bioengineering
Article
TCA Cycle and Its Relationship with Clavulanic Acid
Production: A Further Interpretation by Using a Reduced
Genome-Scale Metabolic Model of Streptomyces clavuligerus
Howard Ramirez-Malule 1,* , Víctor A. López-Agudelo 1, David Gómez-Ríos 2, Silvia Ochoa 2,
Rigoberto Ríos-Estepa 3, Stefan Junne 4and Peter Neubauer 4
Citation: Ramirez-Malule, H.;
López-Agudelo, V.A.;
Gómez-Ríos, D.; Ochoa, S.;
Ríos-Estepa, R.; Junne, S.;
Neubauer, P. TCA Cycle and Its
Relationship with Clavulanic Acid
Production: A Further Interpretation
by Using a Reduced Genome-Scale
Metabolic Model of Streptomyces
clavuligerus.Bioengineering 2021,8,
103. https://doi.org/10.3390/
bioengineering8080103
Academic Editors: Peter Neubauer,
Christoph Herwig and Cornelia
Kasper
Received: 1 May 2021
Accepted: 16 July 2021
Published: 22 July 2021
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1Escuela de Ingeniería Química, Universidad del Valle, Cali 25360, Colombia; [email protected]
2Grupo de Investigación en Simulación, Diseño, Control y Optimización de Procesos (SIDCOP),
Departamento de Ingeniería Química, Universidad de Antioquia UdeA, Medellín 050010, Colombia;
3Escuela de Biociencias, Universidad Nacional de Colombia sede Medellín, Medellín 050010, Colombia;
4Chair of Bioprocess Engineering, Institute of Biotechnology, Technische Universität Berlin, D-13355 Berlin,
*Correspondence: howard.ramirez@correounivalle.edu.co
Abstract:
Streptomyces clavuligerus (S. clavuligerus) has been widely studied for its ability to produce
clavulanic acid (CA), a potent inhibitor of
β
-lactamase enzymes. In this study, S. clavuligerus cultivated
in 2D rocking bioreactor in fed-batch operation produced CA at comparable rates to those observed
in stirred tank bioreactors. A reduced model of S. clavuligerus metabolism was constructed by using
a bottom-up approach and validated using experimental data. The reduced model was implemented
for in silico studies of the metabolic scenarios arisen during the cultivations. Constraint-based
analysis confirmed the interrelations between succinate, oxaloacetate, malate, pyruvate, and acetate
accumulations at high CA synthesis rates in submerged cultures of S. clavuligerus. Further analysis
using shadow prices provided a first view of the metabolites positive and negatively associated with
the scenarios of low and high CA production.
Keywords:
Streptomyces clavuligerus; clavulanic acid; fed-batch cultivation; TCA cycle intermediate;
flux balance analysis; single-use bioreactor; shadow prices
1. Introduction
Streptomyces clavuligerus has been widely studied for its ability to produce clavulanic
acid (CA), a potent inhibitor of
β
-lactamase enzymes [
1
–
4
]. In this sense, many important
achievements have been made regarding nutrients, environmental and operational condi-
tions on CA production as previously reviewed by Ser et al. [
5
]. Although recent studies
have contributed to a deeper understanding of S. clavuligerus metabolism [
6
–
8
] as a basis
for strain engineering, system biology, and downstream processing (see recent review by
López-Agudelo et al. [
9
]), the interaction of byproduct accumulation and product synthesis
is not understood yet. Nonetheless, several S. clavuligerus strains have been engineered,
yielding up to 6.7 g L
−1
of CA in the supernatant [
10
,
11
]. Nevertheless, some steps in the
clavam pathway (Figure 1), wherein CA is synthesized, remain unclear or even unknown,
which hinders a detailed understanding of CA regulation and production [12–18].
Bioengineering 2021,8, 103. https://doi.org/10.3390/bioengineering8080103 https://www.mdpi.com/journal/bioengineering
Bioengineering 2021,8, 103 2 of 16
Figure 1. Schematic pathway of clavam biosynthetic pathway in S. clavuligerus.
In addition, a metabolic relationship between the tricarboxylic acid (TCA) cycle inter-
mediates and antibiotic production has been observed in Streptomycetes sp. [
19
]. This rela-
tionship has been experimentally validated for the case of CA biosynthesis in S. clavuligerus
cultivated in fed-batch and continuous modes [
20
,
21
]. Some experimental studies sug-
gest that CA production is favored during fed-batch operation under phosphate limita-
tion [
22
,
23
]. The differences in CA titers observed in fed-batch cultivations with defined
media, along with the identification of the role of TCA cycle intermediates on the synthesis
of CA in S. clavuligerus might help to identify metabolic targets susceptible to genetic
modification for rational strain engineering.
Bioengineering 2021,8, 103 3 of 16
Classical approaches for studying the nutritional, genetic, and environmental pertur-
bation effects on metabolic rates include the use of constraint-based models (CBM) and
genome-scale metabolic models (GEM) [
24
,
25
]. These models contain the stoichiometric
information of the cell-specific metabolism, Boolean relationships between genes, proteins,
and reactions, as well as nutritional constraints. GEMs work under the optimization of
a cellular objective (usually the maximization of the biomass growth rate) with the aim
of predicting carbon flux distributions [
26
]. Five GEMs have been published and used
as tools for assessing the complexity of S. clavuligerus metabolism in silico, especially in
connection with antibiotics production (CA and cephamycin C) [
20
,
21
,
27
–
29
]. The previous
stoichiometric metabolic models for S. clavuligerus were not reconstructed de novo from
the organism-specific reference sequences; instead, rather new biochemical information
was added to the first GEM without changing its initial structure [
9
,
21
]. Only recently,
an automated “bottom-up” reconstruction of the S. clavuligerus metabolism based on a
reference genome sequence for the strain (refseq GCA_001693675.1) was used as a model
draft. The initial draft was systematically curated, validated, and used for describing
metabolic events during CA production in fed-batch cultures [21].
In summary, the “top-down” approach used in the first reconstructions of the
S. clavuligerus metabolism used the genome annotation as a starting point for reconstructing
the initial metabolic network [
30
]. Nevertheless, this strategy tends to generate large and
complex networks with many gaps, blocked reactions, and non-connected metabolites in
poorly characterized metabolic pathways [
31
]. On the contrary, “bottom-up” reconstruc-
tion uses the biochemical and organism-specific information as a starting point leading to
higher quality manually-curated metabolic networks [31].
Application of GEMs in S. clavuligerus studies has focused on the interplay between
the antibiotics’ biosynthesis in the cephalosporin and clavam pathway and the Central
Carbon Metabolism (CCM). The complexity and quality of GEMs are increasing with
the availability of improved high-quality sequences, and hence, better gene annotations.
Nevertheless, the mathematical solution of the optimization problem under pseudo-steady
state assumption frequently shows a large number of reactions with a zero flux. The
majority of analyses are focused on the central metabolism and specific pathways activated
under certain conditions. Reduced models have emerged recently as alternatives to GEMs
which describe the metabolism with a lower number of reactions while preserving the
pathways of interest. Reduced networks also facilitate the inclusion of kinetic equations,
which was infeasible for a whole GEM. Some systematic and algorithmic approaches have
been proposed to reduce a GEM into a small model while maintaining the quality of the
original model. Reduced GEMs for the organisms Pseudomonas putida,Escherichia coli, and
for human metabolism (Recon 2 and Recon 3D) have been recently constructed using a
“bottom-up” strategy. They have been used for integrating high throughput data and
exploiting demanding computational algorithms for parameter optimization in large-scale
kinetic models [
32
–
35
]. Therefore, a consistent model representation of the physiology of
S. clavuligerus might also be reached by a reduced model retaining the predictive capabilities
of the GEM [34].
Some previous reports have been connected to the CA biosynthesis with the avail-
ability of precursors and metabolic flux distribution [
20
,
28
,
36
,
37
]. Additionally, kinetic
studies have proven that CA is susceptible to hydrolysis in aqueous solutions [
38
–
40
].
Recently, a robustified experimental design for
13
C-Metabolic Flux Analysis was conducted
in S. clavuligerus [
41
]. However, metabolic bottlenecks for increasing the CA biosynthesis
rate remain to be identified. In this respect, the high flexibility, lower complexity, and small
size of reduced GEMs are helpful in identifying bottlenecks between the CCM and sec-
ondary metabolite production that might be masked in the large-size models. Additionally,
critical parameters that could improve CA production can be investigated by the analysis of
metabolic fluxes, constraint-based modeling approaches, and the interpretation of shadow
prices, resulting from the solution of the Flux Balance Analysis (FBA) problem [25,42].
Bioengineering 2021,8, 103 4 of 16
In this study, experimental data of fed-batch bioreactor cultivations of S. clavuligerus
cultivated under low shear stress conditions (non-stirred) are integrated with constraint-
based modeling using a reduced, curated, and validated model of the microorganism to
understand further the role of central carbon precursors in the synthesis of CA.
2. Materials and Methods
2.1. Strain and Cultivations Conditions
S. clavuligerus DSM 41,826 was used for all cultivations. Periodically, mycelium
stock cultures were reactivated and stored at
−
80
◦
C in cryotubes with glycerol solution
(16.7% v/v). The media for inoculum, production, and the feed have been published
previously [
12
,
20
,
43
]. One point two microliters of glycerol stock were transferred to 50 mL
of seed medium in 250 mL UltraYield
©
shake flasks which were sealed with air-permeable
AirOtop membranes (both from Thomson Instrument Company, Oceanside, CA, USA). The
seed cultures were grown in a rotary shaker incubator for 26 h at 200 rpm and 28
◦
C. For the
second preculture, 10 mL of cultivated seed broth was transferred to 90 mL of bioreactor
batch medium in a 500 mL shake flask. The flasks were covered with AirOtop seals, and
cells were grown for 20 h at the same condition as the seed medium. The preculture was
inoculated at 10% v/vinto the bioreactor batch medium. The feed composition was as
follows: glycerol 120 g L−1, (NH4)2SO48gL−1, and K2HPO42gL−1.
Fed-batch cultivations were conducted by duplicate in a 20 L single-use 2D rocking-
motion bioreactor CELL-tainer
®
CT 20 (Cell-tainer Biotech BV, Winterswijk, The Nether-
lands) with a working volume of 1 L during the batch phase as described in [
21
,
43
]. After
36 h, feeding was started at a constant rate of 0.01 L h
−1
for 72 h up to 1.8 L as the final
volume. Cultivations were controlled at 28
◦
C, 0.6 vvm, and a pH-value of 6.8. Samples of
2 mL were taken at 12 h intervals for dry cell weight (DCW) determination. Samples were
centrifuged at 15,000 rpm and 4
◦
C for 10 min and dried overnight at 75
◦
C up to constant
weight. The supernatants samples were analyzed via HPLC-DAD and HPLC-RID for CA
and intermediates quantifications, respectively, as described previously [44,45].
2.2. Bottom-Up Reconstruction of a Reduced Genome-Scale Metabolic Model of S. clavuligerus
The GEM of S. clavuligerus (iDG1237), recently reported by Gómez-Ríos et al. [
21
],
was used as a bottom-up draft reconstruction. The high-quality GEM model iDG1237 is
the most recent reconstruction of S. clavuligerus metabolism, which includes 1518 metabo-
lites, 2177 reactions, and 1237 genes and represented accurately experimentally observed
phenotypes during CA secretion. The bottom-up strategy consisted of a systematic pro-
cess of manual and semi-automated curation [
31
]. Initially, the main metabolic pathways
of the iDG1237 GEM were identified and used for the generation of a first draft of the
reduced stoichiometric matrix. This draft contained the reactions associated with CCM,
such as glycolysis/gluconeogenesis, pentoses phosphate pathway, TCA cycle, amino acid
metabolism, urea cycle, and clavam pathway. The reactions not associated with those
subsystems were not considered in the reduced draft model. Subsequently, the zero-flux
reactions and dead-end metabolites in the network were identified and eliminated or
curated by filling the gaps with reactions taken from the BiGG Models database [
46
,
47
]
and the reduced model of E. coli [
33
]. Likewise, lumped reactions of biomass precursors
of E. coli that also exist in S. clavuligerus were included [
48
]. The production of biomass
precursors was checked after reduction. The biomass precursors that could not be produced
in the reduced model (named in this study as sclav_red) were identified, and the reactions
required for their production to maintain the consistency of the model were included. In
the case of those reactions not reported in the S. clavuligerus genome, the corresponding
precursors were eliminated from the reduced biomass reaction. The carbon contribution
of those reactions to biomass formation was not statistically significant. The stated steps
were applied iteratively until a reduced metabolic network of S. clavuligerus with high
topological consistency was obtained.
Bioengineering 2021,8, 103 5 of 16
Additionally, thermodynamically infeasible cycles (TICs) were checked and
corrected
[49–51]
. TICs are formed by cyclic reactions carrying fluxes without exchanging
energetic metabolites, such as ATP, and therefore violate the second law of thermodynamics.
The TICs identification and correction was carried out by adapting the methodology described
by López-Agudelo et al. [
52
] and Gómez-Ríos et al. [
21
] (https://github.com/viclopezag/UR-
TICS, last accessed 4 July 2021). Once identified, the TICs formed by erroneous directionality
assignments were corrected by directionality restriction based on a change in the Gibbs free
energy obtained from the eQuilibrator database [
53
]. Similarly, transport reactions were
revised to change the directionality of those only associated with the import and export of
metabolites. All the modifications applied to the model draft are listed in the Supplementary
Table S1.
2.3. Validation of the Sclav_Red Model Using Experimental Data
The sclav_red model validation was performed by comparing the predicted fluxes
at a pseudo-steady state against experimental data of S. clavuligerus in continuous cul-
tures [
20
]. Here, FBA simulations were performed by maximizing the specific growth rate
under the minimization of the Manhattan norm of fluxes by using the “optimizeCbModel”
function along with the “minNorm” option, both included in the COBRA toolbox [
54
]. The
experimental specific growth rate in continuous cultures was compared with the model’s
predictions. No hard constraints were imposed on the optimization. Only soft constraints
were used so that the experimental exchange fluxes were in the range defined by the upper
and lower bounds (File S2). Exchange fluxes of glycerol, O
2
, CO
2
, and CA were constrained
according to the experimental data and were used as lower (Glycerol and O
2
) or upper (CA
and CO
2
) bounds constraints. The bounds were systematically explored by Flux Variability
Analysis (FVA) to avoid unbounded fluxes for the nutrients. Mean square error (MSE)
between the model outputs from FBA and experimental fluxes in continuous cultures at
different dilution rates [20] was computed to evaluate the quality of predictions.
2.4. FBA and Shadow Prices as Tools for Sensitivity Analysis of Metabolic Networks
FBA has been used very often to study the metabolic flux distribution of an entire
metabolic network [
55
–
58
]. Under the assumption of a pseudo-steady state, the net flux of
production and consumption of a metabolite is assumed to be zero. In order to simulate
the present experimental data of this paper, a two-step optimization approach was used.
First, the maximization of CA production was defined as an objective function under the
assumption that during phosphate limitation, CA production is achieved; thus, configuring
the FBA primal problem. Second, the minimization of the Manhattan norm of the absolute
value of all the intracellular fluxes was done by using as a constraint the maximum CA
value obtained in the first step optimization. This FBA optimization was implemented in
the cobra toolbox function “optimizeCbModel” [
54
]. Additional to the vector of fluxes, the
vector of shadow prices per metabolite was calculated with the solution of the optimization
problem. Shadow prices were interpreted as the sensitivity of FBA to flux imbalances
obtained during the solution of the dual problem of linear optimization. The shadow
prices vector relates the change in the objective function of the primal problem (i.e., CA
maximization) when the flux of one of the intracellular metabolites increase or decrease,
resulting in a deviation from the steady state; that is the sensitivity of FBA to flux imbalances
obtained during the solution of the dual problem of the linear optimization. Reznik et al.,
2013 showed the importance of shadow prices in the analysis of metabolic networks. For
instance, a negative value of the shadow price for a metabolite implies that additional
outflow of this metabolite would increase the value of the objective function showing
that the metabolite is actually a limiting compound for the objective [
42
]. The biological
interpretation of shadow prices in metabolic modeling has been summarized as follows:
(i) negative values of shadow prices are obtained for those metabolites whose flux is
limiting the objective; (ii) zero value implies that the objective function is not sensitive to
Bioengineering 2021,8, 103 6 of 16
that metabolite; and (iii) positive values of shadow prices are obtained for those metabolites
with sufficient intracellular flux for reaching the objective.
Further details on shadow prices utilization in metabolic modeling are available
(see references [
25
,
54
]). The experimental growth rate was set as a hard constraint in
the optimization problem with the aim to get a better representation of the metabolic
scenarios, while the measured uptake and secretion rates (when available) were set as
lower (O
2
, glycerol, succinate, oxaloacetate, and malate) and upper (CO
2
, pyruvate and
acetate) bounds, respectively. The IBM CPLEX Studio Optimizer v12.10.0 was used for
the solution of the optimization problems. It is worth mentioning that the sign of shadow
prices calculated by CPLEX solver was negative (
−λ
), and it was considered for the further
biological interpretation of shadow prices results.
Furthermore, the solution space for the fluxes’ distributions of two different metabolic
states of S. clavuligerus was explored by using FVA and flux sampling using the sclav_red
metabolic model. The model was constrained with uptake and secretion rates correspond-
ing to 36 h (batch stage) and 48 h (12 h after starting the fed-batch stage) of cultivation.
The model predictions were contrasted with experimental exchange fluxes for the assayed
metabolites and nutrients using the squared error (SE) as indicated in Equation (1) for a
given metabolite i.MSE was calculated as indicated in Equation (2) to assess the deviation
of model predictions and experimental fluxes for a given metabolic scenario.
SEi=Xi−ˆ
Xi2(1)
MSE =
1
p
p
∑
i=1
SEi(2)
where
p
is the number of experimental fluxes considered in the scenario,
X
is the experi-
mental flux value, and ˆ
Xis the model-predicted flux value.
In addition, the feasible flux range was determined for each reaction via FVA. Alter-
natively to the unique solution provided by the FBA problem, the coordinate hit-and-run
with rounding algorithm (CHRR) [
59
] was used for sampling the solution space for the
explored cultivation conditions (batch and fed-batch stages). The following CHRR parame-
ters were set: the sampling density, nStepsPerPoint = 1848, and the number of samples,
nPointsReturned = 5000. A Kruskal—Wallis test was used to assess whether flux samples
generated using either the batch (36 h) or fed-batch (48 h) constraints stemmed from the
same statistical distribution [
60
]. A principal component analysis (PCA) was applied to
metabolite shadow prices for the identification of those metabolites that contributes to the
changes in flux distributions or phenotypes between the culture conditions under study.
All simulations were carried out in RAVEN 2.0 [
61
] and COBRA Toolbox v3.0 [
54
]
under MATLAB 2020b (see Supplementary File S3).
3. Results and Discussion
3.1. Fed-Batch Cultivation of S. clavuligerus
Figure 2a displays the profiles of biomass as DCW, glycerol, phosphate, and CA
concentrations during fed-batch cultivations of S. clavuligerus in a 2D rocking bioreactor
starting at 5% and finalizing at 9% filling volume. The batch phase lasted for the first 36 h
of cultivation; then, the feeding started at constant feed rate until the 108 h. The maximum
biomass concentration was attained at 52 h with 14.6 g DCW L
−1
. Afterward, a clear de-
crease in biomass concentration was observed due to the phosphate limitation and dilution
due to the feed rate. CA was detectable during the complete phosphate-limited stage up to
a maximum concentration of 370.9
±
14.1 mg L
−1
(Figure 2a). TCA intermediates succinate,
pyruvate, and acetate were accumulated during CA synthesis, while oxaloacetate and
malate were rather constant during the fed-batch cultivation (
Figure 2b,c
). A comparable
metabolic scenario (acetate, succinate, oxaloacetate, and malate accumulation during CA
synthesis) has already been reported by Ramirez-Malule and co-workers in continuous
cultures with low CA titers [
20
]; however, the accumulation levels of CA and the TCA
Bioengineering 2021,8, 103 7 of 16
cycle intermediates was almost 10-fold higher during fed-batch operation in comparison
with the reported results for continuous mode. The higher extracellular concentration of
those metabolites is linked to the higher biomass, more active energetic metabolism, and
specialized metabolites production [21].
Figure 2.
Fed-batch cultivation of S. clavuligerus. (
a
) Cell dry weight (circle), phosphate (square),
glycerol (diamond), and clavulanic acid (triangle) as a function of cultivation time. (
b
) Succinate
(circle) and oxaloacetate (triangle) as a function of cultivation time. (
c
) Malate (circle), pyruvate
(diamond), and acetate (triangle) as a function of cultivation time.
Previously, a difference in the metabolic performance of S. clavuligerus cells has been
observed when cultivating at different shear conditions and hence, intracellular oxygen
uptake flux. The 2D rocking bioreactor is characterized by enhanced gas mass transfer
due to large film surface and combination of vertical and horizontal motion leading to
wave formation and gas suction into the liquid phase with lower shear stress than stirred
tank bioreactors [
62
]. CA productivity in S. clavuligerus is intimately related to intracellular
Bioengineering 2021,8, 103 8 of 16
oxygen availability since the biosynthetic pathway requires molecular oxygen in those
reactions catalyzed by the clavaminate synthase. The metabolic scenario observed in this
work resembles that observed in a stirred tank bioreactor with comparable CA productivity,
suggesting that intracellular oxygen availability was likely the same [
21
,
43
]. In this case,
the large headspace and low volume favored the oxygen uptake and hence, CA secretion,
compared with cultivations at similar conditions in 2D rocking bioreactor but higher (5-fold)
operating volume.
The observed succinate accumulation was reported as a result of increased activity
of nitrogen metabolism and hence, with an existing phosphate limitation, due to CA
production. Succinate is released as a byproduct in the reaction steps of CA biosynthesis
catalyzed by the clavaminate synthase, an
α
-ketoglutarate iron-dependent oxygenase (see
Figure 1) [
63
–
65
]. Such activation of specialized metabolism in S. clavuligerus occurs under
phosphate limitation probably as part of an energy-regulating mechanism that controls
ATP generation and ATP saving according to the anabolism requirements [21].
Pyruvate and acetate accumulations during CA production (Figure 2c) were associated
with the requirements of the CCM and CA biosynthesis, respectively [
20
,
21
]. Oxaloac-
etate and malate were rather constant during fed-batch operation with smooth variations
linked to phosphate and limited availability of amino acids, which affect growth and
energetic metabolism [
21
]. All those accumulations supported the higher activity of the
TCA cycle and energetic metabolism expected during the CA biosynthesis. Bushell and
co-workers [
37
] reported that feeding amino acids from the oxaloacetate family improved
the CA yield more than 10-fold compared with non-supplemented chemostat cultivations,
thus resuming a bottleneck that otherwise occurs. Previously, a reaction mechanism for
the N-acetyl-glycyl-clavaminic acid formation involving acetate by an ATP-grasp enzyme
was proposed [
12
]. This hypothesis is supported by the consistent acetate accumulations in
chemostat and fed-batch cultures (this work). Furthermore, acetylated compounds, such
as N-acetyl-glycyl-clavaminic acid and N-acetyl-clavaminic acid, have been suggested as
intermediates in the clavam pathway [
66
,
67
]. The experimental results of this work and
those reported by us previously [
20
,
21
,
43
] suggest a clear interrelation between the TCA
cycle fluxes, the availability of intermediates along with pyruvate and acetate accumulation,
and CA biosynthesis.
3.2. Construction of a Reduced Model of S. clavuligerus Metabolism
Reduced metabolic models are condensed representations of an organism’s biochem-
ical network. The mathematical analysis of the biochemical network allows studying in
silico the cellular phenotypes observed
in vivo
. This reduced representation leads to a
reduction in computational time of complex simulations without loss of fidelity. Addition-
ally, it is expected that the reduction of a high-quality GEM may generate a reduced model
with equivalent accuracy. The first draft of the reduced model included the reactions of
CCM and numerous gaps, single connected and dead-end metabolites as a consequence of
insufficient connection between reactions in the network. Thus, 317 reactions were added,
including the lumped reactions summarizing some minor metabolic pathways. The added
reactions and lumped reactions (identified as LMPD) are detailed in Supplementary Table
S1. Forty-two reactions were modified to fix the single-connected or dead-end condition
of some metabolites. Finally, the directionality of 20 reactions was modified during the
thermodynamic curation. The reduced metabolic network encompassed 652 reactions,
1552 metabolites, and 1284 genes. The validation results for the sclav_red model against
pseudo-steady state experimental data [20] are presented in Table 1. The sclav_red model
(-mat format) is available in the Supplementary Material (File S1).
Bioengineering 2021,8, 103 9 of 16
Table 1.
Comparison of rate predictions of sclav_red with experimental data in continuous cultures
of S. clavuligerus at different dilution rates (D) obtained by Ramirez-Malule et al. [20].
Reaction
D = 0.045 D = 0.035 D = 0.050
FBA Exp. SE FBA Exp. SE FBA Exp. SE
Growth (h−1)0.039
0.045
0 0.034
0.035
0 0.044 0.050 0
O2−
0.375
−
1.665
1.662
−
0.322
−
1.621
1.686
−
0.511
−
1.848
1.787
Glycerol −
0.695
−
1.110
0.172
−
0.639
−
0.968
0.108
−
0.728
−
0.728
0
CO20.067
0.067
0 0.023
0.023
0 0.253 0.253 0
Clavulanate 0.017
0.398 0.145
0.015
0.357 0.117
0.020 0.435
0.172
Phosphate 0.000 N/A N/A 0.000 N/A N/A 0.000 N/A N/A
Glutamate −
0.350
N/A N/A −
0.310
N/A N/A −
0.400
N/A N/A
MSE 0.396 0.382 0.392
Flux units in (mmol.(gDCW *h)
−1
). FBA: flux balance analysis data for the sclav_red model, Exp: experimental
data from [20], N/A: no experimental data available.
The MSE for the reduced model of S. clavuligerus metabolism was lower than for
previously reported GEMs. A complete performance evaluation of S. clavuligerus GEMs
was made by Gómez-Ríos et al. [
21
]. Although the iDG1237 model has the lowest MSE
reported for a S. clavuligerus metabolic model [
21
], the sclav_red model exhibited a MSE in
the same order of magnitude, which means that both models have a similar performance
in the prediction of this specific experimental scenario. Indeed, it was expected that the
parent GEM would have a lower error since it is the most updated GEM for the organ-
ism. The sclav_red constituted a condensed representation of the metabolic capabilities of
S. clavuligerus, with a special focus on the CCM and CA biosynthesis. This model constitutes
a more ‘computationally’ efficient metabolic network compared to the large-scale iDG1237.
This network could be applied for exploring metabolic phenotypes using constraint-based
methods that require high computational power, such as flux sampling [
68
], thermodynam-
ically constrained stoichiometric models that use energy balance as a constraint [
69
], or
large-scale kinetic modeling [32,70].
3.3. Flux Distributions in S. clavuligerus at Two Different Metabolic States during Fed-Batch
Cultivations
A constraint-based analysis was conducted at the two different metabolic states
expected during the batch and fed-batch stages of the cultivations, under the assump-
tion of quasi-steady-state conditions between two consecutive data points. The curated
and reduced reconstruction of S. clavuligerus metabolism sclav_red was used in all the
simulations.
The analysis of the two stages of cultivation aimed to explore the metabolic phenotypes
that occurred during the two different operation modes by means of FVA, FBA (Table S2),
and flux sampling. The pseudo-state condition was assumed since the measured growth
rate was rather constant (an elongation of exponential growth phase) in both time steps.
Table 2displays the metabolic constraints used for the in silico studies and the comparison
between the experimental and the simulated flux rates. Notice that in those metabolites
with SE = 0, the exchange flux assumed the lower bound value constraining the search
space, while in some cases, the metabolites assumed values in the range defined by the
upper and lower bounds. According to the results of MSE calculations, the reduced
metabolic model was suitable for representing the glycerol assimilation, CA production,
succinate, oxaloacetate, malate, and acetate secretion fluxes during both batch and fed-
batch stages. However, the specific O
2
consumption and specific CO
2
production rates
showed the highest discrepancies in both operation modes. Likewise, the sclav_red model
provided a better representation of the metabolic scenario of the fed-batch (MSE = 0.86)
stage than the batch stage (MSE = 1.7).
Bioengineering 2021,8, 103 10 of 16
Table 2.
Comparison between experimental and simulated rates in batch and fed-batch cultivations
of S. clavuligerus.
Reaction
Batch (36 h) Fed-Batch (48 h)
FBA Exp. SE FBA Exp. SE
Growth (h−1)0.042 0.042 ±0.004 0 0.031
0.031
±
0.003
0
O2−0.512 −1.350 0.702 −0.776 −1.2 0.180
Glycerol −0.182 −0.182 0 −0.457 −0.47 0
CO20.639 1.640 1.002 0.610 1.4 0.623
Clavulanate 0.002 0.002 0 0.004 0.004 0
Succinate 0.014 0.014 0 0.257 0.007 0.063
Oxaloacetate 0.004 0.004 0 0 0.003 0
Malate 0 0.001 0 0 0 0
Pyruvate 0 0 0 0.038 0.005 0.001
Acetate 0 0 0 0 0 0
MSE 1.700 0.860
Flux units in (mmol.(gDCW *h)
−1
). FBA: flux balance analysis data, Exp: experimental data. All fluxes in the
table were experimentally quantified and soft constrained for the in silico studies. Growth rate was set as a hard
constraint.
Figure 3shows the predicted intracellular flux distributions of S. clavuligerus during
cultivation in the 2D rocking single-use bioreactor at 36 and 48 h. The results showed that
the high CA production during the fed-batch stage could be associated with the probability
of increased flux through the transketolase (TKT1) enzyme, which favors the production of
glyceraldehyde
−
3 phosphate (G3P) from the pentose phosphate pathway and glycolysis.
Availability of G3P favors the CA biosynthesis as this metabolite is included in the synthesis
of N
2
-(2-carboxyethyl)-arginine, the first intermediate of the clavam pathway produced
via N
2
-(2-carboxyethyl)-arginine synthase (CEAS) (Supplementary File S2). Similarly, the
clavaminate synthase (CS2) was also activated by this increased flux in the clavam pathway
towards clavaminic acid, a point of bifurcation for the biosynthesis of CA and clavam 5S
compounds. One of the main observed phenotypes in our experimental data evidenced
the link between TCA intermediates, such as succinate, oxaloacetate, and malate, and CA
production (Figure 2). The in silico analyses suggested that the accumulation of succinate,
malate, and oxaloacetate was associated with a high activation of the glyoxylate shunt
via isocitrate lyase (ICL). Additionally, the anaplerotic reaction catalyzed by the phospho-
enolpyruvate carboxylase (PPC) acted as the main contributor of oxaloacetate, which along
with glutamate, generates arginine, the C5 precursor for CA production. Thus, the sim-
ulations showed higher probabilities for fluxes through aspartate transaminase (ASPTA)
and a lumped reaction summarizing the steps of arginine production (LMPD_33_arg-L_c,
Figure 3). The obtained flux distributions support the hypothesis that an increased flux of
G3P favors its condensation with arginine (via CEAS), promoting a higher CA synthesis
rate as a consequence of higher activity in the glycolysis, pentose phosphate shunt, and
anaplerotic reactions, such as ICL and PPC.
The shadow prices were used to analyze the effect of accumulation or depletion of
metabolites in the CA production through the deviation of steady-state. As in the case of
flux distributions, the shadow prices were computed for each metabolite in the metabolic
network for both stages, batch and fed-batch (Table S2). Therefore, the size of the data
matrix was 1552 metabolites
×
2 datasets (batch and fed-batch). This matrix contained the
shadow prices of the 1552 metabolites in both conditions. Although dimensionality reduc-
tion techniques such PCA are not usually done for two sets of conditions, we decided to
apply this analysis for a clear visualization of the most important metabolites contributing
to the objective function for each experimental condition, as shown in Figure 4.
Bioengineering 2021,8, 103 11 of 16
Figure 3.
Summary of intracellular and exchange flux distributions in the S. clavuligerus reduced
metabolic network for Table 2. Distributions indicated with blue, and orange represent the metabolic
scenarios in batch (36 h) and fed-batch mode (48 h), respectively. Dotted lines represent flux ranges
of FVA. The significantly different distributions (p< 0.001, Kruskal—Wallis test) have been marked
with an asterisk (*). The reaction names with the prefix EX_ corresponds to the exchange reactions
of consumed or produced metabolites by S. clavuligerus. glu__L: L-glutamate, o2: Oxygen, glyc:
glycerol, co2: CO
2
, pi: phosphate, clav: clavulanic acid, nh4: NH
4+
. For a better discrimination of
flux sampling distributions, please refer to Figure S1.
Blue vectors in opposite directions indicate that shadow price profiles in the metabolic
network differ under batch and fed-batch operations. Therefore, the sclav_red model
predicted a different use of the metabolites for CA production during the batch and fed-
batch operation modes. The 70 metabolites with the highest changes in the shadow prices
are represented in Figure 4. Co-factors and energetic metabolites, such as tetrahydrofolate,
biotin, S-adenosyl-methionine, NAD
+
, NADP
+
, ATP, GTP, and CoA, were predicted to have
high changes in shadow prices values. As an example, the shadow price values of biotin
(btn[c]) were 3.0 and 0.28 for batch and fed-batch modes, respectively. Both positive values
indicate an important role of biotin on CA biosynthesis since biotin is a key cofactor that
maintains metabolic homeostasis. It is essential for the correct operation (carboxylation)
of fatty acids, the TCA cycle, and amino acids metabolism [
71
,
72
]. Given the connections
between the TCA, amino acid metabolism, and CA biosynthesis, biotin might have an
indirect influence on CA production.
Bioengineering 2021,8, 103 12 of 16
Figure 4.
PCA of shadow price changes per metabolite in batch and fed-batch operation mode of
S. clavuligerus cultivation. PC1: Principal component 1, PC2, Principal component 2.
Phosphate limitation required for activation of CA biosynthesis affects ATP availabil-
ity. The negative shadow prices for ADP/ATP and NADP
+
/NADPH in both conditions
(Figure 4) suggest that CA biosynthesis coexisted with the reduction in ATP generation,
and conversely, the high synthesis rate of those cofactors was connected with low or no
CA production. It has been reported that one of the biological roles of antibiotics synthesis,
apart from defense, is to adjust the ATP synthesis/consumption in phosphate scarcity
conditions [
73
]. Similarly, the positive shadow prices of metabolites of the clavam pathway
imply that high CA synthesis rates also favor the production of metabolites beyond the
clavaminic acid bifurcation, i.e., the 5-S clavams compounds.
Table 3lists some relevant metabolites that belong to the central metabolism with
their respective shadow price value. Like the observed in the flux distribution and the
experimental data, metabolic intermediates of TCA cycle, such as succinate, oxaloacetate,
malate, pyruvate, citrate, and fumarate, had positive shadow prices during the phase
of CA production, i.e., fed-batch stage under phosphate limitation. This suggests that
these metabolites positively influence CA biosynthesis, and therefore, the intracellular
accumulation of some TCA cycle intermediates would improve CA synthesis. Conversely,
the accumulation of intermediates of the pentose phosphate pathway negatively influences
the CA production since those metabolites are associated with nucleotide synthesis and
anabolism for biomass production but not secondary metabolism. As in the case of TCA
intermediates, shadow prices of amino acids related to the synthesis of the C-5 precursor of
CA, such as glutamine, arginine, asparagine, aspartate, and glutamate, indicated a positive
influence on CA biosynthesis.
Bioengineering 2021,8, 103 13 of 16
Table 3.
Shadow price values of some metabolite intermediates of the central carbon metabolism in
S. clavuligerus.
Metabolites
Shadow Prices (CPLEX)
Batch (36 h) Fed-Batch (48 h) Pathway
α-ketoglutarate 0 0.05
Glycolysis and TCA
cycle
Pyruvate 0 0.02
Citrate 0 0.05
Isocitrate 0 0.05
Fumarate 0 0.02
Acetate 0 0.02
Succinate 0 0.02
Oxaloacetate 0 0.02
Malate 0 0.02
Erythrose 4 phosphate 0 −0.40
Pentose phosphate
pathway
Fructose 6 phosphate 0 −0.38
Xylulose 5 phosphate 0 −0.39
Sedoheptulose 7-phosphate 0 −0.37
L-glutamate 0.5 0.05
Amino acids
synthesis
L-glutamin 0.5 0.05
L-arginine 1.5 0.05
L-asparagin 0.5 0.02
L-aspartate 0.5 0.02
4. Conclusions
In this work a reduced model of S. clavuligerus metabolism was successfully con-
structed and used as an analysis tool for the study of metabolic scenarios characterized by
high CA production. The reduced reconstruction used a bottom-up approach starting from
a high-quality genome-scale model and retained the predictive capacity of the large model
while requiring less computational power for reaching a feasible solution. This is the first
reduced genome-scale model of S. clavuligerus developed and validated using experimental
data. A reduced network of S. clavuligerus would make possible a further construction of a
kinetic genome-scale network, something infeasible for a large-scale metabolic model.
The metabolic scenario observed for the cultivation explored in this work resembles
the biomass and CA productivity obtained in stirred tank bioreactors but without the
shear stress effects associated with the axial-impeller equipment. The high volumetric
gas transfer rate of the 2D rocking bioreactor favored the metabolic fluxes towards the
clavams pathway under phosphate-limited conditions. The in silico analyses using the
reduced model confirmed the interrelation between the accumulation of acetate, pyruvate
and the TCA cycle intermediates (succinate, oxaloacetate, and malate) and CA production.
The shadow prices provided the first view of those metabolites negatively and positively
associated with the scenarios of low and high production, the first attained during the early
stages of cultivation and the latter under phosphate limitation.
Supplementary Materials:
The following are available online at https://www.mdpi.com/article/
10.3390/bioengineering8080103/s1, File S1: reduced model of S. clavuligerus in mat format, File S2:
Plot of 64 fluxes distributions during two metabolic states in batch and fed-batch operations, File
S3: Supplementary scripts. Figure S1: intracellular and extracellular flux sampling distributions
representing metabolic scenarios in batch (36 h) and fed-batch (48 h) culture mode of S. clavuligerus.
Table S1: List of modifications to the curated sclav_red model, Table S2: FBA, FVA, and Shadow
Prices obtained for the two simulated metabolic states during batch and fed batch cultivation. Text
S1: Explorative gene knockouts to improve the CA production.
Author Contributions:
Conceptualization, H.R.-M., S.J. and P.N.; methodology, H.R.-M., S.J. and
V.A.L.-A.; software, H.R.-M. and V.A.L.-A.; validation, H.R.-M. and V.A.L.-A.; formal analysis, H.R.-
M. and V.A.L.-A.; investigation, H.R.-M., S.J. and V.A.L.-A.; resources, H.R.-M., R.R.-E., S.J. and
P.N.; data curation, H.R.-M. and V.A.L.-A.; writing—original draft preparation, H.R.-M., V.A.L.-
Bioengineering 2021,8, 103 14 of 16
A. and D.G.-R.; writing—review and editing, H.R.-M., V.A.L.-A., D.G.-R., R.R.-E., S.O., S.J. and
P.N.; visualization, H.R.-M. and V.A.L.-A.; supervision, H.R.-M., R.R.-E., S.O., S.J. and P.N.; project
administration, H.R.-M., R.R.-E., S.O., S.J. and P.N.; funding acquisition, H.R.-M., R.R.-E., S.O., S.J.
and P.N. All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by the Universidad del Valle C.I. 21108, and MINCIENCIAS—
Colombia: Postdoctoral fellowship number 848-2019 (grant number 80740-291-2020), and the joint
mobility grant of MINCIENCIAS, grant number 80740-057-2019, and the German Federal Ministry of
Education and Research, grant number 01DN19005.
Data Availability Statement: Not applicable.
Acknowledgments:
V.A.L.-A. thanks MINCIENCIAS for the economic support of the national Ph.D.
scholarship (Convocatoria 727 de 2015) during his Ph.D. studies.
Conflicts of Interest: The authors declare no conflict of interest.
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