metabolic flux profiling of escherichia coli mutants in central carbon metabolism using gc-ms

Metabolic flux profiling of Escherichia coli mutants in centralcarbon metabolism using GC-MS

Eliane Fischer and Uwe Sauer

Institute of Biotechnology, ETH Zurich, Zurich, Switzerland

Wedescribe here a novelmethodology for rapid diagnosis ofmetabolic changes, which is based onprobabilistic equationsthat relate GC-MS-derived mass distributions in proteino-genic amino acids to in vivo enzyme activities. Thismetabolicflux ratio analysis by GC-MS provides a comprehensiveperspective on central metabolism by quantifying 14 ratiosof fluxes through converging pathways and reactions from[1-13C] and [U-13C]glucose experiments. Reliability andaccuracy of this method were experimentally verified bysuccessfully capturing expected flux responses ofEscherichiacoli to environmental modifications and seven knockoutmutations in all major pathways of central metabolism.Furthermore, several mutants exhibited additional, unex-pected flux responses that provide new insights into thebehavior of the metabolic network in its entirety. Mostprominently, the low in vivo activity of the Entner–

Doudoroff pathway in wild-type E. coli increased up to acontribution of 30% to glucose catabolism in mutants ofglycolysis and TCA cycle. Moreover, glucose 6-phosphatedehydrogenase mutants catabolized glucose not exclusivelyvia glycolysis, suggesting a yet unidentified bypass of thisreaction. Although strongly affected by environmentalconditions, a stable balance between anaplerotic and TCAcycle fluxwasmaintained by all mutants in the upper part ofmetabolism. Overall, our results provide quantitative insightinto flux changes that bring about the resilience ofmetabolicnetworks to disruption.

Keywords: Entner–Doudoroff pathway; flux analysis;fluxome; METAFoR analysis; pentose phosphate path-way.

Comprehensive and quantitative understanding of bio-chemical reaction networks requires not only knowledgeabout their constituting components, but also informationabout the behavior of the network in its entirety. Towardthis end, systems-oriented methodologies were developedthat simultaneously access the level of reaction intermedi-ates [1] or rates of reactions [2–5], also referred to as themetabolome [6] and the fluxome [7], respectively. The mostimportant property of biochemical networks are the per senonmeasurable in vivo reaction rates, which may beestimated by so-called metabolic flux analysis that providesa holistic perspective on metabolism.In its simplest form, metabolic flux analysis relies on flux

balancing of extracellular consumption and secretion rateswithin a stoichiometric reaction model [5]. To increasereliability and resolution of such flux balancing analyses,additional information may be derived from 13C-labeling

experiments. In this approach, 13C-labeled substrates areadministered and 13C-labeled products of metabolism areanalyzed by methods that distinguish between differentisotope labeling patterns, in particular NMR and MS[2,3,8]. In the most advanced methodology, a comprehen-sive isotope isomer (isotopomer) model of metabolism isused tomapmetabolic fluxes in an iterative fitting procedureon the isotopomer pattern of network metabolites that arededuced from NMR or MS data [2]. This global datainterpretation enables integrated and quantitative consid-eration of all physiological and 13C-labeling data. Typically,protein hydrolysates are subjected to NMR or GC-MSanalysis, which provides not only isotopomer pattern of theamino acids but also of their related precursor moleculesthat are key components of central metabolism. With thepresently available models and software, these isotopomerbalancing methods have attained a high level of precisionand applicability [2,9,10].In contrast to isotopomer balancing, direct analytical

interpretationof 13C-labelingpatternshas longbeenusednotonly to identify biochemical pathways and reactions but alsoto quantify individual flux partitioning ratios [3,11,12]. Suchanalytically deducedflux ratioswere also used successfully asconstraints for metabolic flux analysis [13–15]. Based onprobabilistic equations, a more general methodology wasdeveloped to simultaneously identify network topology andmultiple flux partitioning ratios [16,17]. This metabolic fluxratio analysis was based on the detection of 13C-labelingpatterns inproteinogenic aminoacids byNMRanalysis, andprovides direct evidence for a particular flux.Global isotopicdata interpretation by isotopomer balancing and strictlylocal metabolic flux ratio analysis are largely independent.

Correspondence to U. Sauer, Institute of Biotechnology,

ETH Zurich, CH-8093 Zurich, Switzerland.

Fax: + 41 1 633 10 51, Tel.: + 41 1 633 36 72,

E-mail: [email protected]

Abbreviations: MDV, mass distribution vector; G6P, glucose-

6-phosphate; F6P, fructose-6-phosphate; P5P, pentose phosphates;

E4P, erythrose-4-phosphate; PEP, phosphoenolpyruvate;

OAA, oxaloacetate; OGA, 2-oxoglutarate; PTS, phosphoenol

pyruvate:glucose phosphotransferase system; PP pathway, pentose

phosphate pathway; ED pathway, Entner–Doudoroff pathway;

TCA cycle, tricarboxylic acid cycle; CDW, cellular dry weight.

(Received 29 August 2002, revised 10 December 2002,

accepted 7 January 2003)

Eur. J. Biochem. 270, 880–891 (2003) � FEBS 2003 doi:10.1046/j.1432-1033.2003.03448.x

Hence, the favorable agreement of results obtained by bothapproaches for the same experimental data provides strongevidence for their reliability [18,19].Here we develop a novel methodology for metabolic flux

ratio analysis based on GC-MS data from [1-13C] and[U-13C]glucose experiments. This methodology is used formetabolic network analysis in Escherichia coli strains withknockoutmutations in all major pathways of central carbonmetabolism. The analyses presented here provide not onlynovel insights into central metabolism but represent alsoexperimental verification of the reliability of metabolic fluxratio analysis by GC-MS.

Materials and methods

Strains, media, and growth conditions

The nomenclature of the employed E. coli knockoutmutants indicates the affected genes (Table 1). Unlessindicated otherwise, aerobic batch cultures were grown at37 �C in 500 mL baffled shake flasks with 50 mL of M9minimal medium on a gyratory shaker at 200 r.p.m.Anaerobic cultures were grown in 100 mL sealed glassflasks containing 50 mL medium that was gassed with N2

prior to sterilization for 10 min. TheM9medium containedper litre of deionized water: 0.8 gNH4Cl, 0.5 gNaCl, 7.52 gNa2HPO4, and 3.0 g KH2PO4. The following componentswere sterilized separately and then added (per litre of finalmedium): 2 mL of 1 MMgSO4, 1 mL of 0.1 M CaCl2, 1 mLof 1 mgÆL)1 thiamine HCl (filter sterilized), and 10 mL of atrace element solution containing (per litre) 16.67 gFeCl3Æ6H2O, 0.18 g ZnSO4Æ7H2O, 0.12 g CuCl2Æ2H2O,0.12 g MnSO4ÆH2O, 0.18 g CoCl2Æ6H2O, and 22.25 gNa2EDTAÆ2H2O. Filter-sterilized glucose was added to afinal concentration of 3 g per litre. For 13C-labelingexperiments, glucose was added either entirely as the[1-13C] labeled isotope isomer (> 99%; Euriso-top, GIF-sur-Yvette, France) or as a mixture of 20% (w/w) [U-13C](13C, > 98%; Isotech, Miamisburg, OH) and 80% (w/w)natural glucose. The 13C-enrichment of [U-13C]glucose wasindependently determined to be 98.7% from cells grownexclusively on [U-13C]glucose.

Analytical procedures and physiological parameters

Cell growth wasmonitored bymeasuring the optical densityat 600 nm (D600). Glucose concentrations were determined

enzymatically using a commercial kit (Beckman, Palo Alto,CA, USA). The following physiological parameters weredetermined during the exponential growth phase in batchcultures as described previously [7]: Maximum growth rate,biomass yield on glucose, and specific glucose consumptionrate, using a predetermined correlation factor of 0.44 gcellular dry weight (CDW) per D600 unit.

Sample preparation and GC-MS measurements

Aliquots of batch cultures were harvested during the mid-exponential growth-phase, defined as D600 of 0.8–1.5, andcentrifuged at 14 000 g at room temp. for 5 min. Pelletswere washed once in 1 mL 0.9% (w/v) NaCl and hydro-lyzed in 1.5 mL 6 M HCl at 105 �C for 24 h in sealed glasstubes. The hydrolysate was dried in a vacuum centrifugeat room temperature and derivatized at 85 �C in 50 lLtetrahydrofurane (Fluka, Switzerland) and 50 lL ofN-(tert-butyldimethylsilyl)-N-methyl-trifluoroacetamide(Fluka, Switzerland) for 60 min [20]. 1 lL of derivatizedsample was injected into a series 8000 GC, combinedwith anMD 800 mass spectrometer (Fisons Instruments,Beverly, MA, USA), on a SPB-1 column (SUPELCO,30 m · 0.32 mm · 0.25 lm fused silica) with a splitinjection of 1 : 20. GC conditions were: carrier gas(helium) flow rate at 2 mL per min, oven temperatureprogrammed from 150 �C (2 min) to 280 �C at 3 �C permin, source temperature at 200 �C and interface tempera-ture at 250 �C. Electron impact (EI) spectra were obtainedat )70 eV. GC-MS raw data were analyzed using thesoftware package MassLab (Fisons), avoiding detectoroverload and isotope fractionation as described [20].The amino acids analyzed by GC-MS were aspartate,

glutamate, glycine, histidine, isoleucine, leucine, phenyl-alanine, proline, serine, threonine, tyrosine, and valine for[U-13C]glucose experiments and aspartate, isoleucine, leu-cine, phenylalanine, serine, threonine, tyrosine, and valinefor [1-13C] experiments.

Bioreaction network

The considered E. coli bioreaction network was describedpreviously [18] but included additionally the ED pathway[21] and threonine aldolase [22] (Fig. 1). The amino-acid-carbon skeletons were derived from the metabolic inter-mediates PEP, Pyruvate, P5P, E4P, OAA, and OGA asdescribed [16].

Table 1. E. coli strains used in this study. The original strain designation is given in parentheses.

Strains Relevant characteristics Reference

MG1655 Wild-type K12 strain (k– F – rph-1) [44]

W3110 Wild-type K12 strain (k– F – IN(rrnD-rrnE)1 rph-1) [44]

JM101 [F – traD36 lacIq D(lacZ)M15 proA+B+ supE thi D(lac-proAB)] [45]

Zwf G6P dehydrogenase-deficient K10 (DF2001) [46]

Pgi Phosphoglucose isomerase-deficient W3110 (LJ110) [47]

PfkA Phosphofructokinase-deficient K10 (AM1) [48]

PykAF Pyruvate kinase-deficient JM101 (PB25) [49]

Mae/Pck Malic enzymes (ScfA and Mae)- and PEP carboxykinase-deficient K12 (EJ1321) [50]

SdhA/Mdh Succinate dehydrogenase- and malate dehydrogenase-deficient MG1655 (DL323) [29]

FumA Fumarase A-deficient K12 (EJ1535) [30]

� FEBS 2003 Metabolic flux profiling in E. coli (Eur. J. Biochem. 270) 881

Correction for naturally occurring isotopes

The obtained EI spectral data are sets of ion clusters, eachrepresenting the distribution of mass isotopomers of a givenamino-acid fragment. For each fragment a, a massisotopomer distribution vector (MDV):

MDVa ¼

ðm0Þðm1Þðm2Þ� � �ðmnÞ

266664

377775with

Xmi ¼ 1 ð1Þ

was assigned, where m0 is the fractional abundance offragments with the lowest mass andmi>0 the abundances ofmolecules with higher masses. These higher masses result

from isotope signals that originate from (a) natural abun-dance in non-C-atoms, (b) natural abundance of 13C in thederivatization reagent, and (c) 13C in the carbon skeleton ofthe amino-acid fragment that were incorporated fromnaturally or artificially 13C-labeled substrates. To obtainthe exclusive mass isotope distribution of the carbonskeleton, MDVa were corrected for the natural isotopeabundance of O,N,H, Si, S, andC atoms in the derivatizingagent by using correction matrices as described elsewhere[23], yielding MDV*a. Prior to analysis, the contribution of13C from unlabeled biomass in culture inocula wassubtracted from MDV*a yielding MDVAA according to

MDVAA ¼MDV�a � funlabeled �MDVunlabeled;n

ð1� funlabeledÞð2Þ

Fig. 1. Bioreaction network ofE. coli central carbon metabolism.Arrows indicate the assumed reaction reversibility. Solid arrows indicate precursor

withdrawal for the amino acid analyzed by GC-MS. Inactivated proteins in the investigated knockout mutants are highlighted in boxes. Abbre-

viations: 6PG, 6-phosphogluconate; S7P, seduheptulose-7-phosphate; T3P, triose-3-phosphate; PGA 3-phosphoglycerate.

882 E. Fischer and U. Sauer (Eur. J. Biochem. 270) � FEBS 2003

where funlabeled is the fraction of unlabeled biomass andMDVunlabeled,n is the mass distribution of an unlabeledfragment of length n. Its elements i can be calculated fromthe natural abundances of 12C and 13C according toEqn (3).

MDVunlabeled;nðiÞ ¼ cðn�iÞ0 c

ðiÞ1

ni

� ð3Þ

c0 and c1 represent the natural abundance of12C and 13C,

respectively, and ni

�is a binomial coefficient. The corrected

MDVAA now represent the mass distributions of the carbonskeletons due to substrate incorporation (Fig. 2A).

MDV of metabolites

Amino acids are derived from one or more metabolicintermediates and MDVM of these metabolites (or theirfragments) can easily be derived from the MDVAA, asillustrated schematically in Fig. 2A. If we assume that thecarbon skeleton of an amino acid originates from themetabolites M1 and M2, the mass distribution vectorMDVAA is a combination of the mass distributionsMDVM1 and MDVM2 and can be derived by element-wisemultiplication according to:

MDVAAðiÞ ¼MDVM1 �MDVM2

¼Xij¼0

MDVM1ði� jÞ�MDVM2ðjÞ ð4Þ

MDVM were obtained from a least squares fit to allMDVAA using the MATLAB function lsqnonlin with theadditional constraint that the sum of their element equals 1.

MDV of substrate fragments

A fragment with n carbon atoms of a mixture of uniformlyand naturally labeled substrate has the following massdistribution

MDVS;nUðiÞ ¼ ð1� lÞ cðn�iÞ0 ci1 þ lð1� pÞðn�iÞpi�

ni

�

ð5Þ

where l is the labeled fraction and p is the purity of thelabeled substrate. A fragment of a substrate that is13C-labeled at a specific position can either be unlabeled,thus having the mass distributionMDVunlabeled,n (Eqn 3) orit may contain the 13C-labeled position leading to

MDVS;n1 (i)¼ð1� l �pÞcðn�iÞ0 ci1ni

� þ l �p cðn�iÞ0 ci�11

n�1i�1

�

ð6Þ

A summary of all obtained MDV is given in Table 2.

Calculation of metabolic flux ratios

The intracellular pool of a given metabolite can be derivedfrom other metabolite pools through biochemical pathways(Fig. 2B). The fractional contribution f of a pathway to atarget metabolite pool with MDV1 was determined as:

f ¼MDV1�MDV3

MDV2�MDV3ð7Þ

where MDV2 and MDV3 are the mass distributions of thesource metabolites degraded through the examined and thealternative pathway, respectively. As MDV are vectors and

Fig. 2. Example of the information flow from experimentally deter-

mined mass distributions in amino acids to metabolites (A) and the

calculation of flux ratios (B). Bars illustrating the mass distribution

(m0, m1,…,mn) are drawn to scale for the example of an E. coli batch

culture grown on a mixture of 20% [U-13C] and 80% unlabeled

glucose. Mass distributions of amino-acid fragments (MDVAA) are

obtained from the experimentally determinedMDVa by correction fornatural isotope abundance and unlabeled biomass fraction. Mass

distributions of metabolite fragments (MDVM) are calculated from

MDVAA by using Eqn (4). (B) MDVM of different metabolites are

used to calculate split ratios of diverging pathways and the MDV of

CO2 according to Eqn (9).


not single data points, f represents the least-squares solutionto Eqn (7). Accordingly, usingMDVwith n elements, up ton alternative pathways can be distinguished. For example,the individual contributions of three converging pathways isdetermined as:

f1f2

� �¼ MDV1�MDV4

MDV2�MDV4MDV3�MDV4

� � ð8Þ

with f3 ¼ 1 ) f1 ) f2.The origin of several intracellular metabolite pools can be

determinedwith Eqns (7) and (8). Specifically,MDVMof sixmetabolites and MDVAA of two amino acids were used formetabolic flux ratio analysis (Table 2) together with MDVSof substrate fragments. In some cases, however, themetabolic precursors MDV2 or MDV3 were combinationsof two MDVM. Eqn (4) was applied to calculate the massdistribution of these combinations.

Pentose phosphate pathway

E. coli can potentially catabolize glucose to trioses via threedifferent biochemical pathways, i.e. glycolysis, ED pathway,and PP pathway [24] (Fig. 1). Upon growth on a mixture of[U-13C] and unlabeled glucose, introduction and cleavageof bonds between carbon atoms is reflected in theMDVMofPEP, P5P, and E4P. As glucose catabolism through theglycolysis and the ED pathway yields uncleaved trioses, theactivity of these two pathways is indistinguishable with[U-13C]glucose. The activity of transketolase and trans-aldolase in the nonoxidative PP pathway, however, can beaccessed.

As exchange fluxes between serine and glycine [16] clearlyinfluence the mass distribution of serine, PEP(1)2) was usedto determine the fraction of trioses that were cleaved andrearranged between C1–C2 by the action of transketolase,and compared to the fraction that originates from anunbroken two carbon unit of glucose according to Eqn (7).An upper bound for PEP molecules that were generatedfrom P5P can be calculated assuming that five trioses areproduced from three pentoses and that at least two triosesare rearranged by transketolase. It should be noted that thethus calculated fraction of PEP originating from P5P doesnot necessarily reflect glucose catabolism through the PPpathway, but may likewise result from a reversible exchangeflux via transketolase.Two other metabolites that reflect transketolase and

transaldolase activities are P5P and E4P. P5P moleculesmay be produced either via the oxidative PP pathwayfrom G6P, thus yielding an intact five carbon skeletonfrom a source molecule of glucose, or via the transketolasereaction, which cleaves between C3–C4. Additionally, P5Pmay also originate from E4P and a one carbon unitthrough the combined action of transaldolase and trans-ketolase. The contributions of the three convergingpathways are thus calculated using Eqn (8). As transketo-lase can reversibly cleave P5P and multiple cycling mayoccur through the PP pathway, P5P from G6P iscalculated as a lower bound for the fraction of P5Pmolecules that were generated via the oxidative PPpathway.The second PP pathway intermediate, E4P, is either

produced from F6P as an uncleaved four carbon unit or viathe combined activity of transketolase and transaldolasefrom P5P. The latter introduces E4Pmolecules with cleavedC1–C2 bonds originating from the fraction of P5P that wascleaved between C3–C4. The E4P pool was analyzed usingEqn (7).

Anaplerosis and the TCA Cycle

[U-13C]glucose experiments were also used to distinguishOAA produced either from a four carbon unit via theTCA cycle or from PEP and CO2 via the anapleroticreaction catalyzed by PEP carboxylase (see also Fig. 2).OAA(1)4) can thus be derived from the mass distributionof OGA(2)5) or from a combination of the MDV of PEPwith CO2, according to Eqn (4). As the fractionallabeling of CO2 (lCO2

) is unknown in batch culturesand may be lower than the fractional enrichment of theinput substrate, it was treated as an additional unknownusing

ff � lCO2

� �¼

OAAð1�4Þ �OGAð2�5Þ�PEPð1�3Þ 0

��OGAð2�5Þ

0 PEPð1�3Þ� �

� PEPð1�3Þ 0� �

� � ð9Þ

The fraction of OAA molecules that originate through theTCA cycle is thus determined as 1 ) f. The remainingfraction originates from PEP either through PEP carboxy-lase or through reversible malic enzyme via pyruvate.Additionally, the fraction of OAA(1)4) derived fromglyoxylate via the glyoxylate shunt can be detected as acombination of pyruvate(2)3) and OAA(1)2).

Table 2. Mass distribution vectors used for flux ratio analysis. The

carbon atoms included in each considered fragment are specified for

eachMDVM andMDVAA.MDVS are described by the length n of the

fragment and its 13C-content. U, 20% [U-13C] and 80% unlabeled

glucose experiment; 1, 100%[1-13C]glucose experiment.

Experiment MDV

Metabolite

PEP U PEP(1)3) PEP(2)3) PEP(1)2)1 PEP(1)2)

Pyruvate U Pyruvate(1)3) Pyruvate(2)3)1 Pyruvate(1)3) Pyruvate(2)3)

OAA U OAA(1)4) OAA(2)4) OAA(1)2)1 OAA(1)4) OAA(2)4) OAA(1)2)

OGA U OGA(1)5) OGA(2)5)1 OGA(1)5) OGA(2)5)

E4P U E4P(1)4)P5P U P5P(1)5)

1 P5P(1)5)

Amino acid

Serine U Serine(1)3) Serine(2)3) Serine(1)2)1 Serine(1)3) Serine(2)3) Serine(1)2)

Glycine U Glycine(1)2)1 Glycine(1)2)

Substrate

Glucose U Glc,nU1 Glc,n1 Glc,nunlabeled


Gluconeogenic reactions

Fluxes from the TCA cycle to the lower part of glycolysisvia malic enzyme and PEP carboxykinase can be diagnosedas cleaved C2–C3 bonds in pyruvate and PEP, respectively.The interconversion of malate to pyruvate via the malicenzymes (ScfA and Mae) can thus be determined bycomparing the pyruvate(2)3) and PEP(2)3) fragments usingEqn (7). As the mass distribution of malate is unknown, apyruvate(2)3) molecule produced via malic enzyme wasassumed to have the mass distribution of two combined onecarbon units, each with the fractional 13C-label of the inputglucose. This assumption includes (a) that all malatemolecules are broken between C2–C3, thus are derived fromOGA, and (b) that the fractional enrichment of C2 andC3 inmalate does not differ from the fractional enrichment in theinput substrate. A dilution of the fractional enrichmentmight be observed, for example, in positions where CO2 isintroduced. This, however, may occur only at C1 or C4 ofmalate, thus does not affect the present calculation of thelower bound for malic enzyme activity. If the malate pool isin equilibrium with OAA, intact C2–C3 fragments fromanaplerosis are present in malate. Thus, an upper bound forpyruvate produced throughmalic enzyme can be defined forthe extreme case of full equilibration of the malate andOAA pools.Similarly, PEP carboxykinase activity can be detected in

the cleaved fraction of PEP(2)3) using Eqn (7). As a cleavedC2–C3 bond in PEP may also result from transaldolaseactivity, the thus calculated fraction of PEP originatingfrom OAA remains an upper bound on the PEP carboxy-kinase activity.

C1-metabolism

The reversible exchange of the serine and glycine pools wasquantified by determining the fraction of serine(1)3) origin-ating from glycine(1)2) and a one carbon unit vs. the fractionthat is identical with PEP(1)3) (Eqn 7). Additionally, thefraction of glycine(1)2) derived from serine(1)2) was attainedassuming that the remaining glycine fraction with twoindependent C atoms originates fromCO2 and a one carbonunit through the reversible glycine cleavage pathway orthrough threonine cleavage catalyzed by the threoninealdolase. The latter enzyme was reported to be active inE. coli under some conditions, albeit not those used here[22,25].

Calculation of metabolic flux ratios from [1-13C]glucoseexperiments

To obtain more precise information about the in vivoactivities of the PP and ED pathway and the PEPcarboxykinase, positional labeling was detected from cellsgrown exclusively on [1-13C]glucose. As the MDV of PEPcould not be obtained in [1-13C]glucose experiments, serinewas used instead to quantify the relative contribution ofglycolysis to triose-3P synthesis, compared to the PP andED pathways. The exchange flux with glycine does notchange the label content in serine, unless substantialfractions of glycine or the one carbon unit are producedfrom sources other than serine. The oxidative PP or the ED

pathway both yield unlabeled triose-3P, while glycolysisyields 50% unlabeled and 50% triose-3P that is 13C-labeledat C1 (Eqn 7).If the ED pathway is active, additional label is introduced

at the level of pyruvate, resulting in different MDV ofserine(1)3) and pyruvate(1)3), which can be used to assess therelative contribution of this pathway to pyruvate synthesisusing Eqn (7). Additionally, pyruvate derived through theED pathway is labeled at C1, while pyruvate originatingfrom glycolysis is labeled at C3. The fraction of pyruvatemolecules labeled at C1 can be calculated from the differencebetween pyruvate(1)3) and pyruvate(2)3). This information isused to verify that the label is indeed introduced through theED pathway and not through a gluconeogenic reaction.Finally, PEP(1)2) originating from OAA(1)2) via the PEP

carboxykinase was quantified using Eqn (7) assuming thatthe remaining fraction is identical to serine(1)2).

Error consideration

The experimental measurement error was determined bycomparing the MDVa of amino acids with identical carbonskeletons, and the standard deviation of these redundantdata was used for calculation of the covariance matrix Cmof the measured individual mass intensities. Standard devi-ations of the calculated flux ratios were determined applyingthe law of error propagation Cr ¼ J*Cm*J

T where J is thejacobian matrix and Cr the covariance matrix of the outputvariables. J was obtained numerically for MDVM after theleast-squares fitting step and calculated analytically for thefinal flux ratios.

Results

Sensitivity of metabolic flux ratio analysis usingdifferent mixtures of [U-13C] and unlabeled glucose

For economical reasons, low fractions of expensive13C-labeled substrates are desirable for labeling experiments,provided that analytical resolution and sensitivity aremaintained. To identify an optimal compromise, we grewE. coli MG1655 batch cultures in 5 mL M9 medium withdifferent mixtures of [U-13C] and unlabeled glucose. Whilefully 13C-labeled or unlabeled biomass contained no infor-mation on metabolic fluxes, mixtures of 20/80, 40/60, 60/40,and 80/20 of [U-13C] and unlabeled glucose, respectively,allowed to determine flux ratios that were consistent withinthe experimental error (data not shown). Although thelowest experimental error is achieved at around equimolarfractions of [U-13C] and unlabeled glucose, the 20%[U-13C]glucose mixture enabled very reliable determinationof intracellular flux ratios and was thus used in the furtherexperiments.

Metabolic flux ratio analysis of E. coli under differentenvironmental conditions

While exponentially growing cells are initially in a physio-logical pseudo steady state, metabolic switches occur uponoxygen limitation or accumulation ofmetabolic byproducts.To identify reproducible conditions that faithfully reflect thephysiological state of unlimited, exponentially growing cells,


biomass aliquots were harvested at different time pointsfrom wild-type batch cultures in shake flasks growing on100% [1-13C]glucose or on a 20%/80% mixture of [U-13C]and unlabeled glucose. Overall, the determined origin ofmetabolite pools did not change significantly with the timeof harvest (data partly shown in Fig. 3). The sole exceptionswere increasing fractions of serine derived through glyco-lysis and OAA derived through the TCA cycle uponapproaching stationary phase (Fig. 3), as was observedearlier [7]. Hence, all further analyses were performed withbiomass aliquots harvested at D600 values between 0.8 and1.5.Next, we investigated the metabolic impact of different

levels of aeration from fully aerobic (500 mL baffled shakeflask) to suboptimally aerated (15 mL vials) and anaerobicE. coli batch cultures (Fig. 4). With decreasing oxygenavailability, most prominently, the fraction of OAA origin-ating through the TCA cycle decreases from 44% to 5%.This reveals a branched, noncyclic operation of the TCAcycle to fulfill exclusively biosynthetic requirements, as wasalso shown earlier [7,16,26]. Although the oxidative PPpathway is still active under anaerobic conditions (serinethrough glycolysis), its relative contribution to glucosecatabolism is decreased from 19% to 5% (Fig. 4), whichconcurs with most [7,16] but not all [26] reports. Thefrequently reported upper bound on in vivo PP pathwayactivity obtained from [U-13C]glucose experiments, incontrast (PEP from P5P), is not sensitive to this decrease.Unexpectedly, suboptimally aerated conditions promoterelatively high in vivo malic enzyme activity (pyruvate frommalate). Likewise, the of CO2 originating from air in the[U-13C]glucose experiments decreased with decreasing oxy-

gen availability from 20% to 0%. Thus, introduction ofunlabeled CO2 via carboxylation reactions can be neglectedin vials or anaerobic cultures, but is significant in the betteraerated shake flask cultures. To ensure fully aerobicconditions, all further experiments were conducted in shakeflasks.

Metabolic flux ratio analysis of E. coli mutantsof central metabolism

The above developed metabolic flux ratio analysis byGC-MS was used for metabolic flux profiling of nonlethalmutations in all major pathways of E. coli central meta-bolism (Fig. 1). For this purpose, aerobic batch cultureswere grown in shake flasks with M9 medium containingeither [1-13C]glucose or a 20/80 mixture of [U-13C] andunlabeled glucose, which were identified above as reliableexperimental conditions. Based on the physiological dataobtained from three different wild-type strains, maximumspecific growth rates of 0.5–0.7Æh)1, biomass yields of0.4–0.5 g(CDW)Æg(glucose))1, and specific glucose uptakerates of 6.5–8.5 mmolÆg(CDW))1Æh)1 may be considered asnormal for E. coli (Table 3). Hence, only the Pgi, PfkA,and Mae/Pck mutants exhibited clear physiologicalphenotypes with significantly reduced growth and glucoseuptake rates.While the flux profiles were similar in the three wild-type

strains with small differences in the fractions of serineoriginating from glycine and OAA originating through theTCA cycle (Fig. 5), major changes were seen in the mutants(Fig. 6). Consistent with its severely reduced growth rate,the phosphoglucose isomerase-deficient Pgi mutant exhi-bited a very different flux profile without any glycolytic flux(serine through glycolysis in Fig. 6). Unexpectedly, the EDpathway was found to contribute about 30% to glucosecatabolism in the Pgi mutant (pyruvate through ED

Fig. 4. Origin of metabolic intermediates in E. coli wild-type during

aerobic (white bars), suboptimally aerated (gray bars), and anaerobic

(black bars) growth. The experimental error was estimated from

redundant mass distributions. Asterisks indicate results obtained from

100% [1-13C] glucose experiments. All other results were from 20%

[U-13C] and 80% unlabeled glucose experiments. The fractions of

pyruvate originating from malate and PEP originating from OAA

could not be determined under anaerobic conditions because the OAA

pool is derived exclusively from PEP.

Fig. 3. Influence of harvest time on METAFoR analysis of E. coli

MG1655 in aerobic shake flask batch cultures. The line indicates the

exponential fit with a growth rate of 0.6 h)1 to the D600 readings

(closed circles). Fractions of OAA through the TCA cycle (open cir-

cles), serine from glycine (open triangles), and pyruvate from malate

(ub) (open squares) were obtained from 20% [U-13C] and 80%

unlabeled glucose experiments. Serine through glycolysis (open dia-

monds) was obtained from 100% [1-13C]glucose experiments. Error

bars indicate standard deviations of triplicate experiments.


pathway), so that the remaining 70% are contributed by thePP pathway, which is consistent with the upper bound of80% PEP from P5P (Fig. 6).The PfkA mutant is deficient in the allosterically regula-

ted, major isoform of phosphofructokinase that constitutesabout 90%of the total phosphofructokinase activity [27,28].As phosphofructokinase is required for glucose catabolismvia both glycolysis and PP pathway, the very low specificglucose uptake rate of the PfkA mutant and, as aconsequence, the low growth rate on glucose are expected(Table 3). Consistently, the major fraction of serine is stillgenerated through glycolysis (Fig. 6), probably catalyzed bythe intact minor isoform phosphofructokinase B. However,the flux partitioning into the PP pathway (PEP from P5P) issignificantly increased.Flux profiles of the Zwf and PykAF mutants defective in

G6P dehydrogenase and both pyruvate kinase isoforms,respectively, were somewhat similar to that of the wild-type.Significant flux changes in the Zwf mutant were seen in thereactions related to the PP pathway (data partly shown inFig. 6). A 93% fraction of serine originating throughglycolysis indicates residual PP pathway and/or ED path-way fluxes for glucose catabolism in the range of 7%.Consistent with the previously described metabolic bypassof pyruvate kinase knockout via PEP carboxylase andmalicenzyme [7,18], the PykAF mutant exhibited lower fractionsof OAA originating through the TCA cycle and higherfractions of pyruvate originating from malate (Fig. 6).During the growth on glucose investigated here, simul-

taneous inactivation of the two gluconeogenic reactionscatalyzed by malic enzyme and PEP carboxykinase had nosignificant effect on the flux profile of the Mae/Pck mutant(Fig. 6). This result was expected, as the fractions ofpyruvate originating frommalate and PEP originating fromOAA that are indicative of in vivo malic enzyme and PEPcarboxykinase activity, respectively, were already at detec-tion level in the wild-type strains (Fig. 5). Disruption of theTCA cycle in the Sdh/Mdh and FumA mutants [29,30]reduced primarily the fraction of OAA generated throughthe TCA cycle (Fig. 6). This fraction is zero in the doubleknockout mutant in malate dehydrogenase and succinatedehydrogenase, which reveals complete inactivation of the

Table 3. Aerobic growth parameters of exponentially growing E. coli

strains in [1-13C] and [U-13C]glucose (in parentheses) experiments.

Strain

Growth

rate (h)1)

Biomass

yield (gÆg)1)Glucose uptake

rate (mmolÆg)1Æh)1)

Wild-types

MG1655 0.61 (0.60) 0.39 (0.39) 8.5 (8.6)

W3110 0.55 (0.53) 0.41 (0.43) 7.3 (6.8)

JM101 0.69 (0.68) 0.49 (0.49) 7.7 (7.7)

Mutants

Zwf 0.68 (0.65) 0.53 (0.52) 8.8 (8.8)

Pgi 0.17 (0.15) 0.39 (0.40) 2.5 (2.0)

PfkA 0.08 (0.08) 0.41 (0.41) 1.4 (1.5)

PykAF 0.60 (0.59) 0.41 (n.d) 8.1 (n.d)

Mae/Pck 0.41 (0.44) 0.40 (0.42) 5.7 (5.8)

SdhA/Mdh 0.50 (0.51) 0.43 (0.40) 6.5 (7.1)

FumA 0.67 (0.65) 0.46 (0.45) 8.2 (8.3)

Fig. 5. Origin of metabolic intermediates in the E. coli wild-type strains

MG1655 (white), JM101 (gray), and W3110 (black) during aerobic

exponential growth. The experimental error was estimated from

redundant mass distributions. Asterisks indicate results obtained from

100% [1-13C]glucose experiments. All other results were from 20%

[U-13C] and 80% unlabeled glucose experiments.

Fig. 6. Origin of metabolic intermediates in

E. coli mutants during aerobic exponential

growth. The experimental error was estimated

from redundant mass distributions. Asterisks

indicate results obtained from [1-13C]glucose

experiments. All other results were from 20%

[U-13C] and 80% unlabeled glucose experi-

ments.


TCA cycle and exclusive origin of OAA through theanaplerotic PEP carboxylase. Although knockout of themajor fumarase isoform in the FumA mutant stronglyreduced TCA cycle fluxes, a residual TCA cycle contribu-tion to OAA synthesis of about 16% remains.

Discussion

We introduce here metabolic flux ratio analysis by GC-MSas a novel methodology for flux profiling from 13C-labelingexperiments. This methodology is based on probabilisticequations that relate mass distributions in amino acids tometabolic activities, and quantifies the relative contributionof converging pathways or reactions to metabolic interme-diates. While MS data were used previously to analyticallydeduce individual flux ratios, for example at the G6P node[13,19,31] and in gluconeogenesis [32], the generalizedmethodology presented here simultaneously quantifies 14flux ratios in central metabolism during growth on glucose.The thus obtained metabolic flux profile provides compre-hensive information on in vivo activities of all majorpathways in central carbon metabolism, hence concomi-tantly identifies the network topology. Although similar inscope to previously described metabolic flux ratio analysisby NMR [16,17], GC-MS-based analysis provides a signi-ficant advance in handling and sensitivity, so that biomasssamples as low as 1 mg cellular dry weight may be analyzed.Without the need for time-consuming quantitative physio-logical analysis, this methodology thus paves the road torapid diagnosis of metabolic changes in culture volumesbelow 1 mL.Usingmetabolic flux ratio analysis byGC-MS, we dissect

here flux responses of E. coli central metabolism toenvironmental and genetic modifications for two reasons:to (a) experimentally verify the accuracy of the newmethodology and to (b) identify novel metabolic response.Estimation of in vivo PP pathway activity has receivedconsiderable attention, due to its variability with environ-mental conditions and relevance for NADPH metabolism.For aerobic batch cultures of E. coli, the relative contribu-tion of the PP pathway to glucose catabolism has long beena matter of debate, yielding values between less than 10%to about 50% of glucose consumption [26,33]. For threedifferent E. coli wild-type strains, we show here that the PPpathway contribution to fully aerobic glucose catabolismvaries between 14% and 20% (Figs 5 and 7 A and 7B). Thiscontribution does not change significantly upon mutationsdownstream of triose 3-phosphate. When forced to serve asthe primary route for glucose catabolism in the phospho-glucose isomerase knockout (Fig. 7A), the PP pathwaysupports only a significantly lower growth rate than thatobserved for the wild-type. The strong reduction of PP andED pathway fluxes upon knockout of G6P dehydrogenase(Fig. 7B) reveals the nonessential nature of both pathwaysfor growth on glucose, as the growth physiology of the Zwfmutant was indistinguishable from that of the wild-type.Noticeably, a fraction of about 7% of the serine moleculesdoes not originate from glycolysis in the Zwf mutant. The13C labeling pattern of serine is instead consistent with a lowbut significant flux through either the PP or ED pathway. Asimilar observation was made with other, independentlygenerated G6P dehydrogenase mutants (data not shown).

Such a bypass of the inactivated G6P dehydrogenase maybe catalyzed for example by the periplasmic glucosedehydrogenase, which produces glucono-d-lactone thatcan be further converted to gluconate [24].Consistent with the reported gluconate induction [21],

in vivo activity of the ED pathway was low but notcompletely absent inwild-typeE. coli during aerobic growthon glucose (Figs 4,5, and 7C). In knockout mutants ofglycolysis and TCA cycle, however, the ED pathwaycatalyzes up to 30% of glucose catabolism (Figs 6 and7C). This is surprising because the inducer of this pathway isnot present and, at least for the example of the Pgi mutant,in vitro ED pathway enzyme activities are not significantlyhigher [34]. In the Pgi mutant, this flux rerouting throughthe ED pathway reduces concomitant excess NADPHformation from exclusive glucose catabolism via the PPpathway, which generates twoNADPH compared to one inthe ED pathway per catabolized glucose. This overproduc-tion of NADPH is deleterious, as limited capacity forreoxidation of NADPH is one reason for the low growthrate of phosphoglucose isomerase-deficient E. coli [34].However, exclusive glucose catabolism via the ED pathwaydoes not support growth of E. coli, as double mutants inboth isoforms of phosphofructokinase cannot grow onglucose as the sole carbon source [27].As may be expected from the known genetic regulation,

low or absent in vivo activity of the gluconeogenic reactionscatalyzed by PEP carboxykinase andmalic enzymewas seenin our batch cultures. Consistent with previous flux analysesbased on NMR data [7,18], the sole exception was thePykAF mutant, which bypassed the pyruvate kinasereaction by redirecting carbon flow via PEP carboxylaseand malic enzyme (Fig. 6).A very important flux ratio characterizing the metabolic

state of a culture is the fraction of OAA originating throughthe TCA cycle, which quantifies the proportion to which theTCA cycle is used for energy generation vs. biosyntheticprecursor supply via the anaplerotic PEP carboxylase(Fig. 7D). Consequently, this ratio is influenced by envi-ronmental factors such as growth phase (Fig. 3), aeration(Fig. 4), and overflow metabolism, but to some extentalso by the genetic background of the wild-type strains(Fig. 5), as was noted previously for different organisms[7,16,26,35,36]. Generally, anaplerosis is high under condi-tions that invoke overflowmetabolism, as acetate formationreduces the fraction of intact two carbon units entering theTCA cycle. Metabolic flux ratio analysis by GC-MSsuccessfully captures the effective disruption of the TCAcycle in the Sdh/Mdhmutant (Figs 6 and 7D). Although themajor fumarase isoform is inactivated in the FumAmutant,its respiratory TCA cycle flux is still at about one third ofthat in the wild-type (Fig. 6). This reveals that the tworemaining fumarase isoforms are also important duringgrowth on glucose.Despite the different genetic backgrounds of the

mutants in the upper part of central metabolism andtheir variations in growth rate, however, we observedsurprisingly small deviations in this fraction of OAAoriginating through the TCA cycle. Thus, all mutants thatwere not related to the TCA cycle maintained a similarbalance between anaplerosis and energy generation duringexponential growth.


Most prominently among the presented data, this lastresult provides experimental evidence formetabolic networkresilience to disruption [37–40]. While this was partlypredicted for E. coli from computational network analysis[41] and is obvious from the fact that the investigatedmutants grow inminimal medium, the flux results presentedhere reveal how metabolism manages intracellular fluxredistribution upon disruption of all major pathways. Theseresults are particularly valuable for the verification/falsifi-cation of hypotheses generated from in silico analyses suchas flux balancing [42] or elementary flux mode analyses [43],and will ultimately contribute to a quantitative understand-ing of metabolic networks.

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Supplementary material

The following material is available from http://www.blackwellpublishing.com/products/journals/suppmat/EJB/EJB3448/EJB3448sm.htmTable S1. Mass distributions of metabolite fragments inE. coli mutants grown on [1-13C]glucose.Table S2. Mass distributions of metabolite fragments inE. colimutants grown on 20% [U-13C] and 80% unlabeledglucose.


metabolic flux profiling of escherichia coli mutants in central carbon metabolism using gc-ms

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