be the major transcription inducer of the aceBAK operon (Ramseier et al., 1993; Ryu et al. , 1995). The activity of this transcriptional regulator is modulated by fructose-1-phosphate and fructose-1,6-bisphosphate in micromolar and millimolar concentrations, respectively (Ramseier, 1996; Ramseier et al. , 1993). Under glucose abundant conditions a cra knock out strain does not seem to have a distinct phenotype, while in glucose limited conditions such a gene deletion reduces the flux through the glyoxylate shunt (Nanchen et al. , 2008; Perrenoud & Sauer, 2005). The decreased activity of Cra in a batch culture may be the effect of an increased intracellular fructose-1- phosphate and fructose-1,6-bisphosphate concentration, which are generally low in a steady state culture. An interesting observation in this regulatory network is the antagonistic effect of CRP and Cra on the glyoxylate route in glucose limited cultures. While Cra activates the route, CRP represses it. Furthermore Cra affects also isocitrate dehydrogenase expression. The activity of this enzyme is however reduced by isocitrate dehydrogenase kinase whose expression is also enhanced by Cra (Sarkar et al. , 2008). The second inducer of the aceBAK operon is IHF or the Integration Host Factor. IHF has two different modes of operation. It induces gene expression when acetate is the primary carbon source, gene repression occurs under glucose abundant conditions. For gene induction, IHF interferes with IclR binding, which seems to be still active in the presence of acetate. An IHF knock out reduces glyoxylate route activity, an effect that disappears when iclR is knocked out as well. Under glucose abundant conditions (the gene repression mode of IHF) IHF does not show any effect on the operon. However, an iclR knock out still increases glyoxylate pathway activity (Resnik et al. , 1996). IHF is probably absent in repressing conditions or lacks activity. The intracellular IHF concentration in exponentially growing cells with glucose as carbon sources is quite low, while the concentration increases dramatically when the cells go into a stationary phase metabolism (Ditto et al. , 1994). In this phase the cell prepares for growth on alternative substrates, which might be formed during the initial exponential phase. For E. coli this is mainly acetate. The increase in IHF might thus play a role in the diauxic growth pattern of E. coli by activating the glyoxylate shunt. An overexpression of ihfA and ihfB , encoding for the two subunits of IHF, in exponentially grown cells slightly affects synthesis of rpoH dependent heat shock proteins (Nystrom, 1995). 152 Synergetic effect of the transcriptional regulators ArcA and IclR 4.2 Materials and methods 4.2.1 Strains The strains used in this chapter were preserved as described in paragraph 3.2.1 and are described in the appendix, section A.6 4.2.2 Media The cultivation media and preparation are described in paragraph 3.2.2. 4.2.3 Cultivation conditions Culture conditions and the inoculation protocol are the same as described in paragraph 3.2.3. 4.2.4 Sampling methodology All sampling methodologies are described in section 3.2.4. 4.2.5 Analytical methods 4.2.5.1 Biomass, glucose and organic acids Biomass, glucose and organic acid determination is the same as described in paragraph 3.2.5. 4.2.5.2 Glycogen and trehalose Glycogen and threhalose assays were based on the method described by Parrou and François (1997). The samples were taken as described in §4.2.4 and the pellets were stored at -20 °C. The sample was transferred with two times 250 µl of 0.25 M Na 2CO 3 (Merck, Leuven, Belgium), to a pre-weighed eppendorf tube. This tube was heated for 4 hours at 95 °C, after which it was cooled down on ice with 300 µl of 1 M acetic acid and 300 µl 0.2 M acetate buffer (pH 5.2) added (VWR, Leuven, Belgium). When cooled down, a short spin in the centrifuge collects the condensate from the cap. For the glycogen measurement 400 µl of the sample is mixed with 10 µl isoamylase solution (Sigma, Bornem, Belgium; 4.8 mg/ml, 6625000 U/mg, 1:19 dilution) and 20 µl amyloglucosidase (Fluka, Bornem, Belgium; 59.9 U/mg, 1 mg/ml solution) solution by vortexing and is incubated at 57 °C with shaking. Threhalose measurement was done by incubating 200 µl of sample with 15 µl threhalase solution (Sigma, Bornem, Belgium; 3.7 mg/ml, 1 U/mg, 1:3 dilution) at 37 °C with shaking. The glucose that is formed in these reactions was measured with the GOD-POD method in which 100 µl of sample was added to 400 µl GOD-POD reagents in a microtiter plate reader and measured at 520 nm. The GOD-POD reagents consists of 50 mg ABTS (Roche, Vilvoorde, Belgium), 1010 µl Glucose oxidase (Sigma, Bornem, Belgium; 950 U/ml) and 173 µl POD (Roche, Vilvoorde, Belgium; 20 mg in 500 µl phosphate buffer (0,2 M pH 4.5)) adjusted to 100 ml with 0.2 M phosphate buffer (pH 4.5). A glucose standard curve (Sigma, 153 Chapter 4 P.A) was constructed between 25 µM and 1000 µM (concentration of glucose [mM] = 1.1032 x Absorption unit - 0.0721, R 2 = 0.9997). To determine the glycogen and threhalose recovery, standards were used (0.1 g glycogen in 10 ml 0.25 M Na 2CO 3 and 0.05 g threhalose.2H 2O in 10 ml 0.25 M Na 2CO 3, respectively; Sigma, Bornem, Belgium). These standards were either incubated with the sample in a 1:1 ratio or incubated as such, to evaluate the enzyme activities and the matrix effects of the samples. The analysis was performed in a similar way as described above. 4 glycogen standards and 4 threhalose standards were measured in a 10 times and 5 times dilution with the GOD-POD method. The recovery of the standard was calculated as the amount of glucose measured with the enzyme assay divided by the weighed amount of glucose equivalents. The molar mass of one glycogen monomer was 162 gram per mole for this calculation. 4.2.5.3 Malate synthase and isocitrate lyase activity Samples for these measurements were kept at -80 °C until analysis. A predetermined amount of cells was lysed with the EasyLyse™ cell lysis kit (Epicentre Biotechnologies, Landgraaf, The Netherlands) and the cell extract was kept at 4 °C. Isocitrate lyase assay was adopted from Maloy et al. (1980). This colorimetric method is based on the reaction of glyoxylate, a product of isocitrate lyase, with phenylhydrazine. The reaction mixture is composed of 6 mM magnesium chloride, 4 mM phenylhydrazine, 12 mM L-cystein, 8 mM trisodium isocitrate in a 100 mM potassium phosphate buffer (pH 7). 985 µl of this mixture was added to 15 µl of enzyme extract. Enzyme activity was measured at 324 nm at 30 °C (Uvikom 922 spectrophotometer, BRS, Brussels, Belgium). The malate synthase assay was also adopted from Maloy et al. (1980). This is a colorimetric assay based on the reaction of coenzyme CoA with DTNB (5,5’-dithio-bis-(2-nitrobenzoate)). The reaction mixture of this assay is composed of 15 mM magnesium chloride, 0.2 mM acetyl-CoA, 10 mM glyoxylate and 0.2 mM DTNB in a 100 mM Tris buffer (pH 8). 900 µl of this mixture was added to 100 µl enzyme extract. The enzyme activity was measured at 412 nm at 30 °C. The activity was normalised on the amount of biomass used for the assay and are given in µmol per minute per gram biomass. 4.2.6 Genetic methods The genetic methods used for this chapter are the same as described in section 3.2.6. The primers used are summarised in the appendix, section A.5. 154 Synergetic effect of the transcriptional regulators ArcA and IclR 4.3 Results and discussion 4.3.1 Product yields of the four strains in batch and chemostat cultures The effect of arcA and iclR was both tested in batch cultures, in which the strain grows at maximal growth rate and in a chemostat culture with a growth rate around 0.1 h-1. The measured dilution rates for the chemostat cultures and the maximal growth rates are listed in Table 4.1. The maximal growth rate is dependent on the mutations that have been introduced in the strain. The differences in dilution rate are due to technical issues of the used setup, in which the pump rate is manually adjusted to obtain a certain dilution rate. We assumed that the small deviations in dilution rate of 0.02 h-1 from the average are negligible for this study. Table 4.1 The average maximal growth rates and dilution rates measured for the effluent and influent for the different strains. -1 -1 -1 Strain Dinfluent (h ) Deffluent (h ) µmax (h ) wild type 0.099 ± 0.001 0.100 ± 0.001 0.66 ± 0.02 ∆arcA 0.118 ± 0.001 0.120 ± 0.001 0.60 ± 0.01 ∆iclR 0.085 ± 0.001 0.090 ± 0.001 0.61 ± 0.02 ∆arcA -∆iclR 0.090 ± 0.001 0.093 ± 0.001 0.44 ± 0.03 The arcA and iclR single knock outs do not seem to have a very large effect on the maximal growth rate in comparison with the wild type strain. However the maximal growth rate of the double mutant seems to be affected more. The combined effect of arcA and iclR reduces the maximal growth rate about 38 % in comparison with the wild type strain. This might be caused by an increase in glyoxylate shunt activity and a decreased activity of isocitrate dehydrogenase, which is phosphorylated by isocitrate dehydrogenase kinase. The outer branch of the TCA cycle yields 2 NAD(P)H, with the loss of two carbon dioxide molecules. There should thus be a difference in carbon dioxide excretion rate between the different strains. In order to evaluate the metabolic effects of the regulators, extracellular metabolite samples were taken during the cultures and carbon dioxide excretion and oxygen uptake were continuously measured. The effect on the yields of both regulators is given in Figure 4.2, the average redox and carbon balance for each strain in chemostat and batch is given in Table 4.2. Table 4.2: The average carbon and redox balances for the chemostat and batch cultures Strain Chemostat cultures Batch cultures Carbon balance Redox balance Carbon balance Redox balance (%) (%) (%) (%) wild type 101 99 97 99 ∆arcA 94 93 96 98 ∆iclR 95 94 95 99 ∆arcA -∆iclR 101 118 99 107 155 Chapter 4 0.8 A 0.7 0.6 0.5 0.4 0.3 0.2 Yields (c-mole/c-moleYields glucose) 0.1 0.0 O2 CO2 lactate acetate biomass pyruvate fumarate succinate 0.7 B 0.6 0.5 0.4 0.3 0.2 yield yield (c-mole/c-mole glucose) 0.1 0.0 O2 CO2 lactate wild type biomass fumarate succinate ∆arcA ∆iclR ∆iclR - ∆arcA Figure 4.2: Batch (A) and chemostat (B) product yields of the wild type, ∆arcA , ∆iclR and ∆arcA-∆iclR mutant strains in c-mole per c-mole glucose. The oxygen yield is represented as a positive number although it is actually consumed during the fermentation. The values represented in this graph are the average of at least two separate fermentations, the error bars are the standard deviations calculated on the yields. 156 Synergetic effect of the transcriptional regulators ArcA and IclR The strains behave quite differently in batch and chemostat, which is to be expected because of the reduced growth rate in a chemostat culture. What is striking is the reduced carbon dioxide yield in both the iclR knock out strain and the arcA iclR knock out strain in both fermentation modes. This effect coincides with an increased biomass yield for arcA-iclR in both culture types, with even a near theoretical maximal biomass yield in batch mode, which is 0.65 c-mole/c-mole (Varma & Palsson, 1993a; Varma & Palsson, 1993b). The latter value should be used with caution, because it depends heavily on the P/O ratio of the cell, which is in turn a result of the activity of the NADH dehydrogenases, the electron transport chain ubiquinol oxidases and the F 0F1-ATPase activity (Figure 4.3 shows the effect of these enzymes on biomass yield). Escherichia coli has two different NADH dehydrogenases NDH- I, which is the product of the ndh gene and NDH-II, product of the nuo gene. It also has two different ubiquinol oxidases, bd type and bo type oxidases. Their activity differs. In the case of the NADH dehydrogenases, NDH-I has an H +/e - stoichiometry of 1.5. This means that for each electron going into the electron transport chain, 1.5 protons are translocated over the membrane. NDH-II does not translocate protons over the cytoplasm membrane, thus does not result in any proton motive force (Bogachev et al. , 1996). ) 0.76 e s o 0.74 c u l g 0.72 e l o 0.70 m 0.58 - 0.68 c / 0.60 e l 0.66 o 0.62 m 0.64 - 0.64 c ( 0.62 0.66 x a m 0.60 0.68 , S x 0.58 0.70 Y 100 80 0.72 % 80 b 60 0.74 o 60 ox id 40 as 40 e 20 -I 20 H ND 0 0 % Figure 4.3: Maximal theoretical biomass yield (c-mole/c-mole glucose as a function of the relative activity of NDH-I and bo oxidase activity, based on the calculations of Varma and Palsson (1993a). The shades of gray indicate the maximal theoretical yields for the different relative activities. 157 Chapter 4 Similar studies for the oxidases revealed that bd type oxidase has an H +/e - stoichiometry of 1 and the bo type oxidase a stoichiometry of 2 (Puustinen et al. , 1991). The conversion of the proton gradient, induced by the previously described enzymes, to ATP is the result of F 0F1- ATPase activity. The H +/ATP coupling ratio for Escherichia coli in aerobic as well as in anaerobic conditions has been measured to be 3 (Kashket, 1982; 1983). Hence, the maximal theoretical P/O ratio for E. coli can be reached when it solely relies on NDH-I and bo oxidase activity. These enzymes supply 7 protons for each oxidised NADH, which results in a P/O ratio of 2.33. This ratio correlates with a maximal theoretical biomass yield of 0.766 c- mole/c-mole (Varma & Palsson, 1993a). An overview of the theoretical yields in function of the relative activity of NDH-I and bo oxidase is shown in Figure 4.3. Table 4.3: Transcription factors involved in the regulation of nuo , ndh , cyo and cyd expression with +: transcriptional activation, -: repression and 0: neutral effect (Gama-Castro et al. , 2008; Keseler et al. , 2009). Regulator NADH dehydrogenases ubiquinol oxidases nuo ndh cyo cyd ArcA - + - + CRP 0 0 + 0 FIS + + 0 0 FNR - - - - FruR 0 0 0 + Fur 0 0 - 0 GadE 0 0 + 0 H-NS 0 0 0 - IHF - - 0 0 PdhR 0 - 0 0 NarL - 0 0 0 In a wild type Escherichia coli the relative activities of NDH I and cytochrome bo are 57 % and 32 % of the total NADH dehydrogenase and ubiquinol oxidases activity, respectively (Noguchi et al. , 2004). This results in a P/O ratio of 1.4, which correlates with a theoretical biomass yield of 0.65 c-mole/c-mole. The distribution of the electron fluxes between the oxidases and dehydrogenases is determined by a complex regulatory system which involves ArcA (Table 4.3). ArcA represses nuo and cyo and activates the expression of ndh and cyd . Increased relative activity of NDH I and cytochrome bo induces an increased P/O ratio and thus the maximal theoretical biomass yield (Figure 4.3). However an arcA knock out does not result in a significant change in biomass yield. The high biomass yielding metabolism requires in addition more activation of the glyoxylate shunt to redirect the carbon towards anabolic reactions, thus an additional iclR gene knock out. Hence the increased biomass yield for the arcA-iclR mutant strain (Figure 4.2). The 158 Synergetic effect of the transcriptional regulators ArcA and IclR glyoxylate pathway effect is also observable in the iclR mutant strain, which shows an increase in biomass yield. Figure 4.4: The direct regulatees of the ArcAB dual transcription regulation system for E. coli, the genes shown within the ellipse are also transcriptional regulators of which the regulates are indirectly regulated by ArcA (Keseler et al. , 2009). This effect is only detected in batch cultures, not in chemostat cultures. This might be the effect of the cAMP receptor protein, which is the major regulator in carbon limiting cultures (Nanchen et al. , 2008) and represses the expression of the aceBAK operon (Zhang et al. , 2005). The other known aceBAK regulators, Cra (formally known as FruR) and IHF are described to be minor effectors of the operon (Aviv et al. , 1994; Nanchen et al. , 2008). A 159 Chapter 4 previous report showed in a similar way an increased biomass yield between a ∆cra ∆edd and a ∆cra ∆edd ∆iclR strain; the former strain showed a 40 % biomass yield and the latter a 50 % biomass yield in batch cultures (Sarkar et al. , 2008). These data compared to the data shown in Figure 4.2 indicate that there is a stronger interaction between ArcA and IclR than between Cra and IclR. This interaction might be the result of any number of genes controlled by ArcA which are represented in. 4.3.2 Malate synthase and isocitrate lyase The activity of the glyoxylate route in the different cultures was evaluated through malate synthase and isocitrate lyase activity measurements (Figure 4.6). The malate synthase activity in E. coli is the result of two isoenzymes, malate synthase A and G (Falmagne & Wiame, 1973). Both genes are described as members of different operons, responsible for different pathways, the glyoxylate pathway, malate synthase A and the glycolate pathway, malate synthase G. The transcriptional regulation differs somewhat in both cases. While the glyoxylate gene aceB is repressed by CRP, the glycolate gene glcB is activated by CRP (Figure 4.1 and Figure 4.5) Figure 4.5: Organisation of the of the glc D and glc C operon with the different transcriptional regulators. ArcA and Fis are transcriptional repressors, IHF and CRP are activators. GlgC activates the glc D operon and represses the glc C operon (Keseler et al. , 2009) In both cases ArcA is a repressor of the operons (Pellicer et al. , 1999). In batch operation, the ∆arcA malate synthase activity is increased in comparison with the wild type strain in a chemostat culture. This increase might be the result of the reduced CRP activity in batch, which is the major regulator in chemostat cultures (Nanchen et al. , 2008). However the increased activity in an ∆arcA knock out in a continuous culture and the decreased activity in a batch culture of these strains are contra-intuitive. What would be expected is that in both the cases malate synthase and isocitrate lyase activity increase. The dual transcriptional regulator seems to have a different mode of operation in the different conditions. The results obtained from the chemostat culture have a certain logic, ArcA represses both glcB and aceB expression, thus without translational regulation, both enzyme activities should go up in the knock out strain. The results for the batch cultures do not have a clear reason. In these cultures indirect regulatory effects have to take place to explain the result. Looking at the isocitrate lyase results (Figure 4.6), where ArcA does not have a similar effect (although the activity is rather low), it might be that the indirect effects are found in the regulation of the glc operon. This operon is next to CRP and ArcA also regulated by GlcC, IHF and Fis (Figure 4.5). IHF has limited activity in exponentially growing cells (Aviv et al. , 1994), which leaves Fis and GlcC as candidates. 160 Synergetic effect of the transcriptional regulators ArcA and IclR Fis is a regulator that is abundant during exponential growth (Ali Azam et al. , 1999) and it negatively regulates the glcD operon through GlcC, which is an activator of the operon. ArcA might interact somehow with Fis and activate the operon through GlcC, rather than repress its expression (Keseler et al. , 2009). Relative malate synthase activity 35 30 25 20 15 10 Relative malate synthase activity synthase malate Relative 5 0 WT arcA iclR iclR arcA WT arcA iclR iclR arcA Chemostat Batch Relative isocitrate lyase activity 4 3 2 1 Relative isocitrate lyase activity lyase isocitrate Relative 0 WT arcA iclR iclR arcA WT arcA iclR iclR arcA Chemostat Batch Figure 4.6: The relative activities of malate synthase and isocitrate lyase in batch and chemostat cultures for the wild type strain, the ∆arcA strain, the ∆iclR strain and the ∆arcA-∆iclR mutant strain. The activity was measured in units per gram biomass and is shown relative to the wild type chemostat activity. The activity of isocitrate lyase relative to the wild type in chemostat cultures indicates that in comparison, the glyoxylate pathway in batch cultures is less active. The influence of CRP on the operon seems thus relatively small, based on these results otherwise the activity in batch should be higher than chemostat. IHF on the other hand might be the crucial regulator that results in a difference between both culture modes. In batch cultures the influence of IHF is negligible while at reduced growth rates this regulator shows increased activity (Aviv et al. , 1994). The isocitrate lyase activity of the arcA mutant strain does not show a significant influence in batch cultures, but clearly represses the operon in a low dilution rate continuous culture. A synergetic effect with IclR cannot be derived from the enzyme activity data. At 161 Chapter 4 maximal growth rate the iclR knock out strain increases enzyme activity with or without an arcA knock out, in chemostat cultures, the effect of both ArcA and IclR on the enzyme activity are the same. The phenotype of the ∆arcA ∆iclR strain is clearly a combined effect of different regulatory systems that affects the electron transport chain activity, the glycolate and glyoxylate pathway. 4.3.3 Glycogen and threhalose measurements Biomass measurements as such are imperfect because it is based on the lump amount of mass that has been produced, without knowing what it is composed of. An organism can for instance store certain polymers or fatty acids as a reaction to unfavourable conditions (Alvarez et al. , 1997; Dauvillee et al. , 2005). These compounds will increase the net weight of the biomass and will thus change the relative composition. Hence, a measured increased biomass yield does not automatically mean enhanced biomass biosynthesis. Escherichia coli can store both threhalose and glycogen in different conditions (Giaever et al. , 1988; Hengge- Aronis et al. , 1991; Kandror et al. , 2002; Yang et al. , 1996). 4.3.3.1 Glycogen and threhalose Recovery First, the recovery of standard glycogen and threhalose was measured and the activity of threhalase on glycogen. The average glycogen and threhalose recovery over 4 samples is 91 % and 86 %, respectively (Table 4.4). When a glycogen standard is assayed with the threhalose assay, 20 % of the standard was hydrolysed into glucose. Table 4.4: Glycogen and threhalose recovery test. Assay 1 is the glycogen assay; Assay 2 indicates the threhalose assay. The odd sample numbers were diluted 10 times for glucose measurement, the even sample numbers were diluted 5 times. The standard deviation is calculated based on 2 glucose measurements for each sample. Sample Assay Glucose concentration Standard deviation Average recovery [mM] % Glycogen 1 1 2.41 0.10 92 Glycogen 2 1 2.51 0.08 96 Glycogen 3 1 2.29 0.01 88 Glycogen 4 1 2.32 0.30 89 Glycogen 5 2 0.51 0.01 20 Glycogen 6 2 0.53 0.01 20 Threhalose 1 2 0.97 0.01 87 Threhalose 2 2 0.97 0.03 87 Threhalose 3 2 0.95 0.01 85 Threhalose 4 2 0.97 0.04 86 Threhalose 5 1 0.00 0.00 0 Threhalose 6 1 0.00 0.00 0 162 Synergetic effect of the transcriptional regulators ArcA and IclR This means that if there is glycogen as well as threhalose present in the samples, the threhalose has to be corrected for the glycogen. Threhalose is not detected when assayed with the glycogen assay. This means glycogen does not have to be corrected for the presence of threhalose in the samples. The matrix effect of the samples was tested for every sample. Glycogen and threhalose standards were added to the samples in a 1:1 ratio. The recovery of the standard addition was 98 ± 1 % and 105 ± 1 % for glycogen and threhalose, respectively. Therefore it can be concluded that the sample matrix does not affect the measurement of glycogen, but leads to a slight overestimation of threhalose. 4.3.3.2 Glycogen and threhalose in batch and chemostat cultures Both threhalose and glycogen were measured for the wild type strain and the ∆arcA ∆iclR strain in batch cultures and a chemostat cultures. In batch as well as in chemostat the mutant strain showed a significant increase in glycogen content (Figure 4.7), threhalose could not be detected in either of the strains. However the 1 % increase in glycogen cannot explain the steep increase in biomass yield. Glycogen content 2.0 1.5 1.0 % glycogen 0.5 0.0 WT arcA iclR WT arcA iclR Chemostat Batch Figure 4.7: Glycogen content of a wild type strain and a ∆arcA-∆iclR mutant strain. The glycogen content is expressed in carbon relative to the total amount of biomass carbon. The results shown are the averages of two cultures, measured 4 times. The wild type chemostat culture had a dilution rate of 0.17 ± 0.01 h -1; the ∆arcA-∆iclR strain had a dilution rate of 0.33 ± 0.02 h -1. The carbon balance and redox balance for these experiments are similar to the data shown in Table 4.2. Based on these results one may wonder whether the observed increases (in glycogen content and biomass) are inevitably linked to each other, and if this would be the case what would be the underlying mechanism, triggered by the above mentioned genetic modifications, resulting 163 Chapter 4 in the observed alterations? In an earlier study on the overproduction of glycogen, this type of correlation was also observed. By overexpressing glycogen synthase and ADP-glucose pyrophosphorylase byproduct formation was reduced and biomass yield increased. In that study, the cells were grown on a Luria Broth glucose medium, which contains alternative carbon sources for E. coli . In a normal physiological state, E. coli does not assimilate these carbon sources until all glucose is consumed. However, a glycogen overproducing strain consumed pyruvate, present in the medium, more rapidly than the control strains and showed lower overall byproduct formation. This could be the result of a stimulated gluconeogenesis pathway that occurred at the same time as glycolysis (Dedhia et al. , 1994). A similar effect could have been obtained here, by knocking out both regulatory proteins ArcA and IclR, gluconeogenesis could have been activated, decreasing byproduct formation and increasing biomass yield. However, in order to evaluate this, transcriptome information is necessary. The effect of the mutations on the biomass yield is growth rate dependent. Lower dilution rates result in lower yield, by increasing growth rate; yield increases (Figure 4.8). ∆∆∆arcA- ∆∆∆iclR biomass yield 0.7 0.6 0.5 0.4 0.3 0.2 Biomass yield (c-mole/c-mole glucose) (c-mole/c-mole yield Biomass 0.1 0.0 0.09 0.33 0.44 -1 Dilution rate (h ) Figure 4.8: Biomass yield (c-mole biomass/ c-mole glucose) of the ∆arcA ∆iclR mutant strain in function of the growth rate or dilution rate. 0.44 is the maximal growth rate of the strain. A similar observation was made for an E. coli wild type strain that was cultured in different chemostats at different dilution rates that ranged from low to near critical dilution. Low to intermediate dilution rates resulted in nearly equal biomass yield in that case, but when the critical dilution was approached, biomass yield increased significantly due to the higher biomass yield per mole ATP and reduced carbon dioxide production (Kayser et al. , 2005). In batch the yield decreases again because of overflow metabolism in a non-limiting 164 Synergetic effect of the transcriptional regulators ArcA and IclR environment. The increasing biomass yield per ATP can be the result of less futile cycling and reduced growth associated maintenance. However, these calculations are also based on a set P/O ratio of 1.75. An increasing dilution rate leads to an increasing specific ATP production rates, which again leads to an increased phosphorylation of ArcA, which could then change the P/O ratio (see above). Increased energy production rate also induces both a decrease in rpoS transcription and an increased RpoS proteolysis, which changes the global transcription pattern in E. coli (Ihssen & Egli, 2004; Mika & Hengge, 2005). A ∆arcA-∆iclR strain shows similar effects, although the effect appears already at lower dilution rates that do not approach critical dilution and it is maintained at maximal growth rate. This might be the result of the arcA knock out, which does not control RpoS anymore, which should then again induce stress metabolism. 165 Chapter 4 4.4 Conclusions The effect of global and local regulators on the microbial phenotype will become more and more important in the future. With each mutation that is introduced in an organism the cell will search for an appropriate answer to counteract the loss of function and to optimise its survival potential. They should thus be considered in any genetic engineering scheme that envisages either an industrial application or physiological studies. However, our limited knowledge and the complexity of the genetic regulation make it very difficult to grasp their effects. Furthermore regulators tend to interact with each other which might lead to unpredictable phenotypes. In this study the example of ArcA and IclR were chosen. These regulators were previously mentioned as key in the production of succinic acid (San et al. , 2007). Both control the glyoxylate route and ArcA more specifically was shown to downregulate the TCA cycle in carbon rich, aerobic conditions (Perrenoud & Sauer, 2005; Sunnarborg et al. , 1990). One might lead to the conclusion that by simply knocking out arcA and/or iclR , an increased succinate yield would be observed because the glyoxylate route directly leads to succinate. However every succinate yielding E. coli described up to now required the elimination of succinate dehydrogenase activity, moreover, an ArcA knock out increases succinate dehydrogenase activity (Cunningham & Guest, 1998). Our observation showed that a double knock out of the arcA gene and the iclR gene leads to increased biomass production. In particular in carbon abundant conditions, during batch fermentations, the biomass approached the theoretical maximum, calculated with a P/O ratio of 1.33. But, a more thorough study of the regulation network of the 4 operons ( nuo , ndh , cyo , cyd ) that determine the P/O ratio of E. coli is tightly regulated by ArcA. Changes in the expression of arcA may thus result in changes in P/O ratio, which changes the cells biomass biosynthesis potential. The assumption of a constant P/O ratio (Baart et al. , 2007; Lequeux et al. , 2005; Park et al. , 2007), which is applied in metabolic modelling should therefore be looked at with a certain scrutiny. When the composition of the respiratory chain of an organism is variable given its genotype or environment, it should not be assumed constant but should be measured (Kjeldsen & Nielsen, 2009; Noguchi et al. , 2004). Moreover, this renders the application of chemostat cultures and mixed substrate experiments to validate the cells’ energy requirements and P/O ratio of these organisms rather limited (Vanrolleghem et al. , 1996). The interaction between IclR and ArcA on the glyoxylate operon itself under the different studied conditions gives a rather complicated image of how the whole shunt is regulated. More in particular the activity of malate synthase, that originates from two different operons (ace and glc ) that are antagonistically regulated. In order to uncover the true effect of the regulators on the different genes and enzymes that are involved, transcriptional information will be necessary. Unfortunately it is very difficult to uncover significant differences between conditions and strains with micro-arrays (Lequeux, 2008). For this purpose a high throughput RT-qPCR assay has to be developed that can handle large amounts of gene assays to evaluate the different strains and conditions that are presented here on the transcriptional level. In addition the fluxes of the different strains have to be evaluated further with C13 flux analysis. 166 Synergetic effect of the transcriptional regulators ArcA and IclR The challenge for these types of experiments will be to recreate the same conditions in small scale reactors for the batch and chemostat cultures to limit the cost of labelled material and to obtain accurate enough data to calculate the flux through the glyoxylate route (Zamboni et al., 2009). 167 5 Chapter 5 Metabolic engineering of succinate biosynthesis in Escherichia coli Chapter 5 CONTENTS 5.1 Introduction 171 5.2 Materials and methods 174 5.3 Results and discussion 175 5.3.1 Succinate production 175 5.3.2 Byproducts 176 5.3.3 First steps towards succinate production 182 5.3.4 Specific targets to enhance succinate yield and production rate 186 5.3.5 Summary of succinate yields and volumetric production rates 195 5.4 Conclusions 197 5.5 References 199 170 Metabolic engineering of succinate biosynthesis in Escherichia coli 5.1 Introduction The partial least squares- elementary flux modes analysis presented in Chapter 2 indicate clearly an optimal route towards succinate. This pathway can be subdivided into building blocks (Figure 5.1). These blocks are: substrate uptake, glycolysis/pentose phosphate, TCA, succinate efflux and byproducts. These blocks translate into a mutation scheme that is presented in Figure 5.2. Uptake Glycolysis By-products TCA-cycle REGULATION Transport Figure 5.1: Overview of the cellular mechanisms that were targeted for succinate production, based on the theoretical calculations in chapter 2. Chapter 3 covers how succinate can be exported out of the cell by knocking out dicarboxylic acid importers and overexpressing DcuC, a succinate efflux protein. The regulation of the TCA cycle and the glyoxylate bypass has been analysed in Chapter 4. This analysis led to the discovery of a phenotype that is much more energy efficient than the wild type. ArcA and IclR seem to induce a synergetic effect that leads to a strain that approaches the maximal theoretical biomass yield via a balanced activation of the oxidative TCA (ArcA) and the glyoxylate shunt (IclR and ArcA) in a glucose abundant environment (CRP). However increased TCA and glyoxylate shunt activity does not result in increased succinate production. Only the disruption of the TCA cycle can increase succinate production. Succinate is in the TCA converted either to succinyl-CoA or to fumarate. The redox reaction to fumarate is foreseen by two different enzymes, fumarate reductase and succinate dehydrogenase. Fumarate reductase is in general active under anaerobic conditions while succinate dehydrogenase is active under aerobic conditions (Cunningham & Guest, 1998; Maklashina & Cecchini, 1999). Their affinity for succinate and fumarate differs accordingly. Under anaerobic conditions, fumarate functions as a terminal electron acceptor, which indicates that the affinity of fumarate reductase for fumarate is greater than that for succinate. 171 Chapter 5 H d l a P E C h d a rcA A A c r A A rc A R E l c h i d a A c r A pckA AS NR FNR F I s l a e , p A r i p r Figure 5.2: Mutation strategy based on the optimal succinate production pathway presented in Chapter 2. The genes and their gene products are summarized in the appendix, section A.3. Aerobically, succinate dehydrogenase supplies the cell with additional reductive equivalents that will be reoxidised by the electron transport chain. This enzymes affinity for succinate is much higher than for fumarate (Maklashina et al. , 2006; Maklashina et al. , 2001). A succinate 172 Metabolic engineering of succinate biosynthesis in Escherichia coli dehydrogenase gene knock out will thus be required to produce succinate. Fumarate reductase activity however has to be enhanced in order to increase the flux through the reductive TCA. This route is activated by FNR, fumarate nitrate reductase regulation protein. This protein also reduces the electron transport chain activity, which is competing for NADH with the reductive TCA enzymes (Shalel-Levanon et al. , 2005a, b, c). The role of malic enzyme and PEP carboxykinase is somewhat ambiguous in the context of succinate production. Kinetically, both native E. coli enzymes prefer to convert malate and oxaloacetate into pyruvate and PEP, respectively (Sauer & Eikmanns, 2005). However they have been described in optimised E. coli succinate strains as advantageous reactions in the presence of a high partial pressure of carbon dioxide (Kwon et al. , 2007; Kwon et al. , 2006; Stols & Donnelly, 1997). Their effect will depend on the intracellular metabolite concentrations of PEP, pyruvate, oxaloacetate and malate. In order to evaluate this, knock out strains have to be evaluated. The optimal succinate production pathways presented in Chapter 2 splits the carbon flow first into two streams, one towards citrate synthase and the other to PEP carboxylase. The cell foresees mechanisms that regulate these two TCA entry points to avoid excessive use of the TCA cycle (Kai et al. , 1999; Stokell et al. , 2003; Wohl & Markus, 1972). Both enzymes are allosterically inhibited by TCA cycle intermediates. PEP carboxylase is influenced by malate, succinate and aspartate, three metabolites that may accumulate when succinate is produced. Citrate synthase is inhibited by alfa-ketoglutarate and NADH. These feedback inhibitions can be overcome by introducing point mutations into the sequences of these enzymes at the amino acid residues that are responsible for allosteric binding. For both enzymes these amino acid residues are known. The allosteric inhibition sites of PEP carboxylase are located at lys491, lys620, lys650, and lys773. Replacing the residues 491, 650, and 773 with alanine or serine residues leads to a desensitisation of the enzyme for its inhibitors, but also leads to a decrease in catalytic efficiency. Only the lys620 to serine mutant enzyme maintained the wild type catalytic properties with a significant desensitisation (Yano & Izui, 1997). A similar study identified the different allosteric inhibition sites of citrate synthase. Based on the enzyme structure, 9 sites were identified as potential allosteric inhibition sites. Two mutant enzymes resulted in similar kinetic properties, Y145A and K167A (Francois et al. , 2006; Harford & Weitzman, 1975; Stokell et al. , 2003). E. coli generally converts the majority of its carbon source into acetate and lactate. Under anaerobic conditions, it produces next to these two metabolites, also ethanol and formic acid (Clark, 1989). The acetate pathway has been reviewed previously extensively (De Mey et al. , 2007b; Wolfe, 2005). Many strategies have been proposed to reduce acetate production, primarily for heterologous protein production (De Mey et al. , 2007a; Dedhia et al. , 1994; Kim & Cha, 2003). However acetate formation could never be fully eliminated from the E. coli fermentation processes, which points at alternative reactions that yield acetate. 173 Chapter 5 5.2 Materials and methods 5.2.1 Strains Escherichia coli MG1655 [ λ−, F –, rph–1] was obtained from the Coli Genetic Stock Center (CGSC). The different strains were preserved in a (1:1) glycerol:LB growth medium solution. Summary of the strains used in this work is given in the appendix, section A.6. 5.2.2 Media The cultivation media and preparation are described in paragraph 3.2.2. 5.2.3 Cultivation conditions Culture conditions and the inoculation protocol are the same as described in paragraph 3.2.3. 5.2.4 Sampling methodology All sampling methodologies are described in section 3.2.4. 5.2.5 Analytical methods 5.2.5.1 Biomass, glucose and organic acids Biomass, glucose and organic acid determination is the same as described in paragraph 3.2.5. 5.2.6 Genetic methods The genetic methods used for this chapter are the same as described in section 3.2.6. The primers used are summarised in the appendix, section A.5. 174 Metabolic engineering of succinate biosynthesis in Escherichia coli 5.3 Results and discussion 5.3.1 Succinate production The profile of one of the higher yielding succinate production strains that will be discussed further in this chapter is given in Figure 5.3. This culture illustrates perfectly the problems that occur during the production of succinate. First, acetate is still produced although the known pathways towards acetate are knocked out in strain SUCC044. Second, about 17 % of the carbon is excreted as pyruvate and finally, there is not enough NADH available for an optimal use of the reductive TCA. The latter can be deduced from the low concentrations of lactate and high concentrations of pyruvate that are produced. Strain SUCC044 14 3.5 0.5 4 4 12 3.0 0.4 3 3 10 2.5 0.3 8 2.0 2 2 6 1.5 CDW(g/l) glucose (g/l) 0.2 succinate (g/l) succinate lactate, pyruvate (g/l) lactate, pyruvate 4 1.0 (g/l) fumarate acetate, 1 1 0.1 2 0.5 0 0.0 0.0 0 0 0 2 4 6 8 10 t (h) glucose CDW acetate succinate fumarate lactate pyruvate Figure 5.3: Profile of an aerobic culture of strain SUCC044, with as genotype MG1655 ∆∆∆(ack A-pta ) ∆∆∆pox B ∆∆∆icl R ∆∆∆sdh AB ∆∆∆arc A ∆∆∆dct A ∆∆∆edd ∆∆∆FNR-pro37-dcu C. The CDW was estimated from OD 600nm measurements as described in the materials and methods. The gene names and their product are summarised in the appendix, section A.3. In this chapter some possible solutions are evaluated for the above mentioned problems. Several genes of which their gene products may lead to acetate formation or the reduction thereof are screened. Further, a basic strain is constructed that produces succinate and the effect of succinate transport, PEP carboxylase, malic enzyme, oxaloacetate carboxylase, citrate synthase, and fumarate nitrate reductase regulating protein (FNR) are assessed. 175 Chapter 5 5.3.2 Byproducts A typical E. coli aerobic culture profile is given in Figure 5.4. By nature, E. coli does not produce much succinate, but produces mainly acetate under aerobic conditions. Wild type Escherichia coli MG1655 16 6 2.0 0.5 14 5 0.4 12 1.5 4 10 0.3 8 3 1.0 CDW (g/l) acetate (g/l) acetate glucose (g/l) glucose 0.2 6 2 4 0.5 0.1 (g/l) lactate fumarate, succinate, 1 2 0 0 0.0 0.0 0 2 4 6 8 t (h) CDW glucose acetate succinate fumarate lactate Figure 5.4: Aerobic E. coli batch culture profile. The CDW was estimated from OD 600 nm measurements as described in the materials and methods. As long as there is enough NADH present in the cell, E. coli will also produce some lactate from pyruvate. Lactate is an excellent indicator for the redox state of the cell. When there is enough NADH, lactate will be produced. When NADH is scarce, pyruvate will be formed. Therefore, lactate dehydrogenase will not be knocked out to exploit this property. Similar, pyruvate-formate lyase, which forms formate from pyruvate, should not be knocked out because it can be used excellently as an anaerobiosis indicator. Pyruvate formate lyase has to be activated by an oxygen sensitive activase. The moment the oxygen concentration drops, formic acid will be formed (Knappe et al. , 1974; Knappe & Sawers, 1990; Wagner et al. , 1992). The wild type MG1655 E. coli strain yields about 0.21 (± 0.01) c-mole/c-mole glucose of acetate under aerobic conditions; which originates mostly from pyruvate oxidase ( pox B) and the phosphate acyltransferase-acetate kinase ( pta and ack A) routes. The different genotypes that were examined on acetate production are given in Table 5.1, their acetate yield is presented in Figure 5.5. 176 Metabolic engineering of succinate biosynthesis in Escherichia coli Acetate yields 0.25 0.20 0.15 0.10 yield (c-mole/c-mole glucose) (c-mole/c-mole yield 0.05 0.00 SUCC001 SUCC002 SUCC003 SUCC019 SUCC020 SUCC021 SUCC022 SUCC023 SUCC024 SUCC011 SUCC012 SUCC013 SUCC050 SUCC045 strain Figure 5.5: Acetate yields of the wild type and different mutant E. coli strains. The description of the strains is given in Table 5.1. Table 5.1: The corresponding genotype of the mutant E. coli strains in Figure 5.5 and Figure 5.6. The gene names and their product are summarised in the appendix, section A.3. Strain Assigned code MG1655 (WT) SUCC001 MG1655 ∆(ack A-pta ) ∆pox B SUCC002 MG1655 ∆(ack A-pta ) ∆poxB ∆icl R SUCC003 MG1655 ∆pppc-pro37-ppc SUCC019 MG1655 ∆pppc-pro55-ppc SUCC020 MG1655 ∆pppc-pro8-ppc SUCC021 MG1655 ∆(ack A-pta ) ∆pox B ∆pppc-pro37-ppc SUCC022 MG1655 ∆(ack A-pta ) ∆pox B ∆pppc-pro55-ppc SUCC023 MG1655 ∆(ack A-pta ) ∆pox B ∆pppc-pro8-ppc SUCC024 MG1655 ∆icl R SUCC011 MG1655 ∆arc A SUCC012 MG1655 ∆arc A ∆icl R SUCC013 MG1655 ∆(ack A-pta ) ∆pox B ∆ato AD SUCC050 MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB ∆arc A ∆dct A SUCC045 ∆edd ∆cit DEF ∆FNR-pro37-dcu C E. coli strains that are mutated in their acetate formation pathways, pyruvate oxidase, phosphate acyltransferase and acetate kinase, result in a decreased acetate yield. However acetate formation is not eliminated completely. Strains with increased expression of PEP carboxylase ( ppc ) gave similar effects as the acetate pathway mutant strain. The effect of PEP carboxylase was tested with different promoters with increasing strength (P8 < P55 < P37 (De Mey et al. , 2007c)). With increased promoter strength, which may increase the flux from 177 Chapter 5 PEP to oxaloacetate, no significant decrease in acetate yield could be detected. A combination with the acetate pathway mutation did not seem to affect the acetate yield much either, except for strain SUCC024, which showed an increased acetate yield. The same evolution can be detected for strain SUCC021 in comparison to the strains SUCC019 and SUCC020. This may be the effect of a reduced expression of PEP carboxylase in comparison to the wild type expression and consequently an increased flux towards pyruvate. Nevertheless, the pyruvate yield decreased with the introduction of synthetic promoters in front of ppc (Figure 5.6), none of these strains produce pyruvate anymore. This leads to the conclusion that pyruvate is probably not the substrate for the unknown acetate formation reaction. The effects of promoter P8 on the acetate yield cannot be explained with the data obtained from these experiments. Pyruvate yields 0.25 0.20 0.15 0.10 yield (c-mole/c-mole glucose) (c-mole/c-mole yield 0.05 0.00 SUCC001 SUCC002 SUCC003 SUCC019 SUCC020 SUCC021 SUCC022 SUCC023 SUCC024 SUCC011 SUCC012 SUCC013 SUCC050 SUCC045 strain Figure 5.6: Pyruvate yields of the wild type and different mutant E. coli strains. The description of the strains is given in Table 5.1. The yield for strain SUCC003 was based on only two points (in the beginning and the end of the experiment). All other yields were calculated based on all data points taken during the experiment. An alternative to increase the flux from pyruvate towards the TCA is the activation of the glyoxylate route. Instead of lowering the flux towards pyruvate, the flux from pyruvate (via acetyl-CoA) towards the TCA is increased with the introduction of an additional reaction, malate synthase. Malate synthase converts acetyl-CoA with glyoxylate into malate. As discussed in Chapter 4, IclR and ArcA are the main transcriptional regulators for this pathway. Their effect on acetate and pyruvate formation is given in Figure 5.5 and Figure 5.6. In comparison to the Wild type strain, an IclR and ArcA knock out strain reduces the acetate yield, without accumulation of pyruvate. A combined knock out strain reduces the acetate formation even more, indicating the synergetic effect that was previously discussed. In 178 Metabolic engineering of succinate biosynthesis in Escherichia coli addition, the icl R knock out mutation was combined with the three gene knock outs in the acetate pathways to evaluate the effect of the glyoxylate pathway on pyruvate formation. Figure 5.6 shows clearly that pyruvate accumulation was resolved with this extra ∆icl R mutation. However, the yield shown in this figure was based on the pyruvate concentration measured at the end of the experiment, right before complete glucose depletion. However, this strain showed a somewhat peculiar growth profile. After about 8 hours of growth, a second lag phase seemed to occur, resulting in a diauxie type of profile. The pattern was validated by repeating the cultures. These cultures were intensively sampled around this apparent lag phase (Figure 5.7). Strain SUCC003 10 8 6 CDW (g/l) 4 2 0 0 2 4 6 8 10 t (h) Figure 5.7: Growth profile of two different cultures of strain MG1655 ∆∆∆(ack A-pta ) ∆∆∆pox B ∆∆∆icl R (SUCC003). Notice the apparent secondary lag phase after 8 hours. The metabolite profiles are shown in Figure 5.8. Glucose, acetate and succinate follow more or less a normal fermentation profile. The concentration of glucose decreases exponentially and the succinate and acetate concentrations increase. Lactate and pyruvate on the other hand give a completely different picture. The concentrations of these two metabolites seem to be atypical around the second lag phase that was observed in the biomass concentration data. According to these data, the fermentation profile can be subdivided in three phases, (I) pyruvate, lactate and biomass formation, (II) a lactate and pyruvate assimilation during a growth arrest, and (III) lactate formation phase with exponential growth. During the transition between phase I and II, pyruvate seems to deplete first, followed by lactate during phase II. In the last phase, no pyruvate was detected anymore in the culture medium. Such an evolution was not observed in any of the other strains. This indicates that the activation of the glyoxylate pathway by knocking out icl R changes the intracellular fluxes depending on environmental factors. 179 Chapter 5 Strain SUCC003 12 1.0 0.5 16 14 10 0.8 0.4 12 8 10 0.6 0.3 8 6 CDW (g/l) acetate (g/l) glucose (g/l) 0.4 0.2 6 (g/l) succinate 4 4 0.2 0.1 2 2 0 0 0.0 0.0 0 2 4 6 8 10 t (h) 0.12 0.12 0.10 0.10 0.08 0.08 0.06 0.06 lactate (g/l) lactate pyruvate (g/l) 0.04 0.04 0.02 0.02 0.00 0.00 0 2 4 6 8 10 t (h) CDW glucose acetate succinate lactate pyruvate Figure 5.8: Evolution of the metabolite concentrations in a batch culture of mutant strain MG1655 ∆∆∆(ack A-pta ) ∆∆∆pox B ∆∆∆icl R (SUCC003). The upper graph depicts biomass, glucose, acetate and succinate concentrations, the lower graph shows the pyruvate and lactate concentration during the experiment. Either transcription or the enzyme activities of the glyoxylate pathway seems somehow controlled during the fermentation. The sudden drop of the pyruvate concentration may indicate that at that time, the glyoxylate shunt is activated. The fact that growth decreases ore even arrests at that time may be the result of a decrease in NADH formation, due to the reduced activity of the oxidative TCA. This disturbance in redox might then be compensated by the consumption of the earlier formed lactate. The cell then seems to stabilise its redox household and resumes exponential growth after almost complete consumption of lactate. At this time lactate dehydrogenase activity is reversed again which indicates an excess in NADH that cannot be consumed by the electron transport chain. Finally, pyruvate is not formed anymore, due to the activation of the glyoxylate route but lactate can still be produced due to 180 Metabolic engineering of succinate biosynthesis in Escherichia coli an excess in NADH. This may be the result of an equilibrated flux ratio between the oxidative TCA and the glyoxylate shunt. Isotopic labelling experiments will have to be applied to fully understand the intracellular flux changes (Nöh et al. , 2007). However these changes may either involve changes in the transcript or may be the result of enzyme activity modulations, e.g. allosteric inhibition. Hence, a transcriptome analysis of the different phases is needed. This would require time series transcriptomics data which involves a Bayesian framework for analysis. Such a framework has been developed for microarray data, which is proven to be inaccurate for subtle differences in transcription (Baldi & Long, 2001; Bhaskar et al. , 2007; Lequeux, 2008). RT-qPCR is a more accurate method for gene expression analysis, but cannot rely yet on an extensive tool set like micro-array analysis (Popovici et al. , 2009). In search of the alternative acetate formation reactions, two operons were targeted, the ato D and the cit D operons. AtoA and AtoD are the alpha and beta subunits of acetate CoA- transferase, which is involved in short-chain fatty acid degradation. This enzyme activates the fatty acid into a thioester (Vanderwinkel et al. , 1968). A similar enzyme is described in Trypanosoma brucei as an acetyl:succinate CoA-transferase. This enzyme can thus convert succinate into succinyl-CoA with acetyl-CoA, which then may form acetate. For this reason, an additional succinate dehydrogenase knock out mutation was introduced, to evaluate the effect of this enzyme. Figure 5.5 and Figure 5.6 present the yield of this strain. The additional knock out seems not to result in a decreased acetate nor pyruvate yield. The E. coli acetate CoA-transferase could thus be more substrate specific than the Trypanosoma brucei enzyme or is not active under the tested conditions. The genes cit D, cite , and cit F code for citrate lyase, an enzyme that consists of three subunits, CitD, acyl carrier, CitE, citryl-ACP lyase, and CitF, citrate-ACP transferase (Quentmeier et al. , 1987). This enzyme converts citrate into oxaloacetate and acetate. To analyse the effect of a three gene citrate lyase knock out, a base strain was used that already showed significant succinate production due to an upregulated TCA and glyoxylate shunt (strain SUCC045). Such a genetic background should supply citrate lyase sufficient substrate to convert into acetate and oxaloacetate. However, the citrate lyase mutation did not result into a decreased acetate yield. The current available information on the E. coli enzymatic reactions does not allow the identification of alternative acetate formation reactions. The genes that were identified through database searches and a literature review did not result in a complete elimination of acetate formation. More advanced searches will thus be needed to find the responsible enzymes. Recent advances in bio-informatics made it possible to find alternative substrates for many enzymes in the glycolysis and pentose phosphate pathway of Saccharomyces cerevisiae (de Groot et al. , 2009). This algorithm, applied on the E. coli enzyme database, should allow the identification of novel targets and a more thorough knowledge of the acetate formation reactions in general. 181 Chapter 5 5.3.3 First steps towards succinate production Mutations in the succinate dehydrogenase genes are deemed to be essential for aerobic succinate production. This enzyme efficiently converts succinate into fumarate. A gene knock out of succinate dehydrogenase immediately increases the succinate yield and specific production rate about 5 times (Figure 5.9). This strain converts 0.11 c-mole/c-mole glucose of the carbon into succinate. However, it still forms as much acetate as the wild type. Hence, a second strain was constructed in which the acetate pathway was knocked out and the expression of the glyoxylate route enzymes was enhanced. The latter was obtained by knocking out iclR . These knock out mutations were described above (SUCC003, Figure 5.5), but did not increase succinate yield in comparison to the wild type strain. They did however reduce acetate formation. The addition of a succinate dehydrogenase knock out mutation increased the yield to 0.15 c-mole/c-mole glucose. These mutations have an enhancing effect on succinate production solely in combination with a succinate dehydrogenase knock out mutation. Succinate yield and specific production rate 0.4 0.4 0.3 0.3 0.2 0.2 (c-mole/c-mole glucose) (c-mole/c-mole (c-mole/c-mole biomass/h) (c-mole/c-mole succinate succinate Y 0.1 0.1 q 0.0 0.0 SUCC001 SUCC015 SUCC005 SUCC006 SUCC053 SUCC009 SUCC054 SUCC044 strain Figure 5.9: Succinate yields and specific production rates of the wild type strain and several mutant strains. The description of the strains is given in Table 5.2. In chapter 4, a synergetic effect between IclR and ArcA was proven, leading to an unexpected phenotype. This effect was also tested for succinate production. Additional to ∆icl R, arcA was knocked out, resulting in SUCC006 (Figure 5.9, Table 5.2). This led to an increase in succinate yield (to 0.22 c-mole/c-mole glucose). The specific production rate remained more or less unchanged. The ∆icl R∆arc A knock out combination affects also succinate production. However, with the increasing number of mutations, pyruvate yield increased (Figure 5.10). The accumulation of succinate, and maybe intracellular malate and aspartate may inhibit the 182 Metabolic engineering of succinate biosynthesis in Escherichia coli carbon flux towards the TCA cycle and thus lead to an accumulation of pyruvate. PEP carboxylase is allosterically controlled by these acids and will show a reduced activity when their concentrations rise (Chang et al. , 2009; Gold & Smith, 1974). Table 5.2: The corresponding genotype of the mutant E. coli strains in Figure 5.9 - Figure 5.12. The gene names and their product are summarised in the appendix, section A.3. Strain Assigned code MG1655 (WT) SUCC001 MG1655 ∆sdh AB SUCC015 MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB SUCC005 MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB ∆arc A SUCC006 MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB ∆arc A ∆dct A SUCC053 MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB ∆arc A ∆FNR-pro37-dcu C SUCC009 MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB ∆arc A ∆dct A ∆FNR-pro37-dcu C SUCC054 MG1655 ∆(ack A-pta ) ∆poxB ∆icl R ∆sdh AB ∆arc A ∆dct A ∆edd ∆FNR-pro37-dcu C SUCC044 Increasing succinate export is one of the strategies that may lead to reduced feedback inhibition (Chapter 3). Enhancing succinate export or decreasing succinate import only resulted in slight increases in yield and production rate (Figure 5.9). Only strain SUCC053, which is impaired in succinate import, showed a reduction in pyruvate yield. Its reduction does not lead to an increased succinate yield, but rather induces enhanced biomass production. In comparison with the strains in which DcuC (succinate exporter) is overexpressed (SUCC009 and SUCC054), the biomass yield of this strain is almost double (Figure 5.11). Pyruvate and lactate yield 0.25 0.25 0.20 0.20 0.15 0.15 (c-mole/c-mole glucose) 0.10 0.10 (c-mole/c-mole biomass/h) pyruvate lactate q Y 0.05 0.05 0.00 0.00 SUCC001 SUCC015 SUCC005 SUCC006 SUCC053 SUCC009 SUCC054 SUCC044 strain Figure 5.10: Pyruvate and lactate yields of the wild type strain and several mutant strains. The description of the strains is given in Table 5.2. 183 Chapter 5 With the introduction of DcuC overexpression in the mutant strains, the growth rate reduced significantly. A reason for these observations may be found in the mode of action of these transporter proteins (Davies et al. , 1999a; Davies et al. , 1999b; Janausch et al. , 2001). DctA is a succinate importer, that is a symporter with protons, thus with the influx of succinate through this transporter, the proton gradient is decoupled. The dctA knock out strain will not undergo such a decoupling, leading to an increased influx of protons through ATP synthase which leads to increased biomass synthesis. When DcuC is overexpressed, this effect is reduced because with each succinate that is exported by this protein; two protons enter the cytoplasm, resulting again in a reduced membrane potential and ATP synthesis. With the flux towards biomass reduced, pyruvate starts accumulating again. In none of the strains described above significant amounts of lactate are produced (Figure 5.10). NADH is thus most likely limiting. In Chapter 2, the optimal pathways for NADH generation are described and analysed. One of the pathways that correlates negatively with succinate formation is the Entner Doudoroff pathway. This pathway yields much less NADH than the pentose phosphate and yields less ATP per glucose. A knock out of the first enzyme (phosphogluconate dehydratase) of the ED pathway might then induce an increased succinate yield and growth rate. This could further lead to a decrease in pyruvate formation. Figure 5.9 indicates that not the yield but the production rate increases in a ∆edd strain (SUCC044), the pyruvate yield does not decrease, but increases (Figure 5.10). The increased production rate is actually the result of an increased growth rate (Figure 5.11) which nearly doubles in comparison with the strain without the gene edd knocked out (SUCC054). The effect of lower ATP yield per glucose is thus quite significant, which may indicate that carbon flux through the ED pathway is quite high in the mutant strains. With this knock out the carbon flux will then redistribute over the pentose phosphate pathway and glycolysis. Carbon dioxide yield and the oxygen uptake rate may be indicative for the flux distribution in strain SUCC044. If the pentose phosphate pathway (PP) flux would increase within this strain, carbon dioxide yield should increase because this pathway yields an extra carbon dioxide. The oxygen uptake rate should also increase due to the additional formation of NADPH. However, the data presented in Figure 5.12 indicate a decrease of carbon dioxide yield in comparison with strain SUCC054 but an increase in OUR, which is contra-intuitive. On the other hand, the CO 2 yields of the strains with an upregulated dcuC expression are quite high in comparison with the other mutant strains. This may the effect of any number reactions, for instance: the oxidative TCA, pyruvate dehydrogenase, malic enzyme, or 6- phosphogluconate dehydrogenase activity. Furthermore, an increased flux through the PP pathway may enhance the flux through the reductive TCA, due to an increased NADPH formation, which is implied by the OUR increase. Then the carbon dioxide yield should decrease instead of increase, which is observed. But, if the reductive TCA flux is increased, the flux from PEP to pyruvate would decrease, which should result in a decreased pyruvate yield in strain SUCC044. 184 Metabolic engineering of succinate biosynthesis in Escherichia coli µmax and biomass yield 0.8 0.8 0.6 0.6 ) -1 (h max 0.4 0.4 µ (c-mole/c-mole glucose) 0.2 0.2 biomass Y 0.0 0.0 SUCC001 SUCC015 SUCC005 SUCC006 SUCC053 SUCC009 SUCC054 SUCC044 strain Figure 5.11: Biomass yield and maximal growth rate of the wild type strain and several mutant strains. The description of the strains is given in Table 5.2. Figure 5.10 shows an increased pyruvate yield for this strain, which could be the result of malic enzyme and oxaloacetate decarboxylase activity. By knocking out edd , the second enzyme of the ED pathway is left without substrate. This enzyme however has a secondary function, namely oxaloacetate decarboxylase. It is thus likely that this activity is the cause for the increased pyruvate yield. Carbon dioxide yield and OUR 0.6 0.6 0.5 0.5 0.4 0.4 (c-mole/c-mole glucose) (c-mole/c-mole 0.3 0.3 0.2 0.2 OUR (mole/c-mole biomass/h) (mole/c-mole OUR carbon dioxide carbon Y 0.1 0.1 0.0 0.0 SUCC001 SUCC015 SUCC006 SUCC053 SUCC009 SUCC054 SUCC044 strain Figure 5.12: Carbon dioxide yield and oxygen uptake rate (OUR) of the wild type strain and several mutant strains. The description of the strains is given in Table 5.2. 185 Chapter 5 5.3.4 Specific targets to enhance succinate yield and production rate 5.3.4.1 Anaplerotic and gluconeogenic reactions The gluconeogenic reactions such as malic enzyme, PEP carboxykinase, and oxaloacetate decarboxylase can be the cause for an increased pyruvate production and reduced succinate yield. These reactions are all active simultaneously, which makes it very difficult to modulate (Sauer & Eikmanns, 2005). In a first attempt to understand the fluxes, all gluconeogenic reactions within the PEP – pyruvate – oxaloacetate node were knocked out. This led to a decreased succinate yield and specific production rate (Figure 5.13, Table 5.3). Second, the effect of citrate lyase was evaluated again on succinate production (Strain SUCC045). In this case the succinate yield was reduced, but the rate remained the same in comparison to strain SUCC044. The acetate remained stayed the same, which points at the fact that citrate lyase in E. coli does not catalyse the reaction from citrate to acetate and oxaloacetate, but to acetyl- ACP and oxaloacetate. Acetyl-ACP is further converted into acetyl-CoA (Tsay et al. , 1992), which is then available for malate synthase and citrate synthase or for fatty acid synthesis. Succinate yield and specific production rate 0.5 0.5 0.4 0.4 0.3 0.3 (c-mole/c-mole glucose) (c-mole/c-mole 0.2 0.2 biomass/h) (c-mole/c-mole succinate succinate Y 0.1 0.1 q 0.0 0.0 SUCC047 SUCC044 SUCC051 SUCC045 SUCC046 (I) SUCC046 SUCC046 (II) SUCC046 strain Figure 5.13: Succinate yields and specific production rates of several mutant strains with altered anaplerotic and gluconeogenic reactions. SUCC046 (I) and (II) indicate a different experimental setup. (I) was sparged with air, while in experiment (II) 5% carbon dioxide was added to the sparging gasmix. The description of the strains is given in Table 5.3. The influence of a mutated gluconeogenesis is most explicit on the biomass yield (Figure 5.14). The yield increased about 20% in comparison to strain SUCC044. This may be the result of a decrease in futile cycles, which are described to dissipate up to 8% of the total ATP synthesised (Chen et al. , 2003). Under carbon limitation PEP carboxylase/PEP carboxykinase are reported to form a substantial futile cycle in E. coli (Sauer et al., 1999). The introduction 186 Metabolic engineering of succinate biosynthesis in Escherichia coli of an artificial futile cycle into E. coli with an overexpressed PEP carboxykinase and a newly introduced pyruvate carboxylase reduced the biomass yield. The growth rate was insensitive to this futile cycle. The cell counteracted the effect by increasing the oxygen and substrate consumption rates (Chao & Liao, 1994). The effect of malic enzyme (SUCC055) and PEP carboxykinase (SUCC056) in an E. coli wild type background is given in Table 5.4. Table 5.3: The corresponding genotype of the mutant E. coli strains in Figure 5.13 - Figure 5.16. The gene names and their product are summarised in the appendix, section A.3. Strain Assigned code MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB ∆arc A ∆dct A ∆edd ∆FNR-pro37-dcu C SUCC044 MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB ∆arc A ∆dct A ∆edd ∆maeAB ∆pckA SUCC051 ∆FNR-pro37-dcu C MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB ∆arc A ∆dct A ∆edd ∆cit DEF ∆FNR- SUCC045 pro37-dcu C MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB ∆arc A ∆dct A ∆edd ∆cit DEF SUCC046 ppc *L620S ∆FNR-pro37-dcu C MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB ∆arc A ∆dct A ∆edd ∆eda ∆cit DEF ppc * SUCC047 L620S ∆FNR-pro37-dcu C µmax and biomass yield 0.6 0.6 0.5 0.5 ) 0.4 0.4 -1 (h (h max 0.3 0.3 µ (c-mole/ c-mole glucose) c-mole (c-mole/ 0.2 0.2 biomass Y 0.1 0.1 0.0 0.0 SUCC044 SUCC051 SUCC045 SUCC047 SUCC046 (I) SUCC046 SUCC046 (II) SUCC046 strain Figure 5.14: The maximal growth rates and biomass yields several mutant strains with altered anaplerotic and gluconeogenic reactions. The description of the strains and the experimental setups are given in Table 5.3 and Figure 5.13. Contradictory to the observation described in literature for an artificial futile cycle in which the OUR increased, the expected decrease in oxygen consumption rate does not occur when suspected futile cycles are knocked out (Table 5.4). However, when these mutations are introduced in a succinate producing strain (Figure 5.15, SUCC051), the oxygen uptake rate and the specific glucose uptake rate decrease. The oxygen uptake rate decreases about 15 % and the specific glucose uptake rate about 32 % (data not shown). These data may proof that 187 Chapter 5 futile cycles also occur in cells in the exponential growth phase and that their existence have an effect on the yield and production rate of succinate. Table 5.4: The maximal growth rates, biomass yields and OUR of malic enzyme and PEP carboxykinase mutant E. coli strains. Strain Assigned code Biomass yield µmax OUR (c-mole/c-mole glucose) (h -1) (mole/c-mole biomass/h) MG1655 (WT) SUCC001 0.43 ± 0.02 0.66 ± 0.05 0.26 ± 0.02 MG1655 ∆maeAB SUCC055 0.49 ± 0.02 0.65 ± 0.07 0.29 ± 0.08 MG1655 ∆pckA SUCC056 0.53 ± 0.08 0.63 ± 0.08 0.28 ± 0.08 The influence of citrate lyase is the opposite of that of PEP carboxykinase and malic enzyme. A citrate lyase knock out results in a decrease in biomass yield without a change in growth rate (Figure 5.14, strains SUCC045, SUCC046, and SUCC047). Either the reduction of oxaloacetate or acetyl-ACP may be the cause of this reduced yield. If oxaloacetate would be the limiting factor, then the biomass yields of the strains with enhanced PEP carboxylase would increase again this yield. However the data presented in Figure 5.14 (SUCC046 and SUCC047) do not support this hypothesis. It is thus more likely that the acetyl-ACP pool has become limiting for efficient biomass synthesis. Acetyl-ACP is an intermediate of the fatty acid biosynthesis in E. coli and plays an important role in the initiation of this biosynthetic route (Garwin et al. , 1980; Jackowski & Rock, 1987). Carbon dioxide yield and OUR 0.5 0.5 0.4 0.4 0.3 0.3 (c-mole/c-mole glucose) (c-mole/c-mole 0.2 0.2 OUR (mole/c-mole biomass/h) (mole/c-mole OUR carbon dioxide carbon Y 0.1 0.1 0.0 0.0 SUCC044 SUCC051 SUCC045 SUCC047 SUCC046 (I) SUCC046 SUCC046 (II) SUCC046 strain Figure 5.15: The carbon dioxide yield and OUR of several mutant strains with altered anaplerotic and gluconeogenic reactions. The carbon dioxide measurement of strains SUCC046 (II) may be an underestimation of the reality due to the addition of 5% CO 2 in the sparging gas. The description of the strains and the experimental setups are given in Table 5.3 and Figure 5.13. 188 Metabolic engineering of succinate biosynthesis in Escherichia coli Pyruvate is the main byproduct in the strains described in this section, apart from biomass and carbon dioxide, which are in a way unavoidable. Lactate was never produced in significant amounts (Figure 5.16). Mutations in the gluconeogenic reactions did result in a slight decrease in pyruvate yield (SUCC051). However with the increased biomass formation, these mutations led to a decrease in succinate production. A citrate lyase knock out mutation (SUCC045), also decreased pyruvate formation, which points at the possibility that oxaloacetate or malate are converted into pyruvate due to a hampering reductive TCA or an inefficient citrate synthase. The introduction of a point mutation at amino acid residue 620 (lysine to serine replacement) decreased pyruvate formation even further, however, without resulting in neither increased succinate yield nor specific succinate production rate. The addition of carbon dioxide to the sparging gas decreased pyruvate formation even more, but the succinate production properties of the strain remained the same. Nearly all production yields remained the same, which resulted in a carbon balance that closed for 83.5 % and a redox balance that was 110 %. Hence, about 17 % of the carbon was not detected during analysis. Assuming that only one unknown byproduct is formed, this byproduct has a degree of reduction of 3.33, which points at an acid. All acids with a degree of reduction near 3.33 are detectable with the HPLC method that was used for the analysis of these cultures. However, some amino acids also have a similar degree of reduction. These are histidine, serine, glutamine, glutamate, asparagine, aspartate, and arginine (Nielsen et al. , 2003). Aspartate is the most obvious candidate as byproduct from this list, because PEP carboxylase results the formation of oxaloacetate and it is the immediate product of the transamination reaction of oxaloacetate and glutamate. Unfortunately, NMR analysis, kindly provided by the Research Group for Synthesis and Bioresources Chemistry of Prof. Stevens, could not confirm this. Pyruvate and lactate yield 0.25 0.25 0.20 0.20 0.15 0.15 (c-mole/c-mole glucose) (c-mole/c-mole 0.10 0.10 biomass/h) (c-mole/c-mole pyruvate lactate Y Y 0.05 0.05 0.00 0.00 SUCC047 SUCC044 SUCC051 SUCC045 SUCC046 SUCC046 (I) SUCC046 SUCC046 (II) strain Figure 5.16: The pyruvate and lactate yields of several mutant strains with altered anaplerotic and gluconeogenic reactions. The description of the strains and the experimental setups are given in Table 5.3 and Figure 5.13. 189 Chapter 5 The residual pyruvate formed by strain SUCC046, may be the result of the activity of oxaloacetate decarboxylase, which as mentioned before performs also the second reaction of the Entner Doudoroff pathway. This was tested by knocking out eda , resulting in strain SUCC047. The yields and rates depicted in Figures 5.13-5.16 do not suggest a significant difference between these two strains. 5.3.4.2 Citrate synthase With an increased flux towards oxaloacetate via a mutant PEP carboxylase, the oxaloacetate has to be either reduced via the reductive TCA cycle towards succinate or has to be converted into citrate with acetyl-CoA, which eventually will be converted into malate and succinate through the glyoxylate shunt. However, citrate lyase is allosterically controlled by NADH, alfa-ketoglutarate and to some extent by oxaloacetate. In order to enhance the flux through citrate synthase, a point mutation was introduced in its allosteric inhibition site. In vitro the mutation of two different amino acid residues (K167 and Y145) were equally efficient (Francois et al. , 2006), thus both of them were tested. Figure 5.17 (A) presents the succinate yield of these mutant strains with their reference strain with a wild type citrate synthase. Based on the in vitro data described in literature, both mutations should perform equally well. However, the results give a completely different picture. The K167A citrate synthase mutant strain (SUCC048) shows a significant decrease in succinate yield and specific production rate. Whereas, the Y145A mutant citrate synthase strain (SUCC049) does not show any significant difference with the reference strain SUCC047. There are two possible explanations for these observations, either mutation K167A reduces the citrate synthase activity in vivo in such a way that the flux towards the TCA and the glyoxylate pathway is reduced, resulting in a decreased succinate formation through these two pathways, or the mutation increases the enzyme activity significantly. An “overactive” citrate synthase could pull acetyl-CoA and oxaloacetate away from the glyoxylate shunt and the reductive TCA respectively, leading to a reduced succinate production. The pyruvate and lactate yield data exhibited in Figure 5.17 (B) indicate that the latter hypothesis is more likely. Strain SUCC048 shows a decrease in pyruvate yield and an increase in lactate yield. Lactate formation requires NADH, which is produced in the oxidative TCA. Only an increased flux through citrate synthase towards the oxidative TCA and reduced fluxes through the reductive TCA and glyoxylate shunt can result in an increased NADH availability. This observation is corroborated by the biomass yield, carbon dioxide yield, and oxygen uptake rate data. With an increased availability of NADH, the oxygen uptake rate increases. This further leads to an increased ATP synthesis and thus an increased biomass yield. The increased carbon dioxide yield points at isocitrate dehydrogenase and alfa-ketoglutarate dehydrogenase as possible sources for that NAD(P)H (Figure 5.18 (A) and (B)). 190 Metabolic engineering of succinate biosynthesis in Escherichia coli A B 0.4 0.4 0.16 0.16 0.14 0.14 0.3 0.3 0.12 0.12 0.10 0.10 0.2 0.2 0.08 0.08 (c-mole/c-mole glucose) (c-mole/c-mole (c-mole/c-mole glucose) (c-mole/c-mole (c-mole/c-mole biomass/h) (c-mole/c-mole (c-mole/c-mole (c-mole/c-mole biomass) 0.06 0.06 lactate pyruvate succinate Y succinate Y Y 0.1 0.1 0.04 0.04 q 0.02 0.02 0.0 0.0 0.00 0.00 SUCC047 SUCC048 SUCC049 SUCC047 SUCC048 SUCC049 strain strain Figure 5.17: A. Succinate yields and specific production rates of several mutant strains of which two have a mutated citrate synthase. B. Pyruvate and lactate yields of these strains. The description of the strains is given in Table 5.5. Table 5.5: The corresponding genotype of the mutant E. coli strains in Figure 5.17 and Figure 5.18. The gene names and their product are summarised in the appendix, section A.3. Strain Assigned code MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB ∆arc A ∆dct A ∆edd ∆eda ∆cit DEF ppc * SUCC047 L620S ∆FNR-pro37-dcu C MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB ∆arc A ∆dct A ∆edd ∆eda ∆cit DEF ppc * SUCC048 L620S glt A*K167A ∆FNR-pro37-dcu C MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB ∆arc A ∆dct A ∆edd ∆eda ∆cit DEF ppc * SUCC049 L620S glt A*Y145A ∆FNR-pro37-dcu C Compared to the reference strain SUCC047, the citrate synthase Y145A mutation in strain SUCC049 does not seem to affect the phenotype very much. Except for the pyruvate yield, none of the yields and rates are significantly different (Figure 5.17 and Figure 5.18). Apparently different point mutations that result in similar altered enzyme activities in vitro do not necessarily have the same effect in vivo . A change in mRNA stability may be a possible reason for this difference. The point mutations may have rendered either the mRNA of citrate synthase (K167A) more stable or the mRNA of citrate synthase (Y145A) less stable. These differences will have to be tested with RT-qPCR. 191 Chapter 5 A B 0.6 0.6 0.6 0.6 0.5 0.5 0.5 0.5 ) 0.4 0.4 0.4 0.4 -1 (h (c-mole/c-mole glucose) (c-mole/c-mole max 0.3 0.3 0.3 0.3 µ (c-mole/ c-mole glucose) (c-mole/ c-mole 0.2 0.2 0.2 0.2 OUR (mole/c-mole biomass/h) OUR(mole/c-mole biomass Y carbon dioxide carbon 0.1 0.1 Y 0.1 0.1 0.0 0.0 0.0 0.0 SUCC047 SUCC048 SUCC049 SUCC047 SUCC048 SUCC049 strain strain Figure 5.18: A. Maximal growth rate and biomass yield of several mutant strains of which two have a mutated citrate synthase. B. Carbon dioxide yield and oxygen uptake rate of these strains. The description of the strains is given in Table 5.5. 5.3.4.3 Fumarate nitrate reductase regulating protein To approach the maximal theoretical succinate yield, a certain balance has to be obtained between the glyoxylate shunt, the reductive TCA and the oxidative TCA. A yield of 1.5 mole per mole glucose would require an even distribution of the carbon flux between the glyoxylate shunt and the reductive TCA. If carbon dioxide would be added to the culture in high enough amounts, a yield of 1.71 mole per mole of glucose can be obtained, but then a flux ratio of 18:2:2 (reductive TCA:glyoxylate shunt: oxidative TCA) will be required (Chapter 2). The latter approach will demand an increased reductive TCA, which is normally not active under aerobic conditions. The genes involved in this pathway are controlled by ArcA and FNR (Shalel-Levanon et al. , 2005c; Unden et al. , 2002). FNR is the main transcriptional activator, but only active under anaerobic conditions. This protein is a homodimer that contains iron – sulphur clusters and is sensitive to oxygen. When the protein comes in contact with oxygen it is converted to a monomer called apoFNR which is inactive (Figure 5.19). This results in a very quick adaptation of the cell to changes in the environmental oxygen concentrations (Tran et al. , 2000). Apart from the activation of the reductive TCA, this protein is also described as a repressor of the electron transport chain cytochromes and NADH dehydrogenases (Table 4.4, Chapter 4). It thus not only activates a succinate production pathway, but may help modulate NADH regeneration by the electron transport chain. However, FNR needs to be rendered insensitive to oxygen. 192 Metabolic engineering of succinate biosynthesis in Escherichia coli Figure 5.19: The conversion of FNR into different forms. Oxygen splits off iron and sulphur from the protein, resulting in apoFNR. In anoxic conditions, the active protein is reassembled with iron ions and cystein under the influence of glutathione (Tran et al. , 2000). Based on the protein structure and the reaction mechanisms with oxygen (Figure 5.19), amino acids were identified that desensitised FNR for oxygen. A replacement of leucine 28 with a histidine residue increased the proteins stability significantly (Bates et al. , 2000). Figure 5.20 and Figure 5.21 present the effect of such a mutation on the yields and rates of two different mutant strains. In first instance the mutation was tested with the wild type MG1655 as reference (SUCC001 and SUCC057). The mutated FNR protein does neither influence the pyruvate, lactate, and succinate yield, nor does it influence the production rate (Figure 5.20 (A) and (B)). Biomass yield increased slightly and the carbon dioxide yield and oxygen uptake rate decreased in comparison to the wild type strain (Figure 5.21 (A) and (B)). With the regulatory network of FNR in mind, the latter two results (decrease in OUR and CO 2 yield) may indicate that the mutated FNR protein affects the reductive TCA and reduced electron transport. FNR reduces the activity of the electron transport chain, linked to the OUR and increases the reductive TCA pathway, that starts at PEP carboxylase and which fixes carbon dioxide. Furthermore, the acetate yield of strain SUCC057 was reduced with 40 % (0.09 in comparison to 0.15 c-mole/c-mole glucose for the wild type). This may be the result of a decreased flux towards pyruvate and an increased flux through PEP carboxylase. The effect of FNR (L28H) in a strain that produces succinate, the mutation was introduced in strain SUCC051, resulting in strain SUCC052. There is no notable difference in succinate yield and specific production rate between both strains (Figure 5.20 (A)). Lactate production is not influenced either, however, pyruvate seems to accumulate slightly more (Figure 5.20 (B)). Because previous results showed a decrease in acetate, which is linked to pyruvate, this result is somewhat unexpected. However, the expression of the ace E and ace F subunits of pyruvate dehydrogenase are repressed by FNR (Gama-Castro et al. , 2008). A new bottleneck arises then with the introduction of this mutation. With the inhibition of pyruvate dehydrogenase, acetyl-CoA synthesis will be less efficient. This molecule normally enhances 193 Chapter 5 the activity of PEP carboxylase (Chapter 2, Table 2.11). Nevertheless, the biomass yield was reduced in a strain in which, as described before, some futile cycles were eliminated. A B 0.25 0.25 0.25 0.25 0.20 0.20 0.20 0.20 0.15 0.15 0.15 0.15 (c-mole/c-mole glucose) (c-mole/c-mole (c-mole/c-mole glucose) (c-mole/c-mole (c-mole/c-mole biomass/h) (c-mole/c-mole 0.10 0.10 0.10 0.10 biomass) (c-mole/c-mole lactate pyruvate succinate Y Y succinate Y 0.05 0.05 q 0.05 0.05 0.00 0.00 0.00 0.00 SUCC001 SUCC057 SUCC051 SUCC052 SUCC001 SUCC057 SUCC051 SUCC052 strain strain Figure 5.20: A. Succinate yields and specific production rates of the wild type strain and three different mutant E. coli strains. B. Pyruvate and lactate yields of these strains. The description of the strains is given is Table 5.6. Table 5.6: The corresponding genotype of the mutant E. coli strains in Figure 5.20 and Figure 5.21. The gene names and their product are summarised in the appendix, section A.3. Strain Assigned code MG1655 (WT) SUCC001 MG1655 FNR*(L28H) SUCC057 MG1655 ∆(ackA-pta) ∆poxB ∆iclR ∆sdhAB ∆arcA ∆dctA ∆edd ∆maeAB ∆pckA SUCC051 ∆FNR-pro37-dcuC MG1655 ∆(ackA-pta) ∆poxB ∆iclR ∆sdhAB ∆arcA ∆dctA ∆edd ∆maeAB ∆pckA SUCC052 FNR*(L28H) ∆FNR-pro37-dcuC The redox and carbon balance of this culture are 108 % and 90 %, respectively. Assuming as before that only one byproduct is formed, this byproduct should have a degree of reduction of about 3.33. This implies that 10% of the carbon is converted into aspartate, which is not the case for the reference strain SUCC051. The point mutation in FNR might thus work to enhance the reductive TCA, but does not affect the electron transport chain gene expression enough to reduce NADH regeneration. Hence oxaloacetate accumulates with the formation of aspartate. However, this is only speculation and further analysis will be needed to fully understand these effects. To what extent gene expression is changed by FNR (L28H) under aerobic conditions will have to be evaluated with RT-qPCR. Several promoter strengths could 194 Metabolic engineering of succinate biosynthesis in Escherichia coli be tested then and the effect on the transcript could then be evaluated. 13 C flux analysis may assist to create more insight into the flux changes that FNR brings about. It should certainly help to answer the question whether the reductive TCA is more active in this mutant strain. A B 0.8 0.8 0.6 0.6 0.5 0.5 0.6 0.6 ) 0.4 0.4 -1 (h (c-mole/c-mole glucose) (c-mole/c-mole max 0.4 0.4 0.3 0.3 µ (c-mole/ c-mole glucose) (c-mole/ c-mole 0.2 0.2 OUR (mole/c-mole biomass/h) OUR(mole/c-mole biomass 0.2 0.2 Y carbon dioxide carbon Y 0.1 0.1 0.0 0.0 0.0 0.0 SUCC001 SUCC057 SUCC051 SUCC052 SUCC001 SUCC057 SUCC051 SUCC052 strain strain Figure 5.21: A. Maximal growth rate and biomass yield of the wild type strain and three different mutant E. coli strains. B. Carbon dioxide yield and oxygen uptake rate of these strains. The description of the strains is given in Table 5.6. 5.3.5 Summary of succinate yields and volumetric production rates Figure 5.22 and Figure 5.23 give an overview of the succinate yields and volumetric production rates that were obtained with the mutant E. coli strains described above. The highest yield that could be obtained here was 0.26 gram succinate per gram glucose, which is still a long way from an economically viable yield of 0.8 g/g. The volumetric rates calculated from the final biomass concentration and specific succinate production rates already approach the economical viable rate of 2.5 g/l/h set by the US department of energy (Werpy et al. , 2004). Strain SUCC044 reaches a rate of nearly 2 g/l/h. Further optimisation will require a more thorough knowledge of the transcriptome and fluxome of the mutant strains. The mutations in the genetic regulators induce certain changes in the transcriptome which are difficult to understand purely relying on the strain phenotypes. Similarly, the point mutations in some key enzymes resulted in phenotypes that cannot be explained easily without information about the intracellular fluxes. 195 Chapter 5 Succinate yield 0.35 0.30 0.25 0.20 (g/g glucose) (g/g 0.15 succinate Y 0.10 0.05 0.00 SUCC001 SUCC057 SUCC012 SUCC048 SUCC055 SUCC038 SUCC033 SUCC056 SUCC034 SUCC050 SUCC039 SUCC051 SUCC052 SUCC045 SUCC049 SUCC047 SUCC046 SUCC006 SUCC009 SUCC053 SUCC054 SUCC044 strain Figure 5.22: Overview of the succinate yields that were obtained with the mutant strains constructed in this chapter. The corresponding genotype is given in the appendix, section A.6. Volumetric succinate production rate 2.5 2.0 1.5 (g/l/h) 1.0 succinate r 0.5 0.0 SUCC057 SUCC001 SUCC012 SUCC055 SUCC048 SUCC034 SUCC038 SUCC033 SUCC056 SUCC051 SUCC039 SUCC052 SUCC006 SUCC050 SUCC054 SUCC045 SUCC009 SUCC047 SUCC046 SUCC053 SUCC049 SUCC044 strain Figure 5.23: Overview of the volumetric production rates that were obtained with the mutant strains constructed in this chapter. These rates were calculated from the final biomass concentration in the reactor and the specific succinate production rate. The corresponding genotype is given in the appendix, section A.6. 196 Metabolic engineering of succinate biosynthesis in Escherichia coli 5.4 Conclusions The highest yield of succinate was obtained with strain SUCC044 in which the acetate pathway was knock out, the glyoxylate pathway activity was enhanced through an icl R and arc A knock out, succinate transport was modified, and the Entner Doudoroff pathway was eliminated. In this strain, 27 % of the glucose carbon was converted into succinate. The remaining carbon yielded biomass (36 %), carbon dioxide (16 %), pyruvate (17 %) and minor amounts of acetate (4 %). A study of the acetate formation reactions could not identify the source of the remaining acetate that was produced. Most likely there are yet to be annotated acetate formation reactions in the E. coli reactome. For most strains pyruvate accumulation becomes a problem. The addition of carbon dioxide nearly resolves this problem, but it does not lead to an increased succinate yield. Essentially the problem does not lay solely with the flux from pyruvate to the TCA. The route towards succinate is a subtle balance between two or three pathways and the available reduced equivalents (Chapter 2). This balance can be influenced by a wide range of genes of which most have been tested in this chapter. For instance a point mutation in either PEP carboxylase or citrate synthase reduced pyruvate production. Furthermore, a mutant regulatory protein, FNR, seemed to be able to change the cell physiology. Biomass and carbon dioxide are unavoidable byproducts in a fermentation process. In order to produce, biomass is needed, but the formation of biomass results also in the formation of carbon dioxide and thus a reduced yield. Reducing cell growth will increase the production yield, but will reduce the production rate. The rates obtained in this study are quite steep in comparison to the rates obtained in literature (Chapter 1). However, the yields are somewhat low. Finding the right balance between both is a very difficult exercise. The presence of oxygen is the obvious reason for the high production rates, but also the cause of the low yields. The energy supplied by ATP synthase increases growth rate significantly in comparison to the rate in an anaerobic environment. Introducing an anaerobic high yielding metabolism into a high growth rate aerobic metabolism may thus be the key. The first steps towards such a metabolism have been made with the introduction of a mutated FNR into E. coli . However more research is needed to optimise the expression of this gene. The methodology presented in this work only allows evaluating the phenotype of a certain strain. However, in most cases, transcriptome or fluxome information would come in handy to further optimise the production host. Hence, the next step should be to evaluate the transcriptome and fluxome of these mutant strains with RT-qPCR and 13 C flux analysis. This information will allow for instance further optimising the split ratio between the glyoxylate pathway, the reductive TCA and oxidative TCA. The screening throughput should also be increased in order to speed up the strain development process. Here, strains were screened in reactors, which are quite laborious to set up and to analyse. In principle a strain can be screened in microtiter plates. However, such cultures require low substrate concentrations due 197 Chapter 5 to their lower oxygen transfer rate. The lower substrate concentrations result in lower product formations which are much more difficult to measure by HPLC. More sensitive analytical methodologies, for instance detection by mass spectrometry (GC or LC MS) are then required to evaluate the phenotypes that are obtained. 198 6 Chapter 6 Conclusions & perspectives Conclusions & perspectives Recent years the strain that petrochemistry has put on the environment and economy became more and more clear. This insight led to the identification of new challenges. Oil derived products will become increasingly scarce, leading to a steep increase in food as well as chemical product prices. Hence, alternatives have to be sought. Biotechnology offers an alternative. Biotechnological processes use renewable resources that are mostly derived from plant material. However, because plants are also the cornerstone of our food supply, the competition between food and for instance energy production will have to be addressed. In addition, with the raising of temperatures, production grounds are threatened with degradation, leaving less agricultural space for a combined food and renewable resource production. The answer to this problem may be found in the waste fraction of food production, which is mainly comprised of lignocellulotic biomass. However, the conversion of this biomass into a resource for biotechnological processes needs still a lot of research. Biotechnological evolution Biotechnology has gone through an evolution. Initially, biotechnology was mainly applied for food purposes, for instance beer or wine production. Through screening of microorganisms and spontaneous mutation, the processes were improved. When the biochemistry behind the different phenotypes became clear, screening for spontaneous mutants was replaced with chemical or physical induced mutagenesis. However, this brought some disadvantages along. The mutations are random and may also be adverse for production. With the upcoming of genomics and genetic engineering, rational design was introduced. Rather than screening, now models and calculations took over to find the optimal pathway towards a certain product. This optimal route could then be constructed in a production host. Great efforts were put into the development of novel genetic engineering techniques to manipulate microorganisms in a rational way. Although the success rate depends on the microorganism, nowadays, nearly every cellular level can be modulated through genetic engineering. Furthermore, synthetic biology gained attention, which opened the door for whole pathway engineering and even whole organism engineering. Just recently the synthesis of viruses and bacteria were reported from merely a mixture of nucleotides (Czar et al. , 2009). Despite these great technological advances, still many challenges lay ahead. Rational design requires almost a perfect understanding of the cell and its regulation mechanisms to reach its full potential. The cell however is in many ways still a black box in which certain reactions happen which cannot be explained with the current biochemical and microbiological knowledge. Moreover, even if knowledge exists, it is a tremendous amount of work to study all the cellular mechanisms that are already known. Their complexity make so called “on the back of a coaster” design impossible and require more advanced methodologies to analyse. The data are also spread over a vast number of databases, that each have their own specific purpose and properties, which makes it difficult to query and combine them. In addition, the quality of data is not easy to ensure. Biocyc tries to address these issues with the introduction of a curation system and the combination of many databases into a one microorganism oriented summarising database (Paley & Karp, 2006). Such a system allows the comparison of organisms and the extraction of certain datasets for further processing. Ultimately, the data extracted and 201 Chapter 6 reprocessed may give an indication to which genes may be important for production and which are adverse through comparative and functional genomics for instance. However, this strategy requires reference organisms that already produce the envisaged target molecule, e.g. succinate. For succinate, there are many known natural producing strains that could be used as reference strains. Many other molecules, which may be metabolic intermediates, are more difficult to analyse in such a way. When the aim is to change more general strain properties, such as resistance properties or extremophilic properties (acidic, high temperature, high/low osmotic pressure,…) specific gene targets are very hard to pinpoint. Hence, those properties are difficult to engineer in a rational way. However, nature has developed many evolutionary mechanisms to adapt to harsh environments which can be employed in the lab. For instance, organisms that live in extreme environments have evolved over the ages and rely on many mechanisms for their survival. In the lab, similar mechanisms can be applied either broad spectrum mutagenesis through chemical or physical mutagenesis or more specific mutagenesis with effects on the overall transcription or translation (gTME or ribosome engineering)(Lima et al. , 1997). Eventually the engineering of organisms will not solely be rational design or random mutagenesis, but will move towards a combination of both. The challenge will be when to choose the one or the other. Succinate – economic viability The production processes that have been described in literature mainly aim for high yielding processes and not so much for high production rates. Moreover, for this process, yield and rate seem to be interchangeable. For instance, a succinate producing E. coli shows a significant reduction in production rate with increased yield. However, yields and rates between the described processes are difficult to compare. In most production processes complex media components such as yeast extract or tryptone are used. Within these components there are also carbon sources present. However, this carbon is not always included in the yield calculations, which may mask the true succinate yield. Analogously, the process rate may be overestimated. It is common practice to increase the biomass concentration to obtain sufficient production rate when the specific production rate is low. However, in these cases the inoculum concentration has to be increased also. Therefore a lengthy seed culture process will precede the actual production process, which may have to be included into the overall production rate. However, the production times of these seed cultures are rarely reported. This complicates the comparison of the different processes and the evaluation of genetic targets that are either advantageous or adverse for succinate yield and production rate. Furthermore, the production parameters cannot be weighed against the production parameters of an economical viable process. The reports on the economical viability of biotechnological processes identify two sets of production parameters. The current day production parameters based on the sugar and oil 202 Conclusions & perspectives prices today and the future production parameters, estimated on the future substrate and oil prices. For succinate, currently, a yield of 0.88 g/g, a rate between 1.8 and 2.5 g/l/h and a titer around 80 g/l suffices. However, the processes will have to go through significant improvements in the future due to increasing costs of resources. In the near future a succinate production process should yield between 1 and 1.1 g/g, have a rate of 15 g/l/h and a titer between 150 en 250 g/l to be economically viable. None of the available (reported) processes reach either of these values. In most cases the rate and the titer are still a problem. In addition, the downstream processing of succinate adds considerably to the total production cost (McKinlay et al. , 2007b; Patel et al. , 2006; Song et al. , 2007; Wilke, 1995, 1999). Not only byproduct formation, which is in general other acids, tend to be a problem during production, but also the form in which succinate is produced. All processes are performed near a neutral pH, which means succinate is produced as a salt. Further processing of succinate, relies on the acid form, which is formed at a pH of approximately 4 (Berglund et al. , 1999). This implies that either the production medium or has to be acidified or that ion exchange resins are needed for purification and crystallisation. This problem is similar to the problems that the lactic acid production processes face. To optimise downstream processing for lactic acid, production processes around the acidity constant of lactic acid are optimised. For instance lactic acid bacteria are evolved in the laboratory to more acid resistant phenotypes. However, lactic acid is relatively soluble at low pH, while succinic acid crystallises already at low concentrations. This could make the development of a low pH process a bit easier than for lactic acid, because crystallised succinic acid will not contribute as much to the decoupling of the proton gradient in the cell. However, the pH for such processes should be extremely low, which means acidophilic organisms will have to be either constructed or screened. Optimal candidates for such a process are acidophilic Archaea, which grow at pH’s as low as 1 (Baker-Austin & Dopson, 2007). Yet, a drawback of these organisms is that there is limited knowledge about their genetics and biochemistry, which makes the development of a metabolic engineering strategy more challenging. Mutation strategies As mentioned before, the increasing amount of data that science generates tends to become overwhelming when a metabolic engineering strategy has to be developed. Therefore analysis tools had to be developed that allow researchers to browse through the many datasets. Two tools were used in this work, a comparative and functional genomics toolbox of IMG 1 (Markowitz et al. , 2008) and a elementary flux mode/partial least squares analysis toolbox (Maertens, 2008) (Figure 6.1). The comparative and functional genomics analysis of several natural succinate producing strains together with E. coli and C. glutamicum revealed some reactions in the metabolic network that may be important for succinate production. The natural producing strains differ in quite some reactions from E. coli . 1 Integrated Microbial Genomes of the Joint Genome Institute 203 Chapter 6 to al least squares analysis approaches that were used E. E. coli l l genomics analysis and elementary flux mode /parti uccinate with Figure 6.1: Scheme of the comparative and functiona develop a mutation strategyfor the production of s 204 Conclusions & perspectives The most outspoken differences are the lack of the glyoxylate route, isocitrate dehydrogenase, Entner Doudoroff route and differences in the affinity of PEP carboxykinase in natural succinate producing strains. In addition, the lack of isocitrate dehydrogenase in the natural succinate producing strains explains the auxotrophies that these strains show. These auxotrophies lead to increased substrate costs, but with insight in their biochemistry and genetics the necessary genes, reactions, can be introduced in the future to reduce these costs. However, the presence or absence of a certain gene or function in a natural succinate producing strain does not automatically imply that this gene or function is related to succinate production. A functional and comparative genomics study pinpoints the genes and functions of interest and allows formulating new hypotheses. The elementary flux mode/partial least squares analysis corroborated some of the targets that were identified in the comparative and functional genomics analysis, but also refute some of the targets. For instance the fact that the oxidative TCA cycle (more particular isocitrate dehydrogenase) was not found in any of the natural succinate producing strains may lead to the conclusion that it should be knocked out, but a stoichiometric network analysis did not corroborate this observation. The very small flux that was predicted through this route by elementary flux modes analysis may indicate that this route might not be essential for succinate production but could be beneficial. This analysis also revealed that in order for a natural succinate producing strain to reach the maximal theoretical yield of 1.71 mole succinate per mole of glucose, a pentose phosphate pathway:glycolysis ratio of 6:1 would be needed. Capnophilic organisms however show only small carbon fluxes through the pentose phosphate, which can only lead to a theoretical yield of 1.5 mole succinate per mole glucose (McKinlay et al. , 2007a; McKinlay & Vieille, 2008). A high pentose phosphate flux would also require increased transhydrogenase activity or the introduction of the heterofermentative lactic acid pentose phosphate enzymes with alternative coenzyme specificity. Keeping in mind the enormous progress has been made in genetic engineering, stoichiometric network analysis should no longer be focussed solely on a metabolic network of a single organism, but should be expanded to a broader range of reactions that may be potentially advantageous for production. Therefore, to modulate the flux through the glycolysis and pentose phosphate pathway, alternative reactions such as the Calvin cycle, the Bifidobacterium shunt and the Entner Douderoff pathway were assessed. Each of these reactions have a specific influence on the stoichiometric network. The Calvin cycle enzymes for instance proved to influence the ATP household. Figure 6.1 gives an overview of the applied approach. Increasing a flux through a certain reaction that is advantageous for production is not always straightforward. Many control mechanisms, genetic, enzymatic or other, may modulate the final flux. Both previously described analysis methodologies do not account for these regulatory mechanisms. Therefore, once the optimal pathway has been identified and the production host is chosen, knowledge has to be gathered concerning regulation. For instance, the Krebs cycle and the glyoxylate route are both tightly regulated by several transcription factors. These transcription factors may interact with one and other and their synergetic 205 Chapter 6 effects should therefore be tested. Further, the two entrance points to the TCA cycle, PEP carboxylase and citrate synthase, are allosterically controlled by many TCA cycle intermediates. In order to modulate these regulatory systems, thorough knowledge about their mechanisms is needed. The transcription factor database and enzyme databases are crucial to gather such information. However, a lot of the available information is still focussed on a hand full of microorganisms, e.g. the transcription factor database only focuses on E. coli (Chang et al. , 2009; Gama-Castro et al. , 2008; Keseler et al. , 2009). With the progress in the field of bio-informatics more information will become available for lesser known organism and will allow a more thorough analysis of the regulatory networks of these organisms. However, the developments in the field of genetic engineering may render the application of alternative production hosts obsolete and may even allow the construction of synthetic production hosts. Succinate production A reaction that correlates in all EFM-PLS models with succinate production is the export reaction of succinate. In the literature succinate export activity in E. coli is only described under anaerobic conditions. Apparently the cell uses succinate export for an efficient NADH regeneration via the reductive TCA. DcuC is responsible for this export and is proven to be an antiporter of protons (in) and succinate (out) (Janausch et al. , 2002). However, in order to be active under aerobic conditions, an artificial promoter had to be introduced in front of the dcuC gene. Furthermore, succinate import (DctA) had to be eliminated in order to obtain a higher succinate yielding strain. These experiments however revealed alternative succinate importers. Blast analysis results led to the identification of 8 potential succinate importers. These 8 proteins were knocked out and screened for importer activity. Two were retained after this screening and identified as possible succinate importers, YbhI and YdjN. Changing the two main succinate transporters, DcuC and DctA, resulted in a decreased biomass yield and growth rate. Additional gene knock outs in YbhI and YdjN resulted in an increase in biomass yield and more importantly in an increased growth rate. The former led however to reduced succinate yields, the latter led to enhanced specific production rates. At this time only speculation concerning the exact mode of action of these proteins is possible. For instance, reduced feedback inhibition by succinate might result in increased growth rates and increases in flux towards biomass. Hence, a more thorough analysis of both proteins will be needed to fully understand their contribution to succinate transport. The multitude of strains that were constructed for succinate production ultimately resulted in a production strain that shows a rather low yield (about 0.26 g/g) but with a relatively high production rate (nearly 2 g/l/h). However, many targets were screened that proved to be potentially favourable for succinate production. More particularly the activity of PEP carboxylase or PEP carboxykinase (from a natural succinate producing strain) and citrate synthase will have to be balanced to create the optimal flux ratio between the reductive branch of the TCA, the oxidative branch of the TCA, and the glyoxylate pathway. In order to activate the reductive branch of the TCA more either the pathway will have to be overexpressed or 206 Conclusions & perspectives controlled through a mutated FNR. However, the activity of this mutated regulator is still unclear under the used aerobic conditions. Hence a more thorough transcriptomics study is needed to evaluate its effects. The glyoxylate pathway The analysis of mutant E. coli strains that are involved in the regulation of the TCA cycle and glyoxylate pathway revealed unexpected synergetic effects. This study also shows that the impact of genetic regulators should not be neglected during the development of production hosts. IclR and ArcA are both described in literature as the main regulators (repressors) of the glyoxylate shunt (Perrenoud & Sauer, 2005; Sunnarborg et al. , 1990). Logically, because they are repressors of the aceBAK operon, both should be knocked out to increase succinate yield. However, a ∆icl R∆arc A mutant strain did neither result in an increased succinate yield nor rate but showed an increase in biomass yield. Moreover, this biomass yield reached nearly the theoretical maximum that was predicted for the E. coli metabolic network (Varma & Palsson, 1993). A detailed literature and database review of ArcA revealed its effect on the genes involved in oxidative phosphorylation and consequently, its effect on the P/O ratio. Hence, changes in the expression of arcA may result in changes in the P/O ratio. Furthermore, these changes may alter the cell’s biomass biosynthesis potential. This implies that the P/O ratio should not be assumed constant for an organism that can alter its electron transport chain in a given environment. Consequently, the composition of the electron transport chain should be measured directly to calculate the P/O ratio and the theoretical biomass yield. A high biomass yielding mutant strain may have interesting industrial application. The strain produces negligible amounts of byproducts and has still a growth rate that is about 80 % of that of the wild type. Reduced byproduct formation is an interesting feature for recombinant protein production. Most industrial production E. coli strains form acetate as a byproduct, which lowers productivity and becomes toxic at higher concentrations (De Mey et al. , 2007). Additional experiments should therefore be performed to assess whether these favourable properties are maintained and if the strain can be applied as an industrial protein production host. Strain screening and evaluation The ease with which nowadays genetic modifications can be introduced into a bacterial strain allows the creation of large amounts of mutant strains. These new methodologies have for instance led to the construction of the Keio collection that contains all possible non-lethal single gene mutants of E. coli (http://ecoli.naist.jp/). In this thesis strains were constructed that contained up to 15 knock outs, knock ins and/or point mutations. The creation of such a mutation takes approximately two to three weeks. However, multiple combinations of mutations can be constructed in parallel. This results in a vast amount of mutant strains that have to be evaluated. To obtain production yield and rate information in a controlled environment that does not influence the strain performance, 2 liter bioreactors were chosen. However, the screening in 2 liter reactors is very laborious. Each strain was cultured at least 207 Chapter 6 twice in a reactor and sampled intensively to obtain an acceptable amount of data to calculate the yields and rates. There is thus a discrepancy between the rate with which a mutant strain is created and the rate of strain evaluation. Moreover, the use of 2 liter reactors to screen the different mutant strains does not allow the evaluation of the intracellular flux changes. This setup only allows relating phenotypical changes such as product yield and rates with certain mutations. Insight in the intracellular fluxes will be necessary to further improve the fluxes in the cell towards succinate. Therefore isotope labelling experiments should be performed. In principle such a screening could be carried out in a shake flask, but then the experiment would be influenced by the low oxygen transfer rate when a high glucose concentration is applied or the metabolite concentrations would be too low to measure with a standard HPLC method. In that case alternative measuring methods, for instance with GC MS or LC MSMS will have to be applied to obtain quantitative data. These methods also allow screening in low volume cultures as well as low substrate concentration cultures. Hence, microtiter plate cultures can be used for the screening of mutant strains with labelled substrates (Fischer et al. , 2004; Sauer, 2004). This would permit a high throughput screening of mutant strains and shorten the strain optimisation cycle significantly. However, with an increase in throughput, the amount of data that has to be processed increases and data interpretation becomes the rate limiting step in screening. 208 References References Agarwal, L., Isar, J., and Saxena, R. K. (2005) Rapid screening procedures for identification of succinic acid producers. Journal of Biochemical and Biophysical Methods 63 (1), 24-32 Agarwal, L., Isar, J., Dutt, K., and Saxena, R. K. 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Recent reports of the US department of energy and the GROWTH program of the European commission review a comprehensive list of chemicals that can be produced via biological processes and which may be of great importance to sustain a green chemical industry in the future. Succinate is one of those biochemicals. Today, this compound is synthesised via maleic anhydride, which is produced by a petrochemical production process. The conditions which a biological production processes have to meet to be economically viable are quite strict. Such a process has to obtain a yield of 0.88 g/g, a rate between 1.8 and 2.5 g/l/h and a titer around 80 g/l. None of the available (reported) processes reach either of these values. In most cases the rate and the titer are still a problem. To optimise succinate production via metabolic engineering, first a mutation strategy has to be developed. This strategy can then be applied to a suitable production host. The choice of this host has nowadays become less important due to the recent developments in genetic engineering and synthetic biology. These developments allow the introduction or altering of almost every cellular function. What has become important is the availability of information on the potential host and its genetic accessibility. E. coli is therefore still an excellent host for the development of production processes. Since its isolation vast amounts of information have been gathered and several biological databases are devoted to it. Moreover, almost each cellular function has been modified. However, E. coli does not naturally produce succinate in large amounts. It will have to undergo some genetic modifications to overproduce this chemical. Which modifications are needed can be uncovered in silico . A functional and comparative genomics analyses of natural producing and non-producing strains revealed which genes and reactions may influence succinate production. The optimal biochemical route towards succinate is then uncovered via stoichiometric network analysis. For this analysis, elementary flux modes was combined with partial least squares regression. Both tools resulted in the identification of optimal biochemical production routes for several substrates and allowed to evaluate how reactions that do not naturally occur in E. coli may affect the succinate yield. The transport reaction is one of the reactions that could be identified by the EFM-PLS model. E. coli possesses both succinate import as well as export proteins. However, export is normally only active under anaerobic conditions and import under aerobic conditions. Therefore, the import protein was knocked out and the export protein was expressed with an artificial promoter. These modifications led to an increased succinate yield and production rate, but also revealed alternative import proteins. An analysis of the phenotype of mutant strains in these alternative importers did however not lead to increases in succinate yield. These mutations influenced biomass yield and growth rate. A second route that was identified in the stoichiometric network analysis was the glyoxylate route. This route correlated positively with succinate production and is strongly regulated by 239 Summary the transcription factors ArcA and IclR. In order to gain more insight into the synergy that may exist between both regulators, knock outs in both genes were studied under chemostat and batch conditions. This analysis revealed a synergetic effect between both proteins on the biomass yield. A strain in which both arc A and icl R are knocked out showed a biomass yield that approached the maximal theoretical yield. The single knock out strains did not have such an outspoken phenotype. Finally, several mutations were introduced and evaluated for succinate production and byproduct formation. The formation of acetate was studied in detail to uncover alternative acetate formation reactions. First, the known reactions, acetate kinase, phospho- acetyltransferase and pyruvate oxidase were knocked out. This resulted in a significant decrease in acetate production but not in the total elimination. Several alternatives such as citrate lyase and acetate CoA-transferase were evaluated, but without success. The remaining acetate formation reactions could not be identified. Succinate dehydrogenase can be seen as one of the most crucial enzymes for succinate production. This enzyme converts succinate into fumarate and therefore has to be knocked out to increase production. Strains that possess a succinate dehydrogenase deletion immediately show an increased production. However, pyruvate becomes one of the main byproducts. Several enzymes influence pyruvate production. The most important enzymes in the context of succinate production are PEP carboxykinase, oxaloacetate decarboxylase, malic enzyme, PEP carboxylase, and citrate synthase. The three former reactions are gluconeogenic reactions that can form futile cycles. Deletions in these genes resulted in an increase in biomass yield due to a more energy efficient metabolism, but does not increase succinate yield. Point mutations in PEP carboxylase and citrate synthase increased the flux towards the TCA cycle. The flux ratio between the glyoxylate pathway and the reductive and oxidative TCA cycle can be influenced by these enzymes. The activity of the reductive TCA is however strongly dependent on the availability of reduced equivalents. To modulate this availability a point mutation was introduced in FNR, an anaerobic transcription factor that activates the reductive TCA and represses the electron transport chain. Although none of the developed strains are economically viable yet, many of the mutations that have been introduced show great promise for future improvements. In fact, the next steps in strain development should not be to identify new targets to modify, but rather to fine tune activities of the routes towards succinate in such a way that the theoretical yields can be approached with sufficiently high rates. 240 Samenvatting Samenvatting De hedendaagse problematiek betreffende het klimaat en de slinkende olievoorraden leidt tot een alsmaar toenemende belangstelling voor bio-gebaseerde productie-processen. Niet alleen de productie van bio-energie maar ook van bio-chemicaliën geniet meer en meer aandacht. Het Amerikaanse departement voor energie en het GROWTH programma van de Europese commissie publiceerden recent een uitgebreide lijst chemicaliën die via biologische weg kunnen geproduceerd worden en die in de toekomst van groot belang zullen zijn voor het in stand houden van een groene chemische industrie. Succinaat of barnsteenzuur, is zo een biochemische stof. Op heden wordt deze stof nog geproduceerd vanuit maleïnezuur anhydride dat via petrochemisch weg geproduceerd wordt. De voorwaarden waaraan een biologisch productie proces, in het bijzonder een bulkproductie-proces, moet voldoen om economisch leefbaar te zijn, zijn erg strikt. Zo is een opbrengst van 0.88 g/g, een productiesnelheid tussen 1.8 en 2.5 g/l/h en een titer rond 80 g/l noodzakelijk om te kunnen concurreren met de klassieke chemische processen. Tot op vandaag is er nog geen enkel biologisch productie proces voor succinaat beschikbaar dat voldoet aan deze voorwaarden. Het onderzoek naar een succinaat productie proces focust zich over het algemeen op de productie opbrengst. De productiesnelheden en titer zijn echter nog steeds ontoereikend. Om succinaatproductie te optimaliseren via metabolic engineering dient eerst een mutatie strategie te worden ontwikkeld. Deze strategie kan dan verder uitgevoerd worden in een geschikte productiestam. Met de hedendaagse ontwikkelingen in de moleculaire en synthetische biologie wordt de keuze van de productiestam alsmaar minder belangrijk omdat zo goed als elke functie in een organisme kan worden geïntroduceerd of aangepast. Wat wel belangrijk is, is de beschikbare informatie over het organisme en de genetische toegankelijkheid. Daarom is E. coli nog steeds een uitstekende gastheer voor de ontwikkeling van productieprocessen. Over de jaren heen werd een enorme hoeveelheid informatie reeds verzameld over dit organisme en werden verscheidene databanken hieraan gewijd. Daarenboven kan zowat elk cellulair niveau van dit organisme worden gemodificeerd. E. coli produceert echter niet van nature succinaat. Het zal dus genetisch gemodificeerd moeten worden om deze biochemische molecule te overproduceren. Welke modificaties het organisme moet ondergaan kan achterhaald worden via een analyse van het metabolisme en de genomen van zowel natuurlijk producerende stammen en niet-producerende stammen. Zo kan via vergelijkende studies van de genomen van niet-succinaat producerende en van nature succinaat producerende micro-organismen achterhaald worden welke genen en reacties eventueel een invloed hebben op succinaat productie. De optimale biochemische route naar succinaat kan worden ontsluierd door middel van stoichiometrische netwerk analyse. Hiervoor werden elementary flux mode analyse gecombineerd met partial least squares regressie. Deze voorgaande analyses resulteerden in een optimale productie route voor verschillende substraten en lieten eveneens toe om de invloed te evalueren van reacties die niet eigen zijn aan E. coli . De transport reactie is een van de reacties die werd geïdentificeerd door middel van de EFM- PLS analyse. E. coli bezit van nature succinaat import- en export-eiwitten. Export trad echter enkel op in anaerobe condities, terwijl import vooral optrad onder aerobe condities. Daarom werd het importeiwit uitgeschakeld en werd het exporteiwit tot expressie gebracht onder aerobe condities met een artificiële promoter. Deze ingreep leidde tot een verhoogde succinaat opbrengst en productie snelheid. Een analyse van de mutant stammen resulteerde in de 243 Samenvatting ontdekking van alternatieve succinaat importeiwitten die vervolgens werden uitgeschakeld. De mutant stammen die werden gemodificeerd in deze putative genen, vertoonden een verhoogde biomassa-opbrengst en groeisnelheid. De succinaatopbrengst werd echter niet positief beïnvloed door deze mutaties. The glyoxylaat route werd eveneens geïdentificeerd als een sleutel route voor succinaat productie. Deze route wordt sterk genetisch gereguleerd door ArcA en IclR, die beide het glyoxylaat operon aceBAK represseren. Om een beter inzicht te verwerven in de synergie die bestaat tussen de beide regulatoren werden gen-deletie-mutanten in beide genen bestudeerd onder verschillende condities. Uit deze studie bleek dat beide regulatoren een synergetisch effect hebben op de biomassa-opbrengst. Een stam waarin zowel arcA als iclR is uitgeschakeld vertoont een biomassa-opbrengst die de maximaal theoretische opbrengst benadert. Stammen gemodificeerd in arcA of iclR bleken niet zo een uitgesproken fenotype te bezitten. Finaal werden verschillende mutaties samengebracht en geëvalueerd. Bijproductvorming werd in detail bestudeerd. Meerbepaald de vorming van acetaat werd onderzocht. Verschillende routes die leiden tot acetaatvorming werden een voor een uitgeschakeld. Hierbij werden eerst de bekende routes, acetaat kinase en pyruvaat oxidase uitgeschakeld. Dit leidde tot een significante daling van de acetaat excretie, maar niet tot de eliminatie van acetaat vorming. Hiervoor werden verschillende alternatieve reacties onderzocht, echter zonder succes. De resterende acetaat vormende reactie(s) kon(den) niet worden geïdentificeerd. Succinaat dehydrogenase kan worden gezien als een van de meest cruciale enzymen voor succinaat productie. Dit enzym zet succinaat om in fumaraat en dient dus te worden uitgeschakeld om productie te stimuleren. Stammen met een deletie in dit gen vertonen onmiddellijk een verhoogde opbrengst. Pyruvaat is echter het belangrijkste bijproduct voor deze stammen. Verschillende enzymen kunnen de productie van pyruvaat beïnvloeden. De belangrijkste enzymen voor succinaat productie zijn PEP carboxykinase, malic enzyme , oxaloacetaat decarboxylase, PEP carboxylase en citraat synthase. De eerste drie reacties zijn gluconeogenetische reacties en kunnen futiele cycli vormen. Deleties in deze genen leidden tot verhoogde biomassa opbrengst. Puntmutaties in PEP carboxylase en citraat synthase brachten een verhoogde koolstofstroom naar de Krebs cyclus teweeg. De mate waarin deze enzymen actief zijn is van groot belang. De flux verhouding tussen de glyoxylaat route en de oxidatieve en reductieve Krebs cyclus kunnen door middel van deze enzymen worden beïnvloed. De activiteit van de reductieve Krebs cyclus is echter sterk afhankelijk van de beschikbaarheid van reducerende equivalenten. Om de beschikbaarheid hiervan te sturen werd een puntmutatie aangebracht in FNR, een anaerobe gen regulator die de reductieve TCA activeert en de elektron transport keten represseert. Hoewel de voorwaarden voor een economisch leefbaar proces nog niet werden bereikt, werden reeds verschillende mutaties geïntroduceerd die veelbelovend blijken voor toekomstige productie optimalisatie. Het verdere verloop van dit onderzoek zal zich eerder moeten richten op het verder fijnregelen van de verschillende routes naar succinate, dan op het verder identificeren van genen die de productie kunnen beïnvloeden. 244 Appendix Metabolic model A.1 Metabolic model Mal + Mal CoA + OAA CoA + Cit NADH + H + CO2 + SucCoA → → → OAA + PiOH NADH + H + AcCoA + CO2 → → PEP + ADP + CO2 oA ADP + G6P Pyr + CO2 + NADH + H ATP + Pyr ADP + FBP → → → → → Suc + Glyox P P + iCit = NADPH + H + CO2 + aKGA → H2O + AcCoA + OAA Reaction ATP + GLC NAD + Pyr + C NAD NAD + CoA + aKGA NAD + Mal = NADH + H FBP = FBP G3P + DHAP DHAP = G3P PiOH + NAD + G3P = NADH + H + BPG ADP + BPG = ATP + 3PG 3PG = 2PG 2PG = H2O + PEP ADP + PEP G6P = F6P ATP + F6P Cit =Cit iCit ADP + PiOH + SucCoA = ATP + CoA + Suc FAD + Suc = FADH2 + Fum H2O + Fum = Mal Glyox + AcCoA + H2O iCit Mal + NAD OAA + ATP PEP + CO2 + H2O Code HK PGI PFK ALD TPI G3PDH PGK PGM ENO PyrK PyrD CitSY ACO CitDH AKGDH SucCoASY SucDH FumHY MalDH iCitL MalSY PEPCB PEPCBKN PyrMalCB Enzyme Hexokinase Phophoglucoisomerase Phosphofructokinase Aldolase Triose phosphate isomerase Glyceraldehyde-3-phosphatedehydrogenase Phosphoglycerate kinase Phosphoglycerate mutase Enolase Pyruvate kinase Pyruvate dehydrogenase Citrate synthase Aconitase Isocitrate dehydrogenase alfa-ketoglutatedehydrogenase SuccinylCoA synthetase Succinate dehydrogenase Fumarate hydratase Malate dehydrogenase Isocitrate lyase Malate synthase PEP-carboxylase PEP-carboxykinase Pyruvic-malic carboxylase Group Glycolysis Krebs Glyoxylate bypass Reactions around PEP Reactions around Pyr A3 Appendix NADP + 2 Glu → Gln + ADP + PiOH → 2 PiOH NADPH + H + 6PGL NADPH + H + CO2 + Rl5P 6PG → G6P + Pyr PiOH + F6P → → → FA + AcCoA → → → 1.33 ATP + NAD + 2.33 H2O → aKGA + NH3 + NADPH + H = Glu + NADP + H2O Glu + NH3 + ATP PPiOH + H2O R5P = R1P PEP + GLC AcCoA + 2 NADH + 2 H = Eth + CoA + 2 NAD AcCoA + PiOH + ADP = ATP + Ac + CoA H2O + 6PGL Rl5P = R5P Rl5P = Xu5P R5P + Xu5P = G3P + S7P G3P + S7P = F6P + E4P Xu5P + E4P = F6P + G3P H2O + FBP Reaction Pyr + NADH + H = Lac + NAD Pyr + CoA NADH + H + 1.33 ADP + 1.33 PiOH + 0.5 O2 NADP + G6P NADP + 6PG aKGA + Gln + NADPH + H CO2 + H2O = H2CO3 Code LacDH PFLY EthDHLR AcKNLR Resp H2CO3SY G6PDH LAS PGDH PPI PPE TK1 TA TK2 FBPAS R5P2R1P PTS PPiOHHY GluDH GluLI GluSY Enzyme Lactate dehydrogenase Pyruvate formate lyase Ethanol dehydrogenase (lumped reaction) Phosphate acetyl transferase acetate + kinase Respiration CO2 - HCO3 equilibration reaction Glucose-6-phosphate dehydrogenase Lactonase 6-phosphogluconaatdehydrogenase Phospho pentose isomerase Phospho pentose epimerase Transketolase1 Transaldolase Transketolase2 Fructose-1,6-bisphosphatase Ribose-5-phosphate conversion to ribose-1-phosphate Phosphoenoltransferase PPiOH hydrolase Glutamate dehydrogenase Glutamate-ammonia ligase Glutamate synthase Group Fermentative pathways Aerobic respiration Carbon dioxide PPP PEP dependent transporters PPiOH splitting Glutamate/glutamine biosynthesis A4 Metabolic model → → → → PiOH + NADH + H + aKGA + Ser Asn + Glu + AMP + PPiOH → → Cys + Ac + CoA aKIV + CO2 + NADP + H2O Asn + AMP + PPiOH → → → AMP + PiOH H2O + Gly + MeTHF → → Leu + aKGA + CO2 + CoA + NADH + H Pro + ADP + PiOH + 2 NADP + H2O H2S + Ser + AcCoA H2S + PPiOH + ADP + PAP + Thiored + 3 H2O3 + NADP PAP PAP + H2O Pyr + Glu = aKGA + Ala Val + Pyr = Ala + aKIV Ile + aKGA + CO2 + NH3 + NADP + H2O Glu + ATP + 2 NADPH + 2 H OAA + Glu = Asp + aKGA Asp + ATP + NH3 Thr + Glu + Pyr + NADPH + H H2SO4 + 2 ATP + ThioredH2 + 3 NADPH + 3 H Reaction Asp + Gln + ATP + H2O aKIV + Glu = Val + aKGA aKIV + Glu + AcCoA + NAD + H2O 2 Pyr + NADPH +H NAD + H2O + 3PG + Glu Ser + THF Code AspSY AspTA AspLI AlaTA ValPyrAT ValAT LeuSYLR aKIVSYLR IleSYLR ProSYLR SerLR SerTHM H2SSYLR PAPNAS CysSYLR ketoIsoValerate synthesis - ethylase Enzyme Asparagine synthetase Aspartate transaminase Aspartate Amonia ligase Alanine transaminase Valine Pyruvate aminotransferase Branched chain AA aminotransferase Leucine synthesis (lumped reaction) alfa (lumpedreaction) Isoleucine synthesis (lumped reaction) Prolyne biosynthesis (lumped reaction) Serine biosynthesis Serine transhydroxy m H2S synthesis (lumped reaction) 3' - 5' Bisphosphate nucleotidase Cysteine synthesis (lumped reaction) Group Aspartate/asparagine biosynthesis Alanine biosynthesis Leucine en Valine synthesis Isoleucine synthesis Proline biosynthesis Serine and glycine biosynthesis Cysteine synthesis A5 Appendix → Met + THF + Pyr + Suc → AspSA + ADP + PiOH + NADP Trp + G3P + Pyr + Glu + PPiOH + 2 → → Qa + NADP ThrADP ++ PiOH aKGA + Tyr + CO2 + NADH + H DahpPiOH + → → → → Chor + 2 PiOH Gallic + NADH + H Phe + aKGACO2 + + H2O Shi3P + ADP → → → → Lys + CO2 Dhq + PiOH → ProtoCat + H2O → → AspSA + NADPH + H = HSer + NADP Dhs + NAD Chor + NAD + Glu Reaction Chor + Glu Chor + Gln ++ PRPP Ser H2O + CO2 E4P + PEP + H2O HSer + SucCoA + Cys + MTHF + H2O + CoA + NH3 Asp + ATP + NADPH + H Dhq + NADPH + H Dhq = Dhs + H2O Dhs + NADPH + H = Shi + NADP Shi + ATP Dahp Shi3P + PEP Dhs MDAP + aKGA + Suc + CoA + NADP MDAP HSer + ATP + H2O AspSA + NADPH + H + SucCoA + Glu + Pyr Code Phenylalanine biosynthesis (lumped reaction) Tyrosine biosynthesis (lumped reaction) Tryptophanbiosynthesis Dehydro deoxyphosphoheptonate aldolase Dhq synthase Dhs synthesis Shikimate synthesis Shikimate kinase Dehydro quinatedehydrogenase Chorismate synthesis (lumped reaction) Dhs dehydratase Threonine synthesis (lumped reaction) MDAP synthesis (lumped reaction) Diaminopimelate decarboxylase Methionine synthesis (lumped reaction) Aspartate Semi aldehyde synthese (lumpedreaction) Homoserine dehydrogenase Gallic acid formation Enzyme PheSYLR TyrSYLR TrpSYLR DhDoPHepAD DhqSY DhsSYLR ShiSY ShiKN DhqDH ChorSYLR DhsDH ThrSYLR MDAPSYLR LysSY MetSYLR AspSASY HSerDH GallicSY Group Aromatic amino acids (shikimate route) Threonine,Lysine and methionine synthesis Gallic acid formation A6 Metabolic model → → CarP + Glu + 2 ADP + PiOH → GMP + AMP + Glu + PPiOH Arg + Fum + AMP + PPiOH + PiOH → → XMP + NADH + H dADP Thiored + + H2O dGDP Thiored + + H2O AMP + GDP + PiOH + Fum → Bka IMP + THF + H2O → → → → → dAMP + PiOH dGMP + PiOH dATP + ADP dGTP + ADP GDP + ADP GTP + ADP → → → → A + ATP + NADPH + H + H2O → → Cat + CO2 2 H2O O2 + FADH2 + NAD = FAD + NADH + H → H + CoQ = NAD + CoQH2 → Thiored NADPH + + H = ThioredH2 + NADP 2 H2O2 FADH2 + NAD = FAD + NADH + H AMP + ATP = 2 ADP ADP + ThioredH2 IMP + GTP + Asp Reaction H2CO3 + Gln + 2 ATP + H2O 2 Glu + AcCo Orn + aKGA + Ac + NADP + ADP + PiOH + CoA CarP + Orn + Asp + ATP NADH + NADP + NADH = NADPH + NAD PRPP + 2 Gln + Gly + Asp FA + +CO2 + 6 ATP + 3 H2O AICAR + Fum + 2 Glu + PPiOH + 6 ADP + 6 PiOH AICAR + FTHF GDP + ATP dADP + ATP GMP + ATP IMP + NAD + H2O GDP + ThioredH2 dADP + H2O XMP + ATP + Gln + H2O dGDP + ATP Cat Cat + O2 + H2O ProtoCat dGDP + H2O Code CarPSY OrnSYLR ArgSYLR ThioredRD H2O2ox FAD2NAD CoQ2NAD NADH2NADP H AICARSYLR IMPSYLR AMPSYLR AdKN ADPRD dADPKN dADPPT IMPDH GMPSY GuKN GDPKN GDPRD dGDPKN dGDPPT ProtoCatDC BkaSYLR Enzyme Carbamoyl phosphate synthase Ornithine synthesis (lumped reaction) Arginine synthesis (lumped reaction) Thioredoxine reductase hydroperoxidase FAD2NAD Quinone reductase NADH2NADPH Beta ketoadipate synthesis (lumpedreaction) AICAR synthesis (lumped reaction) IMP synthesis (lumped reaction) AMP synthesis (lumped reaction) Adenylaat kinase ADP reductase dADP kinase dADP phosphatase IMP dehydrogenase GMP Synthase Guanilate kinase GDP kinase GDP reductase dGDP kinase dGDP phosphatase ProtoCat decarboxylase Group Arginine synthesis Oxido reductionreactions Purine nucleotides synthesis Catechol productiondegradation - A7 Appendix 2 → CTP + Glu + ADP + PiOH UMP + CO2 + PPiOH + FTHF + NADPH + H → → → MTHF + NAD THF + NADP → O2 dUDP Thiored + + H2O dCDP + Thiored + H2O → FTHF + ADP + PiOH O + 2 + NAD Gln dTMP + DHF → → → → dTDP + ADP dCMP + PiOH dUTP + ADP THF + FA dTMP + PiOH dUMP + PPiOH dCTP + ADP dUTP + NH3 dTTP + ADP UDP + ADP +CDP ADP CMP + PiOH UTP + ADP PRPP + AMP → → → → → → → → → → → → → → CDP +CDP ATP = CTP + ADP +CDP H2O dCDP + H2O dCDP + ATP UDP + ATP CDP +CDP ThioredH2 Reaction CarP + Asp + PRPP + PiOH + H2O2 + H2O UMP + ATP MeTHF + NADH + H R5P + ATP PRPP + ATP + 3 H2 PPiOH + PiOH + AICAR + aKGA + His + 2 NADH + 2 H THF + Gly + NAD = MeTHF + CO2 + NH3 + NADH + H UDP + ThioredH2 CMP CMP + ATP THF + FA + ATP dCTP + H2O UTP + ATP + Gln + H2O DHF + NADPH + H dTDP + H2O dUDP + ATP dTDP + ATP dTMP + ATP MeTHF + NADP + H2O dUMP + MeTHF dUTP + H2O FTHF + H2O Code UMPSYLR UrKN UDPKN CTPSY CDPKN CDPPT CMPKN CDPRD dCDPKN dCDPPT dCTPDA UDPRD dUDPKN dUTPPPAS dTMPSY dTMPKN dTDPKN dTDPPT DHFRD FTHFSYLR FTHFLY GlyCA MeTHFRD FTHFDF PrppSY HisSYLR Enzyme UMP synthesis (lumped reaction) Uridilate kinase UDP kinase CTP synthase kinaseCDP phosphataseCDP CMP kinase reductaseCDP dCDP kinase dCDP phosphatase dCTP deaminase UDP reductase dUDP kinase dUTP pyrophosphatase Thymidilate synthase dTMP kinase dTDP kinase dTDP phosphatase DHF reductase FTHF synthesis (lumped reaction) Formate THF ligase gcv system MeTHF reductase FTHF deformylase Phosphoribosyl pyrophosphate synthase Histidine biosynthesis (lumped reaction) Group Pyrimidine ribonucleotides synthesis One carbon metabolism Hypothetical Histidine biosynthesis A8 Metabolic model 15 NADP + C181ACP + 13 NADP + C161ACP + 14 NADP + C160ACP + 11 NADP + C141ACP + 12 NADP + C140ACP + 10 NADP + C120ACP + → → → → → → PA + 2 ACP → UDPNAG + PPiOH + CoA Lipa + 6 +ACP 2 Ac + ADP UDP ++ 2 CMP + UMP → → PSer + CMP → PA + ADP Glu + GA6P → AcACP + CO2 → → PEthAn + CO2 → 5 C140ACP + 2 UDPNAG + ATP + 2 CMPKDO + C120ACP PA PA + CTP = CDPDGo + PPiOH CDPDGo + Ser DHAP + NADPH + H = Go3P + NADP DGo + ATP PSer GA6P = GA1P UTP + GA1P + AcCoA 8 CO2 + 8 +ACP 8 H2O C160ACP + C181ACP + Go3P F6P + Gln 7 CO2 + 7 +ACP 7 H2O AcACP +8 MalACP +15 NADPH + 15 H 7 CO2 + 7 +ACP 7 H2O AcACP +7 MalACP +13 NADPH + 13 H 6 CO2 + 6 +ACP 6 H2O AcACP +7 MalACP +14 NADPH + 14 H 6 CO2 + 6 +ACP 6 H2O AcACP +6 MalACP +11 NADPH + 11 H 5 CO2 + 5 +ACP 5 H2O AcACP +6 MalACP +12 NADPH + 12 H AcACP AcACP + CoA = AcCoA + ACP AcACP +5 MalACP +10 NADPH +10 H Reaction AcCoA + ATP + CO2 + H2O = MalCoA + ADP + PiOH MalCoA + =ACP MalACP + CoA MalACP LipaSYLR Code AcCoACB MalCoATA AcACPSY AcCoATA C120SY C140SY C141SY C160SY C161SY C181SY AcylTF Go3PDH DGoKN CDPDGoSY PSerSY PSerDC GlnF6PTA GlcAnMU NAGUrTF - phosphate - 1 - phosphate transaminase Acetylglucosamine - Enzyme AcCoA carboxylase MalCoA ACP transacetylase beta-ketoacyl-ACP synthase AcCoA-ACP transacetylase C12.0-ACP synthesis C14.0-ACP synthesis C14.1-ACP synthesis C16.0-ACP synthesis C16.1-ACP synthesis C18.1-ACP synthesis Acyltransferase Glycerol-3-phosphate dehydrogenase Diacylglycerol kinase CDP-Diacylglycerol synthetase Phosphatidylserine synthase Phosphatidylserine decarboxylase Glutamine fructose-6- Phosphoglucosamine mutase N uridyltransferase Lipid A synthesis (lumped reaction) Group Fatty acids synthesis EnvelopeCell Biosynthesis Lipid A biosynthesis A9 Appendix 2 → → PiOH + RNA → 6 dTTP6 n + 0.04505 Asp ly0.01802 + His + 0.002291DNA + et + 0.03504Phe + rp0.02603Tyr + + Biom Lipid + PiOH + 2 H2O → → Lipid → 2ADP +2ADP 4 PiOH + 2GDP + Pro CMP + PG + PiOH → → CL + CMP → ADP + PiOH Glcg + ADP + PPiOH → → C160ACP + C161ACP + C181ACP + Go3P 0.004561Glcg + 0.0002663 +Lps 0.0008933 Peptido + 0.01446RNA + 0.12270.003642 +Prot Lipid 2 H2O + 0.246dATP + 0.254dGTP + 0.254 dCTP + 0.24 ATP + H2O Peptido + UDP + UMP + 5 ADP + 7 PiOH + NADP G1P + ATP PG PG + CDPDGo 0.0266CL + 0.202PG + 0.7714PEthAn + 0.01702 Cys + 0.04905+ 0.04905Glu Gln 0.1151 + G 0.05405Ile + 0.08408 Leu + 0.06406 Lys0.02903 + M Reaction CDPDGo + Go3P 2ATP + 3H2O + 0.0961 0.05506Ala + 0.04505Arg + As 0.04104 Pro + 0.040040.04705 Ser + 0.01101TThr + 0.07908Val + 2GTP 2 UDPNAG + MDAP + 3 Ala + PEP + 5 ATP + NADPH + H PiOH + DNA 2 H2O + 0.262 ATP0.322 +GTP + 0.2 CTP + 0.216 UTP Code PGSYLR CLSY PeptidoSYLR GlcgSY ATPHY DNASYLR RNASYLR ProtSYLR LipidSYLR BiomSYLR LipidSYLR Enzyme PG synthesis (lumped reaction) Cardiolipin synthase Peptido synthesis (lumpedreaction) Glycogen synthase ATP hydrolysis DNA formation (lumped reaction) RNA formation (lumped reaction) Protein formation (lumpedreaction) Lipid formation (lumped reaction) Biomass synthesis Lipid formation (lumped reaction) Group Cardiolipin synthesis Peptidoglycan synthesis Glycogen metabolism Maintenance - futiele cycle Biomass formation A10 Metabolic model CMPKDO + PPiOH + 2 → AcCoA + G3P + H2O RI15bP + ADP AcCoA + E4P + H2O → → → 2 3PG NADPH + H + CO2 + Rl5P → NADH + H + 6PGL PPiOH + UDPGlc ADPHEP + PPiOH → Lps + 3 ADP + 2 UDP + 3 CMP + 2 CDP → → → F6P → → X6P + PiOH + CoA 6PG + H2O = 2D3DG6P 2D3DG6P = G3P + PYR F6P + PiOH + CoA PEthAn + CMP = CDPEthAn + DGo Lipa + 3 ADPHEP + 2 UDPGlc + 2 CDPEthAn + 3 CMPKDO G1P + UTP RI5P + ATP + H2O Reaction Rl5P = Ar5P G6P = G1P PEP + Ar5P + CTP + 2 H2O PiOH S7P + ATP NAD + G6P NADP + 6PG SUCR + PiOH = F + G6P F + ATP RI15bP + CO2 Code A5PIR PGLCMT CMPKDOSYLR ADPHEPSY UDPGlcSY EthANPT LpsSYLR EDD EDA F6PK X5PK G6PDH PGDH RI5PK RUB SP FK Enzyme Arabinose 5 phosphate isomerase Phosphoglucomutase CMPKDO synthesis (lumpedreaction) ADP-L-glycero-D-mannoheptose-6-epimerase UDP glucose-1-phosphate uridylyltransferase Ethanolaminephosphotransferase LPS Synthesis - truncated version ofLPS (lumped reaction) phosphogluconate dehydratase 2-keto-3-deoxygluconate-6-phosphate aldolase Fructose-6-phosphoketolase Xylulose-5-phosphate phosphoketolase Glucose-6-phosphate dehydrogenase 6-phosphogluconaatdehydrogenase Ribulose-5-phosphate kinase Rubisco Sucrose phosphorylase Fructokinase Group LPS synthesis Additional reaction Entner-Doudoroff Bifidobacterium shunt Heterolactic acid reactions Calvin cycle reactions Sucrose uptake A11 Appendix A.2 Metabolite abbreviations Abbreviation Metabolite ATP Adenosine triphosphate A Adenine Ac Acetate AcACP Acetyl ACP AcCoA Acetyl CoA ACP Acyl carier protein AdoHCy S-Adenosyl homocysteine AdoMet S-Adenosyl methionine ADP Adenosine diphosphate ADPHEP ADP-Mannoheptose AICAR aminoimidazole carboxamide ribonucleotide aKBA Alfa keto boterzuur aKGA Alfa keto glutaarzuur aKIV alpha-keto-isovalerate Ala Alanine AMP Adenosine monophosphate Ar5P Arabinose 5 phosphate Arg Arginine As Adenosine Asn Aspartate Asp Asparagine AspSA Aspartate semialdehyde Biom Biomass Bka Beta ketoadipate BPG 1,3-biphosphoglycerate C Cytosine CarP Carbamoyl phosphate Cat Catechol CDP Citidine diphosphate CDPDGo CDP-diacylglycerol CDPEthAn CDP-ethanolamine Chor Chorismate Cit cisaconitate CL Cardiolipin CMP Citidine monophosphate CMPKDO CMP-2-keto-3-deoxyoctanoate CO2 Koolstofdioxide CoA Coenzyme A Cs Cytidine CTP Citidine triphosphate Cys Cysteine Cyst Cystathionine dADP deoxy ADP Dahp Deoxy arabino heptulosonate A12 Metabolites Abbreviation Metabolite dAMP deoxy AMP dATP deoxy ATP dCDP deoxy CDP dCMP deoxy CMP dCTP deoxy CTP dGDP deoxy GDP dGMP deoxy GMP DGo Diacyl glycerol dGTP deoxy GTP DHAP Dihydroxyaceton phosphate DHF Dihydrofolate Dhq Dehydroquinate Dhs Dehydroshikimate DNA DNA dTDP deoxy TDP dTMP deoxy TMP dTTP deoxy TTP dUDP deoxy UDP dUMP deoxy UMP dUTP deoxy UTP E4P Erythrose-4-phosphate Eth Ethanol F6P Fructose-6-phosphate FA Formic Acid FAD Flavine adenine dinucleotide FADH2 FADH2 FBP Fructose-1,6-biphosphate FTHF Formyl tetrahydrofolate Fum Fumarate G Guanine G1P Glucose-1-phosphate G3P Glyceraldehyde-3-phosphate G6P Glucose-6-phosphate GA1P D-glucosamine-6-phosphate GA6P D-glucosamine-6-phosphate Gallic Gallic acid GDP Guanosine diphosphate GLC Glucose Glcg Glycogen Gln Glutamine Glu Glutamate Gly Glycine Glyox Glyoxylate GMP Guanosine monophosphate Go GLycerol Go3P Glycerol-3-phosphate A13 Appendix Abbreviation Metabolite Gs Guanosine GTP Guanosine triphosphate H Hydrogene H2CO 3 Bicarbonate H2O Water H2O2 Peroxide H2S Hydrogene sulfide H2SO 4 Sulfuric acid HCy Homocysteine His Histidine HSer Homoserine HXan Hypoxanthine iCit isocitrate Ile Isoleucine IMP Inosine monophosphate Lac Lactate Leu Leucine Lipa Lipid A Lipid Lipids Lps Lipo Poly saccharide Lys Lysine Mal Malate MalACP Malonyl ACP MalCoA Malonyl CoA MDAP Meso-diaminopimelate Met Methionine MeTHF Methyleen tetrahydro folate MMalCoA Methylmalonyl CoA MNTHF Forminino tetrahydrofolate MTHF Methyl tetrahydrofolate NAD Nicotinamide adenine dinucleotide NADH NADH NADP Nicotinamide adenine dinucleotide phosphate NADPH NADPH NH 3 Ammonia O2 Oxygen OA Oleinic acid OAA Oxaloacetate OACoA Oleine CoA ODGo Oleinic diacylglycerol ODGo3P 1,2-Oleinic glycerol-3-phosphate OH Hydroxyl OMGo Oleinic monoacylglycerol OMGo 3P 1-Oleinic glycerol-3-phosphate Orn Ornithine OTGo Oleinic triacylglycerol A14 Metabolites Abbreviation Metabolite PA Phosphatidyl acid PalACP Palmitic acid ACP PAP adenosine 3' 5'-bisphosphate PEP Phosphoenolpyruvate Peptido Peptidoglycan PEthAn Phosphatidyl ethanolamine PG Phosphatidyl glycerol Phe Phenylalanine PiOH phosphate PpCoA Propionyl CoA PPiOH Pyrophosphate Pro Proline Prot Protein ProtoCat Protocatechol PRPP 5-phospho-alfa-D-ribosyl-1-pyrophosphate PSer Phosphatidyl Serine Pyr Pyruvate Qa Quinate R1P Ribose-1-phosphate R5P Ribose-5-phosphate RI15bP Ribulose-1,5-bisphosphate Rl5P Ribulose-5-phosphate RNA RNA S7P Sedoheptulose-7-phosphate Ser Serine Shi Shikimate Shi3P Shikimate 3 phosphate Suc Succinate SucCoA Succinyl CoA T Thymidine TDP Thymidine diphosphate THF Tetrahydrofolate Thiored Thioredoxin ThioredH2 Reduced thioredoxin Thr Threonine TMP Thymidine monophosphate Trp Tryptofaan Ts Thymidine TTP Thymidine triphosphate Tyr Tyrosine U Uracil UA Urinic acid UDP Uridine diphosphate UDPGlc UDP glucose UDPNAG UDP N-acetyl glucosamine A15 Appendix Abbreviation Metabolite UMP Uridine monophosphate Ureum Ureum Us Uridine UTP Uridine triphosphate Val Valine Xan Xanthine XMP Xanthosine-5-phophate Xu5P Xylulose-5-phosphate 2D3DG6P 2-keto-3-deoxygluconate-6-phosphate 2PG 2-phosphoglycerate 3PG 3-phosphoglycerate 6PG 6-phosphogluconate 6PGL 6-phosphogluconolacton A16 Genes and gene products A.3 Genes and gene products Gene Gene product aceA Isocitratelyase aceB Malate synthase ackA Acetate kinase acn Aconitase acs AcetylCo synthase adhCEP Ethanol dehydrogenase adhE Acetyldehydrogenase aldH Aldehyde dehydrogenase alsI Ribose-5-phosphate isomerase B / allose-6-phosphate isomerase aspA Aspartate ammonia-lyase aspC Aspartate aminotransferase citDEF Citrate lyase dctA Succinate importer dcuC Succinate exporter eda Oxaloacetate decarboxylase / 2-keto-3-deoxy-6-phosphogluconate aldolase ,/ 2-keto-4-hydroxyglutarate aldolase edd Phosphogluconate dehydratase eno Enolase fbaAB Fructosebisphosphate aldolase fbp Fructose-1,6-bisphosphatase I focA, focB Formate FNT transporter fumABCD Fumarase gapA Glyceraldehyde-3-phosphate dehydrogenase glk PTS, glucokinase glpX Fructose-1,6-bisphosphatase II gltA Citrate synthase gnd 6-phosphogluconate dehydrogenase gpmAM, ytjC Phophoglyceratemutase icd Isocitrate dehydrogenase ilvBG 2HIMN Acetolacetate decarboxylase ldhA Lactate dehydrogenase lldP Lactate transporter maeA,B Malic enzyme mdh Malate dehydrogenase mgsA Methylglyoxal synthase pck Phosphoenolpyruvatecarboxykinase pdh Pyruvate dehydrogenase pfkA,B 6-phophofructokinase pflB, tdcE Pyruvateformatelyase pgi Phosphogluco-isomerase pgi Phosphoglucose isomerase pgk Phophoglyceratekinase poxB Pyruvate oxidase ppc Phosphoenolpyruvatecarboxylase pps Phosphoenolpyruvate synthase pta Acetylphosphotransferase A17 Appendix Gene Gene product pyk Pyruvate kinase rpe Ribose-5-phosphate epimerase rpiA Ribose-5-phosphate isomerase A sdhABCD Succinate dehydrogenase sucAB,lpd α-ketoglutarate dehydrogenase sucCD Succinate thiokinase talA, talB Transaldolase A; transaldolase B tktA, tktB Transketolase I; transketolase II tpi Triosephosphateisomerase ybhA Pyridoxalphosphatase| Fructose-1,6-bisphosphatase zwf Glucose-6-phosphate-1-dehydrogenase A18 EFM-PLS reactions A.4 EFM-PLS reactions Number Reaction Number Reaction Number Reaction Number Reaction 1 AcACPSY 51 GuKN 101 RNASYLR 151 TK1 2 AcylTF 52 H2O2ox 102 ProtSYLR 152 TK2 3 ADPHEPSY 53 H2SSYLR 103 LipidSYLR 153 TPI 4 ADPRD 54 HisSYLR 104 BiomSYLR 154 ValAT 5 AICARSYLR 55 iCitL 105 SucEXT 155 SucDH 6 AKGDH 56 IleSYLR 106 EthEXT 7 aKIVSYLR 57 IMPDH 107 FAEXT 8 AMPSYLR 58 IMPSYLR 108 AcEXT 9 ArgSYLR 59 LAS 109 LacEXT 10 AspSASY 60 LeuSYLR 110 A5PIR 11 AspSY 61 LipaSYLR 111 AcCoACB 12 ATPHY 62 LpsSYLR 112 AcCoATA 13 C120SY 63 LysSY 113 AcKNLR 14 C140SY 64 MalSY 114 ACO 15 C141SY 65 MDAPSYLR 115 AdKN 16 C160SY 66 MeTHFRD 116 AlaTA 17 C161SY 67 MetSYLR 117 ALD 18 C181SY 68 NAGUrTF 118 AspTA 19 CarPSY 69 OrnSYLR 119 CDPDGoSY 20 CDPRD 70 PAPNAS 120 CDPKN 21 ChorSYLR 71 PEPCB 121 CitDH 22 CitSY 72 PEPCBKN 122 CoQ2NAD 23 CLSY 73 PeptidoSYLR 123 DhsSYLR 24 CMPKDOSYLR 74 PFK 124 ENO 25 CMPKN 75 PFLY 125 EthANPT 26 CTPSY 76 PGDH 126 EthDHLR 27 CysSYLR 77 PGSYLR 127 FAD2NAD 28 dADPKN 78 PheSYLR 128 FumHY 29 dCDPKN 79 PPiOHHY 129 G3PDH 30 dGDPKN 80 ProSYLR 130 GlcAnMU 31 DGoKN 81 PrppSY 131 GluDH 32 DhDoPHepAD 82 PSerDC 132 GlyCA 33 DHFRD 83 PSerSY 133 Go3PDH 34 DhqDH 84 Glycerol_up 134 H2CO3SY 35 DhqSY 85 PyrD 135 HSerDH 36 dTDPKN 86 PyrK 136 LacDH 37 dTMPKN 87 PyrMalCB 137 MalCoATA 38 dTMPSY 88 Resp 138 MalDH 39 dUDPKN 89 SerLR 139 NADH2NADPH 40 dUTPPPAS 90 SerTHM 140 PGI 41 FBPAS 91 ShiKN 141 PGK 42 FTHFLY 92 ThrSYLR 142 PGLCMT 43 FTHFSYLR 93 TrpSYLR 143 PGM 44 G6PDH 94 TyrSYLR 144 PPE 45 GDPKN 95 UDPGlcSY 145 PPI 46 GDPRD 96 UDPKN 146 R5P2R1P 47 GlcgSY 97 UDPRD 147 ShiSY 48 GlnF6PTA 98 UMPSYLR 148 SucCoASY 49 GluLI 99 UrKN 149 TA 50 GMPSY 100 DNASYLR 150 ThioredRD A19 Appendix A.5 Primers used for genetic engineering Primer name Sequence (5’-3’) Fw-o-ackA-P1 CTCTATGGCTCCCTGACGTTTTTTTAGCCACGTATCAATTATAGGTACTT GTGTAGGCTGGAGCTGCTTC Rv-o-ackA-P2 CGATTTGGCGGGTTACAAAACAGCACCGCCAGCTGAGCTGGCGGTGTG AAACATATGAATATCCTCCTTAG Fw-ackA-out GTCGTCAAATTCATATACATTATG Rv-ackA-out CGCTCAGACGAACGCCTTTG Fw-arcA-P1 GGTTGAAAAATAAAAACGGCGCTAAAAAGCGCCGTTTTTTTTGACGGTG GTAAAGCCGAGTGTAGGCTGGAGCTGCTTC Rv-arcA-P2 GGTCAGGGACTTTTGTACTTCCTGTTTCGATTTAGTTGGCAATTTAGGTA GCAAACCATATGAATATCCTCCTTAG Fw-arcA-out CTGCCGAAAATGAAAGCCAGTA Rv-arcA-out GGAAAGTGCATCAAGAACGCAA Fw-arcA-1kb CTCAACGCGCCAAATATCACC Rv-arcA-1kb TCGGCTTTACCACCGTCAAAA Fw-arcA-1kb-out GATGGTTGATGGCAATCAGAAC Fw-arcA-0.5kb GTACGCCATTCTGCTGATTGC Fw-atoDA-P1 ACTCCCCTTGCTATTGCCTGACTGTACCCACAACGGTGTATGCAAGAGG GATAAAAA GTGTAGGCTGGAGCTGCTTC Rv-atoDA-P2 TCGGGAAGCCACCGGCTGACAAAACGCGTCATAAAACGCGATATGCGA CCAATCATAA CATATGAATATCCTCCTTAG Fw-atoDA-out TGCAGAAATTTGCACAGTGCG Rv-atoDA-out GTTTGTGGTGTTAACCAAAGCG Rv-BamHI-fnrH2-p37- TTTTGGATCCAAACAATTTGTGCCAGCTTGTTCACACTTTTATGTAAAGT RBS TACCCTTAACTTACATGAAAAAGGTTCTTGACATTTTAAATCCATGTGGT ATATGTCATTTTT ATATCAATTACGGCTTGAGCAGACCT Fv-BamHI-P1 TTTTGGATCC GTGTAGGCTGGAGCTGCTTC Rv-BamHI-pro8 TTTTGGATCCCATCTTTGTTTCCTCCGAGTAACCCGCGAATTATATC Rv-BamHI-pro37 TTTTGGATCCCATCTTTGTTTCCTCCGAGAAAAATGACATATACCACATG G Rv-BamHI-pro37-RBS TTTTGGATCCCTTACATGAAAAAGGTTCTTGACATTTTAAATCCATGTGG TATATGTCATTTTT ATATCAATTACGGCTTGAGCAGACCT Rv-BamHI-pro55 TTTTGGATCCCATCTTTGTTTCCTCCGAGATACCTAAAAATTATACC Rv-BamHI-TS-fnr CCCCGGATCCACATTAAACAATTTGTGCCAGCTTGTTCACAC Fw-citDEF-0.5 CGGCAATGCTCAACATGCC Rv- citDEF-0.5 CATGCACCTGCTTCCTGAACTC Fw-citDEF-out AGGCGGCAGTGAATAGTC Rv- citDEF-out ATTGAGCGGCTGCGTTACC Fw-citDEF-P1 GGAATTGATACCGCATGGTGGCTGGCGAGTTCAGGAAGCAGGTGCATG GTGTAGGCTGGAGCTGCTTC Rv-citDEF-P2 CCGCCAGGACGCGGCAGCTCGTCAAAAGACCCCCGCATGAGAAACAGG TGAAA CATATGAATATCCTCCTTAG Fw-citT-P1 AGGTGGGCTTACCTGGCGGGCGTGATGATTTATTCAGCGTTTGGCGAAC GTA GTGTAGGCTGGAGCTGCTTC Rv-citT-P2 TAATTATTTAAGCACTTGATAAATTTGGAAATATTAATTTTCGGAGAACC CGT CATATGAATATCCTCCTTAG Fw-citT-out GCAGAAGAGAAGGGAAGCAGAG A20 Genetic engineering primers Primer name Sequence (5’-3’) Rv-citT-out TTATCTCCGGCAGTTCGACAG Fw-dctA-P1 CAGGGGTCAATTATGCGCAAACACCCGCACTCGGGGAAGGGAGTGCGG GCATAAGTGATGAGA GTGTAGGCTGGAGCTGCTTC Rv-dctA-P2 CAGGTACCCCATAACCTTACAAGACCTGTGGTTTTACTAAAGGACACCC T CATATGAATATCCTCCTTAG Fw-dctA-out TGCCCGTTGTCGGTCAGTC Rv-dctA-out ATCTTCGAAGAGAGTTACCTGG Fw-dctA-0.5kb GCTTCAACGGGTTGTTTCAGC Rv-dctA CATCACTTATGCCCGCACTC Fw-dctA-0.5kb-out GTGCGGCTGATTCACGAATC Fw-DcuC-out CGAGCTACACCCACAATAACC Rv-DcuC-out TCAGGCTGTTGCCAGGTTTGT Fw-DcuC-out2 CCCACAATAACCACAACCCCA Rv-DcuC-out2 TTCAGGCTGTTGCCAGGTTTG Rv-dcuC-out3 ATCCCAACGCGACAGGCCAA Fw-DcuC-pro8 TGCCCAATAAATTGGCGATGAATGCTGATTAAAATCAAGAAAAACTGCC A---TAACCCGCGAATTATATCATATTG Rv-DcuC-P2 GCTACAATTAACATAACCTTAATAGACCCAACATAAAGAATAATCTGAA TAGCCATATGAATATCCTCCTTAG Fw-DcuC-pro37 TGCCCAATAAATTGGCGATGAATGCTGATTAAAATCAAGAAAAACTGCC ATAAAAATGACATATACCACATGGA Fw-DcuC-pro55 TGCCCAATAAATTGGCGATGAATGCTGATTAAAATCAAGAAAAACTGCC ATATACCTAAAAATTATACCACATCAAC Fw-dcuC-qPCR AGCCACTTCCAGACCAGAGA Rv-dcuC-qPCR CCGATCATCGGTGTACTGAT Rv-EcoRI-P2 TTTTGAATTCCATATGAATATCCTCCTTAG Fw-EcoRI-pro8 GGGGGAATTCCTTTGTCGGAAAACATTCTTAC Fw-EcoRI-pro37 GGGGGAATTCCTTACATGAAAAAGGTTCTTG Fw-EcoRI-pro55 GGGGGAATTCCTTAGAAGGAATTTGTTCTTG Fw-eda-P1 GGCAAAAAAACGCTACAAAAATGCCCGATCCTCGATCGGGCATTTTGAC TTGTGTAGGCTGGAGCTGCTTC Fw-eda-out TGGTAAGCCGGAAATTGATGG Fw-edd-0.5 TCCATACCCAGCATCAGTTCG Rv-edd-0.5 GACGACAAATTTGTAATCAGGCG Fw-edd-out GGAGAAATACCACCCGTCG Rv-edd-out TTTTACACTTTCAGGCCTCGTG Fw-edd-P1 GTTTTCCAGTTTTTCATCAGAGTTTTCTCTCGCCTGATTACAAATTTGTCG TC GTGTAGGCTGGAGCTGCTTC Rv-edd-P2 GAATGAAACGCGTTGTGAATCATCCTGCTCTGACAACTCAATTTCAGGA GCCTTT CATATGAATATCCTCCTTAG Rv-fnr ACATTAAACAATTTGTGCCAGC Fw-fnrH1-P1 CAGAAAAATTTAATGATATGACAGAAGGATAGTGAGTTATGCGGAAAA A GTGTAGGCTGGAGCTGCTTC Rv-fnrH2-P2 TAAACAATTTGTGCCAGCTTGTTCACACTTTTATGTAAAGTTACCCTTAA CATATGAATATCCTCCTTAG Fw- fnr-K-out CGATAACAACGAGCATGTTCTG Rv-fnr-K-out CAATTACGGCTTGAGCAGACC Fw-fnr-mut GATGCAATGCTGGCTGATGC Fw-fnr-2-mut GATGCAATGCTGGCTGATGCTGCAATCCTGGCAATGG A21 Appendix Primer name Sequence (5’-3’) Rv-fnr-mut CAGCATTGCATCCCGTTCACACTCAACG Fw-fnr-mut-out GCGATTTAAGTTCATCACCAGC Rv-fnr-OG-SB TCAGCCAGCATTGCATCCCGTTCACACTCAACGAACATGAGCTTGATCA GCTTGATAATA Fw-fnr-OG-SB CGGGATGCAATGCTGGCTGATGCTGCAATCCTGGCAATGGATAGCACAA CCGCCAGACTG Rv-fnr-ogt-P2 TCAGCGAAAAGAGTGGTTATTGCGCCATGAAGGTTATCTTTTGCTGTAA CATATGAATATCCTCCTTAG Rv-fnr-ogt-out TAATGCATTACGGCCAACTGG Fw-fnr-out TTCATCACCAGCCTTAAACAGC Rv-fnr-out ACGGATCGAATCCCATCAGC Fw-fnr-P1 TGTTAAGGGTAACTTTACATAAAAGTGTGAACAAGCTGGCACAAATTGT TTAATGTGTGTAGGCTGGAGCTGCTTC Fw-fnr-p8 ATGCGCCGTATAATTCGCTTTTCCGGGATCATAGGTCTGCTCAAGCCGT AATTGATATTAACCCGCGAATTATATCATATTG Fw-fnr-p37 ATGCGCCGTATAATTCGCTTTTCCGGGATCATAGGTCTGCTCAAGCCGT AATTGATATAAAAATGACATATACCACATGGA Fw-fnr-qPCR TCTGGCCTTTCTGAATAGGC Rv-fnr-qPCR TTATACGGCGCATTCAGTCT Fw-fnr-up GATGAACCTTCTGCCAGATCA Fw-fnr-up-out TGACGCATAGCGGTACGTTCG Rv-fnr-up-out TTTGCTTAGACTTACTTGCTCC Fw-fnr-up-P1 AACTGTCAACGCAGTTTGTAATTAAAAGATTAACCCATATCTGGTGAAT GAAACAGGTGTAGGCTGGAGCTGCTTC Rv-fnr-up-P2 CTATCCTTCTGTCATATCATTAAATTTTTCTGATTTATTGATCTGGCAGA AGGTTCATCACATATGAATATCCTCCTTAG Fw-FRT GAAGTTCCTATACTTTCTAG Rv-FRT CCTATTCCGAAGTTCCTATTCT Fw-glcB-qPCR TGCACGTTGTTATCCAGCTC Rv-glcB-qPCR AGCGTACAAGCCAACATTGC Fw-gltA-K167A-mut-OG GTAACACATCGCGGCCATGGTCGGCATAGCCGACAGCAGGCGGAACGC GGCA Rv-gltA-K167A-mut-OG TGCCGCGTTCCGCCTGCTGTCGGCTATGCCGACCATGGCCGCGATGTGT Fw-gltA-KO-P1 AAAAATCAACCCGCCATATGAACGGCGGGTTAAAATATTTACAACTTAG C GTGTAGGCTGGAGCTGCTTC Rv-gltA-KO-P2 GCAAATTTAAGTTCCGGCAGTCTTACGCAATAAGGCGCTAAGGAGACC CATATGAATATCCTCCTTAG Fw-gltA-mut-out AAGAGCCAGCGGTACGCACG Rv-gltA-mut-out TGACCCGTCATACCATGATCC Fw-gltA-out TAGGGTTATAAATGCGACTACC Rv-gltA-out AGCTCTGTACCCAGGTTTTCC Rv-gltA-RBS-HindIII TTTTAAGCTT CAATCATTCAACAAAGTTGTTACAAAC Fw-gltA-Y145A-mut-OG GATTGTTAACATCCAGCGAGTCGTGAGCGAACGCCGCCAGCGCGCCGGT AATACC Rv-gltA-Y145A-mut-OG GGTATTACCGGCGCGCTGGCGGCGTTCGCTCACGACTCGCTGGATGTTA ACAATC Fw-iclR-out CGGTGGAATGAGATCTTGCGA Rv-iclR-out ACTTGCTCCCGACACGCTCA A22 Genetic engineering primers Primer name Sequence (5’-3’) Fw-iclR-P1 TTGCCACTCAGGTATGATGGGCAGAATATTGCCTCTGCCCGCCAGAAAA AGGTGTAGGCTGGAGCTGCTTC Rv-iclR-P2 GTTCAACATTAACTCATCGGATCAGTTCAGTAACTATTGCATTAGCTAA CAATAAAACATATGAATATCCTCCTTAG Fw-kan GTGATATTGCTGAAGAGCTTGG Fw-ldhA-out TGTCATTACTTACACATCCCGC Rv-ldhA-out GCATTCAATACGGGTATTGTGG Fw-ldhA-P1 GGTCATTGGGGATTATCTGAATCAGCTCCCCTGGAATGCAGGGGAGCGG CAAGAGTGTAGGCTGGAGCTGCTTC Rv-ldhA-P2 GTAAAATATTTTTAGTAGCTTAAATGTGATTCAACATCACTGGAGAAAG TCTTCATATGAATATCCTCCTTAG Fw-maeA-out GTGAGCGCAGTGTTTTATACG Rv-maeA-out CCGAAGGTGACAAATCCAGC Fw-maeB-out GGTGCATAAACTTTCATATGCAAC Rv-maeB-out GGACTTGCTCAAAGTCTGTAGACT Fw-maeB-P1 ATATTGCAGCTAAACTGCTTACCCTGAATATTCAGGGTAAGCGTGAGAG TTAAAAAAAAGTGTAGGCTGGAGCTGCTTC Rv-maeB-P2 TGGGCAGGGGCTGTTGCCCACACACTTTATTTGTGAACGTTACGTGAAA GGAACAACCAACATATGAATATCCTCCTTAG Fw-NotI-fnrH1-P1 CCCCGCGGCCGCCAGAAAAATTTAATGATATGACAGAAGGATAGTGAG TTATGCGGAAAAA GTGTAGGCTGGAGCTGCTTC Fw-NotI-P1 TTTTGCGGCCGC GTGTAGGCTGGAGCTGCTTC Fw-o-maeA-P1 GATCTGAAAAAGGGAGAGGGAAATAGCCCGGTAGCCTTCACTACCGGG CGCAGGCGTGTAGGCTGGAGCTGCTTC Rv-o-maeA-P2 TGAGGCCGACGCCCTGGCGGTAAAGCAAAGACGATAAAAGCCCCCCAG GGCATATGAATATCCTCCTTAG Fw-o2-maeA-P1 ATGAGATCTGAAAAAGGGAGAGGGAAATAGCCCGGTAGCCTTCACTAC GTGTAGGCTGGAGCTGCTTC Rv-o2-maeA-P2 GGTGTTTTTGTTTTTATCTGCTTTATACTTGAGGCCGACGCCCTGGCGGT AAACATATGAATATCCTCCTTAG Fw-o-pckA-P1 CCCCCAAAAAGACTTTACTATTCAGGCAATACATATTGGCTAAGGAGCA GTGAAGTGTAGGCTGGAGCTGCTTC Rv-o-pckA-P2 CTCCCGTTTTGCTTTCTATAAGATACTGGATAGATATTCTCCAGCTTCAA ATCACATATGAATATCCTCCTTAG Fw-o-pta-P1 GCGGTGCTGTTTTGTAACCCGCCAAATCGGCGGTAACGAAAGAGGATA AACCGTGTAGGCTGGAGCTGCTTC Rv-o-pta-P2 TATTTCCGGTTCAGATATCCGCAGCGCAAAGCTGCGGATGATGACGAGA CATATGAATATCCTCCTTAG Fw-o-pykA-P1 TCGGATTTCATGTTCAAGCAACACCTGGTTGTTTCAGTCAACGGAGTATT ACGTGTAGGCTGGAGCTGCTTC Rv-o-pykA-P2 TGAACTGTAGGCCGGATGTGGCGTTTTCGCCGCATCCGGCAACGTACCA TATGAATATCCTCCTTAG Fw-o-pykF-P1 CCCAGAAAGCAAGTTTCTCCCATCCTTCTCAACTTAAAGACTAAGACTG TCGTGTAGGCTGGAGCTGCTTC Rv-o-pykF-P2 AAAGCGCCCATCAGGGCGCTTCGATATACAAATTAATTCACAAAAGCA ATACATATGAATATCCTCCTTAG Fw-P1-FRT-P2 GTGTAGGCTGGAGCTGCTTCGAAGTTCCTATACTTTCTAGAGAATAG Rv-P1-FRT-P2 CATATGAATATCCTCCTTAGTTCCTATTCCGAAGTTCCTATTCTCTA Fw-pBluescript CGCCAGGGTTTTCCCAGTCACGAG A23 Appendix Primer name Sequence (5’-3’) Rv-pBluescript AGCGGATAACAATTTCACACAGGA Fw-pckA(As)-H1-BamHI TTTTGGATCCGCGGTTAACACCCCCAAAAAGACTTTACTATTCAGGCAA TACATATTGGCTAAGGAGCAGTGAAATGAGCTTATCTGAAAGTTTAGCT AAATACGG Rv-pckA(As)-NotI TTTTGCGGCCGC AAAAGGCCCGAGGCGCATATAGCACCTCGG Rv-pckA-EcoRI-H2-P2 TTTTGAATTCGTGCCTCCCGTTTTGCTTTCTATAAGATACTGGATAGATA TTCTCCAGCTTCAAATCA CATATGAATATCCTCCTTAG Fw-pckA-P1 ACACCCCCAAAAAGACTTTACTATTCAGGCAATACATATTGGCTAAGGA GCAGTGAAGTGTAGGCTGGAGCTGCTTC Rv-pckA-P2 GAAGGTGCCTCCCGTTTTGCTTTCTATAAGATACTGGATAGATATTCTCC AGCTTCAAATCACATATGAATATCCTCCTTAG Fw-pckA-out CGTTTCGTGACAGGAATCACG Rv-pckA-out CTTTCCATTCGCGCCTGGCT Rv-pfkA-BamHI TTTT GGATCC AAAGGAATCTGCCTTTTTCCG Rv-pfkA-HindIII-P2 TTTTAAGCTTTGATAAGCGAAGCGCATCAGGCATTTTTGCTTCTGTCATC GGTTTCAGGG CATATGAATATCCTCCTTAG Fw-pfkA-KO-P1 GACTTCCGGCAACAGATTTCATTTTGCATTCCAAAGTTCAGAGGTAGTC GTGTAGGCTGGAGCTGCTTC Rv-pfkA-KO-P2 TGATAAGCGAAGCGCATCAGGCATTTTTGCTTCTGTCATCGGTTTCAGG G CATATGAATATCCTCCTTAG Fw-pfkA-mut-out AATGGGCTTCCCGTGCATCG Rv-pfkA-mut-out TCGTTTACCAGGTCTTCACGG Fw-pfkA-out AGGACCCCTGTTCCGTCG Rv-pfkA-out GCTTTGTCCATGCCGGATG Fw-pfkA-Q161A-mut-OG CTGCGTGACACCTCTTCTTCTCACGCGCGTATTTCCGTGGTGGAAGTG Rv-pfkA-Q161A-mut-OG CACTTCCACCACGGAAATACGCGCGTGAGAAGAAGAGGTGTCACGCAG Fw-pfkA-RBS-BcuI TTTT ACTAGT AGCCAGACCCGCATTTTGTG Fw-poxB-out GCAGAGCATTAACGGTAGGG Rv-poxB-out GGGATTTGGTTCTCGCATAATC Fw-poxB-P1 GCGTAAATCAATCATGGCATGTCCTTATTATGACGGGAAATGCCACCCT TTGTGTAGGCTGGAGCTGCTTC Rv-poxB-P2 GTCAGATGAACTAAACTTGTTACCGTTATCACATTCAGGAGATGGAGAA CCCATATGAATATCCTCCTTAG Fw-ppc GTATCCTTCACGTCGCATTGGCGCGAATATGCTCGGGCTTTGCTTTTCGT CGTC Rv-ppc GCTGAAGCGATTTCGCAGCATTTGACGTCACCGCTTTTACGTGGCTTTAT AAAA Fw-ppc-EcoRI TTTTGAATTCAAGATTAGCCGGTATTACGC Rv-ppc-FRT CGTGAAGGATACAGGGCTATCAAACGATAAGATGGGGTGTCTGGGGTA ATCCTATTCCGAAGTTCCTATTCT Rv-ppc-HindIII TTTTAAGCTTGTAGGCCGGATAAGGCGCTCG Fw-ppc-H1 GAATGTGTTCTCCCAACGCATCCTTGATGGTTTCTCCCAGCACTTTGCCG AGCATACTGACATTACTACGCAATGCGG Rv-ppc-H2 GGCTTTATAAAAGACGACGAAAAGCAAAGCCCGAGCATATTCGCGCCA ATGCGACGTGAAGGATACAGGGCTATC Fw-ppc-HIS-P1 ACTGACATTACTACGCAATGCGGAATATTGTTCGTTGTGGTGATGGTGA TGGTGCGCCATGTGTAGGCTGGAGCTGCTTC Fw-ppc-HIS-RBS-8 ACATTACTACGCAATGCGGAATATTGTTCGTTGTGGTGATGGTGATGGT GCGCCATCTTTGTTTCCTCCGAGTAACCCGCGA A24 Genetic engineering primers Primer name Sequence (5’-3’) Fw-ppc-HIS-RBS-37 ACATTACTACGCAATGCGGAATATTGTTCGTTGTGGTGATGGTGATGGT GCGCCATCTTTGTTTCCTCCGAGAAAAATGAC Fw-ppc-HIS-RBS-55 ACATTACTACGCAATGCGGAATATTGTTCGTTGTGGTGATGGTGATGGT GCGCCATCTTTGTTTCCTCCGAGATACCTAA Fw-ppc-KO-P1 AAGTATTTCAGAAAACCCTCGCGCAAAAGCACGAGGGTTTGCAGAAGA GG GTGTAGGCTGGAGCTGCTTC Rv-ppc-KO-P2 TTACGCTTATTTAAAGCGTCGTGAATTTAATGACGTAAATTCCTGCTATT TATTCG CATATGAATATCCTCCTTAG Rv-ppc-2-out AATCAGGCTCATCAACCTGATG Rv-ppc-2-P2 GTAGGCCGGATAAGGCGCTCGCGCCGCATCCGGCGCTGTTGCCAAACTC CCATATGAATATCCTCCTTAG Rv-ppc-mut TTTAGCTATGGTCTGCCAGAAATCACCG Fw-ppc-mut ATAGCTAAAGCGGATCATCTCGCCCTGTTCGG Rv-ppc-mut-OG CGTAACCGAACAGGGCGAGATGATCCGCTTTAGCTATGGTCTGCCAGAA ATCACCG Fw-ppc-mut-OG CGGTGATTTCTGGCAGACCATAGCTAAAGCGGATCATCTCGCCCTGTTC GGTTACG Fw-ppc-mut-out TGCGTCCAGGCGAAGATCC Rv-ppc-mut-out CGCCAACGATGTCATGACC Fw-ppc-out CAGGAACTGACTAAACGCACG Rv-ppc-out ACACCTTTGGTGTTACTTGGG Fw-ppc-2-out GAAAACGAGGGTGTTAGAACAG Rv-ppc-2-out ATCAGGCTCATCAACCTGATG Fw-ppc-NotI-P1 TTTTGCGGCCGCAAGTATTTCAGAAAACCCTCGCGCAAAAGCACGAGGG TTTGCAGAAGAGGGTGTAGGCTGGAGCTGCTTC Rv-ppc-P2 CGTGAAGGATACAGGGCTATCAAACGATAAGATGGGGTGTCTGGGGTA ATCATATGAATATCCTCCTTAG Rv-ppc-2-P2 GTAGGCCGGATAAGGCGCTCGCGCCGCATCCGGCGCTGTTGCCAAACTC CCATATGAATATCCTCCTTAG Rv-ppc-3-P2 ATCAAGCCCACCCGCGAACTGATAACCCAGGTAATTCACCATTTTTGCT GGCATTAACATATGAATATCCTCCTTAG Fw-ppc-p8 TCCTTCACGTCGCATTGGCGCGAATATGCTCGGGCTTTGCTTTTCGTCGT CTAACCCGCGAATTATATCATATTG Fw-ppc-p37 TCCTTCACGTCGCATTGGCGCGAATATGCTCGGGCTTTGCTTTTCGTCGT CAAAAATGACATATACCACATGGA Fw-ppc-p55 TCCTTCACGTCGCATTGGCGCGAATATGCTCGGGCTTTGCTTTTCGTCGT CATACCTAAAAATTATACCACATCAAC Fw-ppc-qPCR CTGGCAGGTATCGAGCACTT Rv-ppc-qPCR CTACCTCGGTATCGGCGACT Fw-ppc-RBS-p8 TTTGCCGAGCATACTGACATTACTACGCAATGCGGAATATTGTTCGTTC ATCTTTGTTTCCTCCGAGTAACCCGCGAATTATATCATATTG Fw-ppc-RBS-p37 TTTGCCGAGCATACTGACATTACTACGCAATGCGGAATATTGTTCGTTC ATCTTTGTTTCCTCCGAGAAAAATGACATATACCACATGGA Fw-ppc-RBS-p55 TTTGCCGAGCATACTGACATTACTACGCAATGCGGAATATTGTTCGTTC ATCTTTGTTTCCTCCGAGATACCTAAAAATTATACCACATCAAC Fw-ppsA-P1 TCATCTTCGGGGATCACATAACCCCGGCGACTAAACGCCGCCGGGGATT TATTGTGTAGGCTGGAGCTGCTTC Rv-ppsA-P2 AGATAAATGCGCAGAAATGTGTTTCTCAAACCGTTCATTTATCACAAAA GGATTGTTCGCATATGAATATCCTCCTTAG A25 Appendix Primer name Sequence (5’-3’) Fw-ppsA-out TACCGCGAACTACCTCAGGTA Rv-ppsA-out CGCAACGCTGGGATCAGTCT Fw-pro8 CCGCGAATTATATCATATTGGC Fw-pro8-bis AATTATATCATATTGGCTGAAAATG Fw-pro37 ATGACATATACCACATGGATTTAA Fw-pro55 AAATTATACCACATCAACACCAAC Fw-pta-out CGGCAAATCTGGTTTCATCAAC Rv-pta-out CACAAAACAAAGTGGTAAGTATG Fw-ptsG-0.6kb GTGAACTGAACAAGCCGGTTA Fw-ptsG-0.6kb-out GAAGAGGGTGTTGTAGCGCT Rv-ptsG-0.6kb TGAGAGTGCTCCTGAGTATGG Fw-ptsG-out GAAACGTGATAGCCGTCAAAC Rv-ptsG-out AACGTGGAAGGTTCTATCGTC Fw-ptsG-P1 GCCACGCGTGAGAACGTAAAAAAAGCACCCATACTCAGGAGCACTCTC AATTGTGTAGGCTGGAGCTGCTTC Rv-ptsG-P2 CACCTGTAAAAAAGGCAGCCATCTGGCTGCCTTAGTCTCCCCAACGTCT TACGGACATATGAATATCCTCCTTAG Fw-pykA-out GCGCTGAAGGAATCGCGTTTT Rv-pykA-out CCATAACGGGATGCTGGAGC Rv-pykA-out-2 GAAATCCAGCCGTATAAGCG Fw-pykF-out CAGGCACTCACGTTGGGCT Rv-pykF-out GCATCGAACGCTGGGTTTTAG Fw-sdhAB-out CATGTGGCAGGTGTTGACCG Rv-sdhAB-out2 TCCTGCATTTCTTTGTTACGCCT Fw-sdhAB-P1 CACTGGTGGTTTACGTGATTTATGGATTCGTTGTGGTGTGGGGTGTGTGA GTGTAGGCTGGAGCTGCTTC Rv-sdhAB-P2 TCCGGCACTGGTTGCCTGATGCGACGCTTGCGCGTCTTATCAGGCCTAC GGTCATATGAATATCCTCCTTAG Rv-XbaI-P2 AAAAA TCTAGA CATATGAATATCCTCCTTAG Fw-XbaI-fnr AAAAA TCTAGA ATCAGGCAACGTTACGCGTATGAC Fw-XbaI-fnrend GGGGTCTAGATATCAGGCAACGTTACGCGTATGACCAGCAAGC Fw-ybhI-out AGGTCAGGATGCCACGACG Rv-ybhI-out AGATATCTGCGCACCGTG Rv-ybhI-out2 ACCGTGAAGTGCGCAGAAAGA Fw-ybhI-P1 AAGGACAATCAATAAAGGACTTCTGTATGAGTCATACAGAAAGAACAG GATTTTAA GTGTAGGCTGGAGCTGCTTC Rv-ybhI-P2 GACTGGGGTGAGCGAACGCAGACGCAGCACATGCAACTTGAAGTATGA CGAGAATAT CATATGAATATCCTCCTTAG Fw-ydfJ-out AATGAACAATTCTTGGAGCCAGG Rv-ydfJ-out TATCTGTTGGGGAGTTTTTTTGG Fw-ydfJ-P1 ATGTTTTGTTATATGAATAAAAATCCCCTCTCCGGTAAGAGAAGGGATT AAGGGT GTGTAGGCTGGAGCTGCTTC Rv-ydfJ-P2 AAAACCTAGAAAACGCCAAGGAAACCACAGGATGGGAAAAAACACCT GTGAATT CATATGAATATCCTCCTTAG Fw-ydiF-out GGTAAATGATTTGCGCACGGT Rv-ydiF-out ATGTCCCGTTTTGATCGCAGG Fw-ydiF-P1 CTCTGTGCGCTAATTCTCCATTTGGCGTAGGGAAAATCACATCTGAATC AGGAATTAACA GTGTAGGCTGGAGCTGCTTC A26 Genetic engineering primers Primer name Sequence (5’-3’) Rv-ydiF-P2 ACTCCTGTTAATGAGCCGCTTCAGGCAGGACAAAACCCATCGCCGCATC GATA CATATGAATATCCTCCTTAG Fw-ydjN-out ATACCTTGCAGATCCAGTTTCATCG Rv-ydjN-out GTATGAGGTTTTAAGCTTCTCG Fw-ydjN-P1 GCGCCAACACTATGACTGCTACGCAGTGATAGAAATAATAAGATCAGG AGAACGGGG GTGTAGGCTGGAGCTGCTTC Rv-ydjN-P2 GGAACATTTAAAAAGTAAGGCAACGGCCCCTATACAAAACGGACCGTT GCCAGCATAAGAA CATATGAATATCCTCCTTAG Fw-yfbS-out AGGAGATGGACTACTTCATGGA Rv-yfbS-out TTTGGTCCTCCACAGTCTGG Fw-yfbS-P1 GAGTCTGCGTCGCATCAGGCAATAAGCGCCGGATGCGACATCAGGCTCT TG GTGTAGGCTGGAGCTGCTTC Rv-yfbS-P2 GGGCTTTTTTATGGCAGAATCAAGTCATCCCCCTCAATTAACAAGGATA AGTT CATATGAATATCCTCCTTAG Fw-ygjE-out TGATTGTCTCTATTGATACCCAC Rv-ygjE-out CGGAATGGTGCGGTTTAAAGC Fw-ygjE-P1 TCAAATAACCCTCCCGGAGAGGCTCACCCCTCTCCTTTTTCGCAGGCAT AACACG GTGTAGGCTGGAGCTGCTTC Rv-ygjE-P2 GCTGCGTAAAACTATTGGGTGCCGGAGAGCAATTTCCGGCACCGTCCTC AC CATATGAATATCCTCCTTAG Fw-ygjU-out AGCATTTTTCGCTTCCCGAAG Rv-ygjU-out GGGTGATTCACAACTTCTGGG Fw-ygjU-P1 ATGCGTCGACAGAACGCACCAGGGATGTGCGACAACACAATGAAAGGA TCGAAAA GTGTAGGCTGGAGCTGCTTC Rv-ygjU-P2 GAATTGATCCGTTTAAAGTTGAGAAAACCCCTTCCGCCGTAGACGAAAG GGGTTAAACAA CATATGAATATCCTCCTTAG Fw-yhjE-out TTCATAGAAGAAAAATCACTGGC Rv-yhjE-out ATTGCCGGATGTCTGACATCC Fw-yhjE-out2 ACCTGAAACCAGTTTTATCCACTA Rv-yhjE-out2 GGCAAAGTGTGCTTTGTTCATGC Fw-yhjE-P1 CACTTTTGCTGTGCGTAATATGGCTATTCGTTAGCCAAAAAATAAGAAA AGATT GTGTAGGCTGGAGCTGCTTC Rv-yhjE-P2 TATATTGTATGGAAATTCAGGCCTGATAAGCGTAGCGCATCAGGCTTTT CACTCTTA CATATGAATATCCTCCTTAG Fw-8-RBS CTTTGTTTCCTCCGAGTAACCCGCGA Fw-37-RBS CTTTGTTTCCTCCGAGAAAAATGAC Fw-55-RBS CTTTGTTTCCTCCGAGATACCTAA A27 Appendix A.6 Strains and strain codes Strain Assigned code MG1655 (WT) SUCC001 MG1655 ∆(ack A-pta ) ∆pox B SUCC002 MG1655 ∆(ack A-pta ) ∆pox B ∆icl R SUCC003 MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆ldh A SUCC004 MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB SUCC005 MG1655 ∆(ackA-pta ) ∆pox B ∆icl R ∆sdh AB ∆arc A SUCC006 MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB ∆arc A ∆pts G SUCC007 MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB ∆FNR-pro37-dcu C SUCC008 MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB ∆arc A ∆FNR-pro37-dcu C SUCC009 MG1655 ∆FNR-pro37-dcu C SUCC010 MG1655 ∆icl R SUCC011 MG1655 ∆arc A SUCC012 MG1655 ∆arc A ∆icl R SUCC013 MG1655 ∆arc A ∆icl R ∆edd SUCC014 MG1655 ∆sdh AB SUCC015 MG1655 ∆ldh A ∆sdh AB SUCC016 MG1655 ∆icl R ∆sdh AB SUCC017 MG1655 ∆icl R ∆ldh A ∆sdh AB SUCC018 MG1655 ∆pppc-pro37-ppc SUCC019 MG1655 ∆pppc-pro55-ppc SUCC020 MG1655 ∆pppc-pro8-ppc SUCC021 MG1655 ∆(ack A-pta ) ∆pox B ∆pppc-pro37-ppc SUCC022 MG1655 ∆(ack A-pta ) ∆pox B ∆pppc-pro55-ppc SUCC023 MG1655 ∆(ack A-pta ) ∆pox B ∆pppc-pro8-ppc SUCC024 MG1655 ∆dct A ∆yfb S SUCC025 MG1655 ∆dct A ∆sst T SUCC026 MG1655 ∆dct A ∆ydj N SUCC027 MG1655 ∆dct A ∆ybh I SUCC028 MG1655 ∆dct A ∆yhj E SUCC029 MG1655 ∆dct A ∆ydf J SUCC030 MG1655 ∆dct A ∆ttd T SUCC031 MG1655 ∆dct A ∆cit T SUCC032 MG1655 ∆sdh AB SUCC033 MG1655 ∆sdh AB ∆dct A SUCC034 MG1655 ∆FNR-pro8-dcu C SUCC035 MG1655 ∆FNR-pro37-dcu C SUCC036 MG1655 ∆FNR-pro55-dcu C SUCC037 MG1655 ∆sdh AB ∆FNR-pro37-dcu C SUCC038 MG1655 ∆sdh AB ∆FNR-pro37-dcu C ∆dct A SUCC039 MG1655 ∆sdh AB ∆FNR-pro37-dcu C ∆ybh I SUCC040 MG1655 ∆sdh AB ∆FNR-pro37-dcuC ∆dct A ∆ybh I SUCC041 MG1655 ∆sdh AB ∆FNR-pro37-dcu C ∆dct A ∆ydj N SUCC042 MG1655 ∆sdh AB ∆FNR-pro37-dcu C ∆dct A ∆ybh I ∆ydjN SUCC043 MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB ∆arc A ∆dct A ∆edd ∆FNR-pro37-dcu C SUCC044 MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB ∆arc A ∆dct A ∆edd ∆cit DEF ∆FNR- SUCC045 pro37-dcu C A28 Strains Strain Assigned code MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB ∆arc A ∆dct A ∆edd ∆cit DEF SUCC046 ppc *L620S ∆FNR-pro37-dcu C MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB ∆arc A ∆dct A ∆edd ∆eda ∆cit DEF ppc * SUCC047 L620S ∆FNR-pro37-dcu C MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB ∆arc A ∆dct A ∆edd ∆eda ∆cit DEF ppc * SUCC048 L620S glt A*K167A ∆FNR-pro37-dcu C MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB ∆arc A ∆dct A ∆edd ∆eda ∆cit DEF ppc * SUCC049 L620S glt A*Y145A ∆FNR-pro37-dcu C MG1655 ∆(ack A-pta ) ∆pox B ∆ato AD SUCC050 MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB ∆arc A ∆dct A ∆edd ∆eda ∆maeAB SUCC051 ∆pckA ∆FNR-pro37-dcu C MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB ∆arc A ∆dct A ∆edd ∆eda ∆maeAB SUCC052 ∆pckA FNR*(L28H) ∆FNR-pro37-dcu C MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB ∆arc A ∆dct A SUCC053 MG1655 ∆(ack A-pta ) ∆pox B ∆icl R ∆sdh AB ∆arc A ∆dct A ∆FNR-pro37-dcu C SUCC054 MG1655 ∆maeAB SUCC055 MG1655 ∆pckA SUCC056 MG1655 FNR*(L28H) SUCC057 A29 Acknowledgments Dankwoord Dankwoord Try, fail, try again, fail better 1 Een doctoraat is eigenlijk een aaneenschakeling van anekdotes en mislukkingen die achteraf bekeken, toch eigenlijk nog best meevallen. Alle anekdotes, verhalen, geaccumuleerd over de afgelopen 5 jaar vullen vast en zeker ettelijke pagina’s meer dan de pagina’s die ik hiervoor al heb neergepend. Vaak zijn het ook de ‘mislukkingen’ die de beste anekdotes opleveren, hoewel, mislukkingen? Een citaat van Thomas Edison vult eigenlijk het citaat van Samuel Becket hierboven het best aan: “ Failed? — we haven't failed, we now know the thousands of ways that won't work .”2. Twee betere slagzinnen als deze kon ik zelf niet verzinnen voor wetenschappelijk onderzoek in het veld van de metabolic engineering of als je wil systems biology . Doch, falen, eigenlijk slagen, doe je nooit alleen in een doctoraat. Zeker niet in dit veld. De complexiteit is zo groot en de technische kennis die moet opgebouwd worden om dergelijke projecten tot een goed eind te brengen is zo uitgebreid, dat een goed, hecht team onontbeerlijk is. Ik wil dan ook in de eerste plaats het hele MEMORE team, en in het bijzonder team Limab-Biomath (MEMO), bedanken voor hun hulp, inzichten, discussies en steun. Hierbij wil ik ook mijn promotoren Prof. Vandamme en Prof. Soetaert bedanken voor mijn introductie in het metabolic engineering team en omdat ze mij toelieten in dit onderzoeksveld te doctoreren. Team Limab – Biomath (eigenlijk de MEMO groep), bestond toen ik begon nog maar uit een handvol mensen, Marjan en Katja op Limab, en Gaspard en Jo op Biomath. Ik kwam erbij als eerste vreemde eend uit dat MEMORE project, maar werd onmiddellijk met open armen ontvangen. Ik werd door Marjan ingeleid in de wereld van fermentaties (“je moet er mee praten”, Marjan over onze coli’s in de reactor die vaak niet deden wat we wilden) en Katja leerde me zowat elk toestel (vooral de reactoreenheden) openschroeven. Bij fermentaties in het begin van het project was de slagzin, “ try, fail, try again, fail better” eigenlijk zeer toepasselijk. Die dagen was een goede dag, een dag waarop je in het fermentatielabo toekwam en je niet moest beginnen het schuim – “black arts” door sommigen in het project genoemd – van tussen de reactoren te kuisen. De reactoropstellingen waren heel lang een grote horde om te nemen en ik moet zeggen, zonder Gaspard waren we er nooit in geslaagd om die horde te nemen. Gaspard had het jaar voordien al in Lund wat ervaring kunnen opdoen met Sartorius reactoren en samen hebben we eigenlijk vele dagen, avonden en nachten doorgebracht in het fermentatielabo om die reactoren te laten doen wat we wilden. Het was vaak afzien, maar we hebben heel vaak goed gelachen terwijl we aan het “prutsen” waren. Gaspard is ook de “master-scripter” binnen het project, in Linux uiteraard, het “betere operating system”. Zowat iedereen op Limab gebruikt vandaag zijn python scriptje om HPLC data te verwerken en eigenlijk werden het gros van de programmaatjes in het fermentor labo ook door Gaspard geschreven. We hebben ook heel wat plezier gehad bij de soms waanzinnige experimenten die we samen hebben uitgevoerd. De prijs voor ‘meest complexe opstelling’ gaat echter naar Jo. Twee reactoren verbonden aan elkaar, in chemostaat, gepulseerd, 15 pompen, 4 vaten, 2 reactor vaten, twee units, 2 PC’s, ongeveer 20 tubings en bijna 100 lijnen code om het geheel te sturen. Een opstelling om “u” tegen te zeggen… en dat voor Jo’s eerste opstelling… stress… . Ik vermoed dat die eerste opstelling geleid heeft tot zijn voorkeur voor het kuisen van een reactor, boven het opstellen ervan, iets wat hij altijd met plezier doet. Maar dit is niet 1 Samuel Becket in Worstward Ho (1983) 2 Thomas Edison (1847-1931) iii Acknowledgments het enige waarvoor we allen op Jo kunnen rekenen. Jo introduceerde ondermeer stoichiometrische netwerk analyse en de multivariate statistiek in het team, iets wat voor mijn werk cruciaal is gebleken. Ik kon daarenboven ook nog rekenen op Jo’s kritische blik die elk woord en elke zin heeft gewikt en gewogen in mijn hele doctoraat. Ik stribbelde nogal vaak tegen, maar apprecieer enorm de input die Jo samen met Gaspard, Hendrik en Marjan heeft gegeven. Jo en Gaspard zaten natuurlijk niet alleen op Biomath, zij deelden een bureau met Brecht en Aditya die zich mochten buigen over de ontwikkeling van optimal experimental design tools en dynamische modellen van het metabolisme. De problemen die bij dergelijk onderzoek komen kijken zijn van een totaal andere orde dan deze bij het klassieke experimentele werk en werden volgens mij daarom te vaak ondergewaardeerd. Wat me het meest is bijgebleven eigenlijk van het bureau op Biomath zijn de plotse pieken in decibels die soms een Doppler effect vertoonden – legendarisch. De reden daarvoor is mij nog altijd niet helemaal duidelijk, waarschijnlijk omdat het/de doel/doelpersoon wisselend was en er enkele briljante spindoctors (die nu ook gewone doctors zijn) aanwezig waren die zowat elke situatie een compleet andere betekenis kunnen geven. Dat heb jij, Gino, waarschijnlijk snel aan de lijve mogen ondervinden toen je, spijtig genoeg tegen het einde van het project, het team vergezelde. Gino bracht ons de Enter Doudoroff route bij, waarvoor eeuwige dank, want een knock out in de ED route verhoogde de groeisnelheid met 100% wat mijn aantal uren nachtwerk significant deed dalen. Iemand die ik zeker niet mag vergeten is Ellen. Het was waarschijnlijk vaak afzien, omdat je de ene keer een fosfaat meting moest doen, de andere keer fermentaties voorgeschoteld kreeg of mRNA moest extraheren en uiteindelijk nog een LC-MSMS moest temmen in niet de meest gemakkelijke omstandigheden. Vooral ook omdat mijn planningen niet bepaald bekend staan als “op het gemak”. Ik vind het bewonderenswaardig hoe je dat allemaal met grote precisie en plezier deed en met spijt in het hart zag ik je vertrekken naar Ablynx. Zoals je gemerkt hebt bij het lezen van mijn doctoraat, werd een hele rist mutanten gescreend en geanalyseerd. Hiervoor ben ik schatplichtig aan Prof Cunin, Prof Charlier en in het bijzonder aan Pery en Sarah die instonden voor de mutantconstructie in het project. While everyone thought in the beginning of the project that the construction of mutants would be the bottleneck in the project, Pery proved everyone wrong by creating high throughput mutants at a rate that I could only dream of doing fermentations in. I am still amazed by the number of modifications you were able to make in a single E. coli strain, seemingly with ease. Ik zou ook het Delftse team, Prof Heijnen (Sef), Walter, Peter, Wouter, Jan, Cor, Hilal, Dirk, Rob, Mlawule en Evelien (die samen met de LC MS en bioscoop naar Gent zijn gekomen) willen bedanken voor de goede samenwerking. De tips die Walter, Dirk en Rob gaven bij de reactoropstellingen waren cruciaal voor het welslagen van onze experimenten. Ik kon ook altijd rekenen op de input van Prof. Heijnen die zeker een grote hulp was bij het interpreteren van het ietwat vreemde fenotype van de mutanten beschreven in hoofdstuk 4. Eduardo, Hendrik, Sophie, Annelies, Steven, Katelijne, en nu dit laatste jaar Frederik en Stijn. Jullie hebben elk zich toegelegd op een deeltje van de puzzel, met veel trying en veel failing . iv Dankwoord Ik dreef jullie waarschijnlijk bij momenten tot de waanzin, en sommigen onder jullie – niet nader genoemd – konden mij zelfs op een onbewaakt moment tot wanhopen brengen. Uiteindelijk hebben jullie (Stijn en Frederik binnen een kort) een prachtig werkstuk afgeleverd en ongelofelijk veel werk verzet. Eduardo was erbij toen de eerste fermentaties werden opgezet en we het medium optimaliseerden. Weken aan een stuk zoeken naar de volgorde waarin we onze vitamine-mineraal oplossing moesten maken, de HPLC methode bijstellen, en schuim, schuim en nog eens schuim… . De anaerobe fermentaties… Hendrik… nu al bijna in de laatste rechte lijn van zijn doctoraat. Wie had ooit kunnen denken dat selenium de oplossing was? Nog nooit hebben we zoveel verschillende dingen geprobeerd met een reactor als tijdens jou thesis en ik moet zeggen, nog nooit hadden we zoveel bijgeleerd over onze opstelling. Sophie… laat ons eens 6 stammen screenen op een week, dat moet wel lukken… en inderdaad, Sophie is erin geslaagd. Dat jaar hadden we het geluk niet op de hand, maar uiteindelijk, met wat EFM’s, qPCR en stalen zenuwen, bleek ik toch gelijk te hebben als optimist (“Geen zorgen maken, het komt wel goed”). Annelies kwam erbij in het tweede semester van dat jaar. Op 3 maanden tijd deden we 8 batchen en 8 chemostaten, hielp je bij het opstellen van enzymassays en analyseerde je honderden stalen met de HPLC en biochemistry analyser. Altijd heel stil, maar buitengewoon efficiënt en accuraat. Het jaar daarop kwam Steven op het lab, vanuit Antwerpen. We wisten net een jaar dat we eigenlijk met de verkeerde E. coli bezig waren, dus was ik alle experimenten net aan het herhalen die ik eigenlijk tijdens de eerste jaren samen met Eduardo, Sophie, Annelies en Hendrik heb gedaan. Steven kreeg enkele tientallen batch screeningsexperimenten en een hele reeks chemostaten voorgeschoteld en deed daarnaast nog eens met plezier een hele reeks extra fermentaties die we voor onze nieuwe qPCR aanpak nodig hadden. Eigenlijk met het grootste gemak slaagde je erin om al die fermentaties uit te voeren en te verwerken, iets wat nooit gelukt zou zijn zonder je hulp. Die nazomer kwam er ook hulp vanuit een onverwachte hoek, Wageningen, meerbepaald Katelijne. Net op het moment dat de eerste priority filing binnen was en een hele hoop patent gerelateerd werk op ons afkwam, kwam Katelijne als reddende engel bij ons een afstudeerstage doen. In principe moest ik op dat ogenblik aan het schrijven zijn, maar eigenlijk had ik wat moeite om afscheid te nemen van het experimentele werk. Gelukkig kon ik op Katelijne vertrouwen om heel wat werk in het lab van mij over te nemen, anders stond er nog steeds geen tekst op papier. In het laboratorium is metabolic engineering uiteraard niet het enige onderzoeksveld en over de jaren heen zijn er ook heel wat mensen met wie ik mijn bureau heb gedeeld. Annelies en Cassandra waren mijn eerste bureaugenoten die mij meteen geïntegreerd hebben in het labo. Daarna kwamen Manu erbij – medeoprichter van ons mini-voetbal ploegje en altijd te vinden voor een leuke wetenschappelijke discussie, Karl – onze eerste post-doc manager met grappige vernederlandste Duitse zegswijzen, de altijd goedlachse Mlawule, LC MS experte Evelien, Beatriz – met wie ik altijd interessante discussies had over chromatografie, Piet – onze bioethanol expert en last but not least , de teamgenoten, Ellen, Marjan, hendrik, Dries en toch ook een beetje, Simon. Ik mag ook Monique niet vergeten – die mij onder andere de weg toonde naar de glasblazerij en de centrale werkplaatsen, Marianne – die altijd bereid was te helpen met de HPLC, het opzoeken van nieuwe producten, of het identificeren van een contaminatie, Jacqueline en Katrien – die me hielpen bij het organiseren van mijn trips naar de verschillende cursussen, ik denk dat ik zeker ergens was verloren gelopen of iets was vergeten zonder jullie en Sofie – “Waarom doet dat dehydrogenase toch niet wat ik wil?” v Acknowledgments (altijd leuk om over mannitol dehydrogenase te discussiëren met je). Margriet wil ik zeker ook bedanken voor de vele uren op TT, de niet altijd evidente patent aanvragen en al het werk dat dit met zich meebrengt. Ook wil ik nog onze nieuwe projectgenoten, Nele, Isabelle (die ik ook nog wil bedanken voor de vele mRNA extracties en cDNA syntheses), Dries en Maarten een hart onder de riem steken. Het loopt nooit van een leien dakje, maar, try, fail, try again, fail better … . Verder bedank ik ook nog alle andere collega’s op Limab – InBio.be, over de jaren heen al zo veel, voor de toffe samenwerking, de leuke uitstapjes naar de zee, de vlaamse ardennen,… het voetballen, het supporteren voor de voetbalploeg (zelfs al verloren we met 30-4). Tijdens mijn doctoraat ontstond een – onvoorzien groot – nevenprojectje, namelijk een RT- qPCR project van zo een 2000 genen in E. coli . Spijtig genoeg ging het allemaal wat trager dan verwacht en heeft dit mijn finale versie niet gehaald, maar ik wil toch even de mensen hier danken die me geholpen hebben om dit project op te starten. Gaspard, voor de bio- informatica tools, Jo, voor alle hulp bij de chaotische administratie en de inzichten in de proefopzet, Ellen en Isabelle voor de mRNA extracties en cDNA syntheses, Steven, Sophie en Katelijne voor de hulp bij de fermentaties, Elen Ortenberg, voor de hulp bij de proefopzet, Prof. Van Criekinge voor de toegang tot het Biotrove toestel en de hulp bij de bio-informatica tools, Johan Vandersmissen, voor de qPCR en de Biotrove opleiding, Filip Pattyn, voor de hulp bij de high-throughput primer design en Jan Hellemans en Jo Vandesompele voor de verhelderende inzichten in het gebruik van referentiegenen. Om dit dankwoord af te sluiten – en ik hoop oprecht dat ik niemand vergeten ben – wil ik mijn ouders nog bedanken, voor de vele jaren steun. Vooral voor dit laatste jaar waarin ik even terug mocht genieten van de luxe om thuis te wonen om te schrijven en de uitgebreide maaltijden die daarbij horen. Ik beloof op de een of andere manier die papieren torens die ik heb verzameld ergens netjes op te bergen, nu ik weer wat meer tijd heb. Ook Sven, mijn broer, hoort thuis is deze lange rij, weliswaar als antipode, die me op tijd van het werk onttrok door met me door Kroatië, Italië, Griekenland en Cuba te trekken met als risico compleet verkeerd te rijden, maar dat maakt het juist spannend… . vi Curriculum vitae Curriculum vitae Joeri Beauprez (° March 17 th 1981) Education 1999-2004 Engineering in applied biological sciences – Chemistry (Master of bio- engineering), Faculty of Bioscience engineering, Ghent University, Ghent, Belgium. Erasmus exchange: August 2002 – February 2003, Helsinki University, Helsinki, Finland. Master thesis: ‘Co-enzyme degradation and regeneration in doughsystems.’ Promotor: em. Prof. dr. ir. E.J. Vandamme 2005- PhD-student in applied biological sciences, Faculty of Bioscience engineering, University of Ghent, Ghent, Belgium. ‘Metabolic modelling and engineering of Escherichia coli for succinate production .’ Promotor: Prof. dr. ir. W. Soetaert and em. Prof. dr ir E.J. Vandamme Courses: Bioreaction engineering, PhD course, 21-27 May, 2005. Technical University of Denmark, Lyngby, Denmark. Advanced course on Applied Genomics of Industrial Fermentations, PhD course, 3-7 October, 2005. Delft University of Technology, Delft, The Netherlands. Research stay at the Technical University of Delft, 31 October - 14 November 2005. TATAA open qPCR course, 18 – 21 June, 2007. Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium. Publications: Peer review Parmentier, S., Beauprez, J., Arnaut, F., Soetaert, W., Vandamme, E.J.(2005). Gluconobacter oxydans NAD-dependent polyol dehydrogenases activity: screening, medium optimization and application for enzymatic polyol production. Biotechnology Letters, 27 (5), 305-311. De Muynck, C., Beauprez, J., Soetaert, W. and Vandamme, E. (2006). Boric acid as a mobile phase additive for high performance liquid chromatography separation of ribose, arabinose and ribulose. Journal of Chromatography A, 1101 (1-2), 115-121. ix Curriculum vitae De Muynck, C., Van der Borght, J., De Mey, M., De Maeseneire, S.L., Van Bogaert, I.N.A., Beauprez, J., Soetaert, W. and Vandamme, E. (2007). Development of a selection system for the detection of L-ribose isomerase expressing mutants of Escherichia coli. Applied Microbiology and Biotechnology, 76 (5), 1051-1057. De Mey, M., Lequeux, G.J., Beauprez, J., Maertens,J., Van Horen, E., Soetaert, K.W., Vanrolleghem, P.A. and Vandamme, E.J. (2007). Comparison of different strategies to reduce acetate formation in Escherichia coli . Biotechnology Progress, 23 (5), 1053 -1063. Beauprez, J., De Mey, M., Soetaert, W. (2010). Metabolic engineering for optimization of a succinic acid producer strain. Process Biochemistry. submitted. Patents Beauprez, J., Baart, G.J., Foulquié Moreno, M.R., Maertens, J. and Heijnen, J.J. (2008). Bacterial strains for enhanced succinate production. PCT application number: PCT/EP2009/058173; Filing date: June 1, 2009 Parmentier, S., Vandamme, E.J., Beauprez, J. and Arnaut, F. (2007). Dough comprising nucleotide pyrophosphatase inhibitor. EP1771073. Parmentier, S., Vandamme, E.J., Beauprez, J. and Arnaut, F. (2006).Nucleotide pyrophosphatase inhibitor and coenzyme regenerating systems. WO/2006/000066 Conference proceedings De Mey, M., Lequeux, G., Van Nieuland, K., Beauprez, J., De Maesenaeire, S., Maertens, J. Soetaert, W., Vandamme, E.J. (2005). Evaluation of current methods for nucleic acid quantification. Abstract Book of the Renewable Resources and Biorefineries Conference. 19/9/2005-21/9/2005, Ghent, Belgium. p. 28. De Mey, M., De Maesenaeire, S., Beauprez, J., Van Nieuland, K., Soetaert, W., Vandamme, E.J. (2005). Comparison of methods for nucleic acid quantitation.. Abstract Book the 12th European Conference on Biotechnology: Bringing Genomes to Life (ECB12). 21/8/2005- 24/8/2005, Copenhagen, Denmark. p. 159 De Mey, M., De Maesenaeire, S., Beauprez, J., Van Nieuland, K., Soetaert, W., Vandamme, E.J. (2005). Quantification of nucleic acids. Abstracts of the World Congress of Industrial Biotechnology and Bioprocessing. 20/4/2005-22/4/2005, Orlando, Florida. x Curriculum vitae Foulquié Moreno, M., De Mey, M., Boogmans, S., Beauprez, J., Soetaert, W., Vandamme, E., Cunin, R. (2006). A novel system for the fine tuning of genes. First International Symposium on System Biology: From Genomes to In silico and back. 1/6/2006-2/6/2006, Murcia, Spain. p. 52. Donckels, B., Beauprez, J., Maertens, J. and Vanrolleghem, P. (2006). Accelerating the transition between steady states in chemostat fermentations through the application of bioprocess control. ESBES-6. 27/08/2006-30/08/2006, Salzburg, Austria. De Mey, M., Beauprez, J., Cunin, R., Soetaert, W., Vandamme, E.J. and Foulquié Moreno, M..(2006). A novel system for the fine tuning of genes. Metabolic engineering VI. 1/10/2006- 5/10/2006, Noordwijkerhout, The Nederlands. De Muynck C., Saerens K., Beauprez, J. and Vandamme E.J. (2006). Sweet Biotechnology: rare sugars, enzymes and co-enzyme regeneration. Rare Sugars Congres. 21/11/2006- 14/11/2006, Kanaga, Japan. Beauprez, J., Maertens, J., Baart, G., Remedios Foulquie Moreno, M., Taymaz, H., Van Horen, E., Lequeux, G., Vancoppenolle, E., Cunin, R., Mashego, M., Bhagwat, A., Donckels, B., Boogmans, S., De Pauw, D., Van Gulik, W., Vanrolleghem, P., Heijnen, J.J. , Delmeire, D., De Baets, B., Soetaert, W. and Vandamme, E.J. (2007). Metabolic Engineering and Dynamic Modelling of E. coli for the Production of Chemicals from Renewable Resources. IMAGE conference. 30/04/2007 – 3/05/2007, Montreal, Canada. Beauprez, J., Maertens, J., Foulquié Moreno, M.R., Roelants, S., Lequeux, G., Van Horen, E., Baart, G., Cunin, R., Charlier, D., Heijnen, J.J., Soetaert, W. (2008) Identification of essential mutations for the optimization of succinate production with E. coli . Metabolic engineering VII, 14/09/2008-19/09/2008, Puerto Vallarta, Mexico Attended Conferences Metabolic engineering VI. 2006, Noordwijkerhoudt, The Netherlands. IMAGE conference. 2007, Montreal, Canada. Metabolic engineering VII. 2008, Puerto Vallarta, Mexico. Student guidance: Practical exercices ‘General Microbiology’. (2005-2008) Practical exercices ‘Biocatalysis and enzymetechnology’. (2006) xi Curriculum vitae Eduardo Arturo Morelos Pulido, summer research internship (2005). Study of fluxes of carbon in Escherichia coli with a black box approach. Eduardo Arturo Morelos Pulido, M.Sc. thesis (2005-2006). Enhancing succinate production in chemostat cultures by overexpressing DcuC in Escherichia coli . Hendrik Waegeman, M.Sc. thesis (2006-2007). Invloed van de aëratie-condities op de succinaatproductie van Escherichia coli. Sophie Roelants, M.Sc. thesis (2006-2007). Model-gebaseerde optimalisatie van succinaatproductie door Escherichia coli. Annelies Delbeke, B.Sc. thesis (2006-2007). Studie van iclR en arcA bemiddelde regulatie van de glyoxylaatcyclus in Escherichia coli MG 1655. Steven Van Brussel, B.Sc thesis (2007-2008). Studie van Escherichia coli onder verschillende fermentatiecondities Sven Deketele, B.Sc. thesis (2007-2008). Opschaling van E. coli succinaat productieproces. Katelijne Bekers, Masters internship (2008-2009). Evaluatie van verschillende E. coli mutantstammen voor de productie van succinaat. Other activities: Secretary of the international congres Renewable Resources and Biorefineries 2005, Ghent 19 th - 21 st of September 2005. Secretary of the international congres Renewable Resources and Biorefineries 2007, Ghent 4 th - 6 th of June 2007. Tutor in the scientists at work program (2005-2006). ‘Biotechnology in our daily lives’ xii