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RES E A R C H Open Access
Metabolic engineering of a tyrosine-
overproducing yeast platform using targeted
metabolomics
Nicholas D. Gold
1†
, Christopher M. Gowen
2†
, Francois-Xavier Lussier
1
, Sarat C. Cautha
2
, Radhakrishnan Mahadevan
2,3
and Vincent J. J. Martin
1*
Abstract
Background: L-tyrosine is a common precursor for a wide range of valuable secondary metabolites, including
benzylisoquin oline alkalo ids (BIAs) and many polyketide s. An industrial ly tractable yeast strain optimi zed for
production of L-tyrosine could serve as a platform for the developmen t of BIA and polyketide cell fac tories. This
study applied a targeted metabolomics approach to evaluate metabolic engineering strategies to increase the
availability of intracellular L-tyrosine in the yeast Saccharomyces cerevisiae CEN.PK. Our engineering strategies
combined localized pathway engineering with global engineering of central metabolism, facilitated by genome-scale
steady-state modelling.
Results: Addition of a tyrosine feedback resistant version of 3-deoxy-D-arabino-heptulosonate-7-phosphate synthase
Aro4 from S. cerevisiae was combined with over expressio n of either a tyrosine feedback resistant yeast chorismate
mutase Aro7, the native pentafunctional arom protein Aro1, native prephenate dehydrogenase Tyr1 or cyclohexadienyl
dehydrogenase TyrC from Zymomonas mobilis. Loss of aromatic car bon was limited by eliminating phenylpyr uvate
decarboxylase Aro10. The TAL gene from Rhodobacter sphaeroides was used to produce coumarate as a simple
test case of a heterologous by-product of tyrosine. Additionally, multiple strategies for engineering global metabolism to
promote tyrosine production were evaluated using metabolic modelling. The T21E mutant of pyruvate kinase Cdc19 was
hypothesized to slow the conversion of phosphoenolpyruvate to pyruvate and accumulate the former as precursor to
the shikimate pathway. The ZWF1 gene coding for glucose-6-phosphate dehydrog enase was deleted to create
an NADPH deficiency designed to force the cell to couple its growt h to tyrosine product ion via overe xpresse d
NADP
+
-dependent prephenate dehydrogenase Tyr1. Our engineer ed Zwf1
−
strain expressing TYRC ARO4
FBR
and
grown in the presence of methionine achieved an intracellular L-tyrosine accumulation u p to 520 μmol/g DCW
or 192 mM in the cytosol, but sustained flux through this pathway was found to depend on the complete elimination
of feedback inhibition and degradation pathways.
Conclusions: Our targeted metabolomics approach confirmed a likely regulatory site at DAHP synthase and identified
another possible cofactor limitation at prephenate dehydrogenase. Additionally, the genome-scale metabolic model
identified design strategies that have the potential to improve availability of erythrose 4-phosphate for DAHP synthase
and cofactor availability for prephenate dehydrogenase. We evaluated these strategies and provide recommendations
for further improvement of aromatic amino acid biosynthesis in S. cerevisiae.
Keywords: L-tyrosine, Saccharomyces cerevisiae, Metabolic engineering, Targeted metabolomics, Glucose-6-phosphate
dehydrogenase, Pyruvate kinase, Prephenate dehydrogenase, Cyclohexadienyl dehydrogenase, Phenylpyruvate
decarboxylase, Aromatic amino acids
* Correspondence: vincent.martin@concordia.ca
†
Equal contributors
1
Department of Biology and Centre for Structural and Functional Genomics,
Concordia University, 7141 Sherbrooke West, Montreal, QC H4B 1R6, Canada
Full list of author information is available at the end of the article
© 2015 Gold et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution License
(http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://
creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Gold et al. Microbial Cell Factories (2015) 14:73
DOI 10.1186/s12934-015-0252-2
Background
Saccharomyces cerevisiae is the host of choice for produc-
tion of high value plant-specific secondary metabolites
with numerous pharmaceutical, industrial, and nutritional
applications [1–5]. Commercialization of this technology
depends on significant improvements in product yield,
titre, and productivity. Although targeted optimization of
heterologous pathways will be vital to maximize product
yields, a more general strategy with some promise is to
develop platform strains that are engineered for the
production of common plant metabolite pre cursors.
ThearomaticaminoacidL-tyrosineisakeyprecursor
in the biosynthesis of both polyketides and benzyliso-
quinoline alkaloids [6], making it a useful target for
metabolic engineering in yeast.
Synthesis of aromatic amino acids in yeast proceeds
via the shikimate pathway, which consists of seven enzym-
atic reactions leading to the generation of chorismate, the
common precursor to all three aromatic amino acids
(Fig. 1) [7]. The first committed step of the shikimate
pathway is the condensation of phosphoenolpyruvate
(PEP) and erythrose 4-phosphate (E4P) to form 3-deoxy-
D-arabino-heptulosonate-7-phosphate (DAHP). In yeast
this reaction is catalyzed by one of two DAHP synthase
Fig. 1 Aromatic amino acid biosynthesis and degradation pathways in S. cerevisiae. The native aromatic amino acid biosynthesis and degradation
pathways are indicated with solid black arrows. Overexpression of the non-native or engineered enzymes is indicated using blue font, including
tyrosine ammonia lyase (TAL) from R. sphaeroides [19] and the NAD
+
-dependent prephenate dehydrogenase (TyrC) from Z. mobilis, the feedback-resistant
DAHP synthase Aro4
K229L
, and the feedback-resistant chorismate mutase Aro7
G141S
. Native genes that are overex pressed in this study are shown using a
green font, while knockout of the first step in the aromatic amino acid degradation pathway, Aro10, is indicated by a ‘prohibited’ symbol. Dotted lines
indicate allosteric inhibition by phenylalanine of Aro3 and by tyrosine of Aro4 and Aro7. Boxed metabolites were measured in this study. Metabolite
abbreviations: PEP, phosphoenolpyruvate; E4P, erythrose-4-phosphate; DAHP, 3-deoxy-D-arabinoheptulosonate-7-phosphate; DHQ, 3-dehydroquinate;
DHS, dehydroshikimate; SHIK, shikimate; S3P, shikimate-3-phosphate; EPSP, 5-enolpyruvyl-shikimate-3-phosphate; CHOR, chorismate; ANTH, anthranilate;
TRP, L-tryptophan; IPY, indole pyruvate; IAA, indole acetaldehyde; IAC, indole acetate; TRP-OL, tryptophol; PREPH, prephenate; PPY, phenylpyruvate; PHE,
L-phenylalanine; PAA, phenylacetaldehyde; PAC, phenylacetate; PHE-OL, phenylethanol; TYR, L-tyrosine; COU, coumarate; 4HPP, 4-hydroxyphenylpyruvate;
4HPAA, 4-hydroxyphenylacetaldehyde; 4HPAC, 4-hydroxyphenylacetate; TYR-OL, tyrosol
Gold et al. Microbial Cell Factories (2015) 14:73 Page 2 of 16
(EC 2.5.1.54) isozymes, Aro3 and Aro4, which are alloste-
rically inhibited by phenylalanine and tyrosine, respectively
[8]. DAHP is then consumed by Aro1, a pentafunctional
enzyme that catalyzes five reactions including shikimate
synthesis from dehydroshikimate (DHS) [9]. The last con-
version step to chorismate is carried out by chorismate
synthase Aro2 (EC 4.2.3.5). Carbon is diverted away from
the tryptophan biosynthesis branch by the activity of chor-
ismate mutase Aro7 (EC 5.4.99.5), which catalyzes the
conversion of chorismate to prephenate, the last precursor
common to both phenylalanine and tyrosine. Aro7 is allo-
sterically inhibited by tyrosine and activated by tryptophan
[10]. Prephenate dehydrogenase Ty r1 (EC 1.3.1 .1 2) cat-
alyzes the conversion of prephenate to the α-keto acid
4-hydroxyphenylpyruvate (4HPP). Finally, both Aro8
(EC 2.6.1.57) and Aro9 (EC 2.6.1.58) can reve rsibly
transaminate 4HPP to L-tyrosine.
Previous approaches to engineering aromatic amino
acid overproduction in yeast have generally focused on
both deregulation of the key checkpoints in tyrosine bio-
synthesis and the removal of degradation pathways [11].
The essential regulatory modification is the removal of
feedback inhibition of DAHP synthase. In previous work,
knocking out ARO3 and ARO4 and overexpressing
feedback-resistant mutants of ARO4 and ARO7 resulted
in a 5.5-fold increase in intracellular tyrosine and a 200-
fold increase in extracellular aromatic compounds relative
to a reference strain in chemostat growth, corresponding
to a 4.5-fold flux increase through the aromatic amino
acid biosynthesis pathway [11]. Additionally, while many
prephenate dehydrogenases are allosterically inhibited by
tyrosine or 4HPP [12],TYR1 is only known to be regulated
transcriptionally by phenylalanine [13], although TYR1 has
not been previously targeted for metabolic engineering
purposes.
Tyrosine degradation proceeds via the Ehrlich path-
way, in which 4HPP is decarboxylated by phenylpyruvate
decarboxylase Aro10 or by pyruvate decarboxylases [14]
(Fig. 1). The resulting aldehyde can then either be oxi-
dized to 4-hydroxyphenylethanol (tyrosol) or reduced to
4-hydroxyphenylacetate (4HPAC). Koopman et al.
showed reduced loss of tyrosine to Ehrlich pathway by-
product formation by eliminating Aro10, as well as pyru-
vate decarboxylases Pdc5 and Pdc6 [15].
This study systematically combines these localized
pathway engineering approaches with global engineering
of central metabolism, facilitated by steady-state modelling.
A genome-scale model of yeast metabolism, iMM904 [16]
was use d in the steady-state strain design algori thms, Opt-
Knock [17] and GDLS [18] to identify genes to overexpress
or delete to enhance the tyrosine yield of S. cerevisiae.A
targeted metabolomics approach was employed to query
the effects of each of the genetic variations applied. In all,
nineteen metabolites from glucose to coumarate, via the
aromatic amino acid production pathway, were monitored
over time. The contribution of tyrosine pools toward
potential downstream use was e valuate d by catalyzing
the c onversion of tyrosine to coumarate using tyrosine
ammonia lyase ( TAL; EC 4.3.1.23) from Rhodobacter
sphaeroides [19]. This enzyme represent s the first step
in the production of many polyketides , inc luding narin-
genin and the prenylated flavonoid xanthohumol from
hops (Humulus lupus) [19].
Results
In this study, a targeted metabolomics approach was
employed to systematically examine the impacts of multiple
metabolic engineering strategies for the production of
tyrosine in S. cerevisiae. Specific concentrations of several
components of the aromatic amino acid biosynthesis path-
way over the fermentation time course are shown in Fig. 2,
and additional metabolite concentrations are included
as additional information (Additional file 1: Figure S1,
Additional file 2: Figure S2, Additional file 3: Figure S3,
Additional file 4: Figure S4, Additional file 5: Figure S5,
Additional file 6: Figure S6). These conce ntration data
were also used to estimate the change s in Gibbs free
energy of reaction across the pathway (Additional f ile
7: Figure S7). A minimally engineered reference strain
(TY920; Table 1 and Additional file 8: Table S1) harboured
the following modifications compared to wild-type: (i) the
committed first step into the shikimate pathway was
deregulated by overexpression of the K229L tyrosine
feedback-resistant mutant ARO4
FBR
allele in an ARO3
ARO4 haploid background; (ii) the ARO10 gene was de-
leted to reduce the loss of aromatic amino acid carbon to
the Ehrlich pathway; and (iii) the TAL gene from R.
sphaeroides was overexpressed to enable transformation
of tyrosine into coumarate as a test of the strain’scapacity
to host heterologous pathways deriving from tyrosine.
This strain represents a basic set of modifications found to
be important to tyrosine or phenylalanine production by
previous studies [15].
Aromatic amino acid pathway engineering
Relative to the control strain TY757, overexpression of
ARO4
FBR
in TY920 improved the coumarate yield by
more than two-fold (Fig. 2) and total specific carbon
measured downstream of the condensation of PEP and
E4P by greater than six-fold (Fig. 3) without disturbing
growth or overflow metabolism (Tables 2 and 3). This
represents a carbon increase of about 0.5 mmol/g DCW
to the aromatic amino acid pathway. Levels of PEP and
E4P did not change, but every metabolite measured
downstream of DHS inclusively showed an increase
(Fig. 2 and Additional file 1: Figure S1-A). Thus, as dem-
onstrated previously [11, 15], adding a deregu lated
Gold et al. Microbial Cell Factories (2015) 14:73 Page 3 of 16
DAHP synthase had a clear carbon benefit to the shi-
kimate and aromatic amino acid pathways.
To improve on the output of strain TY920 we tested
the relative impact of three separate strategies, each one
overexpressing a second gene in addition to ARO4
FBR
in
the Aro10
−
host (Table 1): the native ARO1 gene in
strain TY985 to increase pull on the product of
Aro4
K229L
; the G141S tyrosine feedback-resistant mutant
chorismate mutase ARO7
FBR
allele in strain TY952 to
deregulate control over the branch point between
tryptophan and tyrosine/phenylalanine [11]; and either
the native prephena te dehydrogenase TYR1 in strain
TY1018 or cyclohexadienyl dehydrogenase TYRC (EC
1.3.1.79) from the bacterium Zymomonas mobilis, which
is known to be feedback-insensitive to tyrosine [12], in
strain TY954 to increase the conversion of prephenate
to 4HPP (Additional file 1: Figure S1-B to D). Maximum
specific growth rate, growth yield, and glucose uptake
rate were not affected by overexpression of ARO1,
ARO7
FBR
, TYR1 or TYRC in addition to ARO4
FBR
Fig. 2 Targeted metabolite analysis of S. cerevisiae strains used in this study. Complete phenotypic descriptions of the strains are given in Table 1.
Metabolite levels are shown in specific concentrations per g DCW. Low and high ends of concentration ranges per metabolite represented by
white and black, respectively. Abbreviations: PEP, phosphoenolpyruvate; E4P, erythrose 4-phosphate; DHS, dehydroshikimate; PPH/PPY, prephenate/
phenylpyruvate measured as a mixed peak by HPLC-PDA; 4HPP, 4-hydroxyphenylpyruvate; 4HPAA, 4-hydroxyphenylacetaldehyde; 4HPAC,
4-hydroxyphenylac etate. I ntra an d extra denot e intra-ce llular and extra- cellular metabolit es, respectively
Gold et al. Microbial Cell Factories (2015) 14:73 Page 4 of 16
(Table 2). Interestingly, in all strains, ΔG
γ
of the reac-
tions DAHP synthase catalyzed by Aro4 and 3DHQ
synthase catalyzed by the first step of Aro1 (Additional
file 7: Figure S7) was estimated to be greatest in magni-
tude. Although in this estimation, the concentrations
of DAHP and 3DHQ were not measured and assumed
to be 1 m M, this findin g is consistent with the hypoth-
esis that these reactions are maintained furthest from
equilibrium and are therefore most likely to be actively
regulated [20].
In order to evaluate the capacity of the engineered
strains for industrial polyketide or alkaloid production,
we overexpressed the TAL gene in all strains [19]. In the
absence of TAL, coumarate was never detected. While S.
cerevisiae W303 is known to consume coumarate via the
activity of phenylacrylic acid decarboxylase Pad1, which
catalyzes its conversion to 4-vinylphenol [21, 22],
CEN.PK cultures spiked with coumarate did not show
reduction in the initial coumarate concentration over a
period of 48 h (data not shown). Overexpression of
ARO1, ARO7
FBR
, TYR1 or TYRC in addit ion to ARO4
FBR
were all effective at increasing coumarate yields, up to
ten-fold relative to TY757 and four-fold relative to
TY920 (Fig. 2 and Additional file 1 : Figure S1). How-
ever, with final (96 h) titers averaging 120 μMforall
four strains, no statistically significant difference was
obser ved between these four strategies on coumarate
production.
In strain TY985 overexpressing ARO1, a decrease in
DHS concentration and increases in shikimate and chor-
ismate concentrations relative to TY920 are consistent
with the expected increase in Aro1 activity. However,
strain TY985 did not produce more total specific carbon
downstream of DHS inclusively, and after the end of log
phase (24 h) total specific carbon was down by about
Table 1 Saccharomyces cerevisiae strains tested in this study
Strain name Phenotype of host Genes added on plasmids
TY757 Aro10
−
Zwf1
+
Cdc19
+
TAL
TY920 Aro10
−
Zwf1
+
Cdc19
+
TAL ARO4
FBR
TY985 Aro10
−
Zwf1
+
Cdc19
+
TAL ARO4
FBR
ARO1
TY952 Aro10
−
Zwf1
+
Cdc19
+
TAL ARO4
FBR
ARO7
FBR
TY954 Aro10
−
Zwf1
+
Cdc19
+
TAL ARO4
FBR
TYRC
TY1018 Aro10
−
Zwf1
+
Cdc19
+
TAL ARO4
FBR
TYR1
TY1041 Aro10
−
Zwf1
−
Cdc19
+
TAL ARO4
FBR
TYRC
TY1040 Aro10
−
Zwf1
−
Cdc19
+
TAL ARO4
FBR
TYR1
TY1031 Aro10
−
Zwf1
−
Cdc19
low
TAL ARO4
FBR
TYRC
TY1032 Aro10
−
Zwf1
−
Cdc19
low
TAL ARO4
FBR
TYR1
Fig. 3 Total specific carbon measured for metabolites affected by engineering of localized pathway and global metabolism. Estimated using a
single 2-parameter exponential rise to maximum curve fit in SigmaPlot11.0 and values taken at 48 h. Referring always to strain TY920, strains
TY757 and TY985 were evaluated downstream of DHS inclusively, strain TY952 downstream of prephenate/phenylpyruvate inclusively, and TYR1
and TYRC strains, grown with or without methionine, downstream of 4HPP inclusively
Gold et al. Microbial Cell Factories (2015) 14:73 Page 5 of 16
29 % when compared to TY920 (Fig. 3). Most of this
reduced carbon was due to low levels of tyrosol and
tyrosine (Additional file 1: Figure S1-B), which points
to possible down-regulation of the expression of Ehrlich
pathway machinery in TY985 [14]. Also, initial increases
in the concentrations of shikimate, chorismate, prephenate
and also 4HPP were observed at 12 h in strain TY985
when compared with TY920. This initial spike in concen-
tration was then moderated, possibly pointing to a robust
regulation of intracellular metabolite concentrations.
Overexpression of ARO7
FBR
(TY952) was effective at
increasing the prephenate pool and depleting the choris-
mate pool as expected (Fig. 2 and Additional file 1: Figure
S1-C). No significant change was observed in the profiles
of 4HPP, tyrosine or tyrosol in TY952 when compared
with TY920 (Additional file 1: Figure S1-C).
TYR1 and TYRC overexpression had generally the
same effect even though they were cloned under different
constitutive promoters (TDH3prom-TYRC, TEF1prom-
TYR1; Additional file 8: Table S1). Both contributed to
overall higher levels of 4HPP (Fig. 2 and Additional file 1:
Figure S1-D) when compared to strain TY920. The in-
creases in 4HPP were not reflected in tyrosine, but a
three-fold improvement in coumarate production was
seen by 48 h. The extra pull on prephenate was reflected
in a net decrease in phenylalanine measured, but trypto-
phan concentrations did not change.
Engineering the host core metabolism for improved
precursor and cofactor pools
Model-guided platform strain design
In addition to localized changes affecting enzyme activity
and regulation of the aromatic amino acid biosynthesis
pathway, global changes to yeast core metabolism could
be beneficial by improving availability of the precursors
PEP and E4P, reducing carbon waste to compe ting by-
products, and shifting cofactor pools to favour product
biosynthesis. To this end, the iMM904 model of yeast
metabolism [16] was used along with strain design algo-
rithms targeting tyrosine overproduction from glucose
with the reasoning that manipulations to core metabolism
that result in improved tyrosine production could be
equally valuable for many products deriving from tyrosine
or other aromatic amino acids. For central metabolites like
tyrosine, network complexity and redundancy frequently
require several concurrent knockouts in order to obtain a
growth-coupled design. Because it uses a global search,
OptKnock [17] is computationally intensive and therefore
limited to searching a relatively small number of simultan-
eous knockouts in genome-scale models. OptKnock was
not able to find a growth-coupled solution for tyrosine
after searching up to four knockout s, therefore the
Genetic Design b y Local Search (GDLS) [18] algorithm
was used to expand the search for higher numbers of
knockouts. GDL S by definition doe s not n e cessarily
find a global optimum, so its solutions are dependent on
the quality of initial conditions provided to the algorithm.
When no initial conditions are provided, GDLS begins its
search from the wild-type model, i.e. with all reactions in
place. Under these conditions, GDLS was unable to find a
tyrosine producing design when searching up to ten reac-
tion knockouts. To address this challenge, GDLS was first
run with chorismate production as the target, and the
resulting solution was used as an initial condition for a
second iteration of GDLS with tyrosine as the target. This
two-step approach resulted in a growth-coupled strain de-
sign that produces tyrosine at up to 60 % of the theoretical
yield (Additional file 2: Figure S2).
Table 2 Growth characteristics and glucose uptake
Strain YNB +
Gluc
μ
MAX
Y
X/S
Glucose uptake
(g DCW/g glucose) (mmol/g DCW/h)
TY757 0.168 ± 0.021 0.0041 ± 0.0003 94.2 ± 14.0
TY920 0.162 ± 0.035 0.0040 ± 0.0004 88.9 ± 38.9
TY952 0.161 ± 0.030 0.0038 ± 0.0003 94.0 ± 21.1
TY985 0.163 ± 0.021 0.0038 ± 0.0004 121.3 ± 30.9
TY1018 0.170 ± 0.024 0.0038 ± 0.0005 120.6 ± 40.0
TY954 0.167 ± 0.017 0.0037 ± 0.0010 105.3 ± 23.5
TY1040 0.061 ± 0.019 0.0024 ± 0.0004 55.6 ± 18.2
TY1040 w/ Met 0.088 ± 0.008 0.0025 ± 0.0005 60.4 ± 18.9
TY1032 w/ Met 0.059 ± 0.004 0.0035 ± 0.0005 61.6 ± 15.5
TY1041 0.072 ± 0.006 0.0024 ± 0.0001 100.2 ± 34.8
TY1041 w/ Met 0.089 ± 0.018 0.0022 ± 0.0002 70.7 ± 23.1
TY1031 w/ Met 0.080 ± 0.011 0.0033 ± 0.0002 57.8 ± 14.6
Table 3 Overflow metabolism of acetate
Strain YNB +
Gluc
Acetate
production
Acetate max @
time
Acetate
re-uptake
TY757 0.977 ± 0.149 12.9 ± 2.2 @ 12 h 0.672 ± 0.125
TY920 0.918 ± 0.142 11.0 ± 1.6 @ 12 h 0.693 ± 0.209
TY952 1.261 ± 0.075 14.4 ± 0.4 @ 12 h 0.698 ± 0.019
TY985 1.742 ± 0.072 23.2 ± 1.3 @ 12 h 1.433 ± 0.150
TY1018 1.750 ± 0.118 20.2 ± 0.7 @ 12 h 1.018 ± 0.061
TY954 1.517 ± 0.270 12.2 ± 2.6 @ 8 h 0.417 ± 0.012
TY1040 0.754 ± 0.320 9.5 ± 0.3 @ 24 h 0.377 ± 0.145
TY1040 w/ Met 1.091 ± 0.111 10.7 ± 1.3 @ 12 h 0.276 ± 0.073
TY1032 w/ Met 2.239 ± 0.664 19.0 ± 6.2 @ 8 h 0.679 ± 0.154
TY1041 1.994 ± 0.694 15.1 ± 1.5 @ 12 h 0.224 ± 0.027
TY1041 w/ Met 1.578 ± 0.389 12.8 ± 0.9 @ 24 h 0.351 ± 0.215
TY1031 w/ Met 1.551 ± 0.209 13.5 ± 1.6 @ 8 h 0.403 ± 0.071
All uptake/production rates in mmol/g DCW/h. Maximum detected values in
mmol/g DCW
Gold et al. Microbial Cell Factories (2015) 14:73 Page 6 of 16
The knockout strategy proposed by GDLS combines
multiple simultaneous strategies for dire cting flux
towards tyrosine (Fig. 4). GDL S , like many strain design
algorithms, is based on the search for strategies resulting in
a stoichiometrically growth-coupled strain, meaning that
export or accumulation of the desired target is necessary in
order to reach the optimal growth state. An important
implication of this is that all proposed manipulations are
intended to be implemented as a complete set and the
resulting strain must be adapted in exponential growth
phase to reach a near-optimal growth phenoty pe. Prac-
tically, however, implementation o f all proposed manip-
ulations may not be possible in many situations. In our
case, for example, knockout of the pyruv ate carboxylase
reaction (gene: PYC1, PYC2,reactionID:PC)was
shown to completely prevent production of oxaloacetate
during growth on glucose, necessitating supplementation
of aspartate to the medium [23]. As a result of this limi-
tation, we evaluated the individual predictions provided by
GDLS for their physiological contribution to improved
tyrosine production. Knockout of the aromatic amino acid
degradation pathway is achieved by removing the phenyl-
pyruvate decarboxylase (ARO10, reaction ID: PPYRDC), a
strategy that has already been explored experimentally
[15]. Because this strategy was already well established, de-
letion of ARO10 was implemented in all of our design
strains. In our hands, in a strain overexpressing a tyrosine
insensitive DAHP synthase, deletion of ARO10 resulted in
at least 4 times less tyrosol and 1.5 times more coumarate
than the wild-type background after 48 h (data not
shown). Further, knockouts to the pyruvate decarboxylase
reaction (PD C1, PYRD C), pyruvate carboxylase, mito-
chondrial malate dehydrogenase (MDH1,MDHm),
and the malate mitochondrial transporter (MTM1,
MA Ltm) were proposed b y the GDL S solution. All of
these mutations would m inimize carbon flux below
the pyruvate node, possibly promoting aromatic amino
acid biosynthesis derived from the precursors PEP and
E4P, but experimental i mplementation of these knock-
outs would have introduced many known auxotro-
phies not predicted by the model stoichiometry alone
[23, 24]. As a strategic substitute for these deletions ,
we sele cted a point mutation of the main pyruvate
kinase isoform CDC19 previously shown to reduce
that enzyme’s activity and result in accumulation of
PEP [25]. In addition , the GDLS design eliminates a
competing drain on glycolytic flux w ith the knockout
of the 3-phosphoglycerate dehydrogenase (SER3,
PGCD), the first step in serine and glycine biosynthesis.
This knockout is predicted to not result in auxotrophy be-
cause of the existence of an alternative synthesis route
from alanine via glyoxylate aminotransferase [26]. Finally,
the knockout of glucose 5-phosphate dehydrogenase
(ZWF1, G6PDH2) has the potential to improve both pre-
cursor availability and cofactor pools. In particular, the
ZWF1 knockout was found to be important to achie ve
complete growth-coupling of tyrosine production
(Additional file 2: Figure S2). If the design is implemented
with ZWF1 still intact, tyrosine export is predicted to vary
over a range due to alternate optimal solutions. This is
due to the fact that either tyrosine or phenyla lanine can
be exported equally well in this scenario, according to
the steady-state model (not shown). In light of this, the
potential impacts of ZWF1 kn ockout on tyrosine pro-
duction are two-fold. First, in the absence of the oxida-
tive pentose phosphate pathway, the important biomass
Fig. 4 An overview of an in silico strain design for growth-coupled tyrosine production. Metabolic fluxes for wild-type a and mutant b strains
were predicted by maximizing biomass production using the iMM904 model during respiratory growth on glucose. Knockouts obtained using
the GDLS strain design algorithm are shown in red font. The flux distributions are visualized using Omix Visualization software [54], and arrow
width correlates to predicted flux. Reaction edges carrying no flux are shaded grey
Gold et al. Microbial Cell Factories (2015) 14:73 Page 7 of 16
precursors ribose 5-phosphate (R5P) and E4P must be
obtained through a reversal of the non-oxidative pen-
tose phosphate pathway. Under low or absent seduhep-
tulose 1,7-bisphosphatase (SHB17) activity as predicted
by the iMM904 model, E4P must be produced in excess
in order to meet bioma ss requirements for R5P. This
strategy ha s the potential to improve E4P availability
for aromatic amino acid production. Second, the ZWF1
knockout affect s the cell’s ability to regenerate cytosolic
NADPH pools, which in steady-state would promote
prephenate dehydrogenase flux, catalyzed by the
NADPH-generating Tyr1. Finally, the model suggests a
knockout of dihydroxyace tone kinase (DAK1 and
DAK2, DHAK), which prevents a futile cycle allowing
the NADP
+
-dependent glycerol d ehydrogena se from re-
generating NADPH.
Effect of ZWF1 knockout on flux to aromatic amino acid
and coumarate
In order to test the changes to core metabolism for their
impact on aromatic amino acid and coumarate produc-
tion, we tested a knockout of ZWF1.Becausemanipula-
tion of NA DPH p ools has the potential for far-reaching
effect s throughout metabolism, it was desirable to test
Zwf1
−
strains along with mechanisms to alle viate the
effect s of NADPH depletion. Therefore, overexpression
of NADP
+
-dependent Tyr1 (strain TY1040) was predicted
to alleviate NADPH depletion and promote t yrosine
production while NAD
+
-dependent TyrC (TY1041)
would not. Additionally, methionine supplementation
has been shown to be necessary for growth of Zwf1
−
S288C strains [27], presumably beca use it redu ces the
cellular demand for NADPH during amino acid
Fig. 5 Intracellular tyrosine regulatory effects and accumulation. a Intracellular tyrosine and prephenate concentration for strains TY1040 (zwf1Δ
[ARO4
FBR
TYR1]) and TY1041 (zwf1Δ [ARO4
FBR
TYRC]) are shown during growth on methionine-supplemented YNB. b The maximum intracellular
tyrosine concentration observed for all strains. The wild-type tyrosine concentration was reported previously for S. cerevisiae S288C. Error bars in
both panels signify 95 % confidence intervals based on three biological replicates
Gold et al. Microbial Cell Factories (2015) 14:73 Page 8 of 16
synthesis. A s al l Zw f 1
−
strainstestedinthisstudywere
able to grow in minimal medium, the methionine aux-
otrophy that i s associated with the ZWF1 knockout
was not obser ved. Howe ver, knocking out ZWF1 did
result in a reduction of the specific maximum growth
rate, growth yield and glucose uptake rate by about
half for both strains with respe ct to their Zwf1
+
coun-
terparts. Methionine supplementation was able to par-
tially recover maximum spe cific growth rate of both
Zwf1
−
strains ( Table 2).
In Zwf1
+
strains overe xpressing eith er TYR1 or TYRC,
4HPP plateaued at the end of log phase (24–30 h) before
being depleted in stationary phase (Fig. 2, Additional file
3: Figure S3-A and Additional file 4: Figure S4-A). When
ZWF1 was knocked out and TYR1 overexpressed, 4HPP
dropped by about five-fold, as did prephenate/phenyl-
pyruvate and coumarate (Additional file 3: Figure S3-A).
With TYRC overexpre ssion, on the other hand, 4HPP
was overa ll lower but continued to accumulate past 36 h
and by 48 h was higher than for its Zwf1
+
counterpart
(Additional file 4: Figure S4-A). Starting levels of tyro-
sine were higher for both strains and when cultures were
supplemented with methionine, they showed the highest
intracellular tyrosine levels of any tested, reaching 395
and 520 μmol/g DCW, respectively, although these
levels were not sustained (Fig. 2, Additional file 3: Figure
S3-B and Additional file 4: Figure S4-B). The spike in
tyrosine concentration for strains TY1040 and TY1041
at 12 h coincided with a decrea se in prephenate con-
centration beginning at the same time point (Fig. 5a),
despite chorismate levels r emaining re latively constant.
This result is consistent with allosteric inhibition of
Aro7 by tyrosine. DHS was not significantly higher for
Zwf1
−
than for Zwf1
+
strains when overexpressing
TYRC, but was ten-fold greater when overexpressing
TYR1 (Fig. 2). The maximum intracellular tyrosine con-
centration obser ved at any time point for all strains can
be seen in Fig. 5b.
Supplementation of methionine led to imp rovements
in total carbon downstream of 4HPP inclusively for
all Zwf1
−
strains: an 84 % boost for TYR1 (197 to
363 μmol/g DCW) and a 20 % increase for TYRC (from
489 to 600 μmol/g DCW) (Fig. 3). Coumarate produc-
tion was lower for Zwf1
−
strains compared to Zwf
+
strains; between the two Zwf
−
strains, both with and
without methionine supplementation, the TyrC overex-
pressing strain ( TY1041) had a better coumarate output
than the Tyr1 strain (TY1040) (Fig. 2, Additional file 3:
Figure S3-B and Additional file 4: Figure S4-B). This was
contrary to the hypothesis that in a Zwf1
−
background
overexpression of NADP
+
-dependent Tyr1 would help
to alleviate NADPH depletion and promote tyrosine
production while overexpre ssion of NAD
+
-dependent
TyrC would not.
Effect of cdc19
T21E
on flux to aromatic amino acid and
coumarate
The strain designs obtained by GDLS for tyrosine over-
production are intended to capture metabolic effects
from a very broad perspective, but there are many limi-
tations to the practical implementation of strain designs
obtained using these models. In part icular, because regu-
latory and metabolite concentration information is not
well captured in such models, many of the design com-
ponents are either unnecessary or biologically infeasible.
For example, knockout of the PYRDC reaction (through
deletion of PDC1, PDC5, and PDC6) is allowable in the
iMM904 model at steady-state, while yeast strains with
these mutations are not able to grow on glucose [28,
29]. This error is in part because the Crabtree effect and
its regulatory implications are not captured in t his
model. DAHP synthesis together with the final reaction
carried out by Aro1 account for <1 % of PEP flux con-
sumption in growing yea st cells [30]. The balance is
consumed by the major pyruvate kinase isozyme Cdc19,
which catalyzes the majority of pyruvate production dur-
ing growth on glucose. Many of the deletions obtained by
GDLS had the effect of reducing carbon flux beyond the
pyruvate node. One potential way to mimic this design
choice without implementing these knockouts is the
knockdown of pyruvate kinase achieved by a T21E muta-
tion to the CDC19 gene [25]. This mutation mimics the
phosphorylated form of the enzyme and was shown to
have impaired activity and a corresponding increase in
intracellular PEP concentration [25].
To test the contribution of increased PEP pools to-
ward aromatic amino acid production, overexpression of
TYR1 or TYRC along with ARO4
FBR
was moved into a
Zwf1
−
Cdc19
low
strain, giving rise to strains TY1032 and
TY1031, respectively. Neither TY1031 nor TY1032 grew
in the absence of methionine; therefore comparisons
drawn below are with respect to Zwf1
−
Cdc19
+
counter-
part strains grown in the presence of methionine.
Specific maximum growth rate was not higher for Zwf
−
Cdc19
low
strains than it was for Zwf
−
Cdc19
+
strains;
however, growth yield was improved, approaching levels
obser ved in the Zwf1
+
Cdc19
+
background (without
methionine; Table 2). We expe cted to obser ve some
evidence of PEP accumulation and/or a decre ase in
pyruvate or its by-products. Although no direct differences
in PEP or pyruvate were measured, indirect effects ob-
ser ved bear out the presence of the mutation. For both
the TYR1 and TYRC cases, the addition of the cdc19
T21E
mutation led to a drop of about 50 % in total carbon
downstream of 4HPP; from 343 down to 157 μmol/g
DCW for TY1032 and from 600 to 287 μmol/g DCW for
TY1031 (Fig. 3). For Cdc19
low
strains (TY1032 and
TY1031), 4HPP began to disappear as the strains reached
the end of log growth (24–30 h) whereas for TY1041 it
Gold et al. Microbial Cell Factories (2015) 14:73 Page 9 of 16
was obser ved to b e still accu mulating by 48 h ( Fig. 2,
Additional file 3: Figure S3-C and Additional file 4: Figure
S4-C). For both cases with the pyruvate kinase mutant,
tyrosine concentrations were overall much lower than
Zwf
−
Cdc
+
levels and showed a steady decline from the start
of growth. Coumarate was lower for both, but lower from
the start of growth for TYRC whereas for TYR1 it only
dropped off from Zwf1
−
Cdc19
+
levels towards the end of
log. With respect to overflow metabolism, the TYRC strain
showed considerable changes (Additional file 5: Figure
S5-C and Additional file 6: Figure S6-C). Whereas
TY1041 produced and accumulated high amount s of
acetate in the culture supernatant, T Y1031 produced as
mu ch but began to re-assimilate it from the start of log
phase and consumed virtually all of it by 48 h. This re-
sponse profile resembles that generally obser ved for the
Zwf1
+
Cdc19
+
strains (Additional file 6: Figure S6-A).
TY1031 also produced more ethanol than TY1041. Gly-
cerol profiles for both TY1031 and TY1032 were similar
to those observed for their Zwf1
−
Cdc19
+
counterparts ex-
cept that initial concentrations were higher (Additional file
5: Figure S5-C and Additional file 6: Figure S6-C).
Discussion
We created a series of model-driven modifications to
wild-type CEN.PK yeast to divert carbon flux for tyro-
sine overproduction, monitoring nineteen metabolites
over the course of shake-flask fermentations on glucose.
Using a targeted metabolomics time-course strategy we
sought not only to e valuate our ability to overproduce
tyrosine but also to identify pathway bottlenecks that
might present new potential engineering targets.
Cytosolic tyrosine concentration in wild-type yeast has
been previously reported at 0.5 mM [7]. The maximum
intracellular tyrosine measured for our Aro10
−
base case
was 34 μmol/g DCW at 72 h (Fig. 5b). This corresponds
to a concentration of 19 mM in the cell, a 38-fold increase
over the reported value. Although these are different
strains , it is likely that this tyrosine accumulation is
predominantly due to partial disruption of the tyrosine
degradation pathway by knocking out ARO10. With
ARO4
FBR
overexpressed in the Aro10
−
strain, a maximum
tyrosine value of 351 μmol/g DCW or 129 mM was ob-
tained in the cytosol after 30 h, a further improvement of
nearly 7-fold. Zwf1
−
TY1041 expressing TYRC ARO4
FBR
in
the presence of methionine produced our highest recorded
tyrosine level of 520 μmol/g DCW or 192 mM in the cell, a
further gain of 1.5-fold (a 384-fold total increase over wild-
type); however, it is clear that the manipulation of NADPH/
NADP
+
ratios to promote tyrosine formation is not
straightforward, as some ZWF1 deletion strains exhib-
ited little or no improvement over Zwf1
+
strains. We
hypothesize that the effe ct of this deletion on NADPH
pools must be c arefully controlled in order to balance
improved favourability of prephenate dehydrogenase
flux against creation of a separate cofactor limitation at
shikimatedehydrogenase,forexamplebyusingacon-
trolled knock-down of Zwf1 expression.
Although coumarate levels seem to respond to im-
provements in tyrosine concentration at low fluxes, the
TAL enzyme appeared to be quickly saturated, resulting
in high carbon losses in the aromatic amino acid path-
way. All four first-generation attempts to improve on
the chassis strain case (strains TY985, TY952, TY1018
and TY954) resulted in a virtually identical increase in
coumarate (Fig. 2) despite affecting the total carbon in
the aromatic pathways in vastly different ways.
In our hands, overexpression of the ARO4
FBR
variant
alone in an Aro3
+
Aro4
+
Aro10
−
strain increased tyro-
sine and total carbon measured in the shikimate and
aromatic pathways by more than five-fold with respect
to our control strain.
Other than prephenate dehydrogen ase, only one other
reaction in tyrosine biosynthesis involves the reducing
equivalent NADPH directly, namely the NADPH-
dependent 5-dehydroshikimate (DHS) reductase activity
of Aro1. Because of this, tyrosine biosynthesis can be
considered NADPH neutral, apart from the production
of glutamate as substrate for the final aminotransferase
reaction by Aro8. A third reaction, chorismate synthesis
by Aro2, involves NADPH only indirectly, requiring it
for the reduction of its cofactor FMN but not consuming
it [31]. The NADPH deficiency resulting from deletion of
ZWF1 limited carbon flux at the NADPH-dependent DHS
reductase activity of Aro1. This resulted in a build-up of
DHS and backed carbon up to PEP, which also showed
an accumulation. I t was expe cted that partial relief of
NADPH deficiency – either by the addition of methionine
or by increasing flux through the NADP
+
-dependent pre-
phenate dehydrogenase – would improve the thermo-
dynamic favourability of DHS reduction to shikimate. In
all Zwf1
−
strains, methionine supplementation was able to
achieve improved flux through the shikimate pathway,
increased tyrosine concentrations, and higher final couma-
rate titers. Surprisingly, however, overexpression of the
non-native NAD
+
-dependent TyrC was found to outper-
form overexpression of the native NADP
+
-dependent Tyr1
for driving flux through 4HPP. While many prephenate
dehydrogenases are inhibited by tyrosine, the Tyr1 from
S. cerevisiae is only known to be regulated at the transcrip-
tional level [13]. TyrC from Z. mobilis, on the other hand,
is known to be feedback-insensitive to tyrosine [12]. This
result could reflect a previously undescribed inhibition of
the Tyr1 from S. cerevisiae by tyrosine.
An observed decrease in E4P in Zwf1
−
strains may
have been the result of a bottleneck in the reverse of the
non-oxidative pentose phosphate path way when ZWF1
is deleted. To achieve predicted improvements to flux
Gold et al. Microbial Cell Factories (2015) 14:73 Page 10 of 16
through E4P, it may be necessary to overexpress TKL1 as
demonstrated by Curran et al. for the production of
muconic acid [32]. The initial high levels of tyrosine sug-
gest that the cell did try to overcome the deficiency with
Tyr1 activ ity. Howe ver, this resulted in shutdown of
Aro7 as evidenced by the early spike in chorismate. The
growth rate, growth yield and glucose uptake rate were
reduced by about half (Table 2) in the ZWF1 knockout
strains, and we measured overall lower levels of all three
overflow metabolism products (Table 3) monitored for
the TYR1 case. This observation combined with high
PEP measured in these strains may have signalled a
slowdown in glycolysis. Furthermore the reduction in
re-assimilation rates of ethanol, acetate and glycerol
suggested that the diauxic shift normally signalled by
glycolytic intermediates did not occur.
The incorporation of the cdc19
T21E
mutation did not
improve overall flux through the aromatic amino acid
biosynthesis pathway, resulting in a further drop in car-
bon downstream of 4HPP inclusively for both TYR1 and
TYRC strains, by about half compared to their Zwf1
−
Cdc19
+
counterparts. The cdc19
T21E
mutation reigned in
the conversion of PEP to pyruvate, and this restricted
the amount of substrate carbon in the form of acetalde-
hyde the TYRC strain could send to Ald6 to gener ate
NADPH. As a result, the TYRC case showed a greater
than twenty-fold increase in DHS due to the NADPH
requirement of the DHS reductase activity of Aro1.
The use of constraint-based modelling as a platform
for optimization-based strain design ha s generated con-
siderable interest for the past several years, but only a
few examples of practical success using these approaches
have been demonstrated, particularly for secondary me-
tabolite production. One possible explanation for this is
the relatively high energetic and material costs of sec-
ondary metabolites, which frequently require several
reaction knockout s in combination in order to achieve
a growth-coupled phenotype. Be cause most constraint-
based models d o not fully incorporate regulatory and
thermodynamic limitations, model prediction inaccur-
acies are inevitable, and a higher number of combined
knockouts is increasingly likely to become experimen-
tally infea sible. In this study, it wa s determined based
on previous experimental result s for individual knock-
outs [23, 24] that concurrent implementation of all
GDLS design strategies for tyrosine p roduction would
not be feasible. Spe cifically, this mode l inaccuracy
could be attributed to the inability to capture major
regulatory shifts in S. cerevisiae metabolism from fermen-
tative to respiratory growth (i.e. the Crabtree effect) using
current models. In this study, we demonstrate that
computational strain design algorithms depending on
growth-coupling, such as GDLS, can still provide valuable
insight into broad, non intuitive design strategies , but
interpretation and implementation of these strategies
is not straightforward. The deletion of ZWF1 for im-
proved aromatic amino acid pathway flux ha s been
used previou sly in com bination wi th overexpression of
transketolase TKL1 [32] to improve availabilit y of the
precursor E4P. The constraint-based modelling approach
used here confirmed the utility of that manipulation for
improving E4P pools and also identified it as a tool for
shifting cytosolic NADPH pools in favour of tyrosine pro-
duction, reducing carbon loss to phenylalanine.
Conclusions
In this study, we syste matically evaluated both rational
pathway engineering and model-driven strain design
strategies for the improvement of tyrosine production .
In Aro10
−
strains overexpressing deregulated aromatic
amino acid biosynthesis enzymes, this approach demon-
strated possible cofactor limitation at prephenate de-
hydrogenase, indicated by accumulation of prephenate
by these strains. Genome-scale modelling identified a
ZWF1 knockout strategy as a potential solution to this
by changing NADPH/NADP
+
ratios in the cytosol to
make the prephenate dehydrogenase reaction more
thermodynamically favourable. Our results indicate that
this strategy is able to improve tyrosine accumulation
in vivo, but careful control of its effects on cofactor
pools is critical to avoid unwanted effects. Additionally,
our findings confirm the importance of transcript- and
protein-level deregulation of the aromatic amino acid
pathway and the complete removal of potential degradation
pathways for sustained diversion of carbon flux through
tyrosine toward higher value secondary products.
Materials and methods
Strains and plasmids
Full descriptions of the Saccharomyces cerevisiae strains
and plasmids used in this study are given in Additional
file 8: Table S1. Escherichia coli DH5α was used to main-
tain and propagate plasmids. E. coli was grown at 37 °C
and 200 rpm in LB medium supplemented with 100 μg/
mL of ampicillin. S. cerevisiae was grown at 30 °C and
150 rpm in either the rich medium YPD or the defined
SD medium [YNB supplemented with 2 % (w/v) glucose]
[33]. When required, 200 μg/mL geneticin (G418) and
200 μg/mL hygromycin were added to YPD, and SD
medium was suppleme nted with amino acids to comple-
ment specific auxotrophic requirements [34].
Plasmid construction
The DNA assembler method [35] was used to construct
the plasmids used in this study. The different DNA parts
were amplified by PCR, run on agarose gel and individu-
ally purified using Qiagen Gel Purification kit (Valencia,
CA, USA). All primers used in this study are listed in
Gold et al. Microbial Cell Factories (2015) 14:73 Page 11 of 16
Additional file 9: Table S2. DNA parts (promoter, gene,
terminator) were pooled with a linearized plasmid and
transformed in the appropriate yeast singly auxotrophic
strain by electroporation, as described by Shao et al.
[35]. Assemb ly was selected for by growth on minimal
medium, and the resulting plasmids were recovered
from yeast and transformed into E. coli for maintenance.
Sanger sequencing confirmed correct assembly of the
parts. Promoters and terminators required for assembly
were amplified from S. cerevisiae CEN.PK genomic
DNA. The genes coding for the aromatic amino acid
synthesis enzymes were assembled in centromeric shut-
tle plasmids derived from pGREG506 and pGREG503
[36]. The feedback inhibition-resistant version of the
DAHP synthase ARO4 was obtained by introducing the
K229L mutation by PCR, using S. cerevisiae CEN.PK
genomic DNA as template [11, 37]. Similarly, the G141S
mutation was introduced into ARO7 to create a feedback
inhibition-insensitive version of the chorismate mutase
[11]. Native coding genes for TYR1 and ARO1 were
PCR-amplified from S. cerevisiae CEN.PK genomic
DNA. TYRC from Z. mobilis and TAL from R. sphaeroides
were synthesized and codon-optimized for S. cerevisiae by
DNA 2.0 (Menlo Park, CA, USA). All heterologous genes
were cloned under different constitutive yeast promoters:
ARO4
FBR
under pFBA1 in plasmid pTY978, ARO1 under
pPYK1 in pTY502, ARO7
FBR
under pPDC1 in pTY688,
TYR1 under pTEF1 in pTY1035, and TYRC under pTDH3
in pTY500. TAL was assembled under pPMA1 into a 2 μ
shuttle vector derived from pYES2 (Life Technologies,
Carlsbad, CA, USA), giving rise to pTY350. Plasmids
pTY338 and pTY51 served as empty vector controls.
Gene knockouts and knock-ins
Chromosomal gene knock-outs were done by homolo-
gous recombination, using antibiotic marker-containing
disruption cassettes created by PCR, as described by
Gueldener et al. [38]. Integration cassettes contained
two 40-nt regions of homology corresponding to the 5′
and 3′ ends of the target locus. The loxP-flanked kanMX
and loxLE/RE-flanked hphNT1 cassettes were amplified
from the pUG6 and pZC3 vectors, respectively [38, 39].
The mutant allele coding for Cdc19
T21E
was amplified
from genomic DNA extracted from W303-based strain
JR201, provided courtesy of J. Rabinowitz [25], using
primers upstream of the gene and downstream of a
kanMX marker. The resulting PCR cassette was integrated
into CEN.PK111-61A by homologous recombination.
Transformations into y east of knock-out or knock-in
cassettes (a s well a s all plasmids) were performed by
the lithium acetate method [40]. After transformation,
cells were plated on YPD agar containing 200 μg/mL G418
and/or hygromycin, as appropriate. Presence of an anti-
biotic marker linked to a gene knockout or insertion was
confirmed by PCR. Sanger sequencing validated the pres-
ence of the CDC19 mutant allele (61 A > G + 62 C > A +
63 C > G). Single mutations were made in one of the com-
patible mating types of the triple auxotroph (ura3 leu2
his3) haploid wild-type, CEN.PK111-61A MATα or
CEN.PK111-5B MATa. Mutations were compiled by mat-
ing single mutation strains or by transforming a second
deletion cassette into an existing knockout strain. Strains
H703 and H712 were mated to generate H919. ARO10
was deleted with a loxLE-hphNT1-loxRE cassette in strain
H749 to generate H1045. Strains H749 and H919 were
mated t o generate H876. The antibiotic marker wa s re-
moved from H703 only, using the Cre recombinase
plasmid pSH47 [38], to generate strain H837. Different
three-plasmid combinations were transformed into host
strains, giving rise to the “TY” test strains listed in Table 1.
Model-guided strain design
Genome-scale constraint-based metabolic modelling
[41] was used to predict and evaluate the impacts of
metabolic gene deletion on the metabolic phenotype.
Simulations were done on the in silico reconstruction of
yeast metabolism iMM904 [16] using the COBRA Toolbox
v2.0 [42] in Matlab using CPLEX ILOG for optimization.
All uptake fluxes d uring strain design simulations were set
at zero except for glucose and oxygen exchange, which
were set with lower bounds of −10 mmol/gDCW/h. The
OptKnock strain design algorithm [17] was implemented in
MA TLAB, searching up to four simultaneous reaction dele-
tions with the outer objective of maximizing an artificial
cytosolic L-tyrosine exchange flux. Genetic Design by Local
Search (GDLS) [18] wa s performed using an in-house
implementation with the same boundary conditions
and either cytosolic L-tyrosine or cytosolic chorismate
exchange fluxes as the outer obje c tive. G DLS was run
with a neighbourhood size of 2 and a maximum of 10
knockouts. Simulations were tuned to mimic respiratory
growth on glucose, with glucose and oxygen uptake set at
a ratio of 1:1. These conditions are generally able to repro-
duce biomass and by-product yields observed during
steady-state respiratory growth [43].
In order to simulate wild-type flux through the oxidative
pentose phosphate pathway for Fig. 4, additional changes
were required. First, the cytosolic isocitrate dehydrogen-
ase, IDP2 (reaction ID: ICDHy), is knocked out to reflect
its downregulation in glucose conditions [44]. Second, the
oxoadipate/α-ketoglutarate mitochondrial antiporter was
added to allow transport of cytosolic α-ketoglutarate into
the mitochondria [45]. Finally, the cytosolic acetaldehyde
dehydrogenase ALD6 (reaction ID: ALDD2y) wa s pro-
portionally limited to 16 % of the glucose uptake rate
to refle ct it s physiological contribution to cytosolic
NADPH supply [43].
Gold et al. Microbial Cell Factories (2015) 14:73 Page 12 of 16
Analysis of metabolites
Metabolites extraction
For each strain tested, three pre-cultures were seeded from
individual colonies streaked out on minima l medium.
Pre-cultures were grown overnight and then used to in-
oculate 40 mL of YNB with 2 % (w/v) glucose in a 250-mL
shake-flask to a starting OD
600
of 0.05. Growing cultures
were then sampled at regular intervals and optical
densities were read using a TECAN Infinite 200 PRO in
a 9 6-well plate format , diluting the culture ten-fold into
200 μLoffreshmedium.
One mL of culture broth was centrifuged at 21,000 × g
for 3 min and the supernatant was frozen at −20 °C. An
aliquot of 400 μL of the culture supern atant was used
for glucose, organic acid and ethanol analysis, and an-
other 400 μL of supernatant was extracted with 2 vol-
umes of ethyl acetate (EtAc) for analysis of non-polar
aromatic compounds. Prior to analysis, the EtAc extracts
were dried down to completeness in a SpeedVac with no
heating. The dried extracts were suspended in 40 μLof
50 % (v/v) acetonitrile (ACN) and 0.05 % (v/v) trifluoroace-
tic acid (TF A). Intracellular metabolites were obtained as
follows based on extraction studies by Villas-Boas et al. and
Crutchfield et al. [46, 47]. One mL of cultu re broth was re-
moved and immediately quenched by adding it to 5 mL o f
pure methanol (MeOH) chilled in a bath of ethanol and
dry ice. T he mixture was immediately centrifuged at −9°C
and 3000 × g for 5 min. The supernatant was discarded and
the cell pellets were suspended in 400 μL80%(v/v)MeOH
pre-chilled at −20 °C, and then incubated on ice for 15 min.
The mixtures were then centrifuged at 16,000 × g for 5 min
at 4 °C. Supernatants were removed and set aside, and the
pellets were extracted again with a second volume of
400 μL 80 % MeOH. The pooled extracts were dried to
completeness in a SpeedVac with no heatin g. The dried
extract s were suspende d in 200 μL of 0.1 % (v/v) formic
acid (FA) for HPLC analysis.
HPLC analysis
A10μL aliquot of the non-extracted supernatants was
injected, using a Finnigan Surveyor HPLC system, onto an
Aminex HPX-87H column (7.8 × 300 mm, 9 Å, Biorad)
heated to 65 °C. Glucose, glycerol, acetate and ethanol
were resolved isocratically in 5 mM H
2
SO
4
at 0.6 mL/min.
Metabolites were identified and quantitated by a refractive
index detector set to 35 °C using standards.
The EtAc-extracted supernatants were analyzed on an
Eclipse XDB-C18 column (4.6 × 150 mm, 5 μm, Agilent),
using an Agilent 1200 HPLC system equippe d with a
photodiode array detector. Metabolites from 5 μL of the
concentrated extracts were separated at 1 mL/min using
a gradient method where mobile phase A was 0.1 % (v/v)
TFA in water and B was 0.1 % (v/v) TFA in MeOH. The
gradient was as follows: 0–0.5 min 20 % B, 0.5–10 min
20–50 % B, 10.5–18.5 min 50–98 % B, 18.5–21.5 min
98 % B. Various UV wavelengths were used to follow,
in order of elution, tyrosol (276 nm), chor ismate (276 nm),
4-hydroxyphenylacetate (276 nm), 4-hydroxyphenylacetal-
dehyde (285 nm), 4-hydroxyphenylpyruvate (304 nm), cou-
marate (310 nm), tryptophol (276 nm), and prephenate/
phenylpyruvate which are indistinguishable (290 nm). Pre-
phenate and phenylpyruvate were measured as a mixed
peak by RP-HPLC/PDA; however, the kinetics of the signal
(its maximum typically coinciding with that of chorismate
and being exhausted completely within 36 h) suggests that
the peak consisted predominantly of prephenate. Thus, we
herein refer to prephenate/phenylpyruvate numbers as
though they connote the metabolite prephenate.
Cell extracts were analyzed by single-reaction monitor-
ing (SRM) mass spectrometry using a Thermo LTQ-MS
equipped with an electrospray ionization source and a
Surveyor HPLC system. Positive mode was used to de-
tect the aromatic amin o acids, which were first resolved
on a Zorbax Eclipse XD B-C18 column (4.6 × 30 mm,
1.8 μm, Agilent). The following gradient was used for
separation of the metabolites: 0–1 min 3 % B, 1–10 min
3–97 % B, 10–12min97%B,wheremobilephaseAwas
0.1 % (v/v) F A in water and B was 0.1 % (v/v) FA in MeOH.
Theflowratewassetat100μL/min with the spray voltage
set to +4 kV and the sheath gas at 5. Tyrosine was detected
as the transition from +182 to +165 m/z, using a collision
induced dissociation (CID) energy of 15 and isolation width
of 1.5 m/z. L-Phenylalanine was detected as the transition
from its parent ion +165 to +120 m/z, using a CID of 20.
L-Tryptophan was monitor ed as the transition from +205
to +188 m/z, using a CID of 15. Standard curves were run
for quantitation.
Negative mode was used for detection of phosphory-
lated sugars and shikimate pathway intermediates. A
Fast Acid Analysis column (7.8 × 100 mm, 9 Å, Biorad)
heated to 65 °C was used with 0.1 % (v/v) FA in water,
running isocratically at 0.6 mL/min. Ten μL of extract
was injected and the flow was split post-column to about
100 μL/min to the E SI source. The spray voltage was set
to −3.6 kV and the sheath gas was set to 5. Although
PEP and E4P co-elute they could be analysed by monitoring
the transition of −167 to −79 m/z for PEP and the trans i-
tion of −199 to −97 m/z for E4P. Isolation width of 1.5 m/z
and CID of 35 was used for all metabolites. Shikimate and
DHS co-eluted as well. The transition of −173 to −155 m/z
was used for shikimate, while DHS was monitored as the
transition of −171 to − 129 m/z . Pyruvate could not be
measured by SRM be cause the LTQ is limited in it s
ability to trap and isolate ions smaller than about
150 m/z. Therefore, pyruvate was measured in full scan
modeinthelowmassrangeusingthetransitionfromits
parent ion −87 m/z to itself with no collision energy. Stand-
ard curves were run for identification and quantitation.
Gold et al. Microbial Cell Factories (2015) 14:73 Page 13 of 16
Calculations
Dry cell weight (DCW) was determined by multiplying
OD
600
values by a conversion factor of 2.01 mg DCW/
mL/OD
600
, a relationship determined in-house from S.
cerevisiae CEN.PK grown in minimal medium.
Maximal growth rate μ
MAX
(1/h) was calculated using
least-squares fitting during the exponential growth phase
using the Doubling Time website [48]. Growth yield Y
X/S
was calculated as the maximum grams of DCW per grams
glucose consumed. Average final titer was estimated based
on a parameter-3 sigmoidal fit to data points using Sigma-
Plot11.0. Rate determinations were made for individual
clones and the average value with 95 % confidence interval
is reported.
Specific carbon totals were estima ted as follows. Spe-
cific carbon was added up at each time point up to
48 h for metabolites downstream of a given enzyme
modification. The data were plotted against time in Sig-
maPlot 11.0 and fitted with a 2-parameter exponential
rise to maximum curve fit. Total specific carbon reported
was taken from the fit at 48 h and the error reported is the
error of the fit.
ANOVA were run on all data sets compared. A Student’s
t-test was performed when values were compared at only a
single time point. The null h ypothesis was reje cted
when p < 0.05.
The following values were used in the estimation of
cytosolic concentrations: 160 μm
3
(or 1.6 × 10
−13
L) for
the volume of the cell [49] and 60 pg for the mass of a
cell [50]. Thus, we assume 1.7 × 10
10
cells/g DCW and
an intracellular volume of 2.7 mL per g D CW.
Estimation of the reaction change in Gibbs free energy
(ΔG
γ
) for each step in the tyrosine biosynthesis pathway
(see Additional file 7: Figure S7) was done as follows.
Standard ΔG
γ
values for each step were calculated using
the component contribution method [51] at an assumed
cytosolic pH of 6.5 and at a temperature of 25 °C. ΔG
γ
for each reaction was calculated using the standard
relationship
ΔG
γ
¼ ΔG
γ
0
þ R T lnQ
where R is the gas constant 8.314 J/mol/K, T is the
temperature in degrees Kelvin, and Q is the reaction
quotient, or the product of the concentrations of all
participating metabolites raised to their stoichiometric
coefficients. For a two reactant and two product reaction
in the form
aA þ bB↔cC þ dD
the reaction quotient would be calculated
Q ¼
C½
c
D½
d
A½
a
B½
b
Intracellular concentrations for PEP, E4P, DHS, SHIK,
and TYR were calculated as described above and as-
sumed to be equivalent to concentrations in the cytosol.
Extracellular concentrations of CHOR, PPH, and 4HPP
were calculated as described above, and the cytosolic
concentrations of these metabolites were assumed to be
equivalent to the extracellular concentrations. Cytosolic
concentrations of the cofactors NADPH, NADP, NADH,
and NAD were set at 151.1, 20.37, 174.8, and 862.9 mM,
respectively, based on literature values for wild-type S.
cerevisiae during batch growth on glucose-rich minimal
media [52]. Cytosolic concentrations of ATP, ADP, and
Pi were set at 4.25, 0.93, and 6.6 M, respectively, based
on literature values for wild-type S. cerevisiae during
batch growth on glucose-rich minimal media [53]. All
other metabolites were assumed to have a concentration
of 1 mM. All extracellular and intracellular metabolite
and biomass concentration data are provided in triplicate
for each strain over the 96 h fermentation time course as
Additional file 10.
Additional files
Additional file 1: Figure S1. Aromatic amino acid pathway metabolite
profiles from deregulation or overexpression of genes impinging on
tyrosine biosynthesis plus TAL in Aro10
−
CEN.PK. a Overexpression of
ARO4
FBR
, strain TY920 versus strain TY757. b Overexpression of ARO1 with
ARO4
FBR
, strain TY985 versus strain TY920. c Overexpression of ARO7
FBR
with ARO4
FBR
, strain TY952 versus strain TY920. d Overexpression of TYR1
and TYRC with ARO4
FBR
, strains TY1018 and TY954 versus strain TY920.
Dehydroshikimate, shikimate, L-tryptophan, L-phenylalanine, and L-tyrosine
were measured intracellularly. Chorismate, tryptophol, prephenate/
phenylpyruvate, 4-hydroxyphenylpyruvate, 4-hydrophenylacetaldehyde,
4-hydroxyphenylacetate, tyrosol, and 4-coumarate were measured
extracellularly. Dotted line indicates allosteric feedback inhibition. Values
represent an average of three biological replicates and error bars represent
95 % confidence intervals.
Additional file 2: Figure S2. Tyrosine-overproducing strain design
obtained using GDLS. Product-growth envelopes are shown using the
iMM904 model with glucose and oxygen uptake set at −10 mmol/g
DCW/h. Whereas the wild-type strain (black line) yields no surplus tyrosine
at optimal growth, the complete GDLS mutant strain (red line) indicates
strong growth coupling and a glucose yield near 60 % theoretical. If the
design is implemented with ZWF1 still intact (blue line), tyrosine export is
predicted to vary over a range due to alternate optimal solutions in
which either tyrosine or phenylalanine can be exported equally.
Additional file 3: Figure S3. Aromatic amino acid pathway metabolite
profiles from overexpression of TYR1 with ARO4
FBR
in different genetic
backgrounds. a In Aro10
−
Zwf1
−
Cdc19
+
versus Aro10
−
Zwf1
+
Cdc19
+
,
strain TY1040 versus TY1018. b In Aro10
−
Zwf1
−
Cdc19
+
with and without
methionine added to the growth medium, strain TY1040. c In Aro10
−
Zwf1
−
Cdc19
+
versus Aro10
−
Zwf1
−
Cdc19
low
with methionine added to
the growth medium, strain TY1032 versus TY1040. Dehydroshikimate,
shikimate, and L-tyrosin e were measured intracellularly. Chorismate,
prephenate/phenylpyruvate, 4-hydroxyphenylpyruvate, and 4-coumarate
were measured ex tracellularly. Values represent an average of three
biological replicates and error bars represent 95 % confidence intervals.
Dashed line indicates allosteric feedback inhib ition.
Gold et al. Microbial Cell Factories (2015) 14:73 Page 14 of 16
Additional file 4: Figure S4. Aromatic amino acid pathway metabolite
profiles from overexpression of TYRC with ARO4
FBR
plus TAL in different
genetic backgrounds. a In Aro10
−
Zwf1
−
Cdc19
+
versus Aro10
−
Zwf1
+
Cdc19
+
, strain TY1041 versus TY954. b In Aro10
−
Zwf1
−
Cdc19
+
with and
without methionine added to the growth medium, strain TY1041. c In
Aro10
−
Zwf1
−
Cdc19
+
versus Aro10
−
Zwf1
−
Cdc19
low
with methionine
added to the growth medium, strain TY1031 versus TY1041.
Dehydroshikimate, shikimate, and L-tyrosine were measured intracellularly.
Chorismate, prephenate/phenylpyruvate, 4-hydroxyphenylpyruvate, and
4-coumarate were measured extracellularly. Values represent an average of
three biological replicates and error bars represent 95 % confidence intervals.
Dashed line indicates allosteric feedback inhibition.
Additional file 5: Figure S5. Shikimate pathway precursors and overflow
metabolites from overexpression of TYR1 with ARO4
FBR
plus TAL in different
genetic backgrounds. a In Aro10
−
Zwf1
−
Cdc19
+
versus Aro10
−
Zwf1
+
Cdc19
+
, strain TY1040 versus TY1018. b In Aro10
−
Zwf1
−
Cdc19
+
with and
without methionine added to the growth medium, strain TY1040. c In
Aro10
−
Zwf1
−
Cdc19
+
versus Aro10
−
Zwf1
−
Cdc19
low
with methionine
added to the growth medium, strain TY1032 versus TY1040. Erythrose
4-phosphate, phosphoenolpyruvate and pyruvate were measured
intracellularly. Glycerol, acetate and ethanol were measured extracellularly.
Values represent an average of thr ee biological replicates and error bars
represent 95 % confidence intervals .
Additional file 6: Figure S6. Shikimate pathway precursors and
overflow metabolites from overexpression of TYRC with ARO4
FBR
plus TAL
in different genetic backgrounds. a In Aro10
−
Zwf1
−
Cdc19
+
versus Aro10
−
Zwf1
+
Cdc19
+
, strain TY1041 versus TY954. b In Aro10
−
Zwf1
−
Cdc19
+
with
and without methionine added to the growth medium, strain TY1041. c In
Aro10
−
Zwf1
−
Cdc19
+
versus Aro10
−
Zwf1
−
Cdc19
low
with methionine
added to the growth medium, strain TY1031 versus TY1041. Erythrose
4-phosphate, phosphoenolpyruvate and pyruvate were measured
intracellularly. Glycerol, acetate and ethanol were measured extracellularly.
Values represent an average of thr ee biological replicates and error bars
represent 95 % confidence intervals .
Additional file 7: Figure S7. Estimation of pathway changes in Gibbs
free energy for strains created in this study. ΔG
γ
values were estimated
using the metabolite concentration data obtained in this study combined
with values obtained from literature and presented here at 12 h of batch
culture on YNB glucose, with supplemented methionine where noted. Bars
represent the ΔG
γ
for each reaction step, while the lines depict the
cumulative ΔG
γ
over the entire pathway. Reactions close to equilibrium are
generally less likely to be limited by enzyme level than reactions that are
further from equilibrium.
Additional file 8: Table S1. Saccharomyces cerevisiae strains used in this
study.
Additional file 9: Table S2. Primers used in this study.
Additional file 10: Intracellular and extracellular metabolite
concentrations and biomass levels are provided in triplicate for
each strain studied over the full experiment time course.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
VJJM and RM jointly conceived the study. NDG, CMG, F-XL and SCC
designed and performed the experiments. SCC and CMG performed
modelling work. NDG, CMG, SCC, VJJM and RM interpreted the data.
NDG and CMG prepared the manuscript. VJJM and RM revised the
manuscript. All authors read and approved the final manuscript.
Acknowledgements
This work was financially supported by Genome Canada, Genome Québec
and the Biorefining Conversions Network (BCN). V.J.J.M. was supported by a
Canada Research Cha ir.
Author details
1
Department of Biology and Centre for Structural and Functional Genomics,
Concordia University, 7141 Sherbrooke West, Montreal, QC H4B 1R6, Canada.
2
Department of Chemical Engineering and Applied Chemistry, University of
Toronto, 200 College Street, Toronto, ON M5S 3E5, Canada.
3
Institute of
Biomaterials and Biomedical Engineering, University of Toronto, 164 College
Street, Toronto, ON M5S 3G9, Canada.
Received: 9 December 2014 Accepted: 6 May 2015
References
1. Fossati E, Ekins A, Narcross L, Zhu Y, Falgueyret JP, Beaudoin GA, et al.
Reconstitution of a 10-g ene pathway for synthesis of the plant alkaloid
dihydrosanguinarine in Saccharomyces cerevisiae. Nat Commun. 2014;5:3283.
doi:10.1038/ncomms4283.
2. Lussier F-X, Colatriano D, Wiltshire Z, Page JE, Martin VJJ. Engineering microbes
for plant polyketide biosynthesis. Comput Struct Biotechnol J. 2012;3.
doi:e201210020. doi:10.5936/csbj.201210020.
3. Matsumura E, Matsuda M, Sato F, Minami H. Microbial production of plant
benzylisoquinoline alkaloids. In: Ramawat KG, Mérillon J-M, editors. Natural
Products. Springer Berlin Heidelberg; 2013. p. 3–24.
4. Paddon CJ, Westfall PJ, Pitera DJ, Benjamin K, Fisher K, McPhee D, et al.
High-level semi-synthetic production of the potent antimalarial artemisinin.
Nature. 2013;496(7446):528–32. doi:10.1038/nature12051.
5. Shin SY, Jung SM, Kim MD, Han NS, Seo JH. Production of resveratrol from
tyrosine in metabolically engineered Saccharomyces cerevisiae. Enzyme
Microb Technol. 2012;51(4):211–6. doi:10.1016/j.enzmictec.2012.06.005.
6. Gleason FK, Chollet R. Plant biochemistry. Sudbury, Mass.: Jones & Bartlett
Learning; 2012.
7. Braus GH. Aromatic amino acid biosynthesis in the yeast Saccharomyces
cerevisiae: a model system for the regulation of a eukaryotic biosynthetic
pathway. Microbiol Rev. 1991;55(3):349–70.
8. Paravicini G, Mosch HU, Schmidheini T, Braus G. The general control
activator protein Gcn4 is essential for a basal level of Aro3 gene-expression
in Saccharomyces-cerevisiae. Mol Cell Biol. 1989;9(1):144–51.
9. Graham LD, Gillies FM, Coggins JR. Over-expression of the yeast multifunctional
arom protein. Biochim Biophys Acta. 1993;1216(3):417–24. doi:http://dx.doi.org/
10.1016/0167-4781(93)90009-3.
10. Schmidheini T, Sperisen P, Paravicini G, Hutter R, Braus G. A single point
mutation results in a constitutively activated and feedback-resistant chorismate
mutase of Saccharomyces-cerevisiae. J Bacteriol. 1989;171(3):1245–53.
11. Luttik MAH, Vuralhan Z, Suir E, Braus GH, Pronk JT, Daran JM. Alleviation of
feedback inhibition in Saccharomyces cerevisiae aromatic amino acid
biosynthesis: Quantification of metabolic impact. Metab Eng. 2008;10(3–4):
141–53. doi:10.1016/j.ymben.2008.02.002.
12. Zhao G, Xia TH, Ingram LO, Jensen RA. An allosterically insensitive class of
cyclohexadienyl dehydrogenase from Zymomonas-mobilis. Eur J Biochem.
1993;212(1):157–65. doi:10.1111/j.1432-1033.1993.tb17646.x.
13. Mannhaupt G, Stucka R, Pilz U, Schwarzlose C, Feldmann H. Characterization
of the prephenate dehydrogenase-encoding gene, Tyr1, from Saccharomyces-
cerevisiae. Gene. 1989;85(2):303–11. doi:10.1016/0378-1119(89)90422-8.
14. Hazelwood LA, Daran JM, van Maris AJA, Pronk JT, Dickinson JR. The ehrlich
pathway for fusel alcohol production: a century of research on Saccharomyces
cerevisiae metabolism. Appl Environ Microbiol. 2008;74(8):2259–66. doi:10.1128/
Aem.02625-07.
15. Koopman F, Beekwilder J, Crimi B, van Houwelingen A, Hall RD, Bosch D,
et al. De novo production of the flavonoid naringenin in engineered
Saccharomyces cerevisiae. Microb Cell Factories. 2012;11. doi:Artn 155.
doi:10.1186/1475-2859-11-155.
16. Mo ML, Palsson BO, Herrgard MJ. Connecting extracellular metabolomic
measurements to intracellular flux states in yeast. BMC Syst Biol. 2009;3.
doi:Artn 37. doi:10.1186/1752-0509-3-37.
17. Burgard AP, Pharkya P, Maranas CD. OptKnock: A bilevel programming
framework for identifying gene knockout strategies for microbial strain
optimization. Biotechnol Bioeng. 2003;84(6):647–57. doi:10.1002/Bit.10803.
18. Lun DS, Rockwell G, Guido NJ, Baym M, Kelner JA, Berger B, et al. Large-scale
identification of genetic design strategies using local search. Mol Syst Biol.
2009;5. doi:Artn 296. doi:10.1038/Msb.2009.57.
19. Xue ZX, McCluskey M, Cantera K, Sariaslani FS, Huang LX. Identification,
characterization and functional expression of a tyrosine ammonia-lyase and
its mutants from the photosynthetic bacterium Rhodobacter sphaeroides.J
Ind Microbiol Biotechnol. 2007;34(9):599–604. doi:10.1007/s10295-007-0229-1.
Gold et al. Microbial Cell Factories (2015) 14:73 Page 15 of 16
20. Kummel A, Panke S, Heinemann M. Putative regulatory sites unraveled by
network-embedded thermodynamic analysis of metabolome data. Mol Syst
Biol. 2006;2:2006 0034. doi:msb4100074.
21. Clausen M, Lamb CJ, Megnet R, Doerner PW. PAD1 encodes phenylacrylic
acid decarboxylase which confers resistance to cinnamic acid in Saccharomyces
cerevisiae. Gene. 1994;142(1):107–12. doi: 0378-1119(94)90363-8.
22. Shin SY, Han NS, Park YC, Kim MD, Seo JH. Production of resveratrol from
p-coumaric acid in recombinant Saccharomyces cerevisiae expressing
4-coumarate:coenzyme A ligase and stilbene synthase genes. Enzyme
Microb Technol. 2011;48(1):48–53. doi:10.1016/j.enzmictec.2010.09.004.
23. Stucka R, Dequin S, Salmon JM, Gancedo C. DNA sequences in chromosomes II
and VII code for pyruvate carboxylase isoenzymes in Saccharomyces cerevisiae:
analysis of pyruvate carboxylase-deficient strains. Mol Gen Genet.
1991;229(2):307–15.
24. van Maris AJ, Geertman JM, Vermeulen A, Groothuizen MK, Winkler AA,
Piper MD, et al. Directed evolution of pyruvate decarboxylase-negative
Saccharomyces cerevisiae, yielding a C2-independent, glucose-tolerant, and
pyruvate-hyperproducing yeast. Appl Environ Microbiol. 2004;70(1):159–66.
25. Xu YF, Zhao X, Glass DS, Absalan F, Perlman DH, Broach JR, et al. Regulation
of yeast pyruvate kinase by ultrasensitive allostery independent of
phosphorylation. Mol Cell. 2012;48(1):52–62. doi:10.1016/j.molcel.2012.07.013.
26. Takada Y, Noguchi T. Characteristics of alanine: glyoxylate aminotransferase
from Saccharomyces cerevisiae, a regulatory enzyme in the glyoxylate
pathway of glycine and serine biosynthesis from tricarboxylic acid-cycle
intermediates. Biochem J. 1985;231(1):157–63.
27. Thomas D, Cherest H, Surdinkerjan Y. Identification of the structural gene
for glucose-6-phosphate-dehydrogenase in yeast - inactivation leads to a
nutritional-requirement for organic sulfur. EMBO J. 1991;10(3):547–53.
28. Hohmann S. Characterization of Pdc6, a 3rd structural gene for pyruvate
decarboxylase in Saccharomyces-cerevisiae. J Bacteriol. 1991;173(24):7963–9.
29. Hohmann S. Pdc6, a weakly expressed pyruvate decarboxylase gene from
yeast, is activated when fused spontaneously under the control of the Pdc1
promoter. Curr Genet. 1991;20(5):373–8. doi:10.1007/Bf00317064.
30. Blank L, Kuepfer L, Sauer U. Large-scale 13C-flux analysis reveals mechanistic
principles of metabolic networ k robustness to null mutations in yeast.
Genome Biol. 2005;6(6):1–16. doi:10.1186/gb-2005-6-6-r49.
31. Quevillon-Cheruel S, Leulliot N, Meyer P, Graille M, Bremang M, Blondeau K,
et al. Crystal structure of the bifunctional chorismate synthase from Saccharomyces
cerevisiae. J Biol Chem. 2004;279(1):619–25. doi:10.1074/jbc.M310380200.
32. Curran KA, Leavitt J, Karim A, Alper HS. Metabolic engineering of muconic
acid production in Saccharomyces cerevisiae. Metab Eng. 2013;15:55–66.
doi:10.1016/j.ymben.2012.10.003.
33. Bergman LW. Growth and Maintenance of Yeast. 2001. p. 9–14.
34. Sherman F. Getting started with yeast. Guide to yeast genetics and
molecular and cell biology, Pt B. Methods Enzymol. 2002;350:3–41.
35. Shao Z, Zhao H. DNA assembler, an in vivo genetic method for rapid
construction of biochemical pathways. Nucleic Acids Res. 2009;37(2):e16.
doi:gkn991 [pii] 10.1093/nar/gkn991.
36. Jansen G, Wu C, Schade B, Thomas DY, Whiteway M. Drag&Drop cloning in
yeast. Gene. 2005;344(0):43–51. doi:http://dx.doi.org/10.1016/j.gene.2004.10.016.
37. Hartmann M, Schneider TR, Pfeil A, Heinrich G, Lipscomb WN, Braus GH.
Evolution of feedback-inhibited beta/alpha barrel isoenzymes by gene
duplication and a single mutation. Proc Natl Acad Sci U S A. 2003;100(3):862–7.
doi:10.1073/pnas.0337566100.
38. Gueldener U, Heinisch J, Koehler GJ, Voss D, Hegemann JH. A second set of
loxP marker cassettes for Cre-mediated multiple gene knockouts in budding
yeast. Nucleic Acids Res. 2002;30(6). doi:ARTN e23. doi:10.1093/nar/30.6.e23.
39. Carter Z, Delneri D. New generation of loxP-mutated deletion cassettes for
the genetic manipulation of yeast natural isolates. Yeast. 2010;27(9):765–75.
doi:10.1002/Yea.1774.
40. Gietz RD, Schiestl RH. Transforming yeast with DNA. Meth Mol Cell Biol.
1995;5:255–69.
41. Edwards JS, Covert M, Palsson B. Metabolic modelling of microbes: the
flux-balance approach. Environ Microbiol. 2002;4(3):133–40. doi:10.1046/
j.1462-2920.2002.00282.x.
42. Schellenberger J, Que R, Fleming RMT, Thiele I, Orth JD, Feist AM, et al.
Quantitative prediction of cellular metabolism with constraint-based
models: the COBRA Toolbox v2.0. Nat Protoc. 2011;6(9):1290–307.
doi:10.1038/nprot.2011.308.
43. Frick O, Wittmann C. Characterization of the metabolic shift between
oxidative and fermentative growth in Saccharomyces cerevisiae by
comparative 13C flux analysis. Microb Cell Fact. 2005;4:30. doi:10.1186/
1475-2859-4-30.
44. Loftus TM, Hall LV, Anderson SL, McAlister-Henn L. Isolation, characterization,
and disruption of the yeast gene encoding cytosolic NADP-specific isocitrate
dehydrogenase. Biochemistry. 1994;33(32):9661–7.
45. Palmieri F, Agrimi G, Blanco E, Castegna A, Di Noia MA, Iacobazzi V, et al.
Identification of mitochondrial carriers in Saccharomyces cerevisiae by
transport assay of reconstituted recombinant proteins. Biochim Biophys
Acta. 2006;1757(9–10):1249–62. doi:10.1016/j.bbabio.2006.05.023.
46. Crutchfield CA, Lu WY, Melamud E, Rabinowitz JD. Mass spectrometry-based
metabolomics of yeast. Methods Enzymol. 2010;470:393–426. doi:10.1016/
S0076-6879(10)70016-1.
47. Villas-Boas SG, Hojer-Pedersen J, Akesson M, Smedsgaard J, Nielsen J. Global
metabolite analysis of yeast: evaluation of sample preparation methods.
Yeast. 2005;22(14):1155–69. doi:10.1002/Yea.1308.
48. Roth V. Doubling Time. 2006. http://www.doubling-time.com/compute.php.
49. Moran U, Phillips R, Milo R. SnapShot: Key Numbers in Biology. Cell.
2010;14(7):1262. doi:ARTN 1262.e1.doi:10.1016/j.cell.2010.06.019.
50. Phillips R, Kondev J, Theriot J, Garcia H. Physical biology of the cell. Second
edition. Garland Science; 2012.
51. Noor E, Haraldsdottir HS, Milo R, Fleming RM. Consistent estimation of
Gibbs energy using component contributions. PLoS Comput Biol.
2013;9(7):e1003098. doi:10.1371/journal.pcbi.1003098.
52. Satrustegui J, Bautista J, Machado A. NADPH/NADP+ ratio: regulatory
implications in yeast glyoxylic acid cycle. Mol Cell Biochem. 1983;51(2):123–7.
53. Zhang J, Sassen T, Ten Pierick A, Ras C, Heijnen JJ, Wahl SA. A fast sensor for
in vivo quantification of cytosolic phosphate in Saccharomyces cerevisiae.
Biotechnol Bioeng. 2014. doi:10.1002/bit.25516.
54. Droste P, Miebach S, Niedenfuhr S, Wiechert W, Noh K. Visualizing multi-omics
data in metabolic networks with the software Omix: a case study. Biosystems.
2011;105(2):154–61. doi:10.1016/j.biosystems.2011.04.003.
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