Tài liệu Báo cáo khoa học: Metabolic flux profiling of Escherichia coli mutants in central carbon metabolism using GC-MS docx

Metabolic flux profiling of Escherichia coli mutants in centralcarbon metabolism using GC-MS Eliane Fischer and Uwe Sauer Institute of Biotechnology, ETH Zu¨rich, Zu¨rich, Switzerland We

Metabolic flux profiling of Escherichia coli mutants in central

carbon metabolism using GC-MS

Eliane Fischer and Uwe Sauer

Institute of Biotechnology, ETH Zu¨rich, Zu¨rich, Switzerland

We describe here a novel methodology for rapid diagnosis of

metabolic changes, which is based on probabilistic equations

that relate GC-MS-derived mass distributions in

proteino-genic amino acids to in vivo enzyme activities This metabolic

flux ratio analysis by GC-MS provides a comprehensive

perspective on central metabolism by quantifying 14 ratios

of fluxes through converging pathways and reactions from

[1-13C] and [U-13C]glucose experiments Reliability and

accuracy of this method were experimentally verified by

successfully capturing expected flux responses of Escherichia

coli to environmental modifications and seven knockout

mutations in all major pathways of central metabolism

Furthermore, several mutants exhibited additional,

unex-pected flux responses that provide new insights into the

behavior of the metabolic network in its entirety Most

prominently, the low in vivo activity of the Entner–

Doudoroff pathway in wild-type E coli increased up to a contribution of 30% to glucose catabolism in mutants of glycolysis and TCA cycle Moreover, glucose 6-phosphate dehydrogenase mutants catabolized glucose not exclusively via glycolysis, suggesting a yet unidentified bypass of this reaction Although strongly affected by environmental conditions, a stable balance between anaplerotic and TCA cycle flux was maintained by all mutants in the upper part of metabolism Overall, our results provide quantitative insight into flux changes that bring about the resilience of metabolic networks 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 knowledge

about their constituting components, but also information

about the behavior of the network in its entirety Toward

this end, systems-oriented methodologies were developed

that simultaneously access the level of reaction

intermedi-ates [1] or rintermedi-ates of reactions [2–5], also referred to as the

metabolome [6] and the fluxome [7], respectively The most

important property of biochemical networks are the per se

nonmeasurable in vivo reaction rates, which may be

estimated by so-called metabolic flux analysis that provides

a holistic perspective on metabolism

In its simplest form, metabolic flux analysis relies on flux

balancing of extracellular consumption and secretion rates

within a stoichiometric reaction model [5] To increase

reliability and resolution of such flux balancing analyses,

additional information may be derived from13C-labeling

experiments In this approach,13C-labeled substrates are administered and13C-labeled products of metabolism are analyzed by methods that distinguish between different isotope labeling patterns, in particular NMR and MS [2,3,8] In the most advanced methodology, a comprehen-sive isotope isomer (isotopomer) model of metabolism is used to map metabolic fluxes in an iterative fitting procedure

on the isotopomer pattern of network metabolites that are deduced from NMR or MS data [2] This global data interpretation enables integrated and quantitative consid-eration of all physiological and13C-labeling data Typically, protein hydrolysates are subjected to NMR or GC-MS analysis, which provides not only isotopomer pattern of the amino acids but also of their related precursor molecules that are key components of central metabolism With the presently available models and software, these isotopomer balancing methods have attained a high level of precision and applicability [2,9,10]

In contrast to isotopomer balancing, direct analytical interpretation of13C-labeling patterns has long been used not only to identify biochemical pathways and reactions but also

to quantify individual flux partitioning ratios [3,11,12] Such analytically deduced flux ratios were also used successfully as constraints for metabolic flux analysis [13–15] Based on probabilistic equations, a more general methodology was developed to simultaneously identify network topology and multipleflux partitioning ratios [16,17] This metabolic flux ratio analysis was based on the detection of13C-labeling patterns in proteinogenic amino acids by NMR analysis, and provides direct evidence for a particular flux Global isotopic data interpretation by isotopomer balancing and strictly local metabolic flux ratio analysis are largely independent

Correspondence to U Sauer, Institute of Biotechnology,

ETH Zu¨rich, CH-8093 Zu¨rich, Switzerland.

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

E-mail: sauer@biotech.biol.ethz.ch

Abbreviations: MDV, mass distribution vector; G6P,

glucose-6-phosphate; F6P, fructose-glucose-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)

Hence, the favorable agreement of results obtained by both

approaches for the same experimental data provides strong

evidence 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 for

metabolic network analysis in Escherichia coli strains with

knockout mutations in all major pathways of central carbon

metabolism The analyses presented here provide not only

novel insights into central metabolism but represent also

experimental verification of the reliability of metabolic flux

ratio analysis by GC-MS

Materials and methods

Strains, media, and growth conditions

The nomenclature of the employed E coli knockout

mutants indicates the affected genes (Table 1) Unless

indicated otherwise, aerobic batch cultures were grown at

37C in 500 mL baffled shake flasks with 50 mL of M9

minimal medium on a gyratory shaker at 200 r.p.m

Anaerobic cultures were grown in 100 mL sealed glass

flasks containing 50 mL medium that was gassed with N2

prior to sterilization for 10 min The M9 medium contained

per litre of deionized water: 0.8 g NH4Cl, 0.5 g NaCl, 7.52 g

Na2HPO4, and 3.0 g KH2PO4 The following components

were sterilized separately and then added (per litre of final

medium): 2 mL of 1MMgSO4, 1 mL of 0.1MCaCl2, 1 mL

of 1 mgÆL)1thiamine HCl (filter sterilized), and 10 mL of a

trace element solution containing (per litre) 16.67 g

FeCl3Æ6H2O, 0.18 g ZnSO4Æ7H2O, 0.12 g CuCl2Æ2H2O,

0.12 g MnSO4ÆH2O, 0.18 g CoCl2Æ6H2O, and 22.25 g

Na2EDTAÆ2H2O Filter-sterilized glucose was added to a

final concentration of 3 g per litre For 13C-labeling

experiments, 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 The13C-enrichment of [U-13C]glucose was

independently determined to be 98.7% from cells grown

exclusively on [U-13C]glucose

Analytical procedures and physiological parameters

Cell growth was monitored by measuring the optical density

at 600 nm (D600) Glucose concentrations were determined

enzymatically using a commercial kit (Beckman, Palo Alto,

CA, USA) The following physiological parameters were determined during the exponential growth phase in batch cultures as described previously [7]: Maximum growth rate, biomass yield on glucose, and specific glucose consumption rate, using a predetermined correlation factor of 0.44 g cellular dry weight (CDW) per D600unit

Sample preparation and GC-MS measurements Aliquots of batch cultures were harvested during the mid-exponential growth-phase, defined as D600of 0.8–1.5, and centrifuged at 14 000 g at room temp for 5 min Pellets were washed once in 1 mL 0.9% (w/v) NaCl and hydro-lyzed in 1.5 mL 6MHCl at 105C for 24 h in sealed glass tubes The hydrolysate was dried in a vacuum centrifuge

at room temperature and derivatized at 85C in 50 lL tetrahydrofurane (Fluka, Switzerland) and 50 lL of N-(tert-butyldimethylsilyl)-N-methyl-trifluoroacetamide (Fluka, Switzerland) for 60 min [20] 1 lL of derivatized sample was injected into a series 8000 GC, combined with an MD 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 split injection of 1 : 20 GC conditions were: carrier gas (helium) flow rate at 2 mL per min, oven temperature programmed from 150C (2 min) to 280 C at 3 C per min, source temperature at 200C and interface tempera-ture at 250C Electron impact (EI) spectra were obtained

at )70 eV GC-MS raw data were analyzed using the software package MassLab (Fisons), avoiding detector overload 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 valine for [1-13C] experiments

Bioreaction network The considered E coli bioreaction network was described previously [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 as described [16]

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

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 lacIqD(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]

Correction for naturally occurring isotopes

The obtained EI spectral data are sets of ion clusters, each

representing the distribution of mass isotopomers of a given

amino-acid fragment For each fragment a, a mass

isotopomer distribution vector (MDV):

MDVa¼

ðm0Þ

ðm1Þ

ðm2Þ

  

ðmnÞ

2 6 6 4

3 7 7

5with

X

mi¼ 1 ð1Þ

was assigned, where m0 is the fractional abundance of

fragments with the lowest mass and mi>0the abundances of

molecules with higher masses These higher masses result

from isotope signals that originate from (a) natural abun-dance in non-C-atoms, (b) natural abunabun-dance of13C in the derivatization reagent, and (c)13C in the carbon skeleton of the amino-acid fragment that were incorporated from naturally or artificially 13C-labeled substrates To obtain the exclusive mass isotope distribution of the carbon skeleton, MDVa were corrected for the natural isotope abundance of O, N, H, Si, S, and C atoms in the derivatizing agent by using correction matrices as described elsewhere [23], yielding MDV*a Prior to analysis, the contribution of

13C from unlabeled biomass in culture inocula was subtracted from MDV*ayielding MDVAAaccording to

MDVAA¼MDV



a funlabeledMDVunlabeled;n

ð1  funlabeledÞ ð2Þ

Fig 1 Bioreaction network of E 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.

where funlabeled is the fraction of unlabeled biomass and

MDVunlabeled,n is the mass distribution of an unlabeled

fragment of length n Its elements i can be calculated from

the natural abundances of 12C and 13C according to

Eqn (3)

MDVunlabeled;nðiÞ ¼ cðniÞ0 cðiÞ1 n

i



ð3Þ

c0and c1represent the natural abundance of12C and13C,

respectively, and ni is a binomial coefficient The corrected

MDVAAnow represent the mass distributions of the carbon

skeletons due to substrate incorporation (Fig 2A)

MDV of metabolites

Amino acids are derived from one or more metabolic

intermediates and MDVM of these metabolites (or their

fragments) can easily be derived from the MDVAA, as

illustrated schematically in Fig 2A If we assume that the

carbon skeleton of an amino acid originates from the

metabolites M1 and M2, the mass distribution vector

MDVAA is a combination of the mass distributions

MDVM1and MDVM2and can be derived by element-wise

multiplication according to:

MDVAAðiÞ ¼ MDVM1 MDVM2

¼Xi j¼0

MDVM1ði  jÞMDVM2ðjÞ ð4Þ

MDVM were obtained from a least squares fit to all

MDVAAusing the MATLAB function lsqnonlin with the

additional constraint that the sum of their element equals 1

MDV of substrate fragments

A fragment with n carbon atoms of a mixture of uniformly

and naturally labeled substrate has the following mass

distribution

MDVS;nUðiÞ ¼ ð1  lÞ c ðniÞ0 ci1þ lð1  pÞðniÞpi

i



ð5Þ where l is the labeled fraction and p is the purity of the

labeled substrate A fragment of a substrate that is

13C-labeled at a specific position can either be unlabeled,

thus having the mass distribution MDVunlabeled,n(Eqn 3) or

it may contain the13C-labeled position leading to

MDVS;n1(i)¼ ð1lpÞcðniÞ0 ci1 n

i

 þlp cðniÞ0 ci11 n1

i1

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 derived

from other metabolite pools through biochemical pathways

(Fig 2B) The fractional contribution f of a pathway to a

target metabolite pool with MDV1 was determined as:

f¼MDV1 MDV3

where MDV2 and MDV3 are the mass distributions of the source metabolites degraded through the examined and the alternative 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 (m 0 , m 1 ,…,m n ) are drawn to scale for the example of an E coli batch culture grown on a mixture of 20% [U- 13 C] and 80% unlabeled glucose Mass distributions of amino-acid fragments (MDV AA ) are obtained from the experimentally determined MDVa by correction for natural isotope abundance and unlabeled biomass fraction Mass distributions of metabolite fragments (MDV M ) are calculated from MDV AA by using Eqn (4) (B) MDV M of different metabolites are used to calculate split ratios of diverging pathways and the MDV of

CO 2 according to Eqn (9).

not single data points, f represents the least-squares solution

to Eqn (7) Accordingly, using MDV with n elements, up to

nalternative pathways can be distinguished For example,

the individual contributions of three converging pathways is

determined as:

f1

f2

 

¼ MDV1 MDV4 MDV2 MDV4 MDV3 MDV4

with f3¼ 1) f1) f2

The origin of several intracellular metabolite pools can be

determined with Eqns (7) and (8) Specifically, MDVMof six

metabolites and MDVAAof two amino acids were used for

metabolic flux ratio analysis (Table 2) together with MDVS

of substrate fragments In some cases, however, the

metabolic precursors MDV2 or MDV3 were combinations

of two MDVM Eqn (4) was applied to calculate the mass

distribution of these combinations

Pentose phosphate pathway

E colican potentially catabolize glucose to trioses via three

different 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 cleavage

of bonds between carbon atoms is reflected in the MDVMof

PEP, P5P, and E4P As glucose catabolism through the

glycolysis and the ED pathway yields uncleaved trioses, the

activity of these two pathways is indistinguishable with

[U-13C]glucose The activity of transketolase and

trans-aldolase in the nonoxidative PP pathway, however, can be

accessed

As exchange fluxes between serine and glycine [16] clearly influence the mass distribution of serine, PEP(1)2)was used

to determine the fraction of trioses that were cleaved and rearranged between C1–C2by the action of transketolase, and compared to the fraction that originates from an unbroken two carbon unit of glucose according to Eqn (7)

An upper bound for PEP molecules that were generated from P5P can be calculated assuming that five trioses are produced from three pentoses and that at least two trioses are rearranged by transketolase It should be noted that the thus calculated fraction of PEP originating from P5P does not necessarily reflect glucose catabolism through the PP pathway, but may likewise result from a reversible exchange flux via transketolase

Two other metabolites that reflect transketolase and transaldolase activities are P5P and E4P P5P molecules may be produced either via the oxidative PP pathway from G6P, thus yielding an intact five carbon skeleton from a source molecule of glucose, or via the transketolase reaction, which cleaves between C3–C4 Additionally, P5P may also originate from E4P and a one carbon unit through the combined action of transaldolase and trans-ketolase The contributions of the three converging pathways are thus calculated using Eqn (8) As transketo-lase can reversibly cleave P5P and multiple cycling may occur through the PP pathway, P5P from G6P is calculated as a lower bound for the fraction of P5P molecules that were generated via the oxidative PP pathway

The second PP pathway intermediate, E4P, is either produced from F6P as an uncleaved four carbon unit or via the combined activity of transketolase and transaldolase from P5P The latter introduces E4P molecules with cleaved

C1–C2bonds originating from the fraction of P5P that was cleaved between C3–C4 The E4P pool was analyzed using Eqn (7)

Anaplerosis and the TCA Cycle [U-13C]glucose experiments were also used to distinguish OAA produced either from a four carbon unit via the TCA cycle or from PEP and CO2 via the anaplerotic reaction catalyzed by PEP carboxylase (see also Fig 2) OAA(1)4) can thus be derived from the mass distribution

of OGA(2)5)or from a combination of the MDV of PEP with CO2, according to Eqn (4) As the fractional labeling of CO2 (lCO 2) is unknown in batch cultures and may be lower than the fractional enrichment of the input substrate, it was treated as an additional unknown using

f

f lCO 2

PEPð13Þ 0

 OGAð25Þ

0 PEPð13Þ

 PEP ð13Þ 0

The fraction of OAA molecules that originate through the TCA cycle is thus determined as 1) f The remaining fraction originates from PEP either through PEP carboxy-lase or through reversible malic enzyme via pyruvate Additionally, the fraction of OAA(1)4) derived from glyoxylate via the glyoxylate shunt can be detected as a combination 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

each MDV M and MDV AA MDV S are described by the length n of the

fragment and its13C-content U, 20% [U-13C] and 80% unlabeled

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

1 Glc,n 1 Glc,n unlabeled

Gluconeogenic reactions

Fluxes from the TCA cycle to the lower part of glycolysis

via malic enzyme and PEP carboxykinase can be diagnosed

as cleaved C2–C3bonds in pyruvate and PEP, respectively

The interconversion of malate to pyruvate via the malic

enzymes (ScfA and Mae) can thus be determined by

comparing the pyruvate(2)3)and PEP(2)3)fragments using

Eqn (7) As the mass distribution of malate is unknown, a

pyruvate(2)3) molecule produced via malic enzyme was

assumed to have the mass distribution of two combined one

carbon units, each with the fractional13C-label of the input

glucose This assumption includes (a) that all malate

molecules are broken between C2–C3, thus are derived from

OGA, and (b) that the fractional enrichment of C2and C3in

malate does not differ from the fractional enrichment in the

input substrate A dilution of the fractional enrichment

might be observed, for example, in positions where CO2is

introduced This, however, may occur only at C1or C4of

malate, thus does not affect the present calculation of the

lower bound for malic enzyme activity If the malate pool is

in equilibrium with OAA, intact C2–C3 fragments from

anaplerosis are present in malate Thus, an upper bound for

pyruvate produced through malic enzyme can be defined for

the extreme case of full equilibration of the malate and

OAA pools

Similarly, PEP carboxykinase activity can be detected in

the cleaved fraction of PEP(2)3)using Eqn (7) As a cleaved

C2–C3 bond in PEP may also result from transaldolase

activity, the thus calculated fraction of PEP originating

from OAA remains an upper bound on the PEP

carboxy-kinase activity

C1-metabolism

The reversible exchange of the serine and glycine pools was

quantified by determining the fraction of serine(1)3)

origin-ating from glycine(1)2)and a one carbon unit vs the fraction

that is identical with PEP(1)3) (Eqn 7) Additionally, the

fraction of glycine(1)2)derived from serine(1)2)was attained

assuming that the remaining glycine fraction with two

independent C atoms originates from CO2and a one carbon

unit through the reversible glycine cleavage pathway or

through threonine cleavage catalyzed by the threonine

aldolase The latter enzyme was reported to be active in

E coliunder some conditions, albeit not those used here

[22,25]

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

experiments

To obtain more precise information about the in vivo

activities of the PP and ED pathway and the PEP

carboxykinase, positional labeling was detected from cells

grown exclusively on [1-13C]glucose As the MDV of PEP

could not be obtained in [1-13C]glucose experiments, serine

was used instead to quantify the relative contribution of

glycolysis to triose-3P synthesis, compared to the PP and

ED pathways The exchange flux with glycine does not

change the label content in serine, unless substantial

fractions of glycine or the one carbon unit are produced

from sources other than serine The oxidative PP or the ED

pathway both yield unlabeled triose-3P, while glycolysis yields 50% unlabeled and 50% triose-3P that is13C-labeled

at C1(Eqn 7)

If the ED pathway is active, additional label is introduced

at the level of pyruvate, resulting in different MDV of serine(1)3)and pyruvate(1)3), which can be used to assess the relative contribution of this pathway to pyruvate synthesis using Eqn (7) Additionally, pyruvate derived through the

ED pathway is labeled at C1, while pyruvate originating from glycolysis is labeled at C3 The fraction of pyruvate molecules labeled at C1can be calculated from the difference between pyruvate(1)3)and pyruvate(2)3) This information is used to verify that the label is indeed introduced through the

ED 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 that the remaining fraction is identical to serine(1)2)

Error consideration The experimental measurement error was determined by comparing the MDVaof amino acids with identical carbon skeletons, and the standard deviation of these redundant data was used for calculation of the covariance matrix Cm

of the measured individual mass intensities Standard devi-ations of the calculated flux ratios were determined applying the law of error propagation Cr¼ J*Cm*JTwhere J is the jacobian matrix and Crthe covariance matrix of the output variables J was obtained numerically for MDVMafter the least-squares fitting step and calculated analytically for the final flux ratios

Results Sensitivity of metabolic flux ratio analysis using different mixtures of [U-13C] and unlabeled glucose For economical reasons, low fractions of expensive

13C-labeled substrates are desirable for labeling experiments, provided that analytical resolution and sensitivity are maintained To identify an optimal compromise, we grew

E coliMG1655 batch cultures in 5 mL M9 medium with different mixtures of [U-13C] and unlabeled glucose While fully13C-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 within the experimental error (data not shown) Although the lowest experimental error is achieved at around equimolar fractions of [U-13C] and unlabeled glucose, the 20% [U-13C]glucose mixture enabled very reliable determination

of intracellular flux ratios and was thus used in the further experiments

Metabolic flux ratio analysis ofE coli under different environmental conditions

While exponentially growing cells are initially in a physio-logical pseudo steady state, metabolic switches occur upon oxygen limitation or accumulation of metabolic byproducts

To identify reproducible conditions that faithfully reflect the physiological state of unlimited, exponentially growing cells,

biomass aliquots were harvested at different time points

from wild-type batch cultures in shake flasks growing on

100% [1-13C]glucose or on a 20%/80% mixture of [U-13C]

and unlabeled glucose Overall, the determined origin of

metabolite pools did not change significantly with the time

of harvest (data partly shown in Fig 3) The sole exceptions

were increasing fractions of serine derived through

glyco-lysis and OAA derived through the TCA cycle upon

approaching stationary phase (Fig 3), as was observed

earlier [7] Hence, all further analyses were performed with

biomass aliquots harvested at D600values between 0.8 and

1.5

Next, we investigated the metabolic impact of different

levels of aeration from fully aerobic (500 mL baffled shake

flask) to suboptimally aerated (15 mL vials) and anaerobic

E coli batch cultures (Fig 4) With decreasing oxygen

availability, 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 TCA

cycle to fulfill exclusively biosynthetic requirements, as was

also shown earlier [7,16,26] Although the oxidative PP

pathway is still active under anaerobic conditions (serine

through glycolysis), its relative contribution to glucose

catabolism is decreased from 19% to 5% (Fig 4), which

concurs with most [7,16] but not all [26] reports The

frequently reported upper bound on in vivo PP pathway

activity obtained from [U-13C]glucose experiments, in

contrast (PEP from P5P), is not sensitive to this decrease

Unexpectedly, suboptimally aerated conditions promote

relatively high in vivo malic enzyme activity (pyruvate from

malate) 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 of unlabeled CO2via carboxylation reactions can be neglected

in vials or anaerobic cultures, but is significant in the better aerated shake flask cultures To ensure fully aerobic conditions, all further experiments were conducted in shake flasks

Metabolic flux ratio analysis ofE coli mutants

of central metabolism The above developed metabolic flux ratio analysis by GC-MS was used for metabolic flux profiling of nonlethal mutations in all major pathways of E coli central meta-bolism (Fig 1) For this purpose, aerobic batch cultures were grown in shake flasks with M9 medium containing either [1-13C]glucose or a 20/80 mixture of [U-13C] and unlabeled glucose, which were identified above as reliable experimental conditions Based on the physiological data obtained from three different wild-type strains, maximum specific growth rates of 0.5–0.7Æh)1, biomass yields of 0.4–0.5 g(CDW)Æg(glucose))1, and specific glucose uptake rates of 6.5–8.5 mmolÆg(CDW))1Æh)1may be considered as normal for E coli (Table 3) Hence, only the Pgi, PfkA, and Mae/Pck mutants exhibited clear physiological phenotypes with significantly reduced growth and glucose uptake rates

While the flux profiles were similar in the three wild-type strains with small differences in the fractions of serine originating from glycine and OAA originating through the TCA 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 ED pathway was found to contribute about 30% to glucose catabolism 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- 13 C] glucose experiments All other results were from 20% [U- 13 C] 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)1to the D 600 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- 13 C] and 80%

unlabeled glucose experiments Serine through glycolysis (open

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

bars indicate standard deviations of triplicate experiments.

pathway), so that the remaining 70% are contributed by the

PP pathway, which is consistent with the upper bound of 80% PEP from P5P (Fig 6)

The PfkA mutant is deficient in the allosterically regula-ted, major isoform of phosphofructokinase that constitutes about 90% of the total phosphofructokinase activity [27,28]

As phosphofructokinase is required for glucose catabolism via both glycolysis and PP pathway, the very low specific glucose uptake rate of the PfkA mutant and, as a consequence, the low growth rate on glucose are expected (Table 3) Consistently, the major fraction of serine is still generated through glycolysis (Fig 6), probably catalyzed by the intact minor isoform phosphofructokinase B However, the flux partitioning into the PP pathway (PEP from P5P) is significantly 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 the reactions related to the PP pathway (data partly shown in Fig 6) A 93% fraction of serine originating through glycolysis indicates residual PP pathway and/or ED path-way fluxes for glucose catabolism in the range of 7% Consistent with the previously described metabolic bypass

of pyruvate kinase knockout via PEP carboxylase and malic enzyme [7,18], the PykAF mutant exhibited lower fractions

of OAA originating through the TCA cycle and higher fractions of pyruvate originating from malate (Fig 6) During the growth on glucose investigated here, simul-taneous inactivation of the two gluconeogenic reactions catalyzed by malic enzyme and PEP carboxykinase had no significant effect on the flux profile of the Mae/Pck mutant (Fig 6) This result was expected, as the fractions of pyruvate originating from malate and PEP originating from OAA that are indicative of in vivo malic enzyme and PEP carboxykinase activity, respectively, were already at detec-tion level in the wild-type strains (Fig 5) Disrupdetec-tion of the TCA cycle in the Sdh/Mdh and FumA mutants [29,30] reduced primarily the fraction of OAA generated through the TCA cycle (Fig 6) This fraction is zero in the double knockout mutant in malate dehydrogenase and succinate dehydrogenase, 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- 13 C]glucose experiments All other results were from 20%

[U- 13 C] 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- 13 C]glucose

experiments All other results were from 20%

[U- 13 C] and 80% unlabeled glucose

experi-ments.

TCA cycle and exclusive origin of OAA through the

anaplerotic PEP carboxylase Although knockout of the

major fumarase isoform in the FumA mutant strongly

reduced 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-MS

as a novel methodology for flux profiling from13C-labeling

experiments This methodology is based on probabilistic

equations that relate mass distributions in amino acids to

metabolic activities, and quantifies the relative contribution

of converging pathways or reactions to metabolic

interme-diates While MS data were used previously to analytically

deduce individual flux ratios, for example at the G6P node

[13,19,31] and in gluconeogenesis [32], the generalized

methodology presented here simultaneously quantifies 14

flux ratios in central metabolism during growth on glucose

The thus obtained metabolic flux profile provides

compre-hensive information on in vivo activities of all major

pathways in central carbon metabolism, hence

concomi-tantly identifies the network topology Although similar in

scope to previously described metabolic flux ratio analysis

by NMR [16,17], GC-MS-based analysis provides a

signi-ficant advance in handling and sensitivity, so that biomass

samples 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 to

rapid diagnosis of metabolic changes in culture volumes

below 1 mL

Using metabolic flux ratio analysis by GC-MS, we dissect

here flux responses of E coli central metabolism to

environmental and genetic modifications for two reasons:

to (a) experimentally verify the accuracy of the new

methodology and to (b) identify novel metabolic response

Estimation of in vivo PP pathway activity has received

considerable 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 been

a matter of debate, yielding values between less than 10%

to about 50% of glucose consumption [26,33] For three

different E coli wild-type strains, we show here that the PP

pathway contribution to fully aerobic glucose catabolism

varies between 14% and 20% (Figs 5 and 7 A and 7B) This

contribution does not change significantly upon mutations

downstream of triose 3-phosphate When forced to serve as

the primary route for glucose catabolism in the

phospho-glucose isomerase knockout (Fig 7A), the PP pathway

supports only a significantly lower growth rate than that

observed for the wild-type The strong reduction of PP and

ED pathway fluxes upon knockout of G6P dehydrogenase

(Fig 7B) reveals the nonessential nature of both pathways

for growth on glucose, as the growth physiology of the Zwf

mutant was indistinguishable from that of the wild-type

Noticeably, a fraction of about 7% of the serine molecules

does not originate from glycolysis in the Zwf mutant The

13C labeling pattern of serine is instead consistent with a low

but significant flux through either the PP or ED pathway A

similar observation was made with other, independently

generated G6P dehydrogenase mutants (data not shown)

Such a bypass of the inactivated G6P dehydrogenase may

be catalyzed for example by the periplasmic glucose dehydrogenase, which produces glucono-d-lactone that can be further converted to gluconate [24]

Consistent with the reported gluconate induction [21],

in vivo activity of the ED pathway was low but not completely absent in wild-type E coli during aerobic growth

on glucose (Figs 4,5, and 7C) In knockout mutants of glycolysis and TCA cycle, however, the ED pathway catalyzes up to 30% of glucose catabolism (Figs 6 and 7C) This is surprising because the inducer of this pathway is not present and, at least for the example of the Pgi mutant,

in vitroED pathway enzyme activities are not significantly higher [34] In the Pgi mutant, this flux rerouting through the ED pathway reduces concomitant excess NADPH formation from exclusive glucose catabolism via the PP pathway, which generates two NADPH compared to one in the ED pathway per catabolized glucose This overproduc-tion of NADPH is deleterious, as limited capacity for reoxidation of NADPH is one reason for the low growth rate of phosphoglucose isomerase-deficient E coli [34] However, exclusive glucose catabolism via the ED pathway does not support growth of E coli, as double mutants in both isoforms of phosphofructokinase cannot grow on glucose as the sole carbon source [27]

As may be expected from the known genetic regulation, low or absent in vivo activity of the gluconeogenic reactions catalyzed by PEP carboxykinase and malic enzyme was seen

in our batch cultures Consistent with previous flux analyses based on NMR data [7,18], the sole exception was the PykAF mutant, which bypassed the pyruvate kinase reaction by redirecting carbon flow via PEP carboxylase and malic enzyme (Fig 6)

A very important flux ratio characterizing the metabolic state of a culture is the fraction of OAA originating through the TCA cycle, which quantifies the proportion to which the TCA cycle is used for energy generation vs biosynthetic precursor 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 extent also 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 overflow metabolism, as acetate formation reduces the fraction of intact two carbon units entering the TCA cycle Metabolic flux ratio analysis by GC-MS successfully captures the effective disruption of the TCA cycle in the Sdh/Mdh mutant (Figs 6 and 7D) Although the major fumarase isoform is inactivated in the FumA mutant, its respiratory TCA cycle flux is still at about one third of that in the wild-type (Fig 6) This reveals that the two remaining fumarase isoforms are also important during growth on glucose

Despite the different genetic backgrounds of the mutants in the upper part of central metabolism and their variations in growth rate, however, we observed surprisingly small deviations in this fraction of OAA originating through the TCA cycle Thus, all mutants that were not related to the TCA cycle maintained a similar balance between anaplerosis and energy generation during exponential growth

Most prominently among the presented data, this last

result provides experimental evidence for metabolic network

resilience to disruption [37–40] While this was partly

predicted for E coli from computational network analysis

[41] and is obvious from the fact that the investigated

mutants grow in minimal medium, the flux results presented

here reveal how metabolism manages intracellular flux

redistribution upon disruption of all major pathways These

results are particularly valuable for the

verification/falsifi-cation of hypotheses generated from in silico analyses such

as flux balancing [42] or elementary flux mode analyses [43],

and will ultimately contribute to a quantitative

understand-ing of metabolic networks

References

1 Weckwerth, W & Fiehn, O (2002) Can we discover novel

path-ways using metabolomic analysis? Curr Opin Biotechnol 13, 156–

160.

2 Wiechert, W (2001)13C metabolic flux analysis Metab Eng 3, 195–206.

3 Szyperski, T (1998)13C-NMR, MS and metabolic flux balancing

in biotechnology research Q Rev Biophys 31, 41–106.

4 Christensen, B & Nielsen, J (1999) Metabolic network analysis Adv Biochem Eng Biotechnol 66, 209–231.

5 Varma, A & Palsson, B.O (1994) Metabolic flux balancing: basic concepts, scientific, and practical use Bio/Technol 12, 994– 998.

6 Oliver, S.G., Winson, M.K., Kell, D.B & Baganz, F (1998) Systematic functional analysis of the yeast genome Trends Bio-technol 16, 373–378.

7 Sauer, U., Lasko, D.R., Fiaux, J., Hochuli, M., Glaser, R., Szyperski, T., Wu¨thrich, K & Bailey, J.E (1999) Metabolic flux ratio analysis of genetic and environmental modulations of Escherichia coli central carbon metabolism J Bacteriol 181, 6679–6688.

8 Wittmann, C (2002) Metabolic flux analysis using mass spectro-metry Adv Biochem Eng Biotechnol 74, 39–64.

Fig 7 Ratios of metabolic fluxes (solid arrows) to the synthesis of boxed metabolites in E coli MG1655 (top values), the Pgi mutant (second values), the Zwf mutant (third values), and the Sdh/Mdh double mutant (bottom values) The values are based on the data shown in Fig 6 (A) Relative contributions of catabolic pathways and PEP carboxykinase to PEP formation from [U- 13 C]glucose experiments (B) Relative contribution of the catabolic pathways to the formation of the serine pool from [1- 13 C]glucose experiments (C) Relative contribution of the catabolic pathways and malic enzyme to the formation of the pyruvate pool from [1-13C] and [U-13C]glucose experiments (D) Relative contributions of anaplerosis and the TCA-cycle to the formation of the OAA pool from [U- 13 C]glucose experiments Dashed arrows symbolize reactions that are not considered for a given flux ratio.

Metabolic flux ratio analysis ofE coli mutants

of central metabolism The above developed metabolic flux ratio analysis by GC-MS was used for metabolic flux profiling. .. similar in the three wild-type strains with small differences in the fractions of serine originating from glycine and OAA originating through the TCA cycle (Fig 5), major changes were seen in the mutants. ..

C1 -metabolism

The reversible exchange of the serine and glycine pools was

quantified by determining the fraction of serine(1)3)

origin-ating from glycine(1)2)and