Wageningen Centre for Food Sciences1 and Food and Bioprocess Engineering Group,2 Wageningen University, PO Box 8129, 6700 EV Wageningen, The Netherlands
NIZO Food Research, PO Box 20, 6710 BA, Ede, The Netherlands3
BioCentrum Amsterdam, Dept of Molecular Cell Physiology, Free University, De Boelelaan 1087, NL-1081 HV Amsterdam, The Netherlands4
Dept of Biochemistry, University of Stellenbosch, Private bag X1, Matieland 7602, Stellenbosch, South Africa5
Author for correspondence: Marcel H. N. Hoefnagel. Tel: +31 317 483435. Fax: +31 317 482237. e-mail: marcel.hoefnagel{at}algemeen.pk.wau.nl
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ABSTRACT |
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Keywords: Metabolic control analysis, in silico modelling, Lactococcus lactis, pyruvate distribution
Abbreviations: ALS, acetolactate synthase; LDH, L-lactate dehydrogenase; MCA, metabolic control analysis; NOX, NADH oxidase; (abbreviations used in rate reactions and equations are defined in Table 1)
The GenBank accession number for the sequence reported in this paper is AY046926.
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INTRODUCTION |
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Lactic acid bacteria are used in milk fermentation and the major product from this process is lactate. For some dairy products, like butter, diacetyl produced by Lactococcus lactis is an important flavour component. Diacetyl is produced in only small amounts and various groups have tried to optimize its production (Monnet et al., 1994b ; Platteeuw et al., 1995
; Swindell et al., 1996
; Lopez de Felipe & Hugenholtz, 1999
). Their experimental strategies have involved the overexpression and deletion of genes intuited to be important for the regulation of the carbon fluxes in L. lactis. Here, we use a more rational combination strategy consisting of (i) a detailed kinetic model of the branches around pyruvate metabolism, (ii) MCA and (iii) experiments to describe the various mutations and to illustrate the use of (i) and (ii) in a more successful metabolic engineering strategy.
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METHODS |
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Cloning of the lactococcal nox gene and construction of the nox overexpression plasmid.
The nox gene of Streptococcus mutans, derived from pNZ2600 (Lopez de Felipe et al., 1998 ), was used as a heterologous probe in Southern blotting experiments to clone the NADH-oxidase(NOX)-encoding gene of L. lactis MG1363. A 1·8 kb fragment of EcoRI-digested chromosomal DNA of L. lactis MG1363 hybridized with this probe. The 1·8 kb fragment was cloned into EcoRI-digested pUC19 and colonies containing the hybridizing fragment were selected by colony blotting, again using the nox gene of S. mutans as a probe. Sequence analysis of the 1·8 kb fragment revealed that it contained the 3' end of a gene that had high similarity with the nox gene of S. mutans, putatively representing the 3' end of the lactococcal nox gene. Moreover, downstream of the 3' end of this putative nox gene, ORFs were found that shared a high level of similarity (43·8% at the protein sequence level) with the ssb gene of B. subtilis, encoding a putative single-stranded DNA-binding protein (Meyer & Laine, 1990
). Following the ssb gene, the 5' end of the previously cloned groES operon of L. lactis was found (Kim & Batt, 1993
).
The 5' end of the lactococcal nox gene was amplified by PCR using L. lactis MG1363 chromosomal DNA as a template and the fully degenerated primer NOX2F (5'-ACNGGNACYGAYCANGCNGCNGGYATHGC-3'; N=A/C/G/T, Y=C/T and H=A/C/T), based on the previously determined N-terminal sequence of the lactococcal NOX protein (Lopez de Felipe & Hugenholtz, 2001 ), combined with primer NOXR (5'-TGACCTGCAGTTCTGCGTCAATTGCTTGACC-3'), which was based on the 3' region of the cloned nox sequence, and was extended with a PstI-generating clamp sequence (indicated in bold). The approximately 400 bp fragment obtained was cloned into pGEMT (Promega) and sequenced; the sequence revealed that the fragment contained the 5' region of nox. This fragment (the NcoIPstI fragment from the pGEMT clone) was used as a probe in the cloning of a hybridizing 1·4 kb EcoRI L. lactis MG1363 chromosomal fragment. Sequence analysis of this fragment revealed that it contained the entire 5' region of the lactococcal nox gene. Upstream of nox, an ORF encoding a hypothetical protein displaying local similarity with a phenylalanine tRNA ligase (78% identity with the YdjD protein of L. lactis IL1403) was found. The sequence of the combined EcoRI fragments, containing the entire noxE sequence (93% identity with the NoxE protein of L. lactis IL1403), has been deposited in the GenBank database under accession number AY046926.
The water-forming NOX-encoding nox gene of L. lactis was amplified by PCR using L. lactis MG1363 chromosomal DNA as a template and the primers NOXF (5'-CGTACCATGGAAATCGTAGTTATCGGTAC-3') and NOXR (5'-CGTATCTAGATTCAAAAGCCTGCCTACTGTGC-3'). The PCR fragment obtained was cloned as a NcoIXbaI fragment (restriction sites were introduced into the primers and are indicated in bold) into similarly digested pNZ8048 (de Ruyter et al., 1996 ; Kuipers et al., 1997
). The resulting plasmid was designated pNZ2610 and contained the lactococcal nox gene translationally fused to the nisA promoter. Transcription of nox in this construct is dependent on the activity of the nisin-inducible nisA promoter, whereas translation of the nox transcript depends on the nisA-derived RBS (de Ruyter et al., 1996
).
Control strain.
L. lactis NZ9000(pNZ8048) was used as a control during this study.
Fermentation.
Cultures were grown at 30 °C in M17 medium (Merck) supplemented with 1% (w/v) glucose. Chloramphenicol and erythromycin were used at 10 and 5 µg ml-1, respectively. Nisin was used at 1 ng ml-1. A 1 l bioreactor (Applikon Dependable Instruments) was inoculated with cells from an overnight culture to an initial OD600 of about 0·1 in 700 ml medium. A pH of 6·5 was maintained by the addition of 2 M NaOH and the stirrer speed was set at 500 r.p.m. Air was bubbled through with a flow rate of 520 ml min-1. The batch cultures used showed exponential growth between about 2 and 5·5 h post-inoculation. The flux distribution was calculated during this exponential (pseudo steady-state) phase.
Analysis of fermentation products.
Glucose, lactate, acetate, formate, ethanol, acetoin and 2,3-butanediol were analysed by HPLC, as described previously (Starrenburg & Hugenholtz, 1991 ). In contrast to the wild-type, the carbon recovery for the mutant strains was incomplete (between 70 and 85% recovery). This may have been due to activity of the pentose phosphate pathway, which leads to CO2 release. However, this can not be confirmed as this pathway was not monitored in this study.
Growth and enzyme assays.
The OD600 was determined and corrected for the optical density of the growth medium. When the OD600 value was too high (>0·75) the sample was diluted with medium.
L-Lactate dehydrogenase (LDH) activity was determined by the method of Hillier & Jago (1982) and the NOX and ALS activities were determined according to Lopez de Felipe et al. (1998)
and Platteeuw et al. (1995)
, respectively. Protein concentrations were determined according to the Bradford method (Bradford, 1976
), with BSA as a standard. Preliminary experiments along these lines have been published previously (Platteeuw et al., 1995
; Lopez de Felipe et al., 1998
), but they were not performed in an isogenic background and did not contain precise measurements of catabolic fluxes. Instead, they consisted merely of end-point determinations of product concentrations. This made them unsuitable for a proper demonstration of the rational engineering procedure we propose here.
The kinetic model.
A set of ordinary differential equations was used to describe the time dependence of the metabolite concentrations. Table 1 gives definitions of the mathematical symbols and abbreviations used in the equations and kinetic models in this study. Please note that the flux of glycolysis is given in C3 units.
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Kashket & Wilson (1973) found that in L. lactis the internal cell volume is 1·5 µl (mg dry weight)-1. We assumed that protein makes up 50% of the dry weight, as it does in E. coli (Neidhardt & Umbarger, 1996
), and that an OD600 of 1 is equal to 0·445 mg dry weight ml-1.
Kinetic parameters.
The kinetic parameters used for the enzymes in reactions 115 (Fig. 1) and the rate equations in Table 2
can be found in Table 3
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RESULTS |
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MCA versus the intuitive approach
In an intuitive approach ALS would be the enzyme of choice for genetic engineering, as it is the first dedicated enzyme in the branch and it is far from equilibrium. But what does MCA say? At first sight it might seem to say the same, i.e. in the wild-type situation the ALS has a flux-control coefficient of 1·0 for the flux to acetoin (Table 4. This qualifies as a high flux-control coefficient, as flux-control coefficients always sum up to 1. However, flux-control coefficients can be negative and the complete MCA analysis shows how relevant this is for this branched pathway the highest flux-control coefficients for the acetolactate branch reside in enzymes outside this branch! (Namely LDH and NOX; Table 4
). This corresponds to an early MCA observation for branched pathways, in which the fluxes over the different branches differ by orders of magnitude (Kacser, 1983
). With hindsight, this can be understood also intuitively: a minor decrease in any branch carrying a major flux from pyruvate might increase the pyruvate concentration more than proportionately provided that the concentration exceeded the apparent Michaelis constant for that major pathway. This could then result in a more than proportional increase in flux through the ALS branch.
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LDH knockout
LDH has the highest (negative) flux-control coefficient (-2·3; Table 4). Thus, a 1% reduction in LDH activity should lead to a 2·3% increase in the flux through the acetolactate branch. After MCA has indicated the best candidate for genetic engineering, modulations substantially exceeding 1% can be test-run in the kinetic model. Such a test-run is an important part of the genetic engineering strategy, since MCA is defined at the original steady state and the size of the optimal perturbation might be limited due to kinetic restrictions in the system. For example, if the acetolactate dehydrogenase and pyruvate dehydrogenase complex branches have only a low capacity these branches might not be able to absorb all of the carbon flow from lactate upon a knockout mutation in LDH. Such an effect was observed in the model calculations, where pyruvate and NADH accumulated upon a deletion in LDH and the glycolytic flux was severely inhibited (Fig. 2b
). Such a major perturbation of primary metabolism is bound to lead to a strong reduction in growth rate and possibly to deleterious effects in Ldh- strains. Therefore, even though all of the remaining flux was directed into the ALS branch, this modulation seemed ill-advised (Fig. 2b
).
Experimentally, the overall carbon flux was reduced by 11% but this reduction was not as severe as the model had predicted. The major product formed was acetoin (50% of the measured product formation rates, Fig. 2), which was in agreement with the model prediction.
Overexpression of NOX
In addition to LDH, NOX also had a high (positive) control coefficient (Table 4). The model predicted that about 20% of the glucose would be converted via the ALS branch if NOX was 40-fold overexpressed, whereas in the experimental set-up 13% was converted to acetoin (Fig. 2c
).
The strongest effect was expected when the LDH knockout mutant was combined with NOX overexpression. The model predicted that, under these conditions, 92% of the pyruvate would be converted via the acetolactate branch, but with a much higher glycolytic flux when compared to the situation in which only LDH was deleted (Fig. 2d). Indeed, in the experimental situation acetoin was the main product (75%) of the recovered carbon.
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DISCUSSION |
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The model
L. lactis is an ideal organism for modelling purposes, as it is well-studied, genetically accessible and can be cultured readily. Furthermore, its anabolic reactions can be simulated as being separate from the catabolic reactions with respect to carbon metabolism: when L. lactis is cultured on rich medium almost all the carbon (95%) from the free-energy source (e.g. glucose) is recovered as external products (e.g. lactate) (Novak et al., 1998 ). This simplifies the modelling process considerably. The only link between anabolism and catabolism that was considered in this study was ATP and this could be modelled as a single step; all ATP-consuming reactions in anabolism were grouped into a single module. Biomass formation from glucose was assumed to be, overall, a redox-neutral process; the homolactic fermentation in the wild-type indicates that catabolism itself is redox neutral.
The accuracy of the model depends largely on the kinetic data on which it was built. These data were taken from the literature and were determined by several different groups working with different organisms and they were not always determined under physiological conditions. Also, sometimes no data were available for L. lactis. In these instances, data either from another streptococcal species or from other bacteria were used (see Table 3). Another limitation of the model is that a constant level of gene expression was assumed, i.e. the enzyme levels that were measured in the wild-type strain were assumed not to change during the genetic manipulation steps.
The model is available online at http://jjj.biochem.sun.ac.za/wcfs.html and it can be run from this site. As well as the standard simulations, the site also allows the user to set various parameters and to test all sorts of mutants.
Designing the optimal strain: reducing trial and error
Indeed, if the kinetic parameters for the system under study are available then the construction of a kinetic model is relatively simple and quick. Even without all of the kinetic parameters available, one can sometimes make simplifications that allow for the construction of a model that can be useful. For instance, here we have focused on the flux distribution over the pyruvate branches and have grouped all of the reactions outside these branches, thereby avoiding modelling all of the glycolytic steps in detail.
Once a kinetic model has been constructed it is simple to calculate the control coefficients on the flux (or on the concentration of a metabolite) that one wishes to optimize. On the basis of the control coefficients one can then design a genetic engineering strategy, which can again be tested in the kinetic model. This latter testing is important, as one will want to make large changes in enzyme concentrations in order to attain biotechnologically interesting improvements. If indeed the perturbation leads to an appreciable improvement of the production strain in silico one can then move to the experimental process of genetic engineering.
All in all, the model we used was not perfect. Therefore, our finding that it was very useful in the engineering strategy, and that it even came close to predicting its results quantitatively, may reflect the phenomenon that metabolic fluxes have limited sensitivities to many kinetic parameters. More generally, it suggests that the frequent adage that biotechnology is not yet ready for the application of mathematical models may be much too pessimistic.
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ACKNOWLEDGEMENTS |
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Received 5 September 2001;
revised 5 November 2001;
accepted 20 November 2001.