Sub-optimal phenotypes of double-knockout of E.coli

930 | Integr. Biol., 2015, 7, 930--939 This journal is ©The Royal Society of Chemistry 2015
Cite this: Integr. Biol., 2015,
7, 930
Sub-optimal phenotypes of double-knockout
mutants of Escherichia coli depend on the
order of gene deletions†
Pratish Gawand,a
Fatumina Said Abukar,‡a
Naveen Venayak,a
Siavash Partow,a
Adilson E. Motterb
and Radhakrishnan Mahadevan*a
Metabolic networks are characterized by multiple redundant reactions that do not have a clear
biological function. The redundancies in the metabolic networks are implicated in adaptation to random
mutations and survival under different environmental conditions. Reactions that are not active under
wild-type growth conditions, but get transiently activated after a mutation event such as gene deletion
are known as latent reactions. Characterization of multiple-gene knockout mutants can identify the
physiological roles of latent reactions. In this study, we characterized double-gene deletion mutants of
E. coli with the aim of investigating the sub-optimal physiology of the mutants and the possible roles of
latent reactions. Specifically, we investigated the effects of the deletion of the glyoxylate-shunt gene
aceA (encoding a latent reaction enzyme, isocitrate lyase) on the growth characteristics of the mutant
E. coli Dpgi. The deletion of aceA reduced the growth rate of E. coli Dpgi, indicating that the activation
of the glyoxylate shunt plays an important role in adaptation of the mutant E. coli Dpgi when no other
latent reactions are concurrently inactivated. We also investigated the effect of the order of the gene
deletions on the growth rates and substrate uptake rates of the double-gene deletion mutants. The results
indicate that the order in which genes are deleted determines the phenotype of the mutants during the
sub-optimal growth phase. To elucidate the mechanism behind the difference between the observed
phenotypes, we carried out transcriptomic analysis and constraint-based modeling of the mutants.
Transcriptomic analysis showed differential expression of the gene aceK (encoding the protein isocitrate
dehydrogenase kinase) involved in controlling the isocitrate flux through the TCA cycle and the glyoxylate
shunt. Higher acetate production in the E. coli DaceA1 Dpgi2 mutant was consistent with the increased
aceK expression, which limits the TCA cycle flux and causes acetate production via overflow metabolism.
Insight, innovation, integration
The current submission reports characterization of double-gene deletion mutants of Escherichia coli in the sub-optimal growth phase, and the impact of gene
deletion order on these mutants. We show that the order of gene deletions leads to different sub-optimal phenotypes, thus potentially affecting the outcome of
adaptive evolution. We use constraint-based modeling and RNAseq to investigate the mechanism involved in the differential phenotypes. RNAseq identifies a
differentially expressed gene with a potential role in the phenotypic differences. Modeling studies consolidate the RNAseq results by predicting feasible flux
distributions that may result from the proposed mechanism. This study demonstrates the integration of phenotypic characterization, metabolic modeling,
and transcriptomics to study a fundamental phenomenon that has implications in microbial genetics and applied fields such as metabolic engineering.
1 Introduction
Effects of deletion of non-lethal genes have been studied in
great detail in the model organism E. coli.1–4
Fewer studies,
however, have focused on systematic characterization of multiple-
gene knockout mutants (higher-order mutants) of E. coli.5,6
Characterization of higher-order mutants can uncover metabolic
phenomena such as synthetic lethality,7
synthetic rescues,8
and
conditional lethality,9
which cannot be observed in single-gene
a
Department of Chemical Engineering and Applied Chemistry,
University of Toronto, Toronto, ON, M5S 3E5, Canada.
E-mail: krishna.mahadevan@mail.utoronto.ca; Fax: +1 416 978 8605;
Tel: +1 416 946 0996
b
Department of Physics and Astronomy, Northwestern University, Evanston, IL,
60208, USA
† Electronic supplementary information (ESI) available. See DOI: 10.1039/c5ib00096c
‡ Present address: Advanced Centre for Biochemical Engineering, University
College London, Bernard Katz Building, Gordon St, London, WC1H 0AH, UK.
Received 5th April 2015,
Accepted 6th June 2015
DOI: 10.1039/c5ib00096c
www.rsc.org/ibiology
Integrative Biology
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knockout mutants. Simple phenotypic characterization of
higher-order mutants has led to the discovery of unknown
metabolic pathways in the central carbon metabolism of
E. coli.5
Systematic genetic perturbations and characterization
of higher-order mutants can help in the in-depth understanding
of metabolic responses, and possibly, roles of redundancies in
the metabolic networks. The data from high-throughput char-
acterization of such mutants can be integrated into genome-
scale metabolic models, leading to increased accuracy of their
predictions.
Given the size of the metabolic network of E. coli (the current
genome-scale model accounts for 2251 metabolic reactions and
1136 metabolites),10
it is extremely difficult to construct and
characterize its complete set of double-gene knockout mutants.
One of the approaches to study higher-order mutants is to
systematically design and characterize mutants from important
metabolic nodes. Important nodes in metabolism can be
identified by analyzing the organization of the metabolic network.
The topology of metabolic networks is well characterized, and
some metabolites are known to be highly connected compared to
others.11,12
Such highly connected metabolites are known as hub
metabolites, and these metabolites generally have important
physiological roles.11,12
One of the hub metabolites in the metabolic network of
E. coli is glucose-6-phosphate (G6P), which is the first branch-
ing point of the carbon flux between glycolysis (the Embden–
Meyerhof–Parnas pathway), the pentose phosphate pathway
(PPP), and the Entner–Doudoroff pathway. Additionally, the uptake
of glucose in E. coli, and hence the cellular concentration of
G6P, depends on the ratio of concentrations of pyruvate
and phosphoenolpyruvate (PEP), which are hub metabolites
themselves. Therefore, gene deletion mutants around the G6P
node show pronounced altered physiologies, and can provide
insights into the glucose metabolism of E. coli. One of the most
important genes in G6P metabolism is pgi which encodes for
phosphoglucose isomerase. The pgi knockout mutant of E. coli
has been intensively studied.2,13
We use higher-order mutants
of E. coli with pgi knockout to investigate two fundamental
questions: (1) can the growth rate of the E. coli Dpgi mutant be
improved by deleting an additional gene? (2) Does the order in
which genes are deleted in higher-order mutants affect the
growth phenotype of the final mutants constructed?
The first question concerns the phenomenon known as
synthetic recovery, in which the deletion of a gene improves
the growth rate of a pre-constructed gene deletion mutant.8,14
It is well known that the deletion of a major metabolic gene
in microorganisms is immediately followed by a sub-optimal
growth phase, where the growth rate of the mutant is lower
than that of the wild type.2,15
The sub-optimal growth rate of
the mutants can be gradually improved by adaptive evolution.16
Additionally, it is also known that during the sub-optimal
growth phase, the mutant activates a number of pathways
that are otherwise inactive during the normal growth phase.2
Activation of pathways during the sub-optimal growth phase
is considered to be a metabolic response that may help the
mutants to cope with the sudden loss of an important gene.
The Entner–Doudoroff pathway and the glyoxylate shunt are
known to be activated in response to deletion of pgi in E. coli
(Fig. 1).2
The reason behind activation of the glyoxylate shunt in
the E. coli Dpgi mutant is the excess NADPH production in the
mutant due to diversion of the flux through the PPP. By
activating the glyoxylate shunt, the mutant balances the excess
NADPH by reducing the flux through the NADPH-producing
reaction isocitrate dehydrogenase in the TCA cycle, and instead
diverting it through isocitrate lyase (encoded by aceA).2
Recently, modeling studies have shown that the activation
of latent reactions may not offer metabolic advantage to the
mutants growing in the sub-optimal growth phase. Rather,
concurrent deletion of multiple latent reactions was predicted to
improve the sub-optimal growth rates of the mutants.14
A logical
explanation for this observation could be that the deletion of
latent reactions eliminates non-optimal metabolic pathways,
thereby forcing the mutants to grow at higher (closer to optimal)
growth rates.14
Cornelius et al.14
define latent reactions as those
that are transiently active in the sub-optimal growth phase,
and are not active in the adaptively evolved growth phase. We
have adopted this definition of latent reactions throughout the
present study.
Due to the contradicting theories about the role of latent
reactions in the sub-optimal growth phase, their physiological
significance remains unclear. We investigated the role of the
glyoxylate shunt gene aceA, encoding isocitrate lyase (ICL), by
characterizing the double-gene knockout mutant E. coli Dpgi
DaceA. The gene aceA is known to be transiently activated in the
E. coli Dpgi mutant, and plays a role by balancing the increased
NADPH concentration.2
Additionally, the deletion of aceA was
computationally predicted by RELATCH (but not by MOMA) to
provide a growth advantage to the E. coli Dpgi mutant (Table 2).
To address the effect of the presence versus the absence of the
latent reaction ICL on the event of major gene ( pgi) loss, we
constructed and characterized two versions of this double-gene
knockout mutant, which differed only by the order in which the
genes were deleted. Finally, we used three constraint-based
modeling techniques to simulate the observed phenotypes of
the mutants and to gain insights into their physiology.
2 Materials and methods
2.1 Strains and plasmids
All the strains and plasmids used in this study were obtained
from Coli Genetic Stock Center (CGSC), Yale University. E. coli
K-12 MG1655 was used as the wild-type control strain and as
the starting strain to construct the gene knockout mutants. All
mutants were constructed using the sequential P1 transduction
method, by transferring the target gene deletion from the
respective E. coli BW25113 mutants from the KEIO collection.4
The double mutants were also distinguished by the order in
which the two genes were deleted. For example, E. coli Dpgi1
DaceA2 and E. coli DaceA1 Dpgi2 were two distinct mutants, where
the subscripts denote the order in which the genes were deleted.
The mutant E. coli Dpgi1 DaceA2 was constructed by first deleting
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the gene pgi followed by deletion of the gene aceA, and the
mutant E. coli DaceA1 Dpgi2 was constructed by deleting the
gene aceA first followed by the gene pgi. The mutants were
isolated on selection medium containing 25 mg mLÀ1
of kanamycin.
The kanamycin cassettes were removed sequentially after each
deletion using the rescue plasmid pCP20. None of the final
mutants tested had the kanamycin resistance gene. All the gene
deletions and the absence of the kanamycin cassette were
confirmed using PCR. Care was taken to avoid any significant
delay between consecutive gene knockouts, or extended cultiva-
tion of the intermediate mutants, thereby ensuring that the
mutants were in their unevolved state. To avoid any additional
random mutations, the mutants were stored at À80 1C immedi-
ately after construction.
2.2 Mutant characterization
All the mutants were characterized for growth and for sugar
utilization and product formation in aerobic shake-flask culti-
vations at 37 1C. The minimal medium described previously
was used as the growth medium.17
The medium contained per
litre: 3.5 g of KH2PO4, 5.0 g of K2HPO4, 3.5 g of (NH4)2HPO4,
0.25 g of MgSO4Á7H2O, 15 mg of CaCl2Á2H2O, 0.5 mg of
thiamine, and 1 mL of trace metal stock. The trace metal stock
was prepared in 0.1 M HCl and consisted of per litre: 1.6 g of
FeCl3, 0.2 g of CoCl2Á6H2O, 0.1 g of CuCl2, 0.2 g of ZnCl2Á4H2O,
0.2 g of NaMoO4, and 0.05 g of H3BO3. Glucose at a concen-
tration of 5 g LÀ1
was used as the only carbon source. Finally,
4-morpholinopropanesulfonic acid (MOPS) (0.1 M) was added
to control the pH.
Seed cultures were prepared by inoculating a fresh colony in
10 mL Luria-Bertani broth. The overnight grown culture was
used to inoculate 150 mL minimal medium in 500 mL baffled
Erlenmeyer flasks to get an initial OD550 of 0.1. Samples were
withdrawn every hour or every two hours, and were analyzed for
OD550, glucose, and acetate concentrations. Glucose and acetate
concentrations were measured using a Bio-Rad HPX-87H cation-
exchange column (5 mM H2SO4 mobile phase, 0.4 mL minÀ1
flow rate, 42 1C column temperature, 20 mL injection volume).
The batch was assumed to be complete when the OD550 reached
a constant value. All studies were carried out in three biological
replicates.
2.3 Constraint-based analysis
Three different constraint-based methods, FBA (Flux Balance
Analysis),18
MOMA (Minimization Of Metabolic Adjustment),15
and RELATCH (RELATive CHange),19
were used to predict the
growth rates and acetate secretion rates of the mutants. Experi-
mentally calculated glucose uptake rates were used to simulate
the glucose exchange flux for FBA. For MOMA and RELATCH,
the wild-type glucose uptake rate and the acetate secretion rate
were used as input for reference models. As FBA is not capable of
estimating the growth rate changes with sequential gene dele-
tions, MOMA and RELATCH were used to calculate the growth
rate changes with different orders of gene deletions. To avoid the
Fig. 1 Metabolic pathways of E. coli (a) under normal growth conditions and (b) after deletion of the gene pgi. The major flux during normal growth is
through the glycolysis pathway, with no flux through either the Entner–Doudoroff pathway or the glyoxylate shunt. Both of these pathways get activated
immediately after the deletion of pgi, and major flux diversion from glycolysis to the PPP takes place.2
The thickness of the arrows indicating fluxes is for
illustrative purpose only, and not to scale.
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problems with alternative optima, MOMA was implemented
using the reference FBA solution with minimum 1-norm. All the
codes were implemented in MATLAB (The Mathworks Inc.,
Natick, MA) using the COBRA Toolbox,20
and CPLEX 11.2 (IBM
ILOG) was used as the LP and QP solver.
2.4 Transcriptomic sample preparation and analysis
RNA was extracted from biological duplicate samples at the mid-
exponential growth phase (OD550 around 0.7). The withdrawn
samples were immediately cooled on ice and the cell pellet was
harvested by centrifugation at 4 1C, washed with cold water, and
the biomass was frozen in liquid nitrogen and stored at À80 1C
until further treatment. The total RNA was extracted from cells
using the RNeasy kit (Qiagen, Hilden, Germany). The quantity of
RNA was assayed using NanoDrop ND-1000 (Thermo Scientific,
Wilmington, MD). The quality of the RNA samples was assayed
using a BioAnalyzer (Agilent Technologies, Palo Alto, CA). One
microgram high-quality total RNA was used for cDNA library pre-
paration. First, the ribosomal RNA was removed using the Epi-
centre Ribo-Zero rRNA removal Kit (Epicentre, Madison, WI). The
enriched poly-A RNA was then used to generate the cDNA library
using the Illumina TruSeq RNA sample prep Kit v2 (Illumina,
San Diego, CA). The generated bar-coded cDNA library, with an
average fragment size of 350–400 bp, was quality checked using
a BioAnalyzer (Agilent Technologies, Palo Alto, CA), and quantified
with qPCR using KAPA SYBR FAST Universal 2Â qPCR Master Mix
(Kapa Biosystem, Wilmington, MD). The quality checked libraries
were then loaded on a flow cell for cluster generation using Illumina
c-Bot and TruSeq PE Cluster Kit v3 (Illumina, San Diego, CA).
Sequencing was done on HiSeq2000 with the TruSeq SBS Kit v3
(Illumina, San Diego, CA). The real-time base call (.bcl) files
were converted to fastq files using CASAVA 1.8.2 (on CentOS 6.0
data storage and computation linux servers).
The transcriptomic data were analyzed using Rockhopper
software for bacterial RNAseq analysis.21
In brief, gene transcript
abundance was determined as the total number of reads, normal-
ized using the upper quartile gene expression, after excluding the
genes with no mapped reads. Biological duplicates were analyzed
for all samples, except for the wild type, in which case only one repli-
cate was used. In this case, Rockhopper used surrogate replicates to
determine differential gene expression. P-values were computed for
differential expression of each gene, from which q-values were
computed using the Benjamini–Hochberg correction.22
3 Results
3.1 Mutant characterization
Table 1 lists the mutants characterized in this study, and Fig. 2
shows the growth profiles of the double mutants with different
orders of gene deletions. The gene pgi encodes the enzyme
Table 1 Growth characteristics of pgi and aceA mutants
Mutant m (hÀ
) qs (mmol per gDW per h) qa (mmol per gDW per h) Yx/s (gDW per mmol)
E. coli wild type 0.351 Æ 0.004a
8.91 Æ 1.80 3.78 Æ 0.90 0.043
E. coli Dpgi 0.159 Æ 0.001 3.89 Æ 0.24 0.00 Æ 0.00 0.047
E. coli Dpgi1 DaceA2 0.139 Æ 0.000 2.73 Æ 0.07 2.42 Æ 0.04 0.051
E. coli DaceA1 Dpgi2 0.128 Æ 0.004 2.42 Æ 0.19 3.16 Æ 0.51 0.053
m = growth rate; qs = glucose uptake rate; qa = acetate secretion rate; Yx/s = biomass yield. The growth rates, glucose uptake rates, and acetate
secretion rates were calculated for the exponential growth phase of the strains. a
The errors represent standard deviations between three
experimental results.
Fig. 2 Growth curves for the double mutants with different orders of gene deletions (a) E. coli Dpgi1 DaceA2 and (b) E. coli DaceA1 Dpgi2. The symbol K represents
biomass,E represents glucose concentration, and ’ represents acetate concentration. The error bars represent standard deviations for three replicates.
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phosphoglucose isomerase, which is the first enzyme of glyco-
lysis and converts G6P into fructose-6-phosphate. The E. coli
Dpgi mutant has been characterized in great detail for its
growth characteristics,16
metabolism,23
and genetic changes
over adaptive evolution.13
Major reported consequences of pgi
inactivation in E. coli are: (1) a severely decreased growth rate
due to the lowered glycolytic flux,2
(2) accumulation of NADPH
pools due to the increased flux through the PPP,23
(3) transient
activation of the Entner–Doudoroff pathway and the glyoxylate
shunt,2
(4) the absence of overflow metabolism resulting in no
acetate production,13
and (5) a slightly increased biomass yield
compared to the wild type.23
Growth characteristics and glucose consumption character-
istics of the wild-type E. coli and E. coli Dpgi are summarized in
Table 1. The characteristics of E. coli Dpgi were found to be
largely in agreement with those reported in the previous studies.
The growth rate of E. coli Dpgi was around 45% of the wild-type
growth rate and the biomass yield was found to be slightly
higher than the wild type (Table 1). The drop in the growth rate
of E. coli Dpgi has been reported to be as low as o20% of
the wild-type growth rate.13
The comparatively higher growth
rate observed in our study could be a result of the elaborate
medium composition. As anticipated, no acetate production
was detected in E. coli Dpgi, due to the absence of overflow
metabolism.2
Among the physiological changes in E. coli Dpgi mentioned
above, NADPH imbalance is a major consequence, perturbing a
significant portion of the metabolic network.13
The NADPH
imbalance is caused due to a high flux of glucose through the
PPP (which produces two moles of NADPH per mole of glucose)
in the absence of pgi, causing accumulation of NADPH. E. coli
counters the NADPH imbalance by accumulating mutations in
NADH/NADPH transhydrogenases encoded by the genes udhA
and pntAB,13
and activating the glyoxylate shunt.24
Activation of
the glyoxylate shunt alleviates the NADPH imbalance by divert-
ing the metabolic flux away from NADPH producing isocitrate
dehydrogenase.24
To investigate whether activation of the
glyoxylate shunt in the E. coli Dpgi mutant is a sub-optimal
metabolic response, we constructed a pair of mutants with
deletion of aceA and pgi in different orders (E. coli Dpgi1 DaceA2
and E. coli DaceA1 Dpgi2). By characterizing the growth rates and
substrate uptake rates of these mutants, we investigated the
effect of the glyoxylate-shunt on the sub-optimal growth phase
of E. coli Dpgi mutants. We hypothesized that if the glyoxylate
shunt was indeed a sub-optimal metabolic response to pgi knock-
out, the absence of the glyoxylate shunt would improve the
growth rate of the mutant E. coli Dpgi.
The properties of E. coli Dpgi and the double mutants
E. coli Dpgi1 DaceA2 and E. coli DaceA1 Dpgi2 are summarized
in Table 1. Growth profiles of the two double mutants are
shown in Fig. 2. The growth rates of all the double mutants were
found to be lower than E. coli Dpgi, suggesting that the glyoxylate
shunt plays an important role during the growth of E. coli Dpgi
immediately after the gene deletion. As the elimination of the
glyoxylate shunt reduced the growth rate of the E. coli Dpgi
mutant, the activation of the glyoxylate shunt may not be
considered as a sub-optimal response. However, of note is the
fact that deletion of latent pathways was shown to improve the
sub-optimal growth rates of the mutants (synthetic rescue) only
when the multiple predicted latent reactions were deleted.14
Thus, to investigate whether the elimination of latent pathways
can indeed improve the sub-optimal growth rates of the mutants,
a significant fraction (or even all) of the latent pathways of the
E. coli Dpgi mutant may have to be deleted. At this stage, our
study suggests that in the presence of additional latent pathways,
the glyoxylate shunt is clearly beneficial to the adaptation of the
mutant E. coli Dpgi. Or more generally, deletion of individual
latent pathways may not improve the sub-optimal growth rates of
single gene deletion mutants, although the deletion of multiple
latent pathways might.
3.2 Effects of the order of gene deletions
Characterization results clearly indicated that the order of gene
deletions affects the growth behaviour of the mutants in the
sub-optimal growth phase. Slight but consistent differences
were observed in the growth rates, glucose uptake rates, and
acetate secretion rates of the mutants with different orders of
gene deletions. As the metabolic network of both mutants was
exactly the same (both had the same genes deleted), the differ-
ences in the properties of the mutants were caused due to non-
stoichiometric differences. The most likely reason for E. coli Dpgi1
DaceA2 to have a higher growth rate compared to E. coli DaceA1
Dpgi2 was the difference in the glucose uptake rate. As the glucose
uptake rate in E. coli is controlled by the intracellular pyruvate
and PEP pools, changes in the concentration of these metabolites
most likely caused the differences in the phenotypes of E. coli
Dpgi1 DaceA2 and E. coli DaceA1 Dpgi2.
Different orders of gene deletions also caused differences in
acetate production rates. The mutant E. coli DaceA1 Dpgi2
accumulated more acetate than the mutant E. coli Dpgi1 DaceA2
(Fig. 2). The highest acetate titre for the mutant E. coli DaceA1
Dpgi2 was 1.4 g LÀ1
at 24 h, and the highest acetate titre for
the mutant E. coli Dpgi1 DaceA2 was 0.5 g LÀ1
at 21 h. The flux
of acetate secretion reactions in the two mutants was also
different with the mutant E. coli DaceA1 Dpgi2 showing 23%
higher acetate secretion rate than the mutant E. coli Dpgi1
DaceA2. Based on the characterization of the two mutants, it
was established that the mutant E. coli Dpgi DaceA could exist in
two distinct metabolic states during the sub-optimal growth
phase.
Implications of the order and timing of gene deletions in
multiple-gene knockout mutants have been previously suggested.25
We show that the order of gene deletions certainly affects the
sub-optimal phenotypes of the double-gene deletion mutants.
The sub-optimal growth phenotype of gene-deletion mutants
is not permanent, and the mutants can adaptively evolve to a
completely different phenotype.2
For example, the single-gene
deletion mutant E. coli Dpgi was shown to diverge into two
completely different phenotypes.2
As a single colony of mutant can
evolve into different phenotypes on adaptive evolution, double-
gene knockout mutants with different sub-optimal phenotypes
are likely to evolve into different final states. Thus, the order of
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gene deletions may play an important role in deciding the final
phenotypes of higher-order mutants.
3.3 Model simulations of the mutant phenotypes
FBA can be used to predict the maximum growth rates of the
gene knockout mutants at a given substrate uptake rate. To
compare the predicted growth rates with the experimentally
observed results, we set the glucose uptake rates as found in the
experiments. The growth rates predicted by FBA were much
higher than the experimentally observed growth rates for the
wild type and all the mutants (the results are summarized in
Table 2). As FBA predicts the maximum possible growth rate for
a given substrate uptake rate, it can be used to estimate the
growth rates of fully evolved strains if enzyme availability is not
a limiting factor.16
As the mutants characterized in our study
were not adaptively evolved, the growth rates did not match the
FBA-predicted growth rates. Similar to the predictions for the
mutants, FBA overestimated the growth rate of the wild-type
strain despite the fact that the wild type did not grow in the sub-
optimal growth phase. This difference between the predicted
and observed values was most likely due to the non-ideal growth
conditions under which the wild type was cultivated. All the
mutants in our study were cultivated in Erlenmeyer flasks with
limited aeration and non-stringent pH control. Additionally, FBA
did not capture acetate production due to overflow metabolism,
which caused overestimation of the growth rates. FBA cannot be
used to simulate the growth rate of the mutants with different
orders of gene deletions. Hence, the growth rates calculated by
FBA for the mutants E. coli Dpgi1 DaceA2 and E. coli DaceA1 Dpgi2
were exactly the same.
MOMA is a constraint-based method that can simulate
growth rates of gene deletion mutants in the sub-optimal growth
phase. As the objective function used for MOMA is not biomass-
maximization, rather, minimization of metabolic adjustment,15
MOMA calculates the growth rate change after a gene deletion
from a given reference state. MOMA can thus be used to simulate
sequential changes to the growth rate with each gene deletion,
and hence can conceptually distinguish between two mutants
with different orders of gene deletions. We first used MOMA to
find the flux distribution of E. coli Dpgi using the wild-type
steady-state flux as the reference. However, MOMA could not
predict the activation of either the Entner–Doudoroff pathway
or the glyoxylate shunt in response to pgi knockout. To check
whether MOMA could predict growth rate differences between
the two mutants E. coli Dpgi1 DaceA2 and E. coli DaceA1 Dpgi2, we
simulated the growth of E. coli Dpgi1 DaceA2 using E. coli Dpgi as
the reference flux (predicted by MOMA), and the growth of
E. coli DaceA1 Dpgi2 using E. coli DaceA as the reference flux
(predicted by MOMA). MOMA results obtained are shown in
Table 2. Though MOMA predicted a reduction in the growth
rate of the mutant E. coli Dpgi, it could not predict reduction in the
glucose uptake rate and the absence of acetate secretion in the
mutant. Additionally, MOMA could also not distinguish between
the mutants with different orders of gene deletions predicting
exactly the same growth rate, glucose uptake rate, and acetate
secretion rate for the two mutants. MOMA thus showed no
improvement over FBA predictions for the mutants in this study.
Finally, we used RELATCH (RELATive CHange)19
to predict
the differential phenotype of the double-gene deletion mutants.
RELATCH uses 13
C metabolic flux analysis (MFA) data and gene
expression data to predict the relative flux distribution change
due to genetic and environmental perturbations.19
Using the
wild-type MFA and gene expression data, first the flux distribu-
tion for E. coli Dpgi and E. coli DaceA mutants were calculated.
The wild-type 13
C MFA data were obtained from a previous
study.2
Though the wild type was cultured in a slightly different
medium for obtaining the 13
C MFA data than the minimal
medium used for strain characterization in this study, no large
differences were anticipated between the internal flux distribu-
tions of the wild type under these two media conditions. Using
this predicted flux distribution and the experimental gene-
expression data for the single-gene knockout mutants, the
phenotype of the double-gene knockout mutants was predicted.
The E. coli Dpgi flux was used as the reference for simulating
the E. coli Dpgi1 DaceA2 and E. coli DaceA was used as a reference
for simulating the E. coli DaceA1 Dpgi2 phenotype. For all the
simulations the parameter values chosen were a = 10 and g = 1.1,
which give the predictions for the unevolved (sub-optimal) state of
the mutants. A summary of the results obtained from RELATCH is
provided in Table 2. RELATCH has been shown to predict the sub-
optimal growth state of single knockout mutants, including pgi,
with much higher accuracy than other constraint-based methods
such as FBA and MOMA.19
In agreement with the previous observa-
tions, we found that RELATCH predicted the phenotype of the
E. coli Dpgi mutant with high accuracy, including reduction in the
growth rate and the glucose uptake rate, and no acetate secretion.
RELATCH was finally used to test whether differential pheno-
types of the double mutants could be explained using differences
in the flux distributions of the mutants. As shown in Table 2,
RELATCH predicted different phenotypes of the double-gene knock-
out mutants. However, these phenotypes were not in agreement
with the experimentally observed phenotypes. Two observations that
Table 2 FBA and MOMA simulations for the higher-order mutants
Mutant
qs (mmol per gDW per h) m (hÀ1
) qa (mmol per gDW per h)
Exp. MOMA RELATCH FBAa
MOMA RELATCH FBA MOMA RELATCH
E. coli wild type 8.91 8.91 8.56 0.82 n.a. 0.35 0 n.a. 4.48
E. coli Dpgi 3.89 8.84 2.35 0.34 0.65 0.18 0 3.78 0
E. coli Dpgi1 DaceA2 2.73 8.85 2.56 0.23 0.65 0.20 0 3.78 0
E. coli DaceA1 Dpgi2 2.42 8.84 1.11 0.20 0.65 0.00 0 3.78 0
a
Growth rates predicted by FBA were based on the experimentally observed glucose uptake rates.
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could not be predicted by RELATCH were: (1) onset of acetate
production by the mutant E. coli Dpgi1 DaceA2 after deletion of
aceA, and (2) non-zero sub-optimal growth of the mutant E. coli
DaceA1 Dpgi2.
By adding constraints based on experiments to the perturbed
metabolic network, RELATCH was used to predict the differential
flux distribution that could explain the differences in the acetate
production of the two double-gene deletion mutants. Multiple
different flux distributions were identified that satisfied the
observed phenotypes of the mutants. However, most flux distri-
butions thus predicted were identified as non-plausible given the
limited capacity of the certain pathways involved in the flux
distributions. For example, RELATCH predicted acetate produc-
tion in double mutants using the pyrimidine deoxyribonucleo-
side utilization pathway enzyme, deoxyribose-phosphate aldolase
(DRPA, EC number 4.1.2.4), which degrades deoxyribose
5-phosphate into glyceraldehyde 3-phosphate and acetaldehyde.
Acetaldehyde was converted into acetate using acetaldehyde
dehydrogenase enzymes. This pathway though metabolically
feasible could not have supported the high flux of acetate
formation observed in the double mutants, as no significant
changes were observed in the expression levels of the genes
encoding the corresponding enzymes (deoC for DRPA and aldB,
adhE, mhpF for aldehyde dehydrogenases) between the two
mutants and the wild type (File RNASeqdata.xlsx, ESI†).
By adding constraints to minimize the contribution of the
peripheral reactions towards acetate production, flux distributions
were identified that agreed with the experimentally observed
secretion rates of acetate. The experimentally observed acetate
secretion rates and growth rates of the mutants could be achieved
by redistribution of fluxes through the acetate metabolism enzyme
pyruvate oxidase (POX encoded by poxB), phosphate acetyl-
transferase (PTAr encoded by pta), and acetyl-CoA synthetase
(ACS encoded by acsA) (Fig. 3). Additionally, fluxes through
Fig. 3 One plausible flux distribution obtained from RELATCH for the mutants (a) E. coli Dpgi, (b) E. coli Dpgi1 DaceA2, (c) E. coli DaceA, and (d) E. coli
DaceA1 Dpgi2. The flux values are indicated beside the respective reactions and expressed in mmol gÀ1
dry cell weight per h. The nomenclature used for
the reactions is from the genome-scale model iAF1260.26
The arrows represent the directions of the fluxes rather than the reversibility of the reactions.
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acetyl-CoA metabolism enzymes were also predicted to be
different between the two mutants. There are 34 different
reactions involving cytoplasmic acetyl-CoA in the model
iAF1260.26
RELATCH predicted multiple different flux distri-
butions among the acetate and acetyl-CoA reactions that could
explain the experimentally observed phenotypes. It was, however,
not possible to identify which flux distributions represented
the actual flux distributions in the two mutants. One plausible
flux distribution for each strain predicted by RELATCH is
shown in Fig. 3. To obtain this flux distribution, addi-
tional constraints were added to RELATCH by blocking the
following reactions: XYLI2 (xylose isomerase catalyzing the
conversion of glucose to fructose), LDH_D (D-lactate dehyro-
genase), DRPA (deoxyribose phosphate aldolase), PFL (pyruvate
formate lyase), GLCDpp (periplasmic glucose dehydrogenase
with ubiquinone-8 as acceptor), and ACt4pp (sodium acetate
symport periplasm).
Although it is very unlikely that the mutants could have used
peripheral pathways such as the pyrimidine deoxyribonucleo-
side utilization pathway for acetate production at the high rates
observed experimentally, we believe that transcript levels alone
cannot be used to draw the conclusion that uncommon path-
ways were not used for acetate production. However, based on
the RELATCH simulations and experimentally observed acetate
secretion rates, these phenotypes were most likely achieved by
reorganization of the fluxes through a few central metabolism
reactions. It is well known that the mutants reorganize the flux
distribution to cope with a loss of a major function,2
however,
we show that reorganization of fluxes in double-gene deletion
mutants also depends on the order of gene deletions. In other
words, the phenotypes of double-gene deletion mutants in the
sub-optimal growth phase are determined by the trajectory of
the genetic changes.
Based on the results obtained from FBA, MOMA, and
RELATCH, we found that the phenotypic differences between
the double gene deletion mutants could not be very well
predicted by the computational methods used. Even though
RELATCH was able to predict the rescue of the E. coli Dpgi
mutant upon deletion of aceA, it could not predict the growth
phenotype of the E. coli DaceA1 Dpgi2 mutant. RELATCH’s better
predictive power was due to the incorporation of the MFA and
gene expression data. Model predictions, stoichiometry and
gene expression data alone could not explain the observed
differences in the phenotypes of the double mutants; these
differences could have been caused by metabolite concentration
changes affecting the kinetics of the metabolic enzymes. Models
incorporating metabolite concentrations and enzyme kinetics
may therefore be able to predict the differential phenotypes of
the mutants more accurately.
3.4 Transcriptome analysis
To identify the role of gene expression levels, if any, in the
phenotypic differences of the mutants, we carried out trans-
criptome analysis.
RNAseq was used to quantify the transcriptome of five
strains: the wild type, E. coli DaceA, E. coli Dpgi, E. coli DaceA1
Dpgi2, and E. coli Dpgi1 DaceA2. The raw data for gene expression
and the list of differentially expressed genes with q o 0.05 are
provided in the ESI† (RawData.xlsx). Few metabolic enzymes
were shown to have significant differential expression between
any two strains; however, some interesting differences in trans-
cript abundance were observed.
There was no observable difference in the gene expression
level for the glycolytic and the TCA cycle transcripts between
E. coli DaceA1 Dpgi2 and E. coli Dpgi1 DaceA2 strains, despite
increased acetate production in the E. coli DaceA1 Dpgi2 strain.
However, the gene aceK was shown to have 40-fold increased
expression in the E. coli DaceA1 Dpgi2 strain as compared to the
E. coli Dpgi1 DaceA2 strain (Fig. 4). The gene aceK is a member of
the aceBAK operon. The protein AceK is responsible for parti-
tioning the isocitrate flux between the TCA cycle and the
glyoxylate shunt by changing the phosphorylation state of the
enzyme isocitrate dehydrogenase (IcdH).27
It has been found that AceK can act as a kinase, inactivating
IcdH and promoting flux towards the glyoxylate shunt, and also
as a phosphatase, activating IcdH and promoting flux towards
the TCA cycle.27
It has been proposed that the partition between
AceK kinase and phosphatase activities is dependent on AMP
levels in the cell, with AMP allosterically inhibiting kinase
activity.28
Fig. 4 Gene expression changes among five analyzed strains: E. coli wild type, E. coli Dpgi, E. coli DaceA, E. coli Dpgi1 DaceA2, and E. coli DaceA1 Dpgi2.
Each panel compares the expression level for a gene across all five strains. The names of the corresponding genes are indicated at the top of the panels.
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A previous study has shown that following the deletion of
pgi, AMP and ADP levels decrease to 89% and 48% of the wild-
type concentration, respectively.3
Since the effect of AMP on
AceK activity has not been quantitatively characterized, it is
difficult to specifically determine the behaviour of AceK as a
repressor or an activator of IcdH, and it is unclear what other
system level effects may determine AceK function. Due to the
significantly decreased levels of AMP and ADP, previous studies
would indicate that AceK maintains kinase activity and acts as
an inhibitor of IcdH. In this case, AceK should further inhibit
IcdH and promote flux to the glyoxylate shunt. However, since
the first step of the glyoxylate shunt has been deleted, there is a
metabolic bottleneck due to all key isocitrate consuming
enzymes being either deleted or repressed. Therefore, increased
acetate levels can likely be attributed to increased acetyl-
CoA levels caused by elevated AceK levels in the E. coli Dpgi1
DaceA2 strain.
4 Conclusions
This study focused on the characterization of the effect of the
glyoxylate shunt gene aceA on the sub-optimal growth phase
of the mutant E. coli Dpgi. Two phenomena were investigated
in the E. coli mutants of pgi and aceA: (1) the possibility of
synthetic recovery of the mutant E. coli Dpgi by aceA deletion
and (2) effects of the order of gene deletion on the double-gene
deletion mutants E. coli DaceA1 Dpgi2 and E. coli Dpgi1 DaceA2.
First, we observed that the deletion of genes from the glyoxylate
shunt could not recover the reduced growth rate in the E. coli
Dpgi mutant, suggesting that individual latent reactions may be
important for the sub-optimal growth phase of the E. coli Dpgi
mutant when the other latent reactions are left active. Second,
we found differences in the physiologies of the higher-order
mutants with different orders of gene deletions, suggesting that
the higher order mutants are not agnostic to the order in which
their genes are deleted. The differences in the phenotypes
could not be explained using metabolic models alone, suggest-
ing a role of regulation and/or metabolite concentrations in
determining the physiology of the higher-order mutants. Based
on the transcriptomic results the main factor for different acetate
production rates was most likely the difference in the expression
level of the gene aceK.
Acknowledgements
We would like to thank the SAMSUNG Global Outreach
Research Program and NSERC Bioconversion Network for
sponsoring the research. We also acknowledge support from
the National Institute of General Medical Sciences Grant
R01GM113238. We would also like to thank Dr Chi-Yip Ho
from the Lunenfeld-Tanenbaum Research Institute, Mount
Sinai Hospital, and Shyamsundar Srinivasan from the Univer-
sity of Toronto for their help in transcriptome analysis, and
Jonas Mu¨ller from the University of Toronto for his help with
experiments.
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