Large-scale 13C-flux analysis reveals distinct
transcriptional control of respiratory and
fermentative metabolism in Escherichia coli
Bart RB Haverkorn van Rijsewijk1,2, Annik Nanchen1, Sophie Nallet1, Roelco J Kleijn1 and Uwe Sauer1,*
1 Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland and 2 Life Science Zurich PhD Program on Molecular Life Sciences, Zurich, Switzerland
* Corresponding author. Institute of Molecular Systems Biology, Wolfgang-Pauli Strasse 16, ETH Zurich, Zurich 8093, Switzerland.
Tel.: þ 41 (0)44 633 3672; Fax: þ 41 (0)44 633 1051; E-mail: sauer@imsb.biol.ethz.ch
Received 22.11.10; accepted 4.2.11
Despite our increasing topological knowledge on regulation networks inmodel bacteria, it is largely
unknownwhich of themany co-occurring regulatory events actually controlmetabolic function and
the distribution of intracellular fluxes. Here, we unravel condition-dependent transcriptional
control of Escherichia coli metabolism by large-scale 13C-flux analysis in 91 transcriptional regulator
mutants on glucose and galactose. In contrast to the canonical respiro-fermentative glucose
metabolism, fully respiratory galactose metabolism depends exclusively on the phosphoenol-
pyruvate (PEP)-glyoxylate cycle. While 2/3 of the regulators directly or indirectly affected absolute
flux rates, the partitioning between different pathways remained largely stable with transcriptional
control focusing primarily on the acetyl-CoA branch point. Flux distribution control was achieved
by nine transcription factors on glucose, including ArcA, Fur, PdhR, IHF A and IHF B, but was
exclusively mediated by the cAMP-dependent Crp regulation of the PEP-glyoxylate cycle flux on
galactose. Five further transcription factors affected this flux only indirectly through cAMP and Crp
by increasing the galactose uptake rate. Thus, E. coli actively limits its galactose catabolism at the
expense of otherwise possible faster growth.
Molecular Systems Biology 7: 477; published online 29 March 2011; doi:10.1038/msb.2011.9
Subject Categories: metabolic and regulatory networks; cellular metabolism
Keywords: central metabolism; fermentative growth; gene regulatory networks; respiratory growth;
transcriptional regulation
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Introduction
To survive environmental vicissitudes, microbes evolved
regulatory systems that sense external (and internal) condi-
tions to mount appropriate cellular responses (Janga et al,
2007; Kotte et al, 2010). A large fraction of these functional
responses concern metabolic adaption to changing nutritional
conditions. While activity of the metabolic enzyme is subject
to multiple levels of regulation, transcriptional regulation is
considered a main mechanism for such metabolic adaptations
in bacteria (Gama-Castro et al, 2008), but there are indications
that its relevance might have been overemphasized (Heine-
mann and Sauer, 2010). Understanding the regulatory pro-
cesses that govern cellular metabolism has become a key focus
in microbial systems biology. Key elements of such systems
biology approaches are genome-scale models of metabolic
stoichiometry (Feist and Palsson, 2008) and the detailed topology
of transcriptional regulatory networks that describe all known
interactions between transcription factors and their target genes
(Gama-Castro et al, 2008). Unfortunately, such topological
networks offer only a static description of potentially occurring
interactions, of which only a subset will be active at any given
point in time and conditions (Harbison et al, 2004; Luscombe
et al, 2004; Balazsi et al, 2005; Moxley et al, 2009).
This condition dependence of transcriptional regulation is
typically assessed by measurements of mRNA abundance
changes. Typically unanswered remains the question which of
all the active regulation processes actually control function, in
particular metabolic function, and to which extent? To assess
metabolic function in the network context, intracellular
reaction rates (i.e., fluxes) must be quantified by methods of
13C-based metabolic flux analysis (Sauer, 2006). By comparing
the network’s flux response with regulatory events, one can
identify actual control of function. In some specific cases this
has been done, e.g., by demonstrating that the redox regulator
ArcA controls tricarboxylic acid (TCA) cycle fluxes during
Molecular Systems Biology 7; Article number 477; doi:10.1038/msb.2011.9
Citation: Molecular Systems Biology 7:477
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& 2011 EMBO and Macmillan Publishers Limited Molecular Systems Biology 2011 1

aerobic batch growth on glucose (Perrenoud and Sauer, 2005),
but exerts no apparent flux control during glucose-limited
growth of Escherichia coli (Nanchen et al, 2008). The cAMP
receptor protein Crp on the other hand specifically controls
phosphoenol-pyruvate (PEP)-glyoxylate cycle fluxes during
glucose-limited growth of E. coli, but not during excess batch
growth (Perrenoud and Sauer, 2005; Nanchen et al, 2008).
So far, only one larger-scale flux study has recently been
concluded, revealing several condition-specific networks of
transcriptional regulation that control metabolic function in
yeast (Fendt et al, 2010). Overall, these results clearly demon-
strate the strong condition dependence ofmetabolic control with
typically sparse networks of active transcriptional control that
control the distribution of fluxes between different pathways.
Focusing on central carbon metabolism of E. coli, we aim
here to systematically identify transcriptional regulators that
control the distribution of metabolic fluxes during aerobic
growth on hexoses. For this purpose we selected 81 transcrip-
tion factors, including all known to directly or indirectly
control central metabolic enzymes, and 10 sigma- and anti-
sigma factors. Of the 81 transcription factors, 41 have one or
more direct target gene that is involved in central carbon
metabolism (Supplementary Table 1). To assess the condition
dependence of transcriptional control of flux, we selected
glucose and galactose as two substrates that are highly similar,
yet lead to distinct growth rates (Soupene et al, 2003), overall
metabolic rates (De Anda et al, 2006; Samir El et al, 2009) and
levels of catabolite repression (Hogema et al, 1998; Betten-
brock et al, 2007). Different from the recent yeast flux study
(Fendt et al, 2010), we quantify here not only flux ratios
but also absolute values for intracellular fluxes by large-scale
13C-constrained flux analysis (Zamboni et al, 2009). From
these data, we show here that E. coli employs substantially
different modes of metabolic operation and pathway usage on
both substrates, and identify the controlling transcription
factors and their target pathways.
Results
Two distinct modes of hexose catabolism on
galactose and glucose
To quantify physiological and intracellular flux difference of
aerobic glucose and galactose catabolism, E. coliwild typewas
grown in separate shake flask experimentswith 3 g l1 of either
the [1-13C] hexose isotope isomere or amixture of 20% [U-13C]
with 80% natural abundance hexose isotope isomers.
Compared with the glucose-grown culture, galactose-grown
cultures grew substantially slower with an about fourfold lower
metabolic rate and almost absent overflow to acetate (Table I).
To elucidate how these different macroscopic rates were
reflected by the intracellular distribution of fluxes, we first
determined ratios of converging fluxes from GC-MS-detected
mass isotope partitioning in proteinogenic amino acids (Table I)
(Zamboni et al, 2009). These ratios indicated major alterations
in the flux connecting oxaloacetate and PEP as well as in the
contribution of the glyoxylate cycle to the oxaloacetate pool.
For a better resolution of these flux differences, we
estimated absolute intracellular fluxes using the physiological
data and the flux ratios of Table I as constraints (Zamboni et al,
2009; Figure 1), and confirmed the results with whole
isotopologue modeling (Kleijn et al, 2005; Supplementary
Tables 2 and 3). Consistent with earlier data (Fischer and
Sauer, 2003b; Perrenoud and Sauer, 2005), we found the
classical glucose flux distribution with B30% of the high
hexose influx channeled into the pentose–phosphate (PP)
pathway and acetate overflow that exceeded the TCAcycle flux
about fourfold. The relative distribution of the much lower
galactose influx was similar to glucose metabolism in the
upper part of metabolism. In the lower part, however, the
distributionwas entirely different with a substantial glyoxylate
shunt flux and an interrupted TCA cycle between 2-oxogluta-
rate and succinate. Conjointly with a higher flux through PEP
carboxykinase, galactose metabolism, hence, relies almost
exclusively on the so-called PEP-glyoxylate cycle for respira-
tory energy generation (Fischer and Sauer, 2003b). Specifi-
cally, two PEP molecules are converted to two acetyl-CoA,
one of which enters the glyoxalate shunt reactions to form
succinate and the other is fused with glyoxlate to form malate.
Both organic acids are converted to oxaloacetate, one of
which enters the next round of the cycle and the other is
decarboxylated to form PEP. With a stoichiometry of 2 PEP-
3CO2þ 4NADHþUQH2þATPþPEP, this PEP-glyoxylate
cycle hence bypasses the cyclic operation of the TCA cycle by
the joint activity of PEP carboxykinase and glyoxylate shunt,
such that their normally considered functions in gluconeogen-
esis and anaplerosis, respectively, are recruited for complete
combustion of hexoses to CO2 (Fischer and Sauer, 2003b).
Intracellular flux partitioning in 91 transcription
factor mutants on glucose and galactose
Having established two different modes of hexose catabo-
lism—rapid respiro-fermentative metabolism on glucose and
Table I Physiological parameters and ratios of converging fluxes during
wild-type growth on glucose or galactose
Glucose Galactose
Wild-type physiologya
Growth rate (h1) 0.61±0.01 0.18±0.01
Hexose uptake (mmol gCDW1 h1) 8.26±0.50 2.00±0.33
Acetate secretion (mmol gCDW1 h1) 4.89±1.52 0.11±0.16
Wild-type flux ratiosb
EMD, ED and PP pathways
Serine through EMP pathway 0.77±0.01 0.79±0.01
Pyruvate through ED pathway 0.07±0.02 0.04±0.05
PEP from PP pathway (upper bound) 0.20±0.07 0.35±0.07
Gluconeogenesis, TCA and glyoxylate shunt
Oxaloacetate from phoshoenolpyruvate 0.68±0.01 0.20±0.03
Oxaloacetate from glyoxylate shunt NA 0.72±0.20
Pyruvate from malate (upper bound) 0.04±0.02 0.02±0.03
Pyruvate from malate (lower bound) 0.01±0.00 0.02±0.03
Phoshoenolpyruvate from oxaloacetate 0.02±0.00 0.20±0.03
C1 metabolism
Serine from glycine 0.31±0.02 0.46±0.02
Glycine from serine 0.98±0.02 0.99±0.02
aValues and standard deviations were obtained from at least three biological
replicates.
bValues are flux ratio values±95% confidence intervals. Two experiments led to
identical results.
Large-scale 13C-fluxes in E. coli regulator KOs
BRB Haverkorn van Rijsewijk et al
2 Molecular Systems Biology 2011 & 2011 EMBO and Macmillan Publishers Limited

slower fully respiratory metabolism via the PEP-glyoxylate
cycle—we next wanted to understand how these modes were
transcriptionally controlled. For this purpose, a large-scale flux
analysis was performed with transcription factor knockout
mutants under both growth conditions. Specifically, we chose
81 mutants of transcription factors that are known to directly
or indirectly control enzymes of central metabolism (Keseler
et al, 2005) and 10 sigma- and anti-sigma factors that control
expression globally (Supplementary Table 1). Fully aerobic
deep-well microtiter plate cultures (Duetz et al, 2000) were
grown on either the 100% [1-13C]-labeled hexose isotope
isomere or a mixture of 20% (wt/wt) [U-13C] and 80% natural
abundance hexose (Fischer and Sauer, 2003a). Physiology and
flux distribution of wild-type deep-well cultures were highly
similar to the above shake flask cultures (Supplementary
Tables 2 and 3). For each deletion mutant, both independently
generated clones in the library were analyzed separately
and yielded similar results, except for the Crp and Cra
mutants. PCR verification revealed that both genes were still
present in one of the two clones, which were therefore
discarded.
On the basis of 13C-determined flux ratios and extracellular
rates, we estimated absolute intracellular fluxes for all mutants
(Supplementary Tables 2 and 3; Zamboni et al, 2005). The
average deviation of absolute fluxes through the 23 central
metabolic reactions for all 91 mutants from thewild-type value
was 19 and 49% on glucose and galactose, respectively
(Figure 2). In contrast to absolute fluxes, the relative flux
partitioning was rather invariant in the entire mutant set.
Genetic perturbations hence primarily affected absolute fluxes
and not the distribution of fluxes as was described before
(Blank et al, 2005; Fischer and Sauer, 2005; Perrenoud and
Sauer, 2005; Ishii et al, 2007; Nanchen et al, 2008).
Next we focussed on flux changes that occurred for key
metabolic pathways in the 91 mutants (Figure 3). On glucose,
transcription factor deletions altered many fluxes, including
those of hexose uptake, glycolysis, pyruvate dehydrogenase,
TCA cycle and acetate secretion (Figure 3A). On galactose,
these flux changes were even larger and additionally included
the glyoxylate shunt. These variations in absolute flux values
may be caused by either a change of the overall fluxmagnitude
(due to or resulting in increased carbon uptake) or changes in
the distribution of flux within the network; hence, affecting
specific flux magnitudes. To distinguish between these
two possibilities, we also compared fluxes normalized to the
uptake rate betweenwild type and the 91 mutants (Figure 3B).
Many transcription factors affected absolute uptake rates.
About 60 of the 91 transcription factor mutants exhibited more
than a 10% deviation in uptake rates compared with the wild
Figure 1 Absolute metabolic fluxes in E. coli during aerobic growth on glucose (A) or galactose (B). Flux arrows are drawn in proportion to the substrate uptake rates
for each condition. The numbers represent absolute flux values (mmol gCDW1 h1). One of two replicate experiments is shown (Supplementary Tables 2 and 3).
The presented fluxes are from one of two independent experiments and were obtained by 13C-constrained flux analysis using the software FiatFlux (Zamboni et al,
2005). They were independently confirmed by flux estimation with a whole isotopologue model (Kleijn et al, 2005; van Winden et al, 2005). Generally, the deviation
between the two independent experiments was 1–5%, and whole isotopologue sensitivity analysis through addition of Gaussian noise confirmed accurate estimation for
all major fluxes (Supplementary Tables 2 and 3).
0%
10%
20%
30%
40%
50%
60%
Glucose Galactose
Av
e
ra
ge
d
ev
ia
tio
n
fro
m
w
ild

ty
pe
Absolute
fluxes
Flux
ratios
Absolute
fluxes
Flux
ratios
Figure 2 Average deviations of absolute fluxes and flux ratios in 91 mutants
from the wild type. Deviations in absolute fluxes and flux ratios are %-differences
compared with the wild-type values for each condition.
Large-scale 13C-fluxes in E. coli regulator KOs
BRB Haverkorn van Rijsewijk et al
& 2011 EMBO and Macmillan Publishers Limited Molecular Systems Biology 2011 3

type under either condition, indicating the importance of
transcriptional regulation in controlling metabolic flux.
The distribution of key fluxes through glycolysis, PP and
Entner–Doudoroff (ED) pathway in upper metabolism, how-
ever, remained constant under both conditions (Figure 3B).
Thus, flux variations in upper metabolism were caused by an
altered magnitude of overall fluxes, but our data do not allow
differentiating between a putative-specific transcriptional
control of pathways in upper metabolism and indirect,
growth-related effects. In key fluxes of lower metabolism
(pyruvate dehydrogenase, anaplerosis, TCA cycle, glyoxylate
cycle, acetate secretion), not only absolute fluxes but also their
distribution changed (Figure 3B). TCA cycle, glyoxylate shunt
and acetate secretion fluxes normalized to uptake rates were
significantly different in several mutants, indicating that in
lower metabolism also the distribution of flux is transcription-
ally controlled.
Identification of transcription factors that control
the distribution of flux at key branch points
To identify individual transcription factors that actually
control the distribution of fluxes within the network, we
focused our attention on key metabolic branch points. As
relative fluxes in upper metabolism remained constant
(Figure 3B), the almost perfect linear correlation between
hexose uptake and glycolytic flux at the glucose-6-phosphate
branch point in the various transcription factor mutants was
expected (Figure 4A). The only exceptions were control
experiments with disruptions of either glycolysis or PP
pathway in the Pgi and Zwf mutants, respectively. Clearly,
transcriptional regulation does not control the distribution
of flux into the PP pathway during growth on glucose or
galactose. Furthermore, flux partitioning at the glucose-6-
phosphate branch point was rigid over a large range of
absolute catabolic fluxes from 1.0 to 9.0mmol g1 h1. This
rigidity in the face of substantially different biomass yields in
the different mutants (Supplementary Tables 2 and 3)
demonstrates that, during hexose degradation, the PP pathway
flux is not determined by the biosynthetic needs for the redox
cofactor NADPH or pentoses, as was previously described for
Bacillus subtilis (Fischer and Sauer, 2005).
As the flux distribution varied significantly in lower
metabolism (Figure 3B), we focused on the acetyl-CoA branch
point inmore detail. For the glucose-grownmutants we plotted
the incoming pyruvate dehydrogenase flux as a function of the
outgoing flux into the TCA cycle (i.e., citrate synthase flux;
A
Absolute fluxes in mutants/wild type
0.01 1.00 0.00 0.01 1.00 100.00
GalactoseGlucose Glucose/galactose uptake
Phosphoglucose Isomerase
6-Phosphofructokinase
Glyceraldehyde-3-phosphate dehydrogenase
Glucose-6-phosphate-1-dehydrogenase
Pyruvate dehydrogenase
Acetate secretion
Succinate dehydrogenase
Malate dehydrogenase
Malate synthase B
B
0.01 1.00 0.00 0.01 1.00 100.00
Glucose/galactose uptake
Phosphoglucose Isomerase
6-Phosphofructokinase
Glyceraldehyde-3-phosphate dehydrogenase
Glucose-6-phosphate-1-dehydrogenase
Pyruvate dehydrogenase
Acetate secretion
Succinate dehydrogenase
Malate dehydrogenase
Malate synthase B
GalactoseGlucose
Substrate uptake normalized fluxes in mutants/wild type
Figure 3 Absolute (A) and substrate uptake normalized (B) flux changes of key metabolic pathways in the 91 mutants compared with the wild type during growth on
glucose (D) and galactose (&). The dashed line indicates the wild-type reference fluxes.
Large-scale 13C-fluxes in E. coli regulator KOs
BRB Haverkorn van Rijsewijk et al
4 Molecular Systems Biology 2011 & 2011 EMBO and Macmillan Publishers Limited

redirecting flux through the TCA cycle instead (Figure 4D).
While not affecting overflow metabolism, also the Crp mutant
abolished the PEP-glyoxylate cycle. This Crp-dependent
control of the PEP-glyoxylate cycle flux was previously
described for slowly growing glucose-limited chemostat
cultures, presumably triggered by high cAMP concentrations
that allosterically activate Crp (Nanchen et al, 2008).
Increased hexose uptake rate results in
altered cAMP-Crp-dependent control of
the PEP-glyoxylate cycle
In contrast to Crp, the activity of the other five transcription
factors is not modulated by cAMP; hence, their influence on
the PEP-glyoxylate cycle must be initiated by different means.
As both Cra (Saier and Ramseier, 1996; Crasnier-Mednansky
et al, 1997) and Mlc (Decker et al, 1998; Kimata et al, 1998;
Plumbridge, 2002) are regulators of the glucose phospho-
transferase system (PTSGlc) that represses the uptake of non-
PTS sugars such as galactose (Misko et al, 1987) and as NagC is
a known direct repressor of the galactose transporter GalP
(Soupene et al, 2003; Samir El et al, 2009), we wondered
whether there was a general relationship between galactose
uptake and the PEP-glyoxylate cycle flux. Indeed, when
plotting the fraction of acetyl-Coenzyme A that enters the
PEP-glyoxylate cycle at the acetyl-Coenzyme A branch point
against the galactose uptake rate for all mutants, the strongly
reduced fraction of carbon entering the PEP-glyoxylate cycle in
the Cra, IHFA, IHF B, Mlc and NagC mutants correlated with
much higher galactose uptake rates than in the wild type
(Figure 5A). AsMlc is actively sequestered to themembrane by
the glucose PTSGlc (Plumbridge, 2002), we hypothesized that
the PTSGlc might have an active role in the regulation of
galactose uptake. We therefore grew the PTSGlc-component
IIAglc mutant Crr on galactose. Consistent with the hypothesis
of a direct or indirect role of the PTSGlc in galactose uptake, the
galactose uptake rate of the Crr mutant was indeed signifi-
cantly increased (Figure 5A). Additionally, the PEP-glyoxylate
cycle was repressed in the Crr mutant, as was also seen for all
other mutant with increased galactose uptake.
To verify causality of the observed correlation between
hexose uptake rate and PEP-glyoxylate cycle usage, we used an
environmental strategy to decrease hexose uptake. Specifi-
cally, we performed glucose- and galactose-limited continuous
culture experiments at different dilution rates (Figure 5B).
Decreased uptake rates due to low dilution rates resulted in
activation of the PEP-glyoxylate cycle, for both growth on
glucose and galactose. As galactose uptake is normally
repressed in the wild type, we used the NagC mutant to enable
derepressed uptake of galactose (Soupene et al, 2003; Samir El
et al, 2009). At a dilution rate of 0.12 h1, resulting in an
uptake of 1.38mmol gCDW1 h1 (g cell dry weight, gCDW),
the NagC mutant exhibited a similar flux phenotype as was
obtained for the wild type. In contrast to the wild type,
however, the NagC mutant was capable to grow at a dilution
rate of 0.35 h1 on galactose, thereby achieving a galactose
uptake rate of 3.53mmol gCDW1 h1 (compared with a
maximal rate of 2.00mmol gCDW1 h1 for wild-type batch
growth). At both increased galactose and glucose uptake rate,
the fraction of carbon entering the PEP-glyoxylate cycle flux at
the acetyl-Coenzyme A branch point was significantly
reduced. Thus, independent of the substrate, the fraction of
acetyl-Coenzyme A that enters the PEP-glyoxylate cycle
decreases with gradually increasing hexose uptake rate. As
the PEP-glyoxylate cycle bypasses the NADPH-forming iso-
citrate dehydrogenase of the TCA cycle, it adds potential
metabolic flexibility to redox metabolism. Apparently, as the
cell enters a condition where higher formation than consump-
tion of NADPH occurs, e.g., during carbon-limited growth in a
chemostat when relatively little biomass is synthesized, the
PEP-glyoxylate cycle is activated, whereas the relative flux
through the NADPH generating PP pathway remains unaltered
(Figure 4A). The PEP-glyoxylate cycle, however, is not the only
mechanism to decouple catabolic carbon flow from NADPH
A
0%
10%
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30%
40%
50%
60%
70%
80%
90%
0.0 2.0 4.0 6.0 8.0
%

O
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o
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v
ia
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P-
gl
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xy
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cy
cl
Hexose uptake rate (mmol gCDW–1 h–1)
Crp Mlc Crr NagC IHF BIHF A
Cra
Hns
TCA cycle
+ acetate
GOX Cycle
Acetyl-CoA
Pyruvate
B
0%
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30%
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60%
70%
80%
0.0 5.0 10.0
%

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cl
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Hexose uptake rate (mmol gCDW –1 h–1)
NAGC GAL (B) WT GLC (B)
WT GAL (B)
WT GLC
(CS)
NagC
GAL
(CS)
TCA cycle
+ acetate
GOX Cycle
Acetyl-CoA
Pyruvate
Figure 5 Fraction of flux entering the PEP-glyoxylate cycle as a function of
hexose uptake rate in batch (A) and chemostat (B) cultures. (A) Batch growth on
galactose: the wild-type value is highlighted by a black circle. The PTSGlc enzyme
IIAglc mutant (crr) is highlighted in red. Values are the average of two independent
experiments obtained by 13C-constrained flux analysis (Supplementary Tables 2
and 3). For mutants with significantly altered flux distributions (labeled), a third
independent experiment was conducted and the error bars represent standard
deviations of these three independent experiments. (B) Chemostat growth on
glucose and galactose: wild type on glucose (J), NagC mutant on galactose is
highlighted in blue (E); values from batch experiments are additionally listed
( ). Nomenclature: CS, chemostat culture; B, batch culture. Two biological
replicates were conducted for each condition and error bars represent the
deviation between the two experiments.
Large-scale 13C-fluxes in E. coli regulator KOs
BRB Haverkorn van Rijsewijk et al
6 Molecular Systems Biology 2011 & 2011 EMBO and Macmillan Publishers Limited

Discussion
From large-scale, 13C-determined intracellular fluxes in 91
transcriptional regulatormutants of E. coli, we identified active
transcriptional control of hexose metabolism during two
surprisingly distinct metabolic states. During maximum
exponential growth, glucose metabolism was characterized
through high metabolic rates with about fourfold higher
overflow metabolism than respiratory TCA cycle fluxes.
Galactose metabolism, on the other hand, was generally much
slower, without overflow metabolism and with the PEP-
glyoxylate cycle (Fischer and Sauer, 2003b) replacing the
TCA cycle for respiration. Approximately 2/3 of the investi-
gated regulators controlled absolute fluxes directly or indir-
ectly through growth-related effects. Transcriptional control
of flux partitioning, in sharp contrast, was confined to the
acetyl-CoA branch point, where hexose catabolism can
proceed via acetate overflow, the respiratory TCA cycle and/
or the respiratory PEP-glyoxlate cycle. The relative flux into
the PP pathway, e.g., remained stable atB30% over an order
of magnitude of hexose uptake rates at very different biomass
yields, demonstrating its primary catabolic rather than
anabolic function in E. coli.
On glucose, transcriptional regulation at the acetyl-CoA
branch point controlled flux partitioning between the respira-
tory TCA cycle and acetate overflow. A negative control of the
TCA cycle/respiration branch was exerted by transcription
factors ArcA, as was previously described (Perrenoud and
Sauer, 2005), PdhR, and IHF A and B. All four are known to
repress target genes in the TCA cycle and/or the respiratory
chain (PdhR (Quail and Guest, 1995; Ogasawara et al, 2007),
ArcA (Iuchi and Lin, 1988, 1992; Park et al, 1997; Liu and De
Wulf, 2004; Perrenoud and Sauer, 2005; Vemuri et al, 2006),
IHFA and B (Bongaerts et al, 1995; Green et al, 1997; Park et al,
1997; Cunningham and Guest, 1998)). Increased capacity of
the TCA cycle and/or respiratory chain in these mutants
probably reduces acetate overflow. A positive control of the
TCAcycle fluxwas exerted by the iron utilization regulator Fur,
presumably through its known target genes in the TCA cycle
(Escolar et al, 1998; Zhang et al, 2005). The potentially novel
regulators GlpR, GlcC, QseB and HdfR could act either via
positive control of TCA cycle/respiration or negative control of
overflow metabolism. Our data do not allow drawing conclu-
sions on their direct transcriptional control, because the flux
alterations might also arise through indirect effects.
On galactose, flux partitioning at the acetyl-CoA branch
point was more complicated with the additional outgoing flux
through the respiratory PEP-glyoxylate cycle and the TCAcycle
flux mainly being confined to the anabolic purpose of
supplying 2-oxoglutarate. Transcriptional control appeared to
focus exclusively on the PEP-glyoxylate cycle, where we could
distinguish two different groups of positively acting transcrip-
tion factors. The only direct regulator was the cAMP receptor
protein Crp (Kolb et al, 1993; Gosset et al, 2004), whose
deletion entirely abolished PEP-glyoxylate cycle fluxes, as was
described earlier for slow growing, glucose-limited chemostat
cultures (Nanchen et al, 2008). Presumably, Crp induces
expression of its target genes aceA and aceB (Gosset et al,
2004; Zhang et al, 2005) upon allosteric activation
through the high cAMP concentrations at low growth rates
(Bettenbrock et al, 2007). The second group consists of the
indirectly acting transcription factors Cra, IHF A, IHF B,
Mlc and NagC, whose deletion likewise abolished the
PEP-glyoxylate cycle flux. Here the mode of action is the
strongly increased galactose uptake rate and concomitantly
decreased cAMP levels, which in turn deactivated Crp. Three
lines of evidence support this hypothesis. First, we obtained
similarly abolished PEP-glyoxylate cycle fluxes when increas-
ing galactose uptake by deleting the glucose PTS-based
repression of galactose uptake in the IIAglc mutant Crr
(Meadow et al, 1990; Hogema et al, 1998). Second,modulation
of galactose and glucose uptake rates through chemostat
cultivations supports the inverse relationship between PEP-
glyoxylate cycle flux and hexose uptake. This finding is
consistent with the reported increased mRNA and protein
levels of PEP carboxykinase and glyoxylate shunt enzymes at
decreased glucose uptake rates (Ishii et al, 2007). Third, the
low intracellular cAMP levels in our mutants with increased
galactose uptake rates are fully consistent with the known
inverse relationship between intracellular cAMP levels and
catabolic rates (Matin and Matin, 1982; Notley-McRobb et al,
1997; Ferenci, 2001; Fischer and Sauer, 2003b) and establish
the link to Crp. Hence, we conclude that the only direct
transcriptional regulator of the PEP-glyoxylate cycle on
galactose is the cAMP-activated Crp protein, while the other
five regulators act indirectly via cAMP and Crp.
By selecting a large-set of transcriptional regulators, includ-
ing all transcription factors known to regulate enzymes of
central metabolism, we demonstrate the extent to which this
flux splitting is subjected to transcriptional regulation. Overall,
our absolute flux data demonstrate that control of flux splitting
during growth on hexoses was confined to the acetyl-CoA
branch point in E. coli. Of the 36 transcription factors known to
target genes in pathways that diverge from the acetyl-CoA
branch point, only one transcription factor on galactose and
five plus potentially four others on glucose showed altered flux
splitting. The primary focus of steady-state transcriptional
control on the acetyl-CoA branch point, and thus themetabolic
decision between the energetically efficient respiration and the
less efficient but more rapid fermentation, was recently also
demonstrated with only relative flux data for Saccharomyces
cerevisiae (Fendt et al, 2010). Likewise, the identified networks
of active transcriptional control of the glucose flux distribution
were of similar size in yeast and E. coli with four and five (plus
four) transcriptional factors, respectively. On galactose, how-
ever, the active yeast network was quite extensive with more
than 10 factors, while fluxes in E. coli where controlled by only
one transcription factor. Why the yeast active transcription
network of respiratory metabolism is much more extensive on
galactose than the E. coli network remains unclear as a similar
number of transcription factors target the TCA cycle (35 for
S. cerevisiae (Monteiro et al, 2008) and 36 for E. coli (Keseler
et al, 2005; Salgado et al, 2006)). It is tempting to speculate
though that it is related to the fact that yeast does not invoke a
novel respiratory pathway such as E. coli but rather needs to
activate its TCAcycle (Fendt et al, 2010). As different metabolic
pathways were involved in E. coli galactose catabolism when
compared with glucose, it was necessary to determine
the much more quantitative and tedious absolute in vivo
fluxes, which then enabled us to identify the single directly
Large-scale 13C-fluxes in E. coli regulator KOs
BRB Haverkorn van Rijsewijk et al
8 Molecular Systems Biology 2011 & 2011 EMBO and Macmillan Publishers Limited

controlling transcription factor Crp from the larger set of
possible candidates.
Previously, suboptimal biomass productivity was described
for glucose-grown B. subtilis (Fischer and Sauer, 2003b).
For batch glucose-grown E. coli, we demonstrate here that
none of the investigated transcription factor mutants exhibited
improved biomass productivity. On galactose in contrast,
optimal growth appears to be transcriptionally repressed,
because the Cra, IHF A, IHF B and NagC mutants grew much
faster at almost unaltered biomass yields. As we could
engineer a similarly improved phenotype by directly increas-
ing the galactose uptake through removal of the glucose
PTS-based repression with a Crr mutant, we provide evidence
that E. coli actively represses its galactose uptake at the
expense of otherwise possible rapid growth.
Materials and methods
Strain and growth conditions
All experiments were performed with E. coli BW25113 and its
otherwise isogenic mutants from the Keio knockout library (Baba
et al, 2006). Experiments were conducted with both independently
generated mutants from the library. For clarity, the here used mutant
nomenclature reflects the deleted genes (Supplementary Table 1).
Batch physiology and flux experiments were performed in 35ml
cultures in 500ml shake flask or 1.2ml cultures in 2ml 96-deep-well
plates (Duetz et al, 2000) at 371C, 300 r.p.m. and a shaking diameter of
5 cm. Chemostat experiments were conducted in eight parallel 10ml
bioreactors as previously described (Nanchen et al, 2006). Briefly,
aeration was achieved by pumping water-saturated air through the
reactors and a constant temperature was assured through incubation
in a 371Cwater bath. Amediumflowof 1.5ml h1 and reactor volumes
of 3.0, 4.3, 7.5 and 12.5ml were used to obtain dilution rates of 0.5,
0.35, 0.2 and 0.12 h1, respectively.
Frozen glycerol stocks were used to inoculate Luria-Bertani (LB)
complex medium, supplemented with 50mg l1 kanamycin for all
mutants. All further cultivations were performed without antibiotics.
After 6 h of incubation at 371C and constant shaking, LB cultures were
used to inoculate M9 minimal medium precultures upon overnight
cultivation. The following day 0.3ml and 10 ml of the M9 precultures
were used to inoculate batch physiological and flux experiments for
shake flask and 2ml 96 deep-well plates, respectively. For chemostat
experiments, the M9 precultures were used to inoculate 10-ml-scale
bioreactors (Nanchen et al, 2006) with M9 minimal medium contain-
ing 13C-labeled substrate at a starting OD600 of 0.05. The medium feed
for 13C-labeled glucose- or galactose-limited chemostat operation was
initiated after 4–8h of batch growth depending on the achieved
biomass.
The M9 medium contained, per liter of deionized water, the
following: 0.8 g (NH4)2SO4, 0.5 g NaCl, 7.5 g Na2HPO4 2H2O and 3.0 g
KH2PO4. The following components were sterilized separately and
then added (per liter of finalmedium): 1ml of 1MMgSO4, 1ml of 0.1M
CaCl2, 0.6ml 0.1M FeCl3 6H2O, 2ml of 1mM filter-sterilized thiamine
HCl and 10ml of a trace element solution containing (per liter) 0.18 g
ZnSO4 7H2O, 0.12 g CuCl2 2H2O, 0.12 g MnSO4 H2O and 0.18 g CoCl2
6H2O. Filter-sterilized glucose or galactose was added to a final
concentration of 3 g l1 and 1 g l1 for batch and chemostat experi-
ments, respectively. For 13C-labeling experiments, glucose or galactose
was added entirely as the [1-13C]-labeled isotope isomere (499%;
Cambridge Isotope Laboratories, Andover, MA) or as amixture of 20%
(wt/wt) [U-13C]-labeled (499%; Cambridge Isotope Laboratories)
and 80% (wt/wt) of natural glucose or galactose.
Physiological parameters
Cell growth was monitored by determining the optical density
at 600 nm (OD600) using a spectrophotometer (Spectra Max Plus,
Molecular Devices, Sunnyvale, CA). Specific growth rates were
determined from log-linear regression of time-dependent changes in
optical density from at least four data points during the exponential
growth phase.
Glucose, galactose and acetate concentrations were quantified
enzymatically using commercial enzyme kits (Enzytec, Switzerland).
The uptake and secretion rates were determined from at least three
technical replicates of two independent shake flask or microtiterplate
experiments from two points (beginning of exponential growth and
mid-exponential growth). The glucose, galactose and acetate concen-
trations during exponential growth were plotted against the corre-
sponding cell dry weights. Cell dry weight was calculated using a
determined conversion factor of 0.41 and 0.51 gCDW) per liter per
OD600 for glucose and galactose, respectively. A linear fit was applied
to calculate the slope. The inverse of the slope of glucose/galactose
concentration against cell dry weight is the biomass yield in gmmol1.
The non-inversed slopes were further multiplied with the growth rate
to get uptake and secretion rates.
Metabolic flux ratio analysis by GC-MS
Aliquots of fractionally 13C-labeled biomass were prepared as
described previously for gas chromatography mass spectrometry
(GC-MS) analysis (Fischer and Sauer, 2003a; Fischer et al, 2004;
Zamboni et al, 2009). Briefly, cell pellets from 1ml culture aliquots at
an OD600 of 0.7–1.5 were collected by centrifugation and hydrolyzed in
6M HCl at 1051C for 24 h in sealed Eppendorf microtubes or 96-well
PCR racks for shake flask and deep-well plate cultures, respectively.
The hydrolysates were dried under a stream of air at B601C and
then derivatized at 851C in 20 ml dimethylformamide (Fluka,
Switzerland) and 20 ml N-(tert-butyldimethylsilyl)-N-methyl-trifluoro-
acetamide with 1% (vol/vol) tert-butyldimethylchlorosilane
(Fluka, Switzerland) for 60min (Fischer et al, 2004).
Derivatized amino acids were analyzed on a 6890 GC system
(Agilent Technologies, Santa Clara, USA) combined with a 5973 Inert
SL MS system (Agilent Technologies, Santa Clara, USA). The GC-MS-
derived mass isotope partitioning of proteinogenic amino acids were
then corrected for naturally occurring isotopes (van Winden et al,
2002). The following eight metabolic flux ratios were calculated, as
described previously (Fischer and Sauer, 2003a; Zamboni et al, 2005,
2009; Nanchen et al, 2007): serine derived through glycolysis, serine
derived through the ED pathway, oxaloacetate originating from PEP,
PEP originating from oxaloacetate, oxaloacetate originating from
the glyoxylate shunt, pyruvate originating from malate (upper and
lower boundaries) and an upper bound on PEP derived through the
PP pathway.
Estimation of absolute fluxes by the
13C-constrained method
Intracellular absolute carbon fluxes were estimated using the software
FiatFlux with the previously described (Fischer et al, 2004; Zamboni
et al, 2005, 2009) stoichiometric model that included all major
pathways of E. coli central carbon metabolism, including the
glyoxylate shunt and ED pathway. The reaction matrix consisted of
25 unknown fluxes and 21 metabolite balances (including the
three experimentally determined rates of glucose/galactose uptake,
acetate secretion and biomass production). To solve the under-
determined system of equations, the eight above metabolic flux
ratios were used as additional constraints, as described before (Fischer
et al, 2004; Zamboni et al, 2009). The first five ratios were used as
equality constraints, while the latter three were used as boundary
constraints.
Fluxes into biomass were calculated from the known metabolite
requirements for macromolecular compounds and the growth-rate-
dependent RNA and protein contents (Emmerling et al, 2002). Using
the FiatFlux software (Zamboni et al, 2005), the sum of the weighted
square residuals of the constraints from both metabolite balances and
flux ratios was minimized using the MATLAB function fmincon, and
the residuals were weighed by dividing through the experimental error
Large-scale 13C-fluxes in E. coli regulator KOs
BRB Haverkorn van Rijsewijk et al
& 2011 EMBO and Macmillan Publishers Limited Molecular Systems Biology 2011 9

(Zamboni et al, 2009). All calculations were performed in Matlab 7.7.0
(The Mathworks Inc., Natick, USA).
Estimation of absolute fluxes by the whole
isotopologue modeling method
High precision estimation of absolute fluxes was performed in selected
cases bywhole isotopologue balancing (Kleijn et al, 2005; vanWinden
et al, 2005). Briefly, we used cumomer balances and cumomer to
isotopologue mapping matrices (Wiechert et al, 1999) to calculate the
isotopologue partitioning of metabolites in a pre-defined stoichio-
metric network model for a given flux set. The flux set that gives the
best correspondence between the measured and simulated 13C-label
partitioning is determined by nonlinear optimization and denoted
as the optimal flux-fit. All calculations were performed in Matlab
7.7.0 (The Mathworks Inc.). The standard deviations for metabolic
fluxes were derived from Monte Carlo simulations (Schmidt et al,
1999). To mimic measurement errors, Gaussian noise was added
to the measured 13C-labeling data, and fluxes were re-estimated,
thereby providing a measure for the sensitivity of the different
fluxes.
Intracellular cAMP measurements
Cells were grown in batch cultures as described above. Of cultures
growing in mid-exponential phase, 1ml aliquots were taken in a
constant temperature room kept at 371C andwere vacuum-filtered on a
0.45mm pore size nitrocellulose filter (Millipore). Samples were
immediately washed with two volumes of fresh M9 medium at a
temperature of 371C, containing the respective carbon source, and
adjusted to pH of the culture at the time of sampling. Afterwashing, the
filter was directly transferred for extraction into 4ml of 60% (vol/vol)
EtOH/H2O kept at 781C for 2min. After extraction, the solution was
separated from the filters and centrifuged to remove residual cell
debris and nitrocellulose. Cell extracts were thawed, dried at 120 m bar
and resuspended in 100 ml deionized H2O, of which 15 ml were
transferred into rubber-sealed HPLC tubes. Intracellular cAMP
concentrations were determined by using an ion-pairing ultrahigh
performance liquid chromatography-tandem mass spectrometry
method (Buescher et al, 2010). Compounds were separated using a
Waters Acquity UPLC with a Waters Acquity T3 end-capped reverse-
phase column (1502.1mm 1.8 mm; Waters Corporation, Milford,
MA, USA) and subsequently detected on a tandem mass spectrometer
(Thermo TSQ Quantum Triple Quadropole with Electron-Spray
Ionization, Thermo Scientific, Waltham, MA, USA).
Supplementary information
Supplementary information is available at the Molecular Systems
Biology website (www.nature.com/msb).
Acknowledgements
We are grateful to Patrick Kiefer from Julia Vorholt’s Lab (ETH Zu¨rich)
for the help in setting up the fast filtration quenching and extraction
method for cAMP measurements, and also to Karl Kochanowski from
Uwe Sauer’s Lab (ETH Zu¨rich) for the help in setting up the chemostat.
Author contributions: BHvR designed the study, performed meta-
bolite measurements, most flux and physiology experiments on
glucose, all flux and physiology experiments on galactose, and
drafted the paper. AN and SN performed some flux and physiology
experiments on glucose. RJK estimated control fluxes by the whole
isotopologue modeling method. US conceived and supervised the
study, and helped drafting the paper. All authors read and approved the
final paper.
Conflict of interest
The authors declare that they have no conflict of interest.
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