Development and Application of
13
C-Labeling Techniques
Analyzing the Pentose Phosphate Pathway of Penicillium chrysogenum
Development and Application of
13
C-Labeling Techniques
Analyzing the Pentose Phosphate Pathway of Penicillium chrysogenum
Proefschrift
ter verkrijging van de graad van doctor aan de Technische Universiteit Delft,
op gezag van de Rector Magnificus prof. dr. ir J.T. Fokkema, voorzitter van het College voor Promoties
in het openbaar te verdedigen op maandag 2 april 2007 te 15:00 uur
door
Roelof Jacobus KLEIJN
ingenieur in de BioprocestechnologieProf. dr. J.J. Heijnen
Samenstelling promotiecommissie:
Rector Magnificus Voorzitter
Prof. dr. J.J. Heijnen Technische Universiteit Delft, promotor Prof. dr. J.T. Pronk Technische Universiteit Delft
Prof. dr. J. Nielsen Technical University of Denmark, Lyngby, Denmark Prof. dr. E. Heinzle Saarland University, Saarbrücken, Germany Dr. W.A. van Winden Technische Universiteit Delft
Dr. D. Schipper DSM
Prof. dr. J.H. de Winde Technische Universiteit Delft, reservelid
tions and the impressions of the senses are the fertile soil in which the seeds must grow.
vii
List of abbreviations ix
Chapter 1 General Introduction 1
Chapter 2 Revisiting the 13C-label distribution of the non-oxidative branch of the pentose phosphate pathway based upon kinetic and genetic evidence
29
Chapter 3 Metabolic flux analysis of a glycerol-overproducing Saccharomyces cerevisiae strain based on GC-MS, LC-MS and NMR derived 13C-labeling data
53
Chapter 4 13C-labeling based metabolic network and flux analysis of penicillin-G producing and non-producing chemostat cultures of Penicillium chrysogenum
81
Chapter 5 13C-labeled gluconate tracing as a direct and accurate method for determining the pentose phosphate pathway split-ratio in Penicillium chrysogenum
129
Chapter 6 Cytosolic NADPH metabolism in penicillin-G producing and non producing chemostat cultures of Penicillium chrysogenum
157
Chapter 7 Discussion and future directions 179
Summary 187
Samenvatting 191
List of Publications 195
Curriculum Vitae 197
ix A list of the most common abbreviations used throughout this thesis:
1,3PG 1,3-Bisphosphoglycerate
2/3PG Combined pool of 2PG and 3PG
2PG 2-Phosphoglycerate
3PG 3-Phosphoglycerate
6PG 6-Phosphogluconate
8-HPA 8-Hydroxypenillic acid
Aald Acetaldehyde
AcCoA Acetyl-Coenzyme A
ACV δ-(L-α-Aminoadipyl)-L-cysteinyl-D-valine
ADP Adenosine diphosphate
AKG α-Ketogluterate
AMP Adenosine monophosphate
ASP Aspertate
AT Acyl-coA:IPN acyl transferase
ATP Adenosine triphosphate
AVCS Non-ribosomal ACV synthetase
C1 Methylenetetrahydrofolate
CID Collisionally induced dissociation
CITR Citrate
CoA Coenzyme A
COSY Correlation spectroscopy
DHAP Dihydroxyacetone-phosphate
DSH Direct sulfhydrylation
E4P Erythrose-4-phosphate
E-C2 Glycolaldehyde moiety covalently bound to the thiamine pyrophosphate/transketolase complex
E-C3 Dihydroxyacetone moiety covalently bound to the enzyme transaldolase
ETOH Ethanol
F6P Fructose-6-phosphate
FAD(H) Flavin adenine dinucleotide
FBP Fructose-1,6-bisphosphate FE Fractional enrichment FUM Fumerate G1P Glucose-1-phosphate G3P Glycerol-3-phosphate G6P Glucose-6-phosphate GAP Glyceraldehyde-3-phosphate
GC-MS Gas chromatography mass spectrometry
GLC Glucose
GLN Gluconate
x
GLYOX Glyoxylate
GOH Glycerol
IPN Isopenicillin-N
IPNS IPN synthetase
LC-MS Liquid chromatography mass spectrometry
M6P Mannose-6-phosphate
MAL Malate
MDV Mass isotopomer distribution vector
MFA Metabolic flux analysis
MG Methylgyoxal
NAD(H) Nicotinamide dinucleotide
NADP(H) Nicotinamide dinucleotide phosphate
NMR Nuclear magnetic resonance
O8P Octulose-8-phosphate
OAA Oxaloacetate
o-OH-PAA Ortho-hydroxyphenylacetic acid
OPC 6-Oxopiperidine-2-carboxylic acid
P5P Combined pool of R5P, X5P and RU5P
PAA Phenylacetic acid
PEP Phosphoenolpyruvate
PIO Penicilloic acid
POA Phenocyacetic acid
PPP Pentose phosphate pathway
PYR Pyruvate R5P Ribose-5-phosphate RQ Respiratory quotient RU5P Ribulose-5-phosphate S7P Sedoheptulose-7-phosphate s2
res Estimated error variance
SER Serine
SFL Summed fractional labeling
SS Summed squared residuals
SUC Succinate
T6P Trehalose-6-phosphate
TA Transaldolase
TCA Tri carboxylic acid
THR Threonine
TK Transketolase
TPP Thiamine pyrophosphate
TS Transsulfuration
General introduction
2
1.1 HISToRy of PENICILLIN PRoDuCTIoN 1.1.1 from discovery to productionMany lives have been saved by the development of antibiotics for the treatment of infectious diseases. In that sense, the discovery of antibiotics is perhaps the most important breakthrough in the history of therapeutic medicine. This discovery is generally attributed to Alexander Fleming, who worked at the St. Mary's Medical School at London University and in 1929 published an article in which he showed that a species of Penicillium (later identified as Penicillium chrysogenum) exhibited strong antibacterial activity towards gram-positive bacteria [21]. Although Fleming was not the first to report on the antibacterial properties of moulds and fungi, he was the first to recognize the importance of his findings [29]. For this reason he was one of the recipients of the Nobel price for medicine in 1945.
In the years following the 1929 publication of Fleming, his results did not evoke much interest outside scientific circles and was merely seen as a scientific curiosity. In fact, at that time it was not considered important that basic research should lead to a practical application. By 1932, Fleming had abandoned his work on the antibacterial agent (which he called penicillin), also because his efforts to stabilize and purify penicillin were to no avail. Almost a decade later Howard Florey and Ernst Chain at the Sir William Dunn School of Pathology at Oxford University were the first to recognize the importance of Fleming’s 1929 publication. Thanks to the pioneering work of Norman Heatley, an extraction procedure based upon a solvent/water mixture was developed which enabled the production and extraction of enough penicillin for clinical trials [38]. After successfully showing the chemotherapeutic action of penicillin in mice injected with a lethal dose of bacteria [5], the first dose of penicillin was administered to a human on January 27, 1941. Ensuing studies demonstrated that injections of penicillin caused rapid recoveries in patients suffering from a variety of infections [1].
From 1941 onwards it became clear that if available in sufficient quantity, penicillin had an enormous potential as antibacterial agent. Studies demonstrated that penicillin inhibited the growth of pathogens such as gas gangrene and syphilis, which were widespread due to the outbreak of World War II. However, due to war conditions the British pharmaceutical industry was incapable of producing sufficient penicillin as materials needed for the production of penicillin were limited. For this reason, Florey and Heatley headed for the United States on June 27, 1941 to urge pharmaceutical companies to enter into mass production.
3
produced by fermentation [29]. Aside from being easier, the fermentative approach remains cheaper and more environmental friendly, making the industrial production of penicillin by fermentation one of the outstanding examples of biotechnology.1.1.2 Strain improvement techniques
In the 60 years following the initial commercial production of penicillin, its biosynthesis developed from a specialty process yielding gram scale quantities, to a bulk process with a world-wide annual production exceeding 60.000 tons [2, 53]. Prices have dropped from a staggering $83.000/kg in 1943 to less than $15/kg nowadays [2, 16, 29, 46]. The penicillin titers of P. chrysogenum have increased accordingly from less than 0.003 g/l for the strain isolated by Fleming in 1929, to 40-50 g/L nowadays [14, 16, 29] (See Figure 1). This increase in titer is partly the result of more optimal cultivation conditions and improved fermentation techniques, but above all due to an ongoing quest for superior strains. Numerous strain improvement programs were initiated to increase the productivity and fitness of P. chrysogenum via classical mutagenesis techniques (e.g. via irradiation and chemical agents) and subsequent screening for better producing strains.
year 1940 1960 1980 2000 Penicillin-G price ($/kg) 20 40 60 80 100 120 Penicillin-G titers (g/L) 0 10 20 30 40 50 NRLL1951 WisQ-176 Estimate Panlabs-8 Lilly-E15.1 Estimate
figure 1 Penicillin prices [2, 29, 46] and titers [16, 23, 29, 46] from the start of commercial production
until today. Titers are listed together with the P. chrysogenum strain in which the titer was attained. From 1980 onwards only estimates for titers are available. Arrows denote the corresponding y-axis for the two curves.
4
producing strain is isolated. Conversely, the development of recombinant DNA technology over the past decades has allowed researchers to introduce precise modifications in the genetic makeup of a cell, thereby redesigning only selected parts of the metabolism. This more rational approach towards strain improvement is referred to as metabolic engineering and increases the chance of successfully directing the metabolic fluxes towards the desired end product. In short, metabolic engineering consists of a first metabolic characterization step in which more insight in the cellular metabolism of the studied strain is obtained, followed by a genetic modification step using recombinant DNA technology, in which this insight is used to improve the properties of the strain. For lack of a complete understanding of the cell, this is a cyclic process; multiple steps of characterization and modification are needed to further improve the strain [60].BOX 1: Taxonomy and morphology of P. chrysogenum
Penicillium is a genus of filamentous fungi belonging to the division of ascomycota (sac fungi).
Characteristic of ascomycota is that they produce spores in a distinctive compartment called the ascus. Species of Penicillium are recognized by their dense brush-like spore-bearing structures. This is also where the name Penicillium comes from (brush is penicillus in Latin). The Penicillium species studied in this thesis, Penicillium chrysogenum (also known as Penicillium notatum), is the main production host of penicillin (see main text). Penicillin prevents the growth of gram-positive bacteria by binding to the peptidoglycan crosslinking enzyme transpeptidase, thereby disrupting bacterial cell wall synthesis. As a result, the formation of crosslinks between peptidoglycan polymers is inhibited, making the affected cells more susceptible to lysis during cell division.
Like with all fungi, spores form the start (germination) and endpoint (sporulation) of the developmental phase of
P. chrysogenum. Spore germination can be divided into
three stages: spore swelling, germtube emergence and germtube elongation [49]. During spore swelling, the spore grows spherically and new cell material is produced. After establishing growth polarity, germtubes emerge from the spore at one or two areas. During the elongation phase the germtube grows and eventually develops into an elongated filament consisting of thin, needle-shaped, multi-cellular structures called a hypha. Growth of the hypha is initially supported by storage compounds in the spore, but as the hypha extends it grows on nutrients taken up from the medium [51]. Hyphal growth is highly polarized and occurs only at the hyphal tip (apex). Periodically branches are formed at or near the hyphal tip, allowing the fungus to form densely branched hyphal elements [25, 48]. The morphologic differentiation of a spore to a hypha leads to different cell-types. In general, a hypha can be divided into three distinct compartments each with its own function: the apical, the subapical and the hyphal compartment (Figure 2) [46]. The apical compartment is situated at the tip of the hypha, contains no cross-walls (septa) and is directly involved in the supply of cellular material necessary for tip extension (e.g. cytoplasmic material and building blocks for wall synthesis). The subapical compartment is
Apical Subapical Hyphal
Spore
figure 2 Structure of a hyphal
5
located further down the hypha. The cells in this compartment are divided by septa, but a (free) flow of nutrients and organelles from one compartment to another is still possible. Most probably, this compartment also plays a role in supplying the hyphal tip with cellular building blocks. The hyphal compartment is found at the basis of the hypha, containing cells with large vacuoles [49, 50]. These cells are believed to be important for creating a sufficient intracellular pressure to ensure transport of protoplasm towards the tip section.
In contrast to unicellular eukaryotes the two processes of cell division (genome duplication and cell division) do not necessarily occur consecutively in fungal cell. As a result, fungal cells contain multiple nuclei. Formation of a new apical compartment consists of three stages: (i) the length of the apical compartment is increased until the volume of cytoplasm per nucleus reaches a critical ratio (ii) genome duplication (mitosis) is initiated and continued until the number of nuclei has doubled (iii) the compartment is divided by a septum, forming a new apical compartment [46]. Aside from the morphological differentiation of a single hypha (microscopic morphology) the interaction of multiple hyphae with each other also causes morphological differentiation at a higher level (macroscopic morphology). Within submerged cultures the interaction of hyphae can lead to distinct particles (pellets), connected networks of hyphae (aggregates), or separate hyphae (dispersed mycelia) [11]. Factors which affect the macroscopic morphology include the level and type of inoculum, medium shear, medium constituents and the pH [51].
1.2 METAboLIC ENGINEERING AND P. chrysOgenum
1.2.1 Production pathway
Metabolic engineering of P. chrysogenum requires fundamental knowledge on the biosynthesis pathway leading to the formation of penicillin (see Figure 3). The first step in the penicillin biosynthesis pathway is the condensation of the three amino acid precursors (valine, L-α-aminoadipic acid and L-cysteine) into the tripeptide δ-(L-α-aminoadipyl)-L-cysteinyl-D-valine (LLD-ACV) by the non-ribosomal peptide synthetase ACV-synthetase. In the second step isopenicillin N synthetase (IPNS) catalyzes the oxidative ring closure of the tripeptide, resulting in a four-membered β-lactam ring fused to a five-membered thiazolidine which is characteristic for all penicillins. In the third and final step the L-α-aminoadipyl side chain of IPN is exchanged for a CoA-activated acyl group by acyl-CoA:isopenicillin-N-acyl transferase (AT). Strictly speaking L-α-aminoadipic acid is not a precursor as it is split-off and (partially) recycled after the formation of the β-lactam nucleus. The type of penicillin produced by P. chrysogenum depends on the acyl side-chain precursor added to the medium. Commonly used side-chain precursors are phenylacetic acid (PAA) and phenoxyacetic acid (POA), leading to the formation of penicillin-G and penicillin-V, respectively. Aside from the above described main reaction pathway, several spontaneous chemical conversions lead to the (irreversible) formation of penicillin side-products such as ortho-hydroxyphenylacetic acid (o-OH-PAA), 6-oxopiperidine-2-carboxylic acid (OPC), 8-hydroxypenillic acid (8-HPA), penicilloic acid (PIO) and bis-ACV [17, 30].
6
COOH H2N HOOC HOOC SH CH3 CH3 CH3 CH3 N SH H O N HOOC H O CH3 CH3 N S O N HOOC H O HOOC NH O COOH COOH COOH CH3 CH3 N S H O N COOH H O COOH CH3 CH3 N S H O N COOH H O COOH NADPH NADP + 1/2 O2 H2O CH3 CH3 N S O N H O COOH COOH HOOC + CoA CH3 CH3 N S O H2N COOH CH3 CH3 N S COOH N COOH HO CO 2 COOH H2N HOOC CH3 CH3 N C S N H O COOH H OH O H2O H2O 2H2O O2 3ATP 3AMPL-Aad L-Cys L-Val
OPC Aad 6-APA 8-HPA Penicillin-G PIO Aad bis-ACV LLD-ACV IPN ACVS IPNS AT TR H2N H2N H2N H2N H2N H2N H2N CoA O COOH ATP + CoA H2O + AMP PAA PAA-CoA PCL CoA AT AT
figure 3 Biosynthesis pathway of penicillin-G and the related by-products. For other abbreviations see
7
increased penicillin production rate [59, 70]. Furthermore, Fierro et al. [18] showed that classical strain improvement has led to a 6-14 fold increase in the copy number of the gene cluster in the nowadays available high penicillin producing strains compared to the Wis54-1255 strain. For obvious reasons there are no public reports on the number of gene clusters in the currently used industrial strains.1.2.2 Primary metabolism
There is a limit to increasing penicillin production via the increased amplification of the penicillin gene cluster. At some point the supply of carbon precursors, cofactors and energy by the primary metabolism will limit penicillin production, causing a shift in the metabolic bottleneck from the production pathway to the primary metabolism [27, 63]. For example, Jorgensen et al. [31] observed that supplementing fed-batch cultivations with cysteine, valine and α-aminoadipic acid increased the penicillin-V production by about 20%, indicating that in the studied strain amino acid availability may limit penicillin production. In general, three possible limitations in the primary metabolism of P. chrysogenum can be identified when synthesizing large amounts of penicillins. These are the supply of the carbon precursors; the supply of energy in the form of ATP; and the availability of electrons in the form of NADPH. All these three constituents of primary metabolism are needed to construct the three amino acid precursors for penicillin synthesis. In addition, ATP and NADPH are essential cofactors for the conversion of the precursors to the product penicillin Several studies have addressed one or more of these potential bottlenecks in high-yielding former production strains of P. chrysogenum [9, 10, 28, 31, 62, 63].
supply of carbon precursor The supply of sufficient carbon precursors for the synthesis
of penicillin is an obvious target when optimizing penicillin production. Van Gulik et al. [63] showed in a chemostat cultivation that flux distributions around the four principal metabolite nodes of penicillin production in P. chrysogenum (glucose-6-phosphate, glyceraldehyde-3-phosphate, mitochondrial pyruvate and mitochondrial isocitrate) are highly flexible. It was therefore considered unlikely that in the studied industrial strain the primary carbon metabolism forms a potential bottleneck in penicillin-G synthesis.
supply of energy The synthesis of penicillin is an energy demanding process. The ATP
supply of reducing power Reducing power in the form of NADPH is important for the
biosynthesis of the two amino acid precursors of the β-lactam nucleus, cysteine and valine. The total stoichiometric NADPH demand for penicillin synthesis ranges from 7-10 mol/mol penicillin [63]. However, similar to the ATP demand for penicillin synthesis, the actual value can be several times higher as a result of unaccounted processes (e.g. the spontaneous oxidation of ACV into bis-ACV, which is reduced back to ACV by thioredoxin reductase at the cost of one NADPH equivalent).
The pentose phosphate pathway (PPP) is the main producer of NADPH in P. chrysogenum. Therefore, an important metabolic network parameter is the fraction of g6p entering the oxidative branch of the PPP in relation to the total uptake of glucose by the cell (from hereon referred to as the PPP split-ratio). In general, an increased demand for NADPH is associated with an increase in the PPP split-ratio [74, 79]. In the past, several studies quantified the relation between the PPP split-ratio and the overproduction of (partly) PPP-originating products [12, 78] or amino acids requiring considerable amount of NADPH for their biosynthesis [54]. Several theoretical studies were also performed on P. chrysogenum in which it was speculated that the PPP split-ratio was strongly correlated to penicillin production [28, 31]. This hypothesis was supported by the results of van Gulik et al. [63], who showed that a stepwise increase in the total metabolic demand for NADPH (by altering between glucose, acetate, ethanol or xylose as carbon source and ammonia or nitrate as nitrogen source) resulted in a stepwise decrease in penicillin-G production. On the other hand, experimental 13C-based flux determinations by Christensen et al. [10] in a slightly different P. chrysogenum strain showed no relation between penicillin production and the flux through the oxidative PPP.
1.3 METAboLIC fLux ANALySIS
1.3.1 Principle
At the basis of investigating the central metabolism of a micro-organism lies a tool called metabolic flux analysis (MFA). MFA, also referred to as metabolite balancing, is a generally applicable characterization method within the field of metabolic engineering, aimed at quantifying intracellular steady state fluxes within a predefined metabolic network model. Via this metabolic snapshot researchers are able to better understand and predict the phenotypic behaviour of a micro-organism as a result of genetic alterations [15, 57] and different environmental conditions [13, 34, 63] Moreover, an accurate description of the metabolic network and the corresponding metabolic fluxes form the basis of kinetic models for in silico network analysis. With these models researchers can predict how changes in the underlying metabolic network (e.g. changed enzyme levels or enzyme properties as a result of genetic modification) affect the fluxes in the cell, making these a powerful tool for metabolic engineering.
1.3.2 Experimental framework
steady-
box 2: The pentose phosphate pathwayThe pentose phosphate pathway (PPP) plays a crucial role in supplying the cell with precursors for amino acid and nucleotide biosynthesis and maintaining the NADP+/NADPH balance. Note
that in order to maintain the NADPH balance the flux through the PPP is generally much larger than the drain on PPP metabolites for the biosynthesis of building blocks. Figure 4 shows the two parts of the PPP: i) the oxidative branch for the production of NADPH and ii) the non-oxidative branch for the synthesis of biomass precursors such as erythrose-4-phosphate (e4p) and ribose-5-phosphate (r5p), and for the recycling of surplus carbon back to glycolysis. Other metabolites within the PPP are glucose-6-phosphate (g6p), 6-phosphogluconate (6pg), ribulose-5-phosphate (ru5p), xylulose-5-phosphate (x5p), sedoheptulose-7-phosphate (s7p), fructose-6-phosphate (f6p) and glyceraldehyde-3-phosphate (gap). Neglecting the withdrawal of precursors for amino acid and nucleotide synthesis, the overall reactions of the oxidative and the non-oxidative branch are:
2 2
3g6p 3H O 6NADP+ + +→3ru5p 3CO+ +6NADPH 6H+ +
(1.1)
3ru5p↔2r5p x5p+ ↔2f6p gap+ (1.2)
In the oxidative branch g6p is irreversibly oxidized and hydrolyzed to 6pg by the enzyme g6p dehydrogenase and gluconolactonase, respectively. The formed 6pg is oxidatively decarboxylated by the enzyme 6pg dehydrogenase yielding ribulose-5-phosphate (ru5p) and CO2. In each
oxidation step one molecule of NADP+ is reduced to NADPH, resulting in the net formation of 2 mol NADPH per mol of g6p converted. In the non-oxidative branch of the PPP ru5p can be isomerized or epimerized into, respectively, r5p or x5p by the enzymes phosphopentose isomerase and epimerase. These metabolites form the starting point for the ensuing carbon transfer reactions catalyzed by transketolase and transaldolase. In these reactions two- (transketolase) and three- (transaldolase) carbon fragments are transferred from a ketose substrate (x5p, s7p, f6p) to an aldose acceptor (r5p, gap, e4p). In most textbooks the transketolase and transaldolase catalyzed reactions are depicted as three highly reversible reactions as shown in Figure 4. It should be stressed that this is an oversimplified representation as argued by van Winden et al. [65] and in chapter 2 of this thesis.
figure 4 Simplified representation of the oxidative and non-oxidative branch of the
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state assumption only holds during the exponential phase of growth. As a result, reported fluxes for batch cultures automatically correspond with those at maximal growth rate. In contrast, continuous and fed batch cultivations allow flux analysis experiments for a whole range of growth rates by simply altering the imposed feed rate and establishing (pseudo) steady states.1.3.3 Mathematical framework
MFA is based on the principle of mass conservation of the intracellular metabolites within a defined stoichiometric network. By measuring the net conversion rates of the extracellular metabolites, assuming (pseudo) steady state for the intracellular metabolite concentrations and neglecting the dilution effects of growth, the mass balances of the metabolites are written as: m 0 S v r R ⋅ = (1.3)
In Eq. 1.3, S is the N-by-V stoichiometry matrix, where N is the number of intracellular metabolites and V the total number of fluxes in the flux vector (v). R is the M-by-V measurement matrix, where M is the number of measured net conversion rates in the rate vector (rm). R contains a unity entry for each measured net conversion rate.
In case the rank of S R
equals the number of fluxes (V), the system of equations is determined and all fluxes can be calculated by inverting the matrix. Often, the system of equations is undetermined (rank S
R
< V) in which case only a linear combination of the fluxes can be calculated [67]: m 0 S S v pinv null r R R = + β (1.4)
In Eq. 1.4 ‘pinv’ denotes the pseudo-inverse and ‘null’ denotes the nullspace. Vector β contains the linear coefficients of the columns that span the null space and thus represent the degrees of freedom that remain after combining the balances and the extracellular measurements. Figure 5 shows the application of MFA for resolving the flux patterns in 4 simplified metabolic networks. Fluxes through linear and diverging nodes can be readily calculated as illustrated by Figure 5-A. A shortcoming of metabolic balancing is, however, that it fails to identify parallel metabolic pathways (Figure B), bidirectional reaction steps and metabolic cycles (Figure 5-C). For all these three networks the set of linear equations is undetermined, and only a subset of fluxes can be quantified. The single column of the nullspace indicates that one additional measurement is needed to determine all fluxes.
1.3.4 Solving underdetermined systems
11
problem is the inclusion of conserved moiety balances such as ATP, NADH and NADPH. A difference between the upper glycolysis and the PPP is the production of NADPH in the latter pathway. Based upon the total NADPH demand of the cell the amount of g6p metabolized via the oxidative branch of the PPP is fixed and consequently also the flux through the upper glycolysis. The result of including conserved moiety balances is depicted in Figure 5-D, where the fluxes through a parallel pathway can be calculated as a result of the conserved moiety balance.Inclusion of conserved moiety balances has its own shortcomings, since these balances do not resolve the flux through bidirectional reactions and are generally based on incomplete stoichiometric information [4, 72]. Uncertainties about the ATP yield of the respiratory system and NADPH cofactor specificities of (iso)enzymes, the presence of transhydrogenation cycles and ATP-wasting futile cycles and the occurrence of as yet unknown sinks of NAD(P)H (e.g. caused by oxidative stress), make flux predictions based upon assumed cofactor balances prone to error.
figure 5 Application of MFA for resolving flux patterns in a diverging pathway (A), a parallel pathway
(B), a bidirectional pathway or metabolic cycle (C) and a parallel pathway with a conserved moiety (D). Pathways A and D produce a determined system of equations and therefore yield a unique flux solution. In contrast, pathways B and C form an underdetermined system. Some of the fluxes can be calculated, while the remaining fluxes are expressed as a linear combination of the minimum-norm solution and the nullspace.
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1.4 13C-LAbELING TECHNIquE1.4.1 Principle
An alternative method for resolving the fluxes through parallel pathway, bidirectional reactions and metabolic cycles is by complementing the MFA method with 13C-labeling experiments. 13C-labeling experiments are based on feeding 13C-labeled substrate to a biological system, allowing the labeled carbon atoms to distribute over the metabolic network, and subsequently measuring the 13C-label distributions of intracellular compounds. Based upon the different rearrangement of the 13C-labeled positions in the intracellular metabolites by different reactions, this method enables the flux distribution through pathways to be discriminated even if the pathways have the same overall stoichiometry.
An example of this is shown in the metabolic network model depicted in Figure 6, where the product can be synthesized via two parallel pathways. As shown before (Figure 5), the system of equations formed by the mass balances is underdetermined and thus does not lead to a unique flux solution. The flux distribution between the two pathways can be determined by feeding a mixture of unlabeled and uniformly labeled [u-13C
3] substrate and measuring the labeling patterns of the product. The product produced via the top pathway will have the same labeling as the substrate, while the bottom pathway yields [1,2-13C
2] and [2,3-13C2] product. The ratio between partially labeled product and fully labeled or unlabeled product is thus a direct measure for the flux distribution between the two parallel pathways. Note that in this
I1 I2 I3 I4 I5 S P v1 v 2 v4 v3 r1 r2 1 1 2 3 4 1 2 v 50 0.5 1 0 1 0 1 0 v 50 0.5 1 1 0 0 0 0 v 50 0.5 S 0 0 1 1 0 0 v 50 0.5 0 1 0 1 0 1 r 100 0 0 0 0 0 1 0 r 100 0 where, 100 100 − − − = − ⇒ = + − β − − ≤ β ≤ i S R = 100
figure 6 Use of 13C-labeling technique for resolving flux patterns in a parallel pathway. Open
circles denote a 12C atom and black circles denote a 13C atom. By feeding a mixture of uniformly
13
case the feeding of [1-13C] substrate does not resolve the flux through the upper and lower pathway, showing the importance of a priori analysing the identifiability of the metabolic fluxes [67] and choosing the appropriate 13C-substrate labeling [45].A major advantage of 13C-labeling experiments is the richness of the acquired 13C-labeling data. The total number of possible combinations of a 12C and a 13C atom in a molecule containing n-carbon atoms is equal to 2n (from hereon these combinations are referred to as isotopomers, short for isotopic isomers). By setting up isotopomer balances (similar to the mass balances for MFA) additional constraints are imposed on the set of flux balances. The maximum number of constraints imposed by an isotopomer balance equals 2n-1: 2n isotopomers minus one dependent fraction. The number of constraints imposed by 13C-labeling data on the set of flux balances is thus much bigger then the constraints imposed by the conserved moiety balances.
1.4.2 Experimental framework
The three most often used methods for quantifying the isotopic enrichment of the intracellular metabolites are 2D [13C,1H] COSY NMR [40], GC-MS [20, 24] and LC-MS [69]. An overview of the advantages and disadvantages of the three analytical platforms is given in Table 1. Table 1 Advantages and disadvantages of the three analytical platforms commonly used for measuring 13C-labeling distributions.
Analysis
platform What is measured? Advantages Disadvantages
LC-MS Mass isotopomers of intracellular metabolites 1. Sensitive 2. Direct 13C-labeling information on metabolites 3. Fast isotopic steady
state 1. Rapid sampling and quenching of metabolism in sample 2. Cell averaged 13 C-labeling information GC-MS Mass isotopomers of proteinogenic intact and fragmented amino acids 1. Sensitive 2. Compartment specific 13C-labeling information in eukaryotic cells 3. Easy sampling 1. Derivatization of sample prior to analysis 2. Indirect 13C-labeling
information on metabolites
3. Slow isotopic steady state NMR Fine structures of proteinogenic amino acids 1. Non-destructive analysis 2. Compartment specific 13C-labeling information in eukaryotic cells 3. Easy sampling 4. No separation technique needed 1. Insensitive 2. Indirect 13C-labeling information on metabolites
14
2D [13c,1h] cOsy nmr NMR measurements of 13C-distributions are based on the splitting of the 1H-NMR signals of protons that are bonded to 13C atoms and on the splitting of the 13C-NMR signals of 13C atoms bonded to 13C atoms. Detected fine structures represent the interaction of the measured 13C atom with its neighbouring 13C atoms. Depending on the number of 13C-13C scalar couplings the following fine structures can be detected: singlet (s, no 13C-13C scalar coupling), doublet (d, one 13C-13C scalar coupling), triplet (t, two identical 13 C-13C scalar couplings) and double doublet (dd, two different 13C-13C scalar couplings). Recently, van Winden et al. [64] developed software specifically aimed at fitting and analyzing NMR spectra from 13C-labeling experiments.Inherent to NMR analysis is its low sensitivity. As a result, the 13C-labeling of the intracellular metabolites is inferred from accumulating biomass components such as proteinogenic amino acids and storage carbohydrates (Figure 7). Knowing the biosynthetic pathway from precursor to amino acid, the labeling patterns of the primary metabolites can be easily deduced. Of all biomass constituents the proteinogenic amino acids contain most information on the labeling of the primary metabolites as
they are synthesized from a large variety of precursors originating from glycolysis, pentose phosphate pathway and TCA cycle. Note that since these precursors are localized in predefined compartments of the cell, the measured isotopic enrichment is compartment specific. Turnover times for biomass constituents are slow (in the order of hours). As a consequence, long 13C-substrate feeding times are needed to reach constant 13C-labeling patterns for the measured biomass constituents (isotopic steady state). Replacement of 12C by 13C takes place mostly by formation of new and washout of old biomass from the chemostat. In a batch and fed-batch cultivation this means that all added substrate has to be 13C-labeled. In a chemostat system three residence times of 13C-feeding are needed to replace 95% (1-e-3) of the original biomass, as calculated by first order washout kinetics. These long 13C-feeding times are a disadvantage of NMR analysis, as they render the method unsuitable for observing metabolic transients and require large amounts of expensive 13C-labeled substrate. Despite the fact that prices for 13C-labeled substrate have dropped drastically over the past years, they remain considerable (e.g. the current price of [u-13C
6]glucose is $175/g). Due to the slow turnover time of biomass, sample harvesting does not have to be rapid. After harvesting and filtering the biomass it is hydrolyzed to free the proteinogenic amino acids, lyophilized and dissolved in D2O prior to analysis.
15
to obtain additional labeling information. As a result, GC-MS does not only measure the mass isotopomers of the intact amino acid molecule, but also of specific amino acids fragments. Sample harvesting and preparation is similar to NMR analysis, the only added step being the derivatization of the lyophilized proteinogenic amino acids to make them volatile for the GC step [8]. Compared to NMR, GC-MS is much more sensitive and thus requires less biomass for analysis.Lc-ms A relatively new technique for directly determining the 13C-labeling distribution of primary metabolites is LC-MS, consisting of a high-performance liquid chromatography step (LC) and a MS identification step. Unlike with NMR and GC-MS this method directly assesses the mass isotopomer distribution of primary metabolites, thereby preventing errors due to assumptions on the amino acids biosynthesis pathways (Figure 7). Turn-over times for primary metabolites are in the order of seconds, as a result only a short 13C-substrate feeding time is needed to reach isotopic steady state (compared to GC-MS and NMR analysis). Since anabolic pathways are generally unidirectional, the inflow of unlabeled carbon from the polymeric biomass constituents can be safely neglected. Possible exceptions to this are the inflow of unlabeled storage carbohydrates [44, 69] and the transamination reactions involved in amino acid synthesis [26]. Due to these exceptions the attainment of isotopic steady state can take several hours.
Because turnover times for primary metabolites are small, rapid sampling and quenching techniques are required to correctly determine the 13C-labeling patterns of the metabolites. The sample preparation protocol for LC-MS analysis consists of the rapid withdrawal of biomass, the immediate quenching of metabolism, the separation of cells from the extracellular liquid, and the extraction of primary metabolites [37]. Note that in compartmented micro-organisms some metabolites are localized in multiple compartments (e.g. pyruvate). LC-MS analysis yields the cell averaged 13C-labeling patterns for these metabolites, since all compartments in the cell are lysed during the metabolite extraction step. It should be noted that the LC-MS platform can give both the mass isotopomer distribution as well as the absolute levels of the primary metabolites.
1.4.3 13C flux analysis methods
17
Local node flux analysis The local node flux analysis approach has long been used to
quantify individual flux partitioning ratios [35]. In one of the earliest report on the use of 13 C-labeling experiments Walsh and Koshland [71] applied NMR spectroscopy to characterize the branch point of the tricarboxylic acid (TCA) cycle and glyoxylate shunt in Escherichia coli. Later on both Malloy et al. [41] and Katz et al. [33] used 13C as a tracer to study specific parts of anaplerosis and the TCA cycle in mammalian organs using NMR and GC-MS, respectively. Other examples of flux partitioning ratios that have been quantified using a local flux analysis approach are the PPP split-ratio [6, 39] and the degree of anaplerosis [52].
A particularly informative methodology based upon the local node flux analysis approach is metabolic flux ratio analysis (METAFOR) [40]. With this method 13C-labeling patterns of proteinogenic amino acids from a single experiment are analytically interpreted to quantify flux partitioning ratios for several converging nodes in the central metabolism of a studied micro-organism. The individually quantified flux partitioning ratios are largely independent of each other and require no input of physiological information (e.g. extracellular conversion rates). Furthermore, the determined flux ratios can be used as additional constraints for metabolic flux analysis, thereby producing a global net flux solution for the complete metabolic network [56, 73].
The METAFOR method was originally developed for NMR-analysis by Szyperski [58]. By growing cells on a mixture of 10% [u-13C
6]glucose and 90% naturally labeled glucose and by measuring the 13C-labeling of the proteinogenic amino acids, non-random 13C-labeling patterns can be identified arising from the incorporation of intact two-carbon and three-carbon fragments originating from a single source molecule of glucose. Syperski [58] developed probabilistic equations to relate the observed multiplet intensities of the 13C fine structures to the relative abundance of the intact carbon fragments. These probabilistic equations enable the derivation of fragment balancing equations, which provide accurate flux information. Fischer et al. [20] modified the METAFOR method for calculating flux partitioning ratios based on GC-MS derived mass isotopomers of proteinogenic amino acids from either [1-13C]glucose or [u-13C
6] glucose experiments. In total 14 ratios of fluxes through converging pathways of the central metabolism of E. coli were identified. As the flux ratios based on GC-MS measurements are intuitively better understandable these will be explained in more detail here. For more detailed information on how to calculate the flux ratios from 13C-NMR multiplet data see Syperski [58] and Maaheimo et al. [40].
Flux partitioning ratios for GC-MS measurements are calculated by setting up mass isotopomer balances around converging metabolite nodes. Consider the case of a converging node consisting of two inflowing fluxes:
1
The mass balance and the mass isotopomer balance for this node are:1 2 3
v +v =v (1.5)
1 A 2 B 3 C
v MDV⋅ +v MDV⋅ =v MDV⋅ (1.6)
where MDVX is the mass isotopomer distribution vector of metabolite X (individual mass isotopomers should sum up to one). Substituting Eq. 1.5 into Eq. 1.6 and solving for the flux partitioning ratio (v1/v2) yields:
C B 1 2 A C
(MDV
MDV )
v
v
=
(MDV
−
−
MDV )
(1.7)An example of a flux ratio calculated via METAFOR is the contribution of the non-oxidative branch of the PPP via transketolase to the synthesis of the triose pool [3, 20]. This specific flux ratio is discussed here since the PPP is one of the focal points of this thesis and because the PPP plays an important role in penicillin synthesis. When cells are grown on a mixture of [u-13C
6]glucose and naturally labeled glucose, glucose metabolized through glycolysis yields trioses with all carbon-carbon bonds left intact, while glucose metabolized via the PPP produces trioses with partially cleaved bonds. The fraction of trioses in which the C1-C2 bond has been formed in a metabolic reaction (e.g. both carbon atoms originate from a different glucose molecule) is calculated from the mass isotopomers of the C1-C2 carbon fragment of phosphoenol-pyruvate (PEP12). Note that mass isotopomers of PEP12 are indirectly obtained via GC-MS analysis of the C1-C2 carbon fragments of the proteinogenic amino acids phenylalanine and/or tyrosine. PEP12 can either be synthesized from an intact two-carbon unit of glucose (GLU2U) or from two one-carbon units of glucose (GLU1UxGLU1U). As a result the fraction of phosphoenol-pyruvate (PEP) formed by at least one action of transketolase (TK) equals:
PEP12 GLU2U
GLU1U GLU1U GLU2U
MDV MDV f(PEP TK) MDV MDV MDV − → = ⋅ − (1.8)
An upper bound for the contribution of the PPP to triose formation can be calculated by assuming that five trioses are produced from three pentoses and that at least two of these trioses are rearranged by a transketolase. The amount of PEP synthesized via the PPP then becomes:
5
f(PEP→PPP)= 2⋅f(PEP→TK) (1.9)
1
up- or downstream, thereby assuming identical 13C-labeling patterns for these metabolites. If this assumption is invalid, it can easily lead to erroneous outcomes for the local node flux analysis. In the above example, the 13C-labeling of gap produced by transketolase is inferred from the labeling of PEP. This inference is based on the assumption that the 13C-labeling of PEP is not influenced by other reactions, such as the gluconeogenic reaction catalyzed by PEP carboxykinase.Another disadvantage of the local node flux analysis approach is that flux partitioning ratios in the studied metabolic network can only be determined for convergent nodes. In the example discussed above the PPP split-ratio can be deduced from the convergent node created by triose molecules originating from either the PPP or glycolysis. The derived flux ratio is an upper bound, as It does not take into account the formation of additional cleaved C1-C2 triose fragments through reversibility of the transketolase. Direct determination of the PPP split-ratio is not possible, since this is a divergent metabolite node.
Whole isotopomer modeling Fundamental to the whole isotopomer modeling approach is
the formulation of a whole isotopomer model as described by Schmidt et al. (1997). This whole isotopomer model defines the complete set of balances of each isotopomer fraction of each individual metabolite pool in the studied network. Combined with the measured uptake and secretion rates the model relates the isotopic enrichment of metabolites to all intracellular fluxes. However, due to the non-linearity of the isotopomer balances, in all but very simple metabolic network models, fluxes can not be expressed as an explicit function of the measured 13C-labeling data. Consequently, flux-patterns are calculated by iteratively fitting simulated 13C-label distributions for a chosen set of metabolic fluxes to the measured 13C-label distributions. The flux-set that gives the best correspondence between the measured and simulated 13C-label distribution is determined by non-linear optimization and denoted as the optimal flux-fit.
The whole isotopomer modeling approach has been applied in many studies to derive metabolic flux patterns from GC-MS [9, 19, 24, 36, 79], NMR [68, 75] and LC-MS [69] measurements. In most of these studies fluxes are estimated by simulating mass isotopomers or relative intensities and fitting these to their measured counterpart. Christensen et al. [9] introduced a slightly different approach by, prior to fitting, transforming the mass isotopomer distributions of the measured proteinogenic amino acids (GC-MS) into summed fractional labelings (SFL). The SFL of an amino acid or fragment thereof is equal the sum of its positional enrichments:
m 0 m 1 m 2 m n m 0 m 1 m 2 m n 0 I 1 I 2 I .. n I SFL I I I .. I + + + + + + + + ⋅ + ⋅ + ⋅ + ⋅ = + + + (1.10)
20
are very reproducible and sensitive to small metabolic variations caused by changes in growth conditions or genetic make-up of the micro-organism.A relative new application of whole isotopomer modeling is the respirometric 13C flux analysis method introduced by Yang et al. [76]. In this method the only 13C-labeling input comes from online CO2 labeling measurements. The information content of CO2 is extremely low, as it contains only one independent isotopomer. Additional 13C-labeling constrains are imposed on the fluxes in the studied network by performing multiple parallel 13C-labeling experiments using different 13C-labeled substrates. However, in general not all (exchange) fluxes in the studied metabolic network can be estimated. Despite this drawback, Yang et al. [77] showed that for a lysine-producing Corynebacterium glutamicum mutant the net fluxes in the glycolysis, TCA cycle, anaplerosis and the PPP could be accurately quantified. Since CO2 does not accumulate in the cell and its labeling can be quantified online, the respirometric method is suitable for instationary flux analysis [47]. In addition the method is non-invasive allowing the study of e.g. biofilms, sediments or immobilized cell cultures, without disturbing or destroying the system.
An obvious advantage of the whole isotopomer modeling approach is insight in all metabolic fluxes of the studied network. Due to the input of quantitative physiological data such as extracellular net conversion rates and biomass composition the determined flux values are absolute. Furthermore, the high information content of the 13C-labeling data constrains the flux fit and results in an over-determined system of equations, allowing researchers to statistically verify the topology of the studied metabolic network model. By investigating the statistical acceptability of the flux fit, shortcomings in the stoichiometry of the metabolic network model can be localized and alterations to the model can be hypothesized and validated [9, 52, 68]. A major disadvantage of the whole isotopomer modeling approach is the high interconnectivity of cellular metabolism and the non-linear fit problem. Incorrectly simulated 13C-labeling patterns in one part of the network as a result of a missing or erroneous reaction may very well propagate and cause incorrectly estimated fluxes throughout the studied network. Furthermore, changes in the metabolite-labeling can be counteracted by changed flux-patterns in other parts of the metabolism. As a result, the fitting-procedure can produce multiple ‘optimal’ flux sets each with their own flux distribution. A good understanding of the studied network and a critical statistical assessment of the flux fitting results is thus essential for accurately determining fluxes.
1.5 AIM AND ouTLINE of THE THESIS
21
by LC-MS [37, 61], provided a novel method for quantifying the 13C-label distribution in the cell. With this method the mass isotopomer fractions of 13C-labeled primary metabolites were directly measured and used for analyzing the metabolic fluxes of Saccharomyces cerevisiae [69]. The above results formed the basis of the research described in this thesis. This thesis aims to apply and further develop the 13C-labeling techniques and the available analytical platforms for the analysis of the metabolic fluxes in P. chrysogenum, thereby specifically focussing on the flux through the non-oxidative branch of the PPP which is of prime importance for penicillin synthesis.Chapter 2 describes the development of a new metabolic network model for the PPP based
upon the established ping-pong kinetic mechanism of the enzymes transketolase and transaldolase. The first 13C-labeling based metabolic flux analysis of the PPP was performed by Follstad et al. [22], who constructed a general metabolic network model in order to determine the degree of reversibility of the individual reactions in the non-oxidative branch of the PPP. Van Winden et al. [65] further investigated the PPP and showed that six additional reactions could take place in the non-oxidative branch of the PPP. All six additional reactions were included in the new stoichiometric model for the PPP, but restructured into metabolite specific, reversible, C2 and C3 fragments producing and consuming half-reactions. It is shown that a stoichiometric model based upon these half-reactions is fundamentally different from the currently applied stoichiometric models and can lead to different label distributions for 13 C-tracer experiments.
Chapter 3 and 4 focus on the application of whole isotopomer models for estimating the
metabolic fluxes in, respectively, S. cerevisiae and the closely related micro-organism P. chrysogenum. In these two studies different analytical platforms were used to quantify the 13C-labeling distribution in the cell. Comparisons were made between the estimated flux patterns for the different techniques and a sensitivity analysis of the flux estimates for important metabolite nodes was performed. In Chapter 3 we investigate a glycerol
hyper-producing tpi1∆nde1,2∆gut2∆ S. cerevisiae strain by measuring the isotopic enrichment of the intracellular primary metabolites both directly (LC-MS) and indirectly from proteinogenic amino acids (NMR and GC-MS). New insight in the carbon and redox metabolism of the studied strain is presented. In addition, it is shown that the three 13C-quantification techniques lead to similar flux-patterns but have different flux sensitivities for important metabolite nodes such as the PPP split-ratio. In Chapter 4 the fluxes of P. chrysogenum are determined under both
penicillin producing and non-producing conditions to examine the effect of penicillin synthesis on primary metabolism. Two analytical platforms are compared: NMR and LC-MS. In addition, we show how labeling redundancies can be used to reconstruct and validate a metabolic network model. In contrast to S. cerevisiae, highly different flux patterns are presented for the two applied 13C-quantification techniques, making it difficult to draw clear conclusions on the effect of penicillin-G production on the primary metabolism of P. chrysogenum.
In Chapter 5 a local node flux analysis approach is used to directly determine the flux
22
method is more accurate than the whole isotopomer modeling approach applied in Chapter 3 and 4. In Chapter 6 the gluconate-tracer method of Chapter 5 is applied to a penicillin-Gproducing and non-producing chemostat culture. In accordance with earlier claims, this study conclusively shows for the first time that the flux through the oxidative branch of the PPP is strongly correlated to β-lactam antibiotic production and that the oxidative branch of the PPP produces the majority of the cytosolic NADPH needed for penicillin synthesis.
Finally, Chapter 7 discusses the outcome of the studies presented in this thesis and provides
23
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