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OptDesign: Identifying Optimum Design Strategies in Strain Engineering for Biochemical Production

Cite this: ACS Synth. Biol. 2022, 11, 4, 1531–1541
Publication Date (Web):April 7, 2022
https://doi.org/10.1021/acssynbio.1c00610

Copyright © 2022 The Authors. Published by American Chemical Society. This publication is licensed under

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Abstract

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Computational tools have been widely adopted for strain optimization in metabolic engineering, contributing to numerous success stories of producing industrially relevant biochemicals. However, most of these tools focus on single metabolic intervention strategies (either gene/reaction knockout or amplification alone) and rely on hypothetical optimality principles (e.g., maximization of growth) and precise gene expression (e.g., fold changes) for phenotype prediction. This paper introduces OptDesign, a new two-step strain design strategy. In the first step, OptDesign selects regulation candidates that have a noticeable flux difference between the wild type and production strains. In the second step, it computes optimal design strategies with limited manipulations (combining regulation and knockout), leading to high biochemical production. The usefulness and capabilities of OptDesign are demonstrated for the production of three biochemicals in Escherichia coli using the latest genome-scale metabolic model iML1515, showing highly consistent results with previous studies while suggesting new manipulations to boost strain performance. The source code is available at https://github.com/chang88ye/OptDesign.

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It is worth noting that the desired down-regulation sometimes may not be achievable when the selective pressure to increase gene expression results in increased fitness.

Introduction

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A growing population and fast economical development are leading to an increasing demand of various daily products and industrial raw materials, many of which are derivatives of oil and petroleum. Over the past decades, important efforts are being made to develop sustainable production processes that convert biomass or other renewable resources to bioproducts through cell platforms. (1) A key challenge in this respect is the design of high-performance strains with efficient metabolic conversion routes to desired products. Recent advances in genome-scale metabolic modeling (GSMM) (2) have made it possible to have a system-level understanding of cell physiology and metabolism, leading to rational prediction of metabolic interventions for strain development. Systems strain design (1) has helped to improve the production of numerous biochemicals, including lycopene, (3) malonyl-CoA, (4) alkane and alcohol, (5) and hyaluronic acid. (6)
A number of tools have been developed for strain design. (7,8) OptKnock, (9) which was developed to block some reactions in metabolic networks, is one of the earliest such tools. OptKnock identifies the knockout targets that lead to maximal biochemical production in the context of flux balance analysis (2) that is subject to mass balance and thermodynamic constraints. This results in a bilevel optimization problem which can be solved through mathematical reformulation into a standard mixed-integer linear program (MILP). (9) The OptKnock model was later extended to consider gene up/down-regulation, (10) swap of cofactor specificity, (11) and introduction of heterologous pathways (12) for biochemical production. It was also adapted to identify synthetic lethal genes for anti-cancer drug development. (13) Some improvement strategies, such as GDBB (14) and GDLS, (15) have been proposed to improve the efficiency of OptKnock in solving the bilevel problem. There also exist numerous approximate solutions to the OptKnock model, including genetic algorithms (16) and swarm intelligence. (17) Designing strains that couple production to growth has received increasing attention in recent years, mainly due to the great production potential of growth-coupled strains in adaptive laboratory evolution. (18) Consequently, a number of computational tools along this direction have been developed to design strains with various growth-coupled phenotypes. (19,20) OptCouple (20) simulates jointly gene knockouts, insertions, and medium modifications to identify growth-coupled designs, although gene expression regulation is not considered. In addition, game theory has been introduced into metabolic engineering. (21,22) NIHBA (22) considers metabolic engineering design as a network interdiction problem involving two competing players (host strain and metabolic engineer) in a max-min game enabling growth-coupled production phenotypes, and the problem is solved by an efficient mixed-integer solver. Furthermore, there are also some studies which do not rely on optimality principles for phenotype prediction. Among these, the minimum cut set (MCS)-based approach, (23,24) which aims to find the smallest number of interventions blocking undesired production phenotypes, has been extensively studied. Despite high computational complexity, MCS-based approaches have successfully predicted strain design strategies leading to in vivo biochemical production. (25) Another important approach of the same kind is OptForce, which identifies metabolic interventions by exploring the difference in flux distributions between the wild type and the desired production strain. (26) OptForce has shown good predictions for in vivo malonyl-CoA production. (4)
The use of computational tools is of undisputed importance to strain development in metabolic engineering. (27) However, there are several limitations which may prevent the wide applicability of the above-mentioned approaches. First, most of the tools focus on prediction of either knockout targets or regulation targets alone, with a few exceptions that are capable of predicting both interventions, such as OptForce (26) and OptRAM. (28) These exceptions highlight that a combination of knockout and up/down-regulation often leads to higher biochemical production compared to a single strategy. OptForce encourages the use of flux measurements while identifying optimum design strategies. OptRAM considers regulatory networks from which transcriptional factors can be optimized for biochemical production. However, both OptForce and OptRAM rely heavily on the precise expression level of regulation targets; for example, desired production phenotypes can only be achieved at the exactly suggested flux values (OptForce) or up/down-regulation fold changes (OptRAM). It is known that gene expression is a complex process with many uncertainties. The underlying strict expression requirements in these approaches may miss theoretically non-optimal but practically feasible design strategies. In addition, both approaches rely on a reference flux vector of the wild type, which can be incorrectly chosen from many steady-state flux distributions if it cannot be uniquely determined. Second, many existing strategies rely on the assumption of optimality principles, for example, maximal growth in OptKnock (9) and derivatives, in the cell metabolism. However, this assumption is not always an accurate representation of how cells respond to metabolic perturbations or environmental changes. (29) NIHBA (22) showed that reducing unnecessary surrogate biological objectives helps to identify many non-optimal but biologically meaningful knockout solutions.
This paper introduces a new computational tool, called OptDesign, that uses a two-step strategy to predict rational strain design strategies for biochemical production. OptDesign has the following capabilities:
(C1) overcomes the uncertainty problem as there is no assumption of exact fluxes or fold changes that cells should have for production. As a result, non-optimal but good feasible solutions are not missed.
(C2) allows two types of interventions (knockout and up/down-regulation).
(C3) disregards the assumption of (potentially unrealistic) optimal growth in the production mode.
(C4) can use with or without reference flux vectors.
(C5) guarantees growth-coupled production (if desired up/down-regulations are achievable in vivo).
OptDesign is the only tool that combines these five capabilities, as shown in Table 1. In the remainder of this paper, we describe OptDesign and benchmark it considering three case studies, demonstrating high consistencies of predicted design strategies with previous in vivo and in silico studies.
Table 1. Comparison of Different Strain Design Toolsa
toolC1C2C3C4C5
OptKnockb×××√×
OptForce (26)×√×××
OptCouple (20)×××√√
OptReg (10)×√×√×
OptRAM (28)×√×××
NIHBA (22)√×√√√
OptDesign (this study)√√√√√
a

(C1) overcomes the uncertainty problem as there is no assumption of exact fluxes or fold changes that cells should have for production, (C2) allows two types of interventions (knockout and up/down regulation), (C3) disregards the assumption of optimal growth in the production mode, (C4) can use with or without reference flux vectors, and (C5) guarantees growth-coupled production (if desired up/down-regulations are achievable in vivo).

b

The original OptKnock may not always achieve growth-coupled production, but its derivative RobustKnock (30) is guaranteed to achieve this.

Materials and Methods

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A metabolic network of m metabolites and n reactions has a stoichiometric matrix S that is formed by stoichiometric coefficients of the reactions. Let J be a set of n reactions and vj the reaction rate of j ∈ J; Sv represents the concentration change rates of the m metabolites. The flux space FS is defined as the space spanned by all possible flux distributions v for the system subject to thermodynamic constraints at steady state (i.e., the concentration change rate is 0 for all the metabolites). Mathematically, FS can be described as
(1)
where lbj and ubj are the lower and upper flux bounds of reaction j, respectively. We use the notation FSw for the wild type and FSm for the mutant strain. Flux balance analysis (FBA) determines a single solution in FS when a surrogate biological objective is provided for 1.
OptDesign recognizes metabolic changes from the wild type to production (mutant) strains. Let v ∈ FSw denote a flux vector of the wild type and Δv denotes the flux change needed for v to transition into a desired production state. Obviously, v + Δv represents a flux vector of the production strain, and it needs to satisfy mass balance and some production requirements, that is, v + Δv ∈ FSm. Note that flux measurements can be used to customize the flux bounds in FSw and FSm if available; otherwise, the flux bounds can be set according to flux variability analysis (FVA) predictions. For example, FSm can be constrained by imposing production requirements on the lower bounds of the production reaction and biomass.
OptDesign introduces the concept of noticeable flux difference δ (mmol/gDW/h) between the wild type strain and the production strain in reactions. OptDesign uses this concept to identify an optimal set of manipulations leading to the production phenotype FSm. To do so, OptDesign performs two key steps of optimization. First, OptDesign identifies a minimal set of reactions that must deviate from their wild-type flux with at least δ in order to achieve FSm. This set of reactions form candidate regulation targets. Second, OptDesign searches through regulation candidates, together with knockout candidates, for the optimal combination of manipulations to maximize biochemical production. The following two subsections are devoted to presenting these two steps in detail.

Selecting Up/Down-Regulation Reaction Candidates

This step of OptDesign is to identify the minimum number of reactions whose flux must have a noticeable change if the cellular metabolism shifts from the wild type to the required production state. A reaction is considered a candidate for up-regulation if its flux in the mutant is at least δ units more than that in the wild type. On the contrary, this reaction is considered for down-regulation if its flux in the mutant is at least δ units fewer than that in the wild type. Note that the above directional up/down-regulation definition is used for computational convenience, and final regulation targets identified from OptDesign will be rationally grouped by contrasting the wild type to the mutant strain by their absolute flux values (which will be detailed later in the section Materials and Methods). In any other situations, this reaction is not considered as a candidate for genetic manipulation. Figure 1 illustrates the above concept with a toy network of five reactions. Suppose δ is set to 2 units for all these five reactions, R4 and R5 are considered for down-regulation and up-regulation, respectively. However, R1, R2, and R3 are not selected as regulation candidates since their flux changes from the wild type to the mutant are within the predefined threshold δ.

Figure 1

Figure 1. Toy metabolic network (A) and flux distributions of the wild type and mutant (B). Symbols in (A) are as follows: S, carbon source; X, biomass; P, product; Mi (i = 1, 2, 3), metabolite name; Ri (i = 1,..., 5), reaction name. Each axis in (B) represents the absolute flux for a reaction.

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An MILP procedure is employed to minimize the number of reactions that must change their flux from the wild type to the mutant by at least δ units. This can be expressed as the following MILP problem
(2a)
(2b)
(2c)
(2d)
(2e)
(2f)
(2g)
where yj+ and yj– are binary variables representing that the flux of reaction j increases and decreases by at least a noticeable level δj > 0 from the wild type to the production phenotype, respectively. Equivalently, yj+ = 1 (yj– = 1) implies Δvj ≥ δj (Δvj ≤ −δj). Constraints 2b–2e are for flux increase and decrease, respectively. Δvjmin and Δvjmax are the lower and upper bounds of flux change Δvj, respectively. Special reactions in which fluxes are not allowed to be decreased (increased), for example, non-growth-associated maintenance, should have a zero value for the lower (upper) bound of their corresponding Δv components. A reaction cannot increase and decrease flux simultaneously, which implies constraint 2f. Constraints 2g describe the flux space of the wild type and mutant strain at steady state, where FSw and FSm are constrained differently by specifying a minimum growth rate and a minimum target production rate, respectively. It is worth noting that the minimum target production is not a requirement for the producing strain. Instead, it is mainly used to identify the set of reactions that must change their flux in order to produce the target compound. In practice, we can fulfil this purpose by setting the minimum target production rate to the maximum theoretical production rate (computed by FBA with an objective to maximize the flux through the reaction acting on the target product).
Note that this procedure does not need a known reference flux vector for both the wild type and the mutant; instead, it takes into account all possible wild-type and mutant flux distributions that meet engineering requirements (e.g., growth rate, production/yield). However, it is recommended to make use of flux measurements for the wild-type and mutant strains if possible in order to select a rational set of regulation targets effectively.

Identifying Optimal Manipulation Strategies

The solution to the MILP (2) results in a flux-increase set F+ (corresponding to reactions with yj+ = 1) and a flux-decrease set F– (corresponding to reactions with .yj– = 1) in addition to the suggested flux change Δv. However, these two sets are not the minimum number of manipulations needed for the required production state as the effects of some manipulations can be propagated to the whole metabolic network. (26) In addition, there is an engineering cost in manipulating reactions (through gene–protein–reaction associations), and therefore, it is assumed there is a limit on the number Km of genetic manipulations, including up/down-regulation and knockout. We allow gene/reaction knockout in this step for two reasons. First, reactions having an unnoticeable flux change (below δj and thus not in F+ or F–) may sometimes be good manipulation targets, especially when they are involved in completing pathways. Taking them as potential knockout targets could improve biochemical production (essentially a relaxation of optimization models). Second, there may be reactions in the regulation candidate set that carry near-zero fluxes in the production strain. From a practical point of view, completely deactivating them by gene knockout is easier than regulating their gene expression precisely to the suggested minute fluxes. Reaction knockout candidates (denoted by set F×) can be selected by a preprocessing approach, (22) which excludes reactions that are essential, irrelevant, or unlikely to be good knockout targets.
Here, we treat the strain design task as a network interdiction problem (22) that maximally forces cells to violate their wild-type phenotypes for production, that is, to choose the optimum manipulations from F+ ∪ F– ∪ F× in favor of biochemical production regardless of what the wild-type flux distribution is. As a result, we develop the following network interdiction problem
(3a)
(3b)
(3c)
(3d)
(3e)
(3f)
(3g)
(3h)
where cP is a coefficient vector for the target biochemical. yj× = 1 represents the knockout of reaction j, leading to zero flux in this reaction as illustrated by constraint 3b. Constraints 3f and 3g limit the allowable number of knockouts and the total number of manipulations.
The above statement is a special bilevel problem and can be formulated to a standard MILP using duality theory (9) (see reformulation in Supporting Information Data 1). The resulting MILP can be handled either by a modern MILP solver or, if numerous alternative solutions are desired in a single run, by the hybrid Benders algorithm. (22)
The solution to problem 3 contains some regulation-associated binary variables that have a value of 1, that is, yj = 1, for some j ∈ F+ ∪ F–. The integer values represent the reaction targets whose flux needs a change for biochemical production. In order to determine how they are to be regulated in experimental implementation, the following classification rule is applied
The output of the model (3) predicts which reaction should be up- or down-regulated by at least the chosen flux change threshold. It does not impose exact fluxes on the mutant strain to guarantee the high production of target chemicals. In this sense, the resulting manipulations suggested by OptDesign could be experimentally more feasible than those obtained by existing tools.

Computational Implementation

OptDesign relies on model reduction and candidate selection for computational efficiency. Genome-scale metabolic (GEM) models can be significantly simplified by compressing linearly linked reactions and removing dead-end reactions (those carrying zero fluxes). Similarly, many reactions can be excluded from consideration with a priori knowledge that, for example, they are vital for cell growth or their knockout is not likely to improve target production. We followed the model reduction and candidate selection procedure (22,31) (the detailed procedure can be found in Supporting Information Data 1), resulting in a much smaller knockout candidate set for each target product in the latest Escherichia coli GEM iML1515. (32) The flux change threshold δj = 1 mmol/gDW/h was used throughout the paper unless otherwise stated. Algorithm 1 presents the pseudocode of OptDesign. It was implemented in MATLAB 2018b to be compatible with the Cobra Toolbox 3.0. (33) All MILPs were solved by Gurobi 9.02. (34) It is worth noting that a couple of minutes is enough for a modern optimization software like Gurobi to identify a reasonably small set of up/down-regulation candidates. The source code is available for download at https://github.com/chang88ye/OptDesign.

Case Studies

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The OptDesign framework was tested by identifying metabolic manipulations for the production of three industrially relevant biochemicals using the latest genome-scale metabolic model iML1515 (32) for E. coli. Glucose was used as the sole carbon substrate, and its maximum uptake rate was set to 10 mmol/gDW/h. These biochemicals include a number of compounds that have been experimentally studied in the literature. In particular, we focussed on the native succinate and non-native lycopene and naringenin (26,35) in our case studies. A comparison between OptDesign predictions and experimentally validated interventions for another nine target biochemicals can be found in Supporting Information Data 2. The heterologous biosynthesis pathway added to iML1515 for the production of two non-native biochemicals can be found in Supporting Information Data 1. The newly added reactions were charge- and mass-balanced. The growth conditions were the same as in iML1515, except that a minimal cell growth of 0.1 h–1 was imposed on mutant strains for biochemical production. (31) All the other parameters remained the same as the original iML1515. (32) At most 10 manipulations including no more than 5 knockouts were allowed, and the restriction on knockout is to intentionally favor gene expression manipulation over gene knockout. All optimization problems in OptDesign were solved by Gurobi 9.02 on a MacBook with a 3.3 GHz Intel Core i5 processor and 16 GB RAM. The optimization process was terminated by multiple stopping criteria whichever was met first, including the time limit (104 seconds) and optimality gap (5%). Indeed, we observed that the incumbent solution did not improve either after 3000 s or when the optimality gap reached 5%.

Case Study 1: Succinate Overproduction

As a starting point, we wondered which reactions are likely to be good regulation targets and how they are distributed in metabolic networks. Therefore, we extracted the candidate regulation targets from the first step of OptDesign and reorganized them into different metabolic subsystems, as shown in Figure 2. It can be observed that the majority of regulation candidates are from the Krebs cycle and fermentation products (i.e., formate, acetate, and ethanol) that have the same precursor acetyl-CoA as succinate. It is suggested that all the reaction candidates from the Krebs cycle should increase their flux and those related to the formation of formate, acetate, and ethanol should lower their activity. This prediction is consistent with many studies of succinate production. (26,36,37) In addition to these two main subsystems, reactions from the glucose metabolism, pyruvate metabolism, and cofactor conversion/formation are also possible regulation targets. For example, the glucose transporter (GLCptspp) predicted for down-regulation here has been a deletion target in another study (38) to enhance succinate production.

Figure 2

Figure 2. Reactions identified as up/down-regulation targets by OptDesign for succinate overproduction. Abbreviations of reaction names are borrowed from the iML1515 model definitions. Up-regulation and down-regulation reactions are in green and blue ovals, respectively. These reactions have been classified into different subsystems represented by orange rectangles.

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Figure 3a shows a few final design strategies identified by OptDesign that can improve succinate production. It suggests eight primary manipulations, including the knockout of five reactions, up-regulation of citrate synthase (CS) and pyruvate dehydrogenase (PDH), and down-regulation of periplasmic ATP synthase (ATPS4rpp). Two crucial enzymes in the formation of the fermentation products lactate and ethanol, that is, lactate dehydrogenase (LDH_D) and acetaldehyde dehydrogenase (ACALD), are suggested to be deactivated as they are considered as competing pathways consuming succinate precursors. These two manipulation targets have been observed in several studies. (39,40) The knockout of FAD reductase (FADRx) increases the availability of NADH, which has shown to be an effective approach to high succinate production. (41) The methylglyoxal synthase (MGSA) pathway to lactate is another primary knockout target predicted by OptDesign. The removal of this minor pathway should result in pyruvate accumulation for succinate biosynthesis. Interestingly, this knockout has been implemented in previous studies, (37,40) resulting in an increased flux in the Krebs cycle. Ribulose-phosphate 3-epimerase (RPE) is another manipulation target identified by OptDesign, whose knockout blocks the conversion of ribose-5-phosphate (R5P) to d-xylulose 5-phosphate (xu5p-D) in the pentose phosphate pathway. (42) Therefore, it is expected that primary glycolytic flux flows into the precursors, for example, phosphoenolpyruvate and pyruvate, of succinate.

Figure 3

Figure 3. Design strategies identified by OptDesign for biochemical production in E. coli. Reaction names and their arrow symbols in the same color mean that they must be manipulated in mutant strains. Reaction names colored only (i.e., red, green, or blue) mean that they are alternative manipulations. Dashed arrows represent a merge of multiple conversion steps to metabolites. Design strategies are summarized in boxes above the simplified metabolic maps. Abbreviations of metabolite names are as follows: g6p, glucose-6-phosphate; f6p, d-fructose 6-phosphate; g3p, glyceraldehyde-3-phosphate; 13dpg, 3-phospho-D-glyceroyl phosphate; 3gp, 3-phospho-d-glycerate; 6pgc, 6-phospho-d-gluconate; ru5p-D, d-ribulose 5-phosphate; r5p, alpha-d-ribose 5-phosphate; xu5p-D, d-xylulose 5-phosphate; dhap, dihydroxyacetone phosphate; mthgxl, methylglyoxal; pep, phosphoenolpyruvate; pyr, pyruvate; lac-D: d-lactate; dxyl5p, 1-deoxy-d-xylulose 5-phosphate; ipdp, isopentenyl diphosphate; frdp, farnesyl diphosphate; ggdp, geranylgeranyl diphosphate; phyto, all-trans-phytoene; ppi, diphosphate; pi, phosphate; gly, glycine; mlthf, 5,10-methylenetetrahydrofolate; flxso, flavodoxin semi oxidized; flxr, flavodoxin reduced; accoa, acetyl-CoA; cit, citrate; icit, isocitrate; akg, 2-oxoglutarate; succ, succinate; fum, fumarate; mal-L, l-malate; oaa, oxaloacetate; hom-L, l-homoserine; thr-L, l-threonine; dhor-S, (S)-dihydroorotate; orot, orotate; malcoa, malonyl-CoA; cma, coumaric acid; cmcoa, coumaroyl-CoA; chal, naringenine chalcone; fad, flavin adenine dinucleotide oxidized; fadh2, flavin adenine dinucleotide reduced. Abbreviations of reaction names are referred to the iML1515 model definitions.

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OptDesign suggests to overexpress two enzymes in the succinate biosynthetic pathway, that is, PDH and CS, which are intuitively straightforward to understand. In anaerobic E. coli, the PDH activity is either low or undetectable in order to maintain redox balance. (43) However, it is observed that an E. coli mutant with activation of PDH for extra NADH improves succinate production. (40) Overexpression of CS, which is also suggested in another study, (26) has been observed to increase the flux in the Krebs cycle in a malic acid production E. coli strain. (44)
OptDesign further predicts that high succinate production requires either up-regulation of glucokinase (HEX1) or down-regulation of a phosphoenolpyruvate-dependent phosphotransferase system (PTS)-related reaction (GLCptspp), both of which have the same effect that glucose transport is favorably through the ATP-consuming HEX1 rather than the more efficient but phosphoenolpyruvate-dependent PTS route. Consequently, it improves the availability of PEP, which is a precursor for biomass formation and many biochemicals including succinate. Both manipulation approaches have been observed to improve the succinate yield. (36) However, the increased ATP demand due to glucose transport via HEX1 has to be mediated by increased ATP production by other means. For this reason, OptDesign suggests down-regulation of ATPS4rpp to reduce cleavage of ATP to ADP in order to meet metabolic energy requirements. This prediction has been also suggested in another study. (45)
OptDesign also identifies a number of additional modification targets, such as pyruvate formate lyase (PFL), phosphotransacetylase (PTAr), and acetate kinase (ACKr), that have been widely used as knockouts to increase the flux toward the Kreb cycle in succinate-focused studies. (37,40) However, here, OptDesign suggests to down-regulate these enzymes instead of deactivating them completely. In addition, phosphoenolpyruvate carboxylase (PPC) and malate dehydrogenase (MDH) are also predicted as promising overexpression targets. This result is consistent with experimental studies that show increased succinate production through up-regulating these two enzymes. (40,46)

Case Study 2: Naringenin Production

A three-step pathway for naringenin was introduced into the metabolic network E. coli (see Figure 3b), and unlimited coumaric acid (cma) was supplemented in the growth medium. (35) OptDesign predicts that naringenin production requires four primary knockouts, one up-regulation and two down-regulations. The first two primary knockouts are dihydroorotic acid dehydrogenases (DHORD2 and DHORD5) that catalyze the oxidation of dihydroorotate to orotate in the pyrimidine biosynthesis pathway. The knockout of the underlying gene pyrD for these two reactions results in a reduced growth rate, (47) which might save the carbon source for naringenin biosynthesis. The knockout of succinate dehydrogenase (SUCDi) creates a surplus of the biosynthetic precursor acetyl-CoA for naringenin, which has been experimentally observed in a previous study. (35) Phosphoenolpyruvate carboxylase (PPC), a metabolic shortcut for the conversion of phosphoenolpyruvate to oxaloacetate and the byproduct phosphate, is also listed as a primary knockout. We postulate that in addition to avoid the accumulation of phosphate, its deletion could not only direct the flux through pyruvate to acetyl-CoA but also reduce the consumption of acetyl-CoA in the Krebs cycle for the mediation of oxaloacetate. In fact, PPC mutants were found to have a flux increase from pyruvate to acetyl-CoA in a 13C-labeling experiment. (48) Two linear reactions, that is, threonine synthase (THRS) and homoserine kinase (HSK), which are involved in the formation of l-threonine from l-homoserine, are predicted as down-regulation targets. This manipulation is expected to reduce carbon consumption in competing pathways, which therefore increases the carbon flux toward naringenin. Another down-regulation target is the inorganic diphosphatase (PPA) that catalyzes the conversion of one ion of pyrophosphate to two phosphate ions. This manipulation is not intuitively straightforward and believed to create a combined effect with other manipulations to boost naringenin production. Since PPA down-regulation produces less phosphate which is needed in the added naringenin biosynthesis pathway, phosphate has to be balanced through an increase in its transport channel, that is, the phosphate transporter (PItex).
Aside from the above primary manipulations, it is also predicted that naringenin production strains must block at least one of the following reactions: two reactions on the Entner–Doudoroff pathway (EDD/EDA), pyruvate synthase (POR5), isocitrate lyase (ICL), and PDH. Blocking EDD/EDA might increase the use of glucolycosis, producing more ATP which is needed in the heterologous naringenin pathway. The removal of POR5 or PDH forces E. coli to use alternative conversion routes from pyruvate to acetyl-CoA without depleting coenzyme A (CoA), another primary precursor for naringenin biosynthesis. The knockout of ICL prevents the malate synthase reaction from consuming acetyl-CoA. In addition, OptDesign also predicts that the up-regulation of acetyl-CoA carboxylase (ACCOAC) helps to increase the production of naringenin, which has been implemented in another study. (35)

Case Study 3: Lycopene Production

A non-native lycopene biosynthetic pathway consisting of three key reactions were added to the metabolic network of E. coli (see Figure 3c). A preliminary execution of OptDesign predicted the need of only one modification, which is the overexpression of the gene encoding dimethylallyltranstransferase (DMATT) or the one encoding geranyltranstransferase (GRTT). While this manipulation intuitively makes sense, gene overexpression only in the upstream biosynthesis pathway of lycopene does not lead to high lycopene production due to a low concentration of precursors, as experimentally illustrated in another study. (49) Therefore, we run our tool again while disallowing DMATT/GRTT to be valid regulation targets. Consequently, a variety of design strategies were identified, as shown in Figure 3c. Specifically, all the design strategies are combinations of seven manipulations, consisting of five knockouts, two up-regulations, and one down-regulation. However, they differ from each other in only two knockout targets. The three primary knockouts, that is, ribose-5-phosphate isomerase (RPI), triose-phosphate isomerase (TPI), and PDH, are linked to two precursors (i.e., glyceraldehyde-3-phosphate and pyruvate) of lycopene biosynthesis. The knockout of RPI reroutes the carbon flux flowing into the lycopene precursors using more effective metabolic routes (e.g., glycolysis) rather than the non-oxidative pentose phosphate pathway, which is consistent with the study, (3) in addition to slowing down cell growth due to reduced ribose-5-phosphate formation for RNA and DNA synthesis. Both TPI and PDH knockouts should immediately increase the availability of the lycopene precursors, with the latter for increased lycopene biosynthesis being already confirmed experimentally in another study. (50) Apart from glyceraldehyde-3-phosphate and pyruvate, acetyl-CoA is also an important precursor to form isopentenyl diphosphate, a building block for lycopene, using a different pathway. Therefore, it is expected that increasing the availability of acetyl-CoA should also improve lycopene. Unsurprisingly, POR5 is predicted as an up-regulation target in compensation for the loss of PDH for acetyl-CoA formation. Also, reducing the amount of acetyl-CoA flowing into the Krebs cycle was found to increase the flux toward isopentenyl diphosphate. (51) This is fulfilled by removing either fumarase (FUM) or SUCDi in this study. Each of these two knockouts has to be paired with an additional knockout outside the Krebs cycle. This leads to three most frequent pairs, that is, the glycine cleavage system (CLYCL) with FUM, CLYCL with SUCDi, and SUCDi with EDD/EDA. The predicted CLYCL knockout is believed to help reduce the cleavage of 3-phospho-d-glycerate into the glycine biosynthetic pathway so that more pyruvate can be accumulated. Alternatively, blocking the Entner–Doudoroff pathway allows more flux into glycolysis, leading to a higher production of the two precursors (i.e., glyceraldehyde-3-phosphate and pyruvate) for lycopene.
The NADPH-dependent flavodoxin reductase (FLDR2) is another primary up-regulation target predicted by OptDesign. Overexpressing FLDR2 is thought to balance the significantly increased ratio of NADPH to NADP+ caused by the last step of the lycopene biosynthetic pathway. Last, it is predicted that reducing the phosphate uptake rate improves lycopene production. This is probably because two out of the three reactions added for lycopene biosynthesis produce diphosphate that can be converted to phosphate, and a flux decrease in this uptake reaction rebalances phosphate in the system.

Discussion

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This paper has presented a new computational tool, called OptDesign, to aid strain development through rational identification of genetic manipulations including reaction knockout and flux up/down-regulation. This tool has been benchmarked via three case studies of different biochemicals, demonstrating its capability of identifying high-quality strain design strategies to improve biochemical production.
OptDesign predicts well in its first computational step a set of candidates that can be potentially used as experimental regulation targets, as shown in the succinate case. In a second computational step, the algorithm further prunes this set to a realistically acceptable size while optimizing biochemical production. Interestingly, many of the predicted manipulations have been experimentally implemented in previous studies. Taking succinate production as an example, 10 out of 14 manipulations (MSGA, ACALD, LDH_D, HEX1, PDH, PFL, PTAr/ACKr, GLCptspp, PPC, and MDH) suggested by OptDesign have been employed in succinate-producing strains. (36,37,40,46) Specifically, it has been shown that engineered E. coli strains KJ060 and KJ073 produce succinate yields of 1.2–1.6 mol/mol glucose after removing completing pathways that lead to the byproducts ethanol, acetate, formate, and lactate. (40) These strains were developed through added acetate in culture media because the deletion of PFL causes acetate auxotrophy under anaerobic conditions. (37) However, OptDesign suggests that there is no need to completely deactivate PFL. Instead, down-regulating it avoids acetate auxotrophy while still achieving high succinate production.a Additionally, it is observed that glucose transport favoring glucokinase over pep-dependent PTS yields higher succinate production. (52) Furthermore, the overexpression of PPC in E. coli for increasing succinate yields has been confirmed in a previous study. (46)
OptDesign also suggests a few new modifications, such as the deletion of FADRx and RPE, the up-regulation of CS, and down-regulation of ATPS4rpp, which to our best knowledge have not been experimentally implemented for succinate production. While up-regulation of CS has been shown to increase malic acid production, (44) it remains unclear whether this manipulation is also useful for succinate production. The suggested flux modifications on FADRx and ATPS4rpp reconfirm the importance of ATP and redox balance in succinate-producing strains. (40) Deletion of RPE showed low flux in the Krebs cycle, (53) suggesting that metabolic bottlenecks may exist upstream of the Krebs cycle. The design strategies predicted by OptDesign imply that a synergistic effect of RPE knockout with the other identified flux modifications can lead to a high production of succinate. Similar observations can also be found for the production of two non-native biochemicals, naringenin and lycopene, studied in this paper.
We have so far assumed that regulation targets can be selected only from the minimal regulation set derived from the first computational step of OptDesign. Under this assumption, it ensures that regulation manipulations are used as few as possible since suggested regulation levels cannot be exactly guaranteed in experimental implementation. However, in the case that multiple metabolic routes exist between two metabolites, the minimal regulation set will have only one of them included. In view of this, we have also computed the maximal regulation set by maximizing the number of reactions that can have noticeable flux changes. Taking lycopene as an example, the number of regulation candidates increases sharply to 119 in the maximal regulation set from 43 in the minimal regulation set (see Supporting Information Data 1). Consequently, the resulting larger solution space makes it possible to identify design strategies with a better minimum guaranteed flux for lycopene (see Figure 4). In both cases, the design strategies identified by OptDesign couple lycopene production with growth, although the strategy from the maximal regulation set yields a higher production rate than that from the minimal regulation set.

Figure 4

Figure 4. Production envelopes of different growth-coupled design strategies consisting of no more than five manipulations for lycopene. The production envelope illustrates the minimum and maximum production rates a production strain can achieve at different growth rates compared to the wild type. The solid-blue production envelope is for the design strategy using the minimal regulation set: ALCD19 (knockout), TKT2 (knockout), DXPS (overexpressed), PItex (overexpressed), and TPI (underexpressed). The dashed red production envelope is for the design strategy using the maximal regulation set: FUM (knockout), R1PK (knockout), ADK3 (overexpressed), PItex (overexpressed), and ADK1 (underexpressed). Reaction names are consistent with the genome-scale metabolic network model of E. coli iML1515.

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OptDesign has two key parameters, that is, the flux change δ and the minimum required growth rate, which influence the quality of solutions for high production. Figure 5 shows the sensitivity of OptDesign to these two parameters on identifying design strategies for succinate production (sensitivity analysis of these parameters for lycopene and naringenin production can be seen in Supporting Information Data 1). It is observed that high succinate production is achieved near the anti-diagonal line in the 2-D parameter space. Low production strategies are seen when both parameters have either a small or big value. This is because when the minimum required growth is high, there is little room to adjust flux for biochemical production; on the contrary, when the minimum required growth is small, large flux changes (and more regulation candidates to choose as indicated in Supporting Information Data 1) can be made to boost biochemical production. δ impacts on production too as it not only affects the candidate regulation set but also the flux of candidate reactions for regulation on metabolic networks (see details in Supporting Information Data 1). In practice, it requires careful selection of δ and growth threshold to yield optimum design strategies.In addition, OptDesign can be used with a reference flux vector v* easily by binding the flux bounds of the wild type to v* in eq 3. Figure 6 shows the production envelopes of the design strategies, identified with/without the use of in silico reference flux vectors, for three target products. It can be observed that the use of reference flux vectors increases the size of production envelopes, and the maximum growth rate of reference-guided mutants is higher than that of reference-free mutants. This may be explained by the fact that fixing the wild-type flux vector v in eq 3 at v* reduces the room for flux adjustments, hence impacting less on growth rate. The production envelopes for succinate also suggest that reference-guided design sometimes could lead to better solutions. Figure 6 also demonstrates the capability of OptDesign to create (strongly) growth-coupled producing strains whose (minimum) target production increases with growth regardless of reference flux vectors.

Figure 5

Figure 5. Influence of δ and minimum growth on succinate production.

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Figure 6

Figure 6. Comparison of production envelopes obtained by OptDesign with and without a reference flux vector for three target products. The reference flux vector for the wild type was computed using parsimonious FBA (pFBA), which minimizes the sum of squared fluxes in the network. (54)

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Furthermore, OptDesign highlights the benefit of flux regulation in strain design. For example, with a limit of 5 manipulations (including knockout and flux regulation), OptDesign found numerous design strategies for naringenin, with the best having a minimum guaranteed production flux of 1.73 mmol/gDW/h. In contrast, some existing strain design tools (e.g., OptKnock (9) and NIHBA (22)) using knockout only did not identify any strategies leading to naringenin production. Similar to OptDesign, there also exist a few tools, for example, OptForce (26) and OptReg, (10) that can identify both flux regulation and knockout targets. We compared OptDesign with OptForce and OptReg in terms of manipulation targets. For succinate production (see Figure 7), it is noticed that there is a large overlap between the design strategies predicted by these tools, and the common interventions which tend to increase the flux flow toward succinate are all from the core central metabolism, highlighting that intervention of these common targets is effective to increase the availability of succinate precursors. In addition, Figure 7 also shows that OptDesign can find more novel manipulations than the other two tools, demonstrating its capability C1 (Table 1) that enables the search for near-optimal alternatives. OptReg identified fewer regulation targets than the others as it tends to couple the maximum growth rate with target production. OptForce eliminated possible near-optimal but important manipulations by restricting its overproduction target, thereby producing fewer intervention targets than OptDesign.

Figure 7

Figure 7. Comparison of different strain design tools without reference flux vectors for succinate overproduction. The intervention targets were identified by using the default genome-scale metabolic network model of E. coli iML1515. (32) A 100% theoretical succinate yield was used in OptForce, and the regulation parameter C in OptReg was set to 0.5. Reaction names are consistent with the iML1515 model.

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Finally, OptDesign has been developed to identify metabolic manipulations regardless of whatever the wild-type flux distribution looks like, and it can be used with flux measurements if available. Indeed, a measured wild-type flux vector can help refine the manipulation candidates, leading to a more accurate prediction of design strategies. In addition, the threshold for noticeable flux change defined in this work can be further adjusted with measured data, and different reactions can have distinct values for this parameter. Dedicated threshold values allow for a better prediction of rational flux modifications. Although OptDesign has been implemented for reaction-level phenotype prediction, it can be easily modified to predict design strategies at the gene level. For example, OptDesign can be applied to metabolic network models with an advanced stoichiometric representation of gene–protein–reaction associations, (55) from which design strategies consisting of gene targets can be identified.

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acssynbio.1c00610.

  • Bilevel problem reformulation, lycopene and naringenin biosynthetic pathway, model reduction, and impact of OptDesign parameters on biochemical production (PDF)

  • Comparison between in silico predictions and in vivo manipulations for nine compounds; knockout and regulation candidates for succinate, lycopene, and naringenin; impact of reference flux vectors (XLSX)

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Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

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  • Corresponding Authors
  • Authors
    • Irene Otero-Muras - Institute for Integrative Systems Biology, UV-CSIC, Valencia 46980, Spain;  Orcidhttps://orcid.org/0000-0003-2895-997X
    • Julio R. Banga - Computational Biology Lab, MBG-CSIC, Pontevedra 36143, Spain
    • Marcus Kaiser - School of Medicine, University of Nottingham, Nottingham NG7 2RD, U.K.
    • Natalio Krasnogor - School of Computing, Newcastle University, Tyne NE4 5TG, U.K.;  Orcidhttps://orcid.org/0000-0002-2651-4320
  • Author Contributions

    S.J. designed the overall project with support from N.K. and Y.W. I.O.M. and J.R.B. helped refine the idea. S.J. performed simulations and data analysis and wrote the manuscript. All the authors reviewed the results and contributed to manuscript revision.

  • Notes
    The authors declare no competing financial interest.

Acknowledgments

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This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) for funding the project “Synthetic Portabolomics: Leading the way at the crossroads of the Digital and the Bio Economies (EP/N031962/1)”. S.J. acknowledges funding from BBSRC Mitigation Fund RG16134-18. I.M.O. and J.R.B. acknowledge funding from MCIN/AEI/10.13039/501100011033 and “ERDF A way of making Europe” through grant DPI2017-82896-C2-2-R (SYNBIOCONTROL). J.R.B. acknowledges funding from MCIN/AEI/10.13039/501100011033 through grant PID2020-117271RB-C22 (BIODYNAMICS). Y.W. acknowledges funding from the National Natural Science Foundation of China (grant no. 61976225). N.K. is funded by a Royal Academy of Engineering Chair in Emerging Technology award.

Note Added After ASAP Publication

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This paper was published ASAP on Apr 7, 2022, with an incorrect mathematical operator in the text on the fourth page, top right column due to a production error. The corrected version was reposted Apr 15, 2022.

Additional Note
a

It is worth noting that the desired down-regulation sometimes may not be achievable when the selective pressure to increase gene expression results in increased fitness.

References

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    We introduce a computational framework termed OptReg that dets. the optimal reaction activations/inhibitions and eliminations for targeted biochem. prodn. A reaction is deemed up- or downregulated if it is constrained to assume flux values significantly above or below its steady-state before the genetic manipulations. The developed framework is demonstrated by studying the overprodn. of ethanol in Escherichia coli. Computational results reveal the existence of synergism between reaction deletions and modulations implying that the simultaneous application of both types of genetic manipulations yields the most promising results. For example, the downregulation of phosphoglucomutase in conjunction with the deletion of oxygen uptake and pyruvate formate lyase yields 99.8% of the max. theor. ethanol yield. Conceptually, the proposed strategies redirect both the carbon flux as well as the cofactors to enhance ethanol prodn. in the network. The OptReg framework is a versatile tool for strain design which allows for a broad array of genetic manipulations.
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  11. 11
    King, Z. A.; Feist, A. M. Optimal cofactor swapping can increase the theoretical yield for chemical production in Escherichia coli and Saccharomyces cerevisiae. Metab. Eng. 2014, 24, 117– 128,  DOI: 10.1016/j.ymben.2014.05.009
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    11
    Optimal cofactor swapping can increase the theoretical yield for chemical production in Escherichia coli and Saccharomyces cerevisiae
    King, Zachary A.; Feist, Adam M.
    Metabolic Engineering (2014), 24 (), 117-128CODEN: MEENFM; ISSN:1096-7176. (Elsevier B. V.)
    Maintaining cofactor balance is a crit. function in microorganisms, but often the native cofactor balance does not match the needs of an engineered metabolic flux state. Here, an optimization procedure is utilized to identify optimal cofactor-specificity "swaps" for oxidoreductase enzymes utilizing NAD(H) or NADP(H) in the genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae. The theor. yields of all native carbon-contg. mols. are considered, as well as theor. yields of twelve heterologous prodn. pathways in E. coli. Swapping the cofactor specificity of central metabolic enzymes (esp. GAPD and ALCD2x) is shown to increase NADPH prodn. and increase theor. yields for native products in E. coli and yeast-including L-aspartate, L-lysine, L-isoleucine, L-proline, L-serine, and putrescine-and non-native products in E. coli-including 1,3-propanediol, 3-hydroxybutyrate, 3-hydroxypropanoate, 3-hydroxyvalerate, and styrene.
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  12. 12
    Pharkya, P.; Burgard, A. P.; Maranas, C. D. OptStrain: a computational framework for redesign of microbial production systems. Genome Res. 2004, 14, 2367– 2376,  DOI: 10.1101/gr.2872004
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    12
    Optstrain: A computational framework for redesign of microbial production systems
    Pharkya, Priti; Burgard, Anthony P.; Maranas, Costas D.
    Genome Research (2004), 14 (11), 2367-2376CODEN: GEREFS; ISSN:1088-9051. (Cold Spring Harbor Laboratory Press)
    This paper introduces the hierarchical computational framework OptStrain aimed at guiding pathway modifications, through reaction addns. and deletions, of microbial networks for the overprodn. of targeted compds. These compds. may range from electrons or hydrogen in biofuel cell and environmental applications to complex drug precursor mols. A comprehensive database of biotransformations, referred to as the Universal database (with >5700 reactions), is compiled and regularly updated by downloading and curating reactions from multiple biopathway database sources. Combinatorial optimization is then used to elucidate the set(s) of non-native functionalities, extd. from this Universal database, to add to the examd. prodn. host for enabling the desired product formation. Subsequently, competing functionalities that divert flux away from the targeted product are identified and removed to ensure higher product yields coupled with growth. This work represents an advancement over earlier efforts by establishing an integrated computational framework capable of constructing stoichiometrically balanced pathways, imposing max. product yield requirements, pinpointing the optimal substrate(s), and evaluating different microbial hosts. The range and utility of OptStrain are demonstrated by addressing two very different product mols. The hydrogen case study pinpoints reaction elimination strategies for improving hydrogen yields using two different substrates for three sep. prodn. hosts. In contrast, the vanillin study primarily showcases which non-native pathways need to be added into Escherichia coli. In summary, OptStrain provides a useful tool to aid microbial strain design and, more importantly, it establishes an integrated framework to accommodate future modeling developments.
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  13. 13
    Pratapa, A.; Balachandran, S.; Raman, K. Fast-SL: An efficient algorithm to identify synthetic lethal sets in metabolic networks. Bioinformatics 2015, 31, 3299– 3305,  DOI: 10.1093/bioinformatics/btv352
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    13
    Fast-SL: an efficient algorithm to identify synthetic lethal sets in metabolic networks
    Pratapa, Aditya; Balachandran, Shankar; Raman, Karthik
    Bioinformatics (2015), 31 (20), 3299-3305CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
    Motivation: Synthetic lethal sets are sets of reactions/genes where only the simultaneous removal of all reactions/genes in the set abolishes growth of an organism. Previous approaches to identify synthetic lethal genes in genome-scale metabolic networks have built on the framework of flux balance anal. (FBA), extending it either to exhaustively analyze all possible combinations of genes or formulate the problem as a bi-level mixed integer linear programming (MILP) problem. We here propose an algorithm, Fast-SL, which surmounts the computational complexity of previous approaches by iteratively reducing the search space for synthetic lethals, resulting in a substantial redn. in running time, even for higher order synthetic lethals. Results: We performed synthetic reaction and gene lethality anal., using Fast-SL, for genomescale metabolic networks of Escherichia coli, Salmonella enterica Typhimurium and Mycobacterium tuberculosis. Fast-SL also rigorously identifies synthetic lethal gene deletions, uncovering synthetic lethal triplets that were not reported previously. We confirm that the triple lethal gene sets obtained for the three organisms have a precise match with the results obtained through exhaustive enumeration of lethals performed on a computer cluster. We also parallelized our algorithm, enabling the identification of synthetic lethal gene quadruplets for all three organisms in under 6 h. Overall, Fast-SL enables an efficient enumeration of higher order synthetic lethals in metabolic networks, which may help uncover previously unknown genetic interactions and combinatorial drug targets.
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  14. 14
    Lun, D. S.; Rockwell, G.; Guido, N. J.; Baym, M.; Kelner, J. A.; Berger, B.; Galagan, J. E.; Church, G. M. Large-scale identification of genetic design strategies using local search. Mol. Syst. Biol. 2009, 5, 296,  DOI: 10.1038/msb.2009.57
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    14
    Large-scale identification of genetic design strategies using local search
    Lun Desmond S; Rockwell Graham; Guido Nicholas J; Baym Michael; Kelner Jonathan A; Berger Bonnie; Galagan James E; Church George M
    Molecular systems biology (2009), 5 (), 296 ISSN:.
    In the past decade, computational methods have been shown to be well suited to unraveling the complex web of metabolic reactions in biological systems. Methods based on flux-balance analysis (FBA) and bi-level optimization have been used to great effect in aiding metabolic engineering. These methods predict the result of genetic manipulations and allow for the best set of manipulations to be found computationally. Bi-level FBA is, however, limited in applicability because the required computational time and resources scale poorly as the size of the metabolic system and the number of genetic manipulations increase. To overcome these limitations, we have developed Genetic Design through Local Search (GDLS), a scalable, heuristic, algorithmic method that employs an approach based on local search with multiple search paths, which results in effective, low-complexity search of the space of genetic manipulations. Thus, GDLS is able to find genetic designs with greater in silico production of desired metabolites than can feasibly be found using a globally optimal search and performs favorably in comparison with heuristic searches based on evolutionary algorithms and simulated annealing.
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  15. 15
    Egen, D.; Lun, D. S. Truncated branch and bound achieves efficient constraint-based genetic design. Bioinformatics 2012, 28, 1619– 1623,  DOI: 10.1093/bioinformatics/bts255
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    15
    Truncated branch and bound achieves efficient constraint-based genetic design
    Egen, Dennis; Lun, Desmond S.
    Bioinformatics (2012), 28 (12), 1619-1623CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
    Motivation: Computer-aided genetic design is a promising approach to a core problem of metabolic engineering-that of identifying genetic manipulation strategies that result in engineered strains with favorable product accumulation. This approach has proved to be effective for organisms including Escherichia coli and Saccharomyces cerevisiae, allowing for rapid, rational design of engineered strains. Finding optimal genetic manipulation strategies, however, is a complex computational problem in which running time grows exponentially with the no. of manipulations (i.e. knockouts, knock-ins or regulation changes) in the strategy. Thus, computer-aided gene identification has to date been limited in the complexity or optimality of the strategies it finds or in the size and level of detail of the metabolic networks under consideration. Results: Here, we present an efficient computational soln. to the gene identification problem. Our approach significantly outperforms previous approaches-in seconds or minutes, we find strategies that previously required running times of days or more. Availability and implementation: GDBB is implemented using MATLAB and is freely available for non-profit use at http://crab.rutgers.edu/∼dslun/gdbb. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.
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  16. 16
    Rocha, I.; Maia, P.; Evangelista, P.; Vilaça, P.; Soares, S.; Pinto, J. P.; Nielsen, J.; Patil, K. R.; Ferreira, E. C.; Rocha, M. OptFlux: an open-source software platform for in silico metabolic engineering. BMC Syst. Biol. 2010, 4, 45,  DOI: 10.1186/1752-0509-4-45
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    16
    OptFlux: an open-source software platform for in silico metabolic engineering
    Rocha Isabel; Maia Paulo; Evangelista Pedro; Vilaca Paulo; Soares Simao; Pinto Jose P; Nielsen Jens; Patil Kiran R; Ferreira Eugenio C; Rocha Miguel
    BMC systems biology (2010), 4 (), 45 ISSN:.
    BACKGROUND: Over the last few years a number of methods have been proposed for the phenotype simulation of microorganisms under different environmental and genetic conditions. These have been used as the basis to support the discovery of successful genetic modifications of the microbial metabolism to address industrial goals. However, the use of these methods has been restricted to bioinformaticians or other expert researchers. The main aim of this work is, therefore, to provide a user-friendly computational tool for Metabolic Engineering applications. RESULTS: OptFlux is an open-source and modular software aimed at being the reference computational application in the field. It is the first tool to incorporate strain optimization tasks, i.e., the identification of Metabolic Engineering targets, using Evolutionary Algorithms/Simulated Annealing metaheuristics or the previously proposed OptKnock algorithm. It also allows the use of stoichiometric metabolic models for (i) phenotype simulation of both wild-type and mutant organisms, using the methods of Flux Balance Analysis, Minimization of Metabolic Adjustment or Regulatory on/off Minimization of Metabolic flux changes, (ii) Metabolic Flux Analysis, computing the admissible flux space given a set of measured fluxes, and (iii) pathway analysis through the calculation of Elementary Flux Modes. OptFlux also contemplates several methods for model simplification and other pre-processing operations aimed at reducing the search space for optimization algorithms. The software supports importing/exporting to several flat file formats and it is compatible with the SBML standard. OptFlux has a visualization module that allows the analysis of the model structure that is compatible with the layout information of Cell Designer, allowing the superimposition of simulation results with the model graph. CONCLUSIONS: The OptFlux software is freely available, together with documentation and other resources, thus bridging the gap from research in strain optimization algorithms and the final users. It is a valuable platform for researchers in the field that have available a number of useful tools. Its open-source nature invites contributions by all those interested in making their methods available for the community. Given its plug-in based architecture it can be extended with new functionalities. Currently, several plug-ins are being developed, including network topology analysis tools and the integration with Boolean network based regulatory models.
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  17. 17
    Choon, Y. W.; Mohamad, M. S.; Deris, S.; Chong, C. K.; Omatu, S.; Corchado, J. M. Gene knockout identification using an extension of bees hill flux balance analysis. BioMed Res. Int. 2015, 2015, 124537,  DOI: 10.1155/2015/124537
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    17
    Gene knockout identification using an extension of Bees Hill Flux Balance Analysis
    Choon Yee Wen; Mohamad Mohd Saberi; Deris Safaai; Chong Chuii Khim; Omatu Sigeru; Corchado Juan Manuel
    BioMed research international (2015), 2015 (), 124537 ISSN:.
    Microbial strain optimisation for the overproduction of a desired phenotype has been a popular topic in recent years. Gene knockout is a genetic engineering technique that can modify the metabolism of microbial cells to obtain desirable phenotypes. Optimisation algorithms have been developed to identify the effects of gene knockout. However, the complexities of metabolic networks have made the process of identifying the effects of genetic modification on desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to a combinatorial problem in obtaining optimal gene knockout. The computational time increases exponentially as the size of the problem increases. This work reports an extension of Bees Hill Flux Balance Analysis (BHFBA) to identify optimal gene knockouts to maximise the production yield of desired phenotypes while sustaining the growth rate. This proposed method functions by integrating OptKnock into BHFBA for validating the results automatically. The results show that the extension of BHFBA is suitable, reliable, and applicable in predicting gene knockout. Through several experiments conducted on Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as model organisms, extension of BHFBA has shown better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes.
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  18. 18
    Sandberg, T. E.; Lloyd, C. J.; Palsson, B. O.; Feist, A. M. Laboratory evolution to alternating substrate environments yields distinct phenotypic and genetic adaptive strategies. Appl. Environ. Microbiol. 2017, 83, e00410– e00417,  DOI: 10.1128/AEM.00410-17
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    18
    Laboratory evolution to alternating substrate environments yields distinct phenotypic and genetic adaptive strategies
    Sandberg, Troy E.; Lloyd, Colton J.; Palsson, Bernhard O.; Feist, Adam M.
    Applied and Environmental Microbiology (2017), 83 (13), e00410-17/1-e00410-17/15CODEN: AEMIDF; ISSN:1098-5336. (American Society for Microbiology)
    Adaptive lab. evolution (ALE) expts. are often designed to maintain a static culturing environment to minimize confounding variables that could influence the adaptive process, but dynamic nutrient conditions occur frequently in natural and bioprocessing settings. To study the nature of carbon substrate fitness tradeoffs, we evolved batch cultures of Escherichia coli via serial propagation into tubes alternating between glucose and either xylose, glycerol, or acetate. Genome sequencing of evolved cultures revealed several genetic changes preferentially selected for under dynamic conditions and different adaptation strategies depending on the substrates being switched between; in some environments, a persistent "generalist" strain developed, while in another, two "specialist" subpopulations arose that alternated dominance. Diauxic lag phenotype varied across the generalists and specialists, in one case being completely abolished, while gene expression data distinguished the transcriptional strategies implemented by strains in pursuit of growth optimality. Genome-scale metabolic modeling techniques were then used to help explain the inherent substrate differences giving rise to the obsd. distinct adaptive strategies. This study gives insight into the population dynamics of adaptation in an alternating environment and into the underlying metabolic and genetic mechanisms. Furthermore, ALE-generated optimized strains have phenotypes with potential industrial bioprocessing applications.
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    Alter, T. B.; Ebert, B. E. Determination of growth-coupling strategies and their underlying principles. BMC Bioinf. 2019, 20, 447,  DOI: 10.1186/s12859-019-2946-7
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    19
    Determination of growth-coupling strategies and their underlying principles
    Alter Tobias B; Ebert Birgitta E; Ebert Birgitta E
    BMC bioinformatics (2019), 20 (1), 447 ISSN:.
    BACKGROUND: Metabolic coupling of product synthesis and microbial growth is a prominent approach for maximizing production performance. Growth-coupling (GC) also helps stabilizing target production and allows the selection of superior production strains by adaptive laboratory evolution. To support the implementation of growth-coupling strain designs, we seek to identify biologically relevant, metabolic principles that enforce strong growth-coupling on the basis of reaction knockouts. RESULTS: We adapted an established bilevel programming framework to maximize the minimally guaranteed production rate at a fixed, medium growth rate. Using this revised formulation, we identified various GC intervention strategies for metabolites of the central carbon metabolism, which were examined for GC generating principles under diverse conditions. Curtailing the metabolism to render product formation an essential carbon drain was identified as one major strategy generating strong coupling of metabolic activity and target synthesis. Impeding the balancing of cofactors and protons in the absence of target production was the underlying principle of all other strategies and further increased the GC strength of the aforementioned strategies. CONCLUSION: Maximizing the minimally guaranteed production rate at a medium growth rate is an attractive principle for the identification of strain designs that couple growth to target metabolite production. Moreover, it allows for controlling the inevitable compromise between growth coupling strength and the retaining of microbial viability. With regard to the corresponding metabolic principles, generating a dependency between the supply of global metabolic cofactors and product synthesis appears to be advantageous in enforcing strong GC for any metabolite. Deriving such strategies manually, is a hard task, due to which we suggest incorporating computational metabolic network analyses in metabolic engineering projects seeking to determine GC strain designs.
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  20. 20
    Jensen, K.; Broeken, V.; Hansen, A. S. L.; Sonnenschein, N.; Herrgård, M. J. OptCouple: Joint simulation of gene knockouts, insertions and medium modifications for prediction of growth-coupled strain designs. Metab. Eng. Commun. 2019, 8, e00087  DOI: 10.1016/j.mec.2019.e00087
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    Pusa, T.; Wannagat, M.; Sagot, M.-F. Metabolic Games. Front. Appl. Math. Stat. 2019, 5, 18,  DOI: 10.3389/fams.2019.00018
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    Jiang, S.; Wang, Y.; Kaiser, M.; Krasnogor, N. NIHBA: a network interdiction approach for metabolic engineering design. Bioinformatics 2020, 36, 3482– 3492,  DOI: 10.1093/bioinformatics/btaa163
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    22
    NIHBA: a network interdiction approach for metabolic engineering design
    Jiang, Shouyong; Wang, Yong; Kaiser, Marcus; Krasnogor, Natalio
    Bioinformatics (2020), 36 (11), 3482-3492CODEN: BOINFP; ISSN:1367-4811. (Oxford University Press)
    Motivation: Flux balance anal. (FBA) based bilevel optimization has been a great success in redesigning metabolic networks for biochem. overprodn. To date, many computational approaches have been developed to solve the resulting bilevel optimization problems. However, most of them are of limited use due to biased optimality principle, poor scalability with the size of metabolic networks, potential numeric issues or low quantity of design solns. in a single run. Results: Here, we have employed a network interdiction model free of growth optimality assumptions, a special case of bilevel optimization, for computational strain design and have developed a hybrid Benders algorithm (HBA) that deals with complicating binary variables in the model, thereby achieving high efficiency without numeric issues in search of best design strategies. More importantly, HBA can list solns. that meet users' prodn. requirements during the search, making it possible to obtain numerous design strategies at a small runtime overhead (typically ~ 1 h, e.g. studied in this article).
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    Apaolaza, I.; Valcarcel, L. V.; Planes, F. J. gMCS: Fast computation of genetic minimal cut sets in large networks. Bioinformatics 2018, 35, 535– 537,  DOI: 10.1093/bioinformatics/bty656
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    von Kamp, A.; Klamt, S. Enumeration of smallest intervention strategies in genome-scale metabolic networks. PLoS Comput. Biol. 2014, 10, e1003378  DOI: 10.1371/journal.pcbi.1003378
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    Enumeration of smallest intervention strategies in genome-scale metabolic networks
    von Kamp, Axel; Klamt, Steffen
    PLoS Computational Biology (2014), 10 (1), e1003378/1-e1003378/13, 13 pp.CODEN: PCBLBG; ISSN:1553-7358. (Public Library of Science)
    One ultimate goal of metabolic network modeling is the rational redesign of biochem. networks to optimize the prodn. of certain compds. by cellular systems. Although several constraint-based optimization techniques have been developed for this purpose, methods for systematic enumeration of intervention strategies in genome-scale metabolic networks are still lacking. In principle, Minimal Cut Sets (MCSs; inclusion-minimal combinations of reaction or gene deletions that lead to the fulfilment of a given intervention goal) provide an exhaustive enumeration approach. However, their disadvantage is the combinatorial explosion in larger networks and the requirement to compute first the elementary modes (EMs) which itself is impractical in genome-scale networks. We present MCSEnumerator, a new method for effective enumeration of the smallest MCSs (with fewest interventions) in genome-scale metabolic network models. For this we combine two approaches, namely (i) the mapping of MCSs to EMs in a dual network, and (ii) a modified algorithm by which shortest EMs can be effectively detd. in large networks. In this way, we can identify the smallest MCSs by calcg. the shortest EMs in the dual network. Realistic application examples demonstrate that our algorithm is able to list thousands of the most efficient intervention strategies in genome-scale networks for various intervention problems. For instance, for the first time we could enumerate all synthetic lethals in E.coli with combinations of up to 5 reactions. We also applied the new algorithm exemplarily to compute strain designs for growth-coupled synthesis of different products (ethanol, fumarate, serine) by E.coli. We found numerous new engineering strategies partially requiring less knockouts and guaranteeing higher product yields (even without the assumption of optimal growth) than reported previously. The strength of the presented approach is that smallest intervention strategies can be quickly calcd. and screened with neither network size nor the no. of required interventions posing major challenges.
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    Harder, B.-J.; Bettenbrock, K.; Klamt, S. Model-based metabolic engineering enables high yield itaconic acid production by Escherichia coli. Metab. Eng. 2016, 38, 29– 37,  DOI: 10.1016/j.ymben.2016.05.008
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    25
    Model-based metabolic engineering enables high yield itaconic acid production by Escherichia coli
    Harder, Bjoern-Johannes; Bettenbrock, Katja; Klamt, Steffen
    Metabolic Engineering (2016), 38 (), 29-37CODEN: MEENFM; ISSN:1096-7176. (Elsevier B. V.)
    Itaconic acid is a high potential platform chem. which is currently industrially produced by Aspergillus terreus. Heterologous prodn. of itaconic acid with Escherichia coli could help to overcome limitations of A. terreus regarding slow growth and high sensitivity to oxygen supply. However, the performance achieved so far with E. coli strains is still low. We introduced a plasmid (pCadCS) carrying genes for itaconic acid prodn. into E. coli and applied a model-based approach to construct a high yield prodn. strain. Based on the concept of minimal cut sets, we identified intervention strategies that guarantee high itaconic acid yield while still allowing growth. One cut set was selected and the corresponding genes were iteratively knocked-out. As a conceptual novelty, we pursued an adaptive approach allowing changes in the model and initially calcd. intervention strategy if a genetic modification induces changes in byproduct formation. Using this approach, we iteratively implemented five interventions leading to high yield itaconic acid prodn. in minimal medium with glucose as substrate supplemented with small amts. of glutamic acid. The derived E. coli strain (ita23: MG1655 ΔaceA ΔsucCD ΔpykA ΔpykF Δpta ΔPicd::cam_BBa_J23115 pCadCS) synthesized 2.27 g/l itaconic acid with an excellent yield of 0.77 mol/(mol glucose). In a fed-batch cultivation, this strain produced 32 g/l itaconic acid with an overall yield of 0.68 mol/(mol. glucose) and a peak productivity of 0.45 g/l/h. These values are by far the highest that have ever been achieved for heterologous itaconic acid prodn. and indicate that realistic applications come into reach.
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    Ranganathan, S.; Suthers, P. F.; Maranas, C. D. OptForce: An optimization procedure for identifying all genetic manipulations leading to targeted overproductions. PLoS Comput. Biol. 2010, 6, e1000744  DOI: 10.1371/journal.pcbi.1000744
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    Otero-Muras, I.; Carbonell, P. Automated engineering of synthetic metabolic pathways for efficient association to reaction rules. biomanufacturing. Metab. Eng. 2021, 63, 61– 80,  DOI: 10.1016/j.ymben.2020.11.012
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    27
    Automated engineering of synthetic metabolic pathways for efficient biomanufacturing
    Otero-Muras, Irene; Carbonell, Pablo
    Metabolic Engineering (2021), 63 (), 61-80CODEN: MEENFM; ISSN:1096-7176. (Elsevier B.V.)
    A review. Metabolic engineering involves the engineering and optimization of processes from single-cell to fermn. in order to increase prodn. of valuable chems. for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial prodn. Metabolic engineering is nowadays on track towards a truly manufg. technol., with reduced times from conception to prodn. enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising prodn. routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an exptl. design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufg. process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomols. in an affordable, inclusive and sustainable bioeconomy.
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    Shen, F.; Sun, R.; Yao, J.; Li, J.; Liu, Q.; Price, N. D.; Liu, C.; Wang, Z. OptRAM: In-silico strain design via integrative regulatory-metabolic network modeling. PLoS Comput. Biol. 2019, 15, e1006835  DOI: 10.1371/journal.pcbi.1006835
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    28
    OptRAM: in-silico strain design via integrative regulatory-metabolic network modeling
    Shen, Fangzhou; Sun, Renliang; Yao, Jie; Li, Jian; Liu, Qian; Price, Nathan D.; Liu, Chenguang; Wang, Zhuo
    PLoS Computational Biology (2019), 15 (3), e1006835/1-e1006835/25CODEN: PCBLBG; ISSN:1553-7358. (Public Library of Science)
    The ultimate goal of metabolic engineering is to produce desired compds. on an industrial scale in a cost effective manner. To address challenges in metabolic engineering, computational strain optimization algorithms based on genome-scale metabolic models have increasingly been used to aid in overproducing products of interest. However, most of these strain optimization algorithms utilize a metabolic network alone, with few approaches providing strategies that also include transcriptional regulation. Moreover previous integrated approaches generally require a pre-existing regulatory network. In this study, we developed a novel strain design algorithm, named OptRAM (Optimization of Regulatory And Metabolic Networks), which can identify combinatorial optimization strategies including overexpression, knockdown or knockout of both metabolic genes and transcription factors. OptRAM is based on our previous IDREAM integrated network framework, which makes it able to deduce a regulatory network from data. OptRAM uses simulated annealing with a novel objective function, which can ensure a favorable coupling between desired chem. and cell growth. The other advance we propose is a systematic evaluation metric of multiple solns., by considering the essential genes, flux variation, and engineering manipulation cost. We applied OptRAM to generate strain designs for succinate, 2,3-butanediol, and ethanol overprodn. in yeast, which predicted high min. predicted target prodn. rate compared with other methods and previous literature values. Moreover, most of the genes and TFs proposed to be altered by OptRAM in these scenarios have been validated by modification of the exact genes or the target genes regulated by the TFs, for overprodn. of these desired compds. by in vivo expts. cataloged in the LASER database. Particularly, we successfully validated the predicted strain optimization strategy for ethanol prodn. by fermn. expt. In conclusion, OptRAM can provide a useful approach that leverages an integrated transcriptional regulatory network and metabolic network to guide metabolic engineering applications.
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    Schuetz, R.; Zamboni, N.; Zampieri, M.; Heinemann, M.; Sauer, U. Multidimensional optimality of microbial metabolism. Science 2012, 336, 601– 604,  DOI: 10.1126/science.1216882
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    Multidimensional optimality of microbial metabolism
    Schuetz, Robert; Zamboni, Nicola; Zampieri, Mattia; Heinemann, Matthias; Sauer, Uwe
    Science (Washington, DC, United States) (2012), 336 (6081), 601-604CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
    Although the network topol. of metab. is well known, understanding the principles that govern the distribution of fluxes through metab. lags behind. Exptl., these fluxes can be measured by 13C-flux anal., and there has been a long-standing interest in understanding this functional network operation from an evolutionary perspective. On the basis of 13C-detd. fluxes from nine bacteria and multi-objective optimization theory, the authors show that metab. operates close to the Pareto-optimal surface of a three-dimensional space defined by competing objectives. Consistent with flux data from evolved Escherichia coli, they propose that flux states evolve under the trade-off between two principles: optimality under one given condition and minimal adjustment between conditions. These principles form the forces by which evolution shapes metabolic fluxes in microorganisms' environmental context.
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    Tepper, N.; Shlomi, T. Predicting metabolic engineering knockout strategies for chemical production: accounting for competing pathways. Bioinformatics 2010, 26, 536– 543,  DOI: 10.1093/bioinformatics/btp704
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    Predicting metabolic engineering knockout strategies for chemical production: accounting for competing pathways
    Tepper, Naama; Shlomi, Tomer
    Bioinformatics (2010), 26 (4), 536-543CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
    Motivation: Computational modeling in metabolic engineering involves the prediction of genetic manipulations that would lead to optimized microbial strains, maximizing the prodn. rate of chems. of interest. Various computational methods are based on constraint-based modeling, which enables to anticipate the effect of genetic manipulations on cellular metab. considering a genome-scale metabolic network. However, current methods do not account for the presence of competing pathways in a metabolic network that may diverge metabolic flux away from producing a required chem., resulting in lower (or even zero) chem. prodn. rates in reality-making these methods somewhat over optimistic. Results: In this article, we describe a novel constraint-based method called RobustKnock that predicts gene deletion strategies that lead to the over-prodn. of chems. of interest, by accounting for the presence of competing pathways in the network. We describe results of applying RobustKnock to Escherichia coli's metabolic network towards the prodn. of various chems., demonstrating its ability to provide more robust predictions than those obtained via current state-of-the-art methods.
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    Feist, A. M.; Zielinski, D. C.; Orth, J. D.; Schellenberger, J.; Herrgard, M. J.; Palsson, B. Ø. Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli. Metab. Eng. 2010, 12, 173– 186,  DOI: 10.1016/j.ymben.2009.10.003
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    Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli
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    Metabolic Engineering (2010), 12 (3), 173-186CODEN: MEENFM; ISSN:1096-7176. (Elsevier B. V.)
    Integrated approaches utilizing in silico analyses will be necessary to successfully advance the field of metabolic engineering. Here, we present an integrated approach through a systematic model-driven evaluation of the prodn. potential for the bacterial prodn. organism Escherichia coli to produce multiple native products from different representative feedstocks through coupling metabolite prodn. to growth rate. Designs were examd. for 11 unique central metab. and amino acid targets from three different substrates under aerobic and anaerobic conditions. Optimal strain designs were reported for designs which possess max. yield, substrate-specific productivity, and strength of growth-coupling for up to 10 reaction eliminations (knockouts). In total, growth-coupled designs could be identified for 36 out of the total 54 conditions tested, corresponding to eight out of the 11 targets. There were 17 different substrate/target pairs for which over 80% of the theor. max. potential could be achieved. The developed method introduces a new concept of objective function tilting for strain design. This study provides specific metabolic interventions (strain designs) for prodn. strains that can be exptl. implemented, characterizes the potential for E. coli to produce native compds., and outlines a strain design pipeline that can be utilized to design prodn. strains for addnl. organisms.
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    Monk, J. M.; Lloyd, C. J.; Brunk, E.; Mih, N.; Sastry, A.; King, Z.; Takeuchi, R.; Nomura, W.; Zhang, Z.; Mori, H.; Feist, A. M.; Palsson, B. O. iML1515, a knowledgebase that computes Escherichia coli traits. Nat. Biotechnol. 2017, 35, 904– 908,  DOI: 10.1038/nbt.3956
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    iML1515, a knowledgebase that computes Escherichia coli traits
    Monk, Jonathan M.; Lloyd, Colton J.; Brunk, Elizabeth; Mih, Nathan; Sastry, Anand; King, Zachary; Takeuchi, Rikiya; Nomura, Wataru; Zhang, Zhen; Mori, Hirotada; Feist, Adam M.; Palsson, Bernhard O.
    Nature Biotechnology (2017), 35 (10), 904-908CODEN: NABIF9; ISSN:1087-0156. (Nature Research)
    There is no expanded citation for this reference.
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    Heirendt, L. Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3. 0. Nat. Protoc. 2019, 14, 639– 702,  DOI: 10.1038/s41596-018-0098-2
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    Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0
    Heirendt, Laurent; Arreckx, Sylvain; Pfau, Thomas; Mendoza, Sebastian N.; Richelle, Anne; Heinken, Almut; Haraldsdottir, Hulda S.; Wachowiak, Jacek; Keating, Sarah M.; Vlasov, Vanja; Magnusdottir, Stefania; Ng, Chiam Yu; Preciat, German; Zagare, Alise; Chan, Siu H. J.; Aurich, Maike K.; Clancy, Catherine M.; Modamio, Jennifer; Sauls, John T.; Noronha, Alberto; Bordbar, Aarash; Cousins, Benjamin; El Assal, Diana C.; Valcarcel, Luis V.; Apaolaza, Inigo; Ghaderi, Susan; Ahookhosh, Masoud; Ben Guebila, Marouen; Kostromins, Andrejs; Sompairac, Nicolas; Le, Hoai M.; Ma, Ding; Sun, Yuekai; Wang, Lin; Yurkovich, James T.; Oliveira, Miguel A. P.; Vuong, Phan T.; El Assal, Lemmer P.; Kuperstein, Inna; Zinovyev, Andrei; Hinton, H. Scott; Bryant, William A.; Aragon Artacho, Francisco J.; Planes, Francisco J.; Stalidzans, Egils; Maass, Alejandro; Vempala, Santosh; Hucka, Michael; Saunders, Michael A.; Maranas, Costas D.; Lewis, Nathan E.; Sauter, Thomas; Palsson, Bernhard Oe.; Thiele, Ines; Fleming, Ronan M. T.
    Nature Protocols (2019), 14 (3), 639-702CODEN: NPARDW; ISSN:1750-2799. (Nature Research)
    Constraint-based reconstruction and anal. (COBRA) provides a mol. mechanistic framework for integrative anal. of exptl. mol. systems biol. data and quant. prediction of physicochem. and biochem. feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biol., biomedicine, and biotechnol. because its functions can be flexibly combined to implement tailored COBRA protocols for any biochem. network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topol. anal., strain and exptl. design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochem. data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, resp. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.
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    Fowler, Z. L.; Gikandi, W. W.; Koffas, M. A. G. Increased malonyl coenzyme A biosynthesis by tuning the Escherichia coli metabolic network and its application to flavanone production. Appl. Environ. Microbiol. 2009, 75, 5831– 5839,  DOI: 10.1128/aem.00270-09
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    Increased malonyl coenzyme A biosynthesis by tuning the Escherichia coli metabolic network and its application to flavanone production
    Fowler, Zachary L.; Gikandi, William W.; Koffas, Mattheos A. G.
    Applied and Environmental Microbiology (2009), 75 (18), 5831-5839CODEN: AEMIDF; ISSN:0099-2240. (American Society for Microbiology)
    Identification of genetic targets able to bring about changes to the metabolite profiles of microorganisms continues to be a challenging task. We have independently developed a cipher of evolutionary design (CiED) to identify genetic perturbations, such as gene deletions and other network modifications, that result in optimal phenotypes for the prodn. of end products, such as recombinant natural products. Coupled to an evolutionary search, our method demonstrates the utility of a purely stoichiometric network to predict improved Escherichia coli genotypes that more effectively channel carbon flux toward malonyl CoA (CoA) and other cofactors in an effort to generate recombinant strains with enhanced flavonoid prodn. capacity. The engineered E. coli strains were constructed first by the targeted deletion of native genes predicted by CiED and then second by incorporating selected overexpressions, including those of genes required for the coexpression of the plant-derived flavanones, acetate assimilation, acetyl-CoA carboxylase, and the biosynthesis of CoA. As a result, the specific flavanone prodn. from our optimally engineered strains was increased by over 660% for naringenin (15 to 100 mg/L/optical d. unit [OD]) and by over 420% for eriodictyol (13 to 55 mg/L/OD).
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    Thakker, C.; Martínez, I.; San, K.-Y.; Bennett, G. N. Succinate production in Escherichia coli. Biotechnol. J. 2012, 7, 213– 224,  DOI: 10.1002/biot.201100061
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    Succinate production in Escherichia coli
    Thakker, Chandresh; Martinez, Irene; San, Ka-Yiu; Bennett, George N.
    Biotechnology Journal (2012), 7 (2), 213-224CODEN: BJIOAM; ISSN:1860-6768. (Wiley-VCH Verlag GmbH & Co. KGaA)
    A review. Succinate has been recognized as an important platform chem. that can be produced from biomass. While a no. of organisms are capable of succinate prodn. naturally, this review focuses on the engineering of Escherichia coli for the prodn. of four-carbon dicarboxylic acid. Important features of a succinate prodn. system are to achieve an optimal balance of reducing equiv. generated by consumption of the feedstock, while maximizing the amt. of carbon channeled into the product. Aerobic and anaerobic prodn. strains have been developed and applied to prodn. from glucose and other abundant carbon sources. Metabolic engineering methods and strain evolution have been used and supplemented by the recent application of systems biol. and in silico modeling tools to construct optimal prodn. strains. The metabolic capacity of the prodn. strain, the requirement for efficient recovery of succinate, and the reliability of the performance under scaleup are important in the overall process. The costs of the overall biorefinery-compatible process will det. the economic commercialization of succinate and its impact in larger chem. markets.
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    Jantama, K.; Haupt, M. J.; Svoronos, S. A.; Zhang, X.; Moore, J. C.; Shanmugam, K. T.; Ingram, L. O. Combining metabolic engineering and metabolic evolution to develop nonrecombinant strains of Escherichia coli C that produce succinate and malate. Biotechnol. Bioeng. 2008, 99, 1140– 1153,  DOI: 10.1002/bit.21694
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    Combining metabolic engineering and metabolic evolution to develop nonrecombinant strains of Escherichia coli C that produce succinate and malate
    Jantama, Kaemwich; Haupt, M. J.; Svoronos, Spyros A.; Zhang, Xueli; Moore, J. C.; Shanmugam, K. T.; Ingram, L. O.
    Biotechnology and Bioengineering (2008), 99 (5), 1140-1153CODEN: BIBIAU; ISSN:0006-3592. (John Wiley & Sons, Inc.)
    Derivs. of Escherichia coli C were engineered to produce primarily succinate or malate in mineral salts media using simple fermns. (anaerobic stirred batch with pH control) without the addn. of plasmids or foreign genes. This was done by a combination of gene deletions (genetic engineering) and metabolic evolution with over 2,000 generations of growth-based selection. After deletion of the central anaerobic fermn. genes (ldhA, adhE, ackA), the pathway for malate and succinate prodn. remained as the primary route for the regeneration of NAD+. Under anaerobic conditions, ATP prodn. for growth was obligately coupled to malate dehydrogenase and fumarate reductase by the requirement for NADH oxidn. Selecting strains for improved growth co-selected increased prodn. of these dicarboxylic acids. Addnl. deletions were introduced as further improvements (focA, pflB, poxB, mgsA). The best succinate biocatalysts, strains KJ060(ldhA, adhE, ackA, focA, pflB) and KJ073(ldhA, adhE, ackA, focA, pflB, mgsA, poxB), produce 622-733 mM of succinate with molar yields of 1.2-1.6 per mol of metabolized glucose. The best malate biocatalyst, strain KJ071(ldhA, adhE, ackA, focA, pflB, mgsA), produced 516 mM malate with molar yields of 1.4 per mol of glucose metabolized.
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    Zhang, X.; Jantama, K.; Moore, J. C.; Jarboe, L. R.; Shanmugam, K. T.; Ingram, L. O. Metabolic evolution of energy-conserving pathways for succinate production in Escherichia coli. Proc. Natl. Acad. Sci. 2009, 106, 20180– 20185,  DOI: 10.1073/pnas.0905396106
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    Metabolic evolution of energy-conserving pathways for succinate production in Escherichia coli
    Zhang, Xueli; Jantama, Kaemwich; Moore, Jonathan C.; Jarboe, Laura R.; Shanmugam, Keelnatham T.; Ingram, Lonnie O.
    Proceedings of the National Academy of Sciences of the United States of America (2009), 106 (48), 20180-20185, S20180/1-S20180/7CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
    During metabolic evolution to improve succinate prodn. in Escherichia coli strains, significant changes in cellular metab. were acquired that increased energy efficiency in two respects. The energy- conserving phosphoenolpyruvate (PEP) carboxykinase (pck), which normally functions in the reverse direction (gluconeogenesis; glucose repressed) during the oxidative metab. of org. acids, evolved to become the major carboxylation pathway for succinate prodn. Both PCK enzyme activity and gene expression levels increased significantly in two stages because of several mutations during the metabolic evolution process. High-level expression of this enzyme- dominated CO2 fixation and increased ATP yield (1 ATP per oxaloacetate). In addn., the native PEP-dependent phosphotransferase system for glucose uptake was inactivated by a mutation in ptsl. This glucose transport function was replaced by increased expression of the GalP permease (galP) and glucokinase (glk). Results of deleting individual transport genes confirmed that GalP served as the dominant glucose transporter in evolved strains. Using this alternative transport system would increase the pool of PEP available for redox balance. This change would also increase energy efficiency by eliminating the need to produce addnl. PEP from pyruvate, a reaction that requires two ATP equiv. Together, these changes converted the wild-type E. coli fermn. pathway for succinate into a functional equiv. of the native pathway that nature evolved in succinate-producing rumen bacteria.
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    Sánchez, A. M.; Bennett, G. N.; San, K.-Y. Efficient succinic acid production from glucose through overexpression of pyruvate carboxylase in an Escherichia coli alcohol dehydrogenase and lactate dehydrogenase mutant. Biotechnol. Prog. 2005, 21, 358– 365,  DOI: 10.1021/bp049676e
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    Efficient Succinic Acid Production from Glucose through Overexpression of Pyruvate Carboxylase in an Escherichia coli Alcohol Dehydrogenase and Lactate Dehydrogenase Mutant
    Sanchez, Ailen M.; Bennett, George N.; San, Ka-Yiu
    Biotechnology Progress (2005), 21 (2), 358-365CODEN: BIPRET; ISSN:8756-7938. (American Chemical Society)
    An adhE, ldhA double mutant Escherichia coli strain, SBS110MG, has been constructed to produce succinic acid in the presence of heterologous pyruvate carboxylase (PYC). The strategic design aims at diverting max. quantities of NADH for succinate synthesis by inactivation of NADH competing pathways to increase succinate yield and productivity. Addnl. an operational PFL enzyme allows formation of acetyl-CoA for biosynthesis and formate as a potential source of reducing equiv. Furthermore, PYC diverts pyruvate toward OAA to favor succinate generation. SBS110MG harboring plasmid pHL413, which encodes the heterologous pyruvate carboxylase from Lactococcus lactis, produced 15.6 g/L (132 mM) of succinate from 18.7 g/L (104 mM) of glucose after 24 h of culture in an atm. of CO2 yielding 1.3 mol of succinate per mol of glucose. This molar yield exceeded the max. theor. yield of succinate that can be achieved from glucose (1 mol/mol) under anaerobic conditions in terms of NADH balance. The current work further explores the importance of the presence of formate as a source of reducing equiv. in SBS110MG(pHL413). Inactivation of the native formate dehydrogenase pathway (FDH) in this strain significantly reduced succinate yield, suggesting that reducing power was lost in the form of formate. Addnl. we investigated the effect of ptsG inactivation in SBS110MG(pHL413) to evaluate the possibility of a further increase in succinate yield. Elimination of the ptsG system increased the succinate yield to 1.4 mol/mol at the expense of a redn. in glucose consumption of 33%. In the presence of PYC and an efficient conversion of glucose to products, the ptsG mutation is not indispensable since PEP converted to pyruvate as a result of glucose phosphorylation by the glucose specific PTS permease EIICBglu can be rediverted toward OAA favoring succinate prodn.
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    Jantama, K.; Zhang, X.; Moore, J. C.; Shanmugam, K. T.; Svoronos, S. A.; Ingram, L. O. Eliminating side products and increasing succinate yields in engineered strains of Escherichia coli C. Biotechnol. Bioeng. 2008, 101, 881– 893,  DOI: 10.1002/bit.22005
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    Eliminating side products and increasing succinate yields in engineered strains of Escherichia coli C
    Jantama, Kaemwich; Zhang, Xueli; Moore, J. C.; Shanmugam, K. T.; Svoronos, S. A.; Ingram, L. O.
    Biotechnology and Bioengineering (2008), 101 (5), 881-893CODEN: BIBIAU; ISSN:0006-3592. (John Wiley & Sons, Inc.)
    Derivs. of Escherichia coli C were previously described for succinate prodn. by combining the deletion of genes that disrupt fermn. pathways for alternative products (ldhA::FRT, adhE::FRT, ackA::FRT, focA-pflB::FRT, mgsA, poxB) with growth-based selection for increased ATP prodn. The resulting strain, KJ073, produced 1.2 mol of succinate per mol glucose in mineral salts medium with acetate, malate, and pyruvate as significant co-products. KJ073 has been further improved by removing residual recombinase sites (FRT sites) from the chromosomal regions of gene deletion to create a strain devoid of foreign DNA, strain KJ091 (ΔldhA ΔadhE ΔackA ΔfocA-pflB ΔmgsA ΔpoxB). KJ091 was further engineered for improvements in succinate prodn. Deletion of the threonine decarboxylase (tdcD; acetate kinase homolog) and 2-ketobutyrate formate-lyase (tdcE; pyruvate formatelyase homolog) reduced the acetate level by 50% and increased succinate yield (1.3 mol mol-1 glucose) by almost 10% as compared to KJ091 and KJ073. Deletion of two genes involved in oxaloacetate metab., aspartate aminotransferase (aspC) and the NAD+-linked malic enzyme (sfcA) (KJ122) significantly increased succinate yield (1.5 mol mol-1 glucose), succinate titer (700 mM), and av. volumetric productivity (0.9 g L-1 h-1). Residual pyruvate and acetate were substantially reduced by further deletion of pta encoding phosphotransacetylase to produce KJ134 (ΔldhA ΔadhE ΔfocA-pflB ΔmgsA ΔpoxB ΔtdcDE ΔcitF ΔaspC ΔsfcA Δpta-ackA). Strains KJ122 and KJ134 produced near theor. yields of succinate during simple, anaerobic, batch fermns. using mineral salts medium. Both may be useful as biocatalysts for the com. prodn. of succinate.
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    Sánchez, A. M.; Bennett, G. N.; San, K.-Y. Novel pathway engineering design of the anaerobic central metabolic pathway in Escherichia coli to increase succinate yield and productivity. Metab. Eng. 2005, 7, 229– 239,  DOI: 10.1016/j.ymben.2005.03.001
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    Novel pathway engineering design of the anaerobic central metabolic pathway in Escherichia coli to increase succinate yield and productivity
    Sanchez, Ailen M.; Bennett, George N.; San, Ka-Yiu
    Metabolic Engineering (2005), 7 (3), 229-239CODEN: MEENFM; ISSN:1096-7176. (Elsevier)
    A novel in vivo method of producing succinate has been developed. A genetically engineered Escherichia coli strain has been constructed to meet the NADH requirement and carbon demand to produce high quantities and yield of succinate by strategically implementing metabolic pathway alterations. Currently, the max. theor. succinate yield under strictly anaerobic conditions through the fermentative succinate biosynthesis pathway is limited to one mole per mol of glucose due to NADH limitation. The implemented strategic design involves the construction of a dual succinate synthesis route, which diverts required quantities of NADH through the traditional fermentative pathway and maximizes the carbon converted to succinate by balancing the carbon flux through the fermentative pathway and the glyoxylate pathway (which has less NADH requirement). The synthesis of succinate uses a combination of the two pathways to balance the NADH. Consequently, exptl. results indicated that these combined pathways gave the most efficient conversion of glucose to succinate with the highest yield using only 1.25 mol of NADH per mol of succinate in contrast to the sole fermentative pathway, which uses 2 mol of NADH per mol of succinate. A recombinant E. coli strain, SBS550MG, was created by deactivating adhE, ldhA and ack-pta from the central metabolic pathway and by activating the glyoxylate pathway through the inactivation of iclR, which encodes a transcriptional repressor protein of the glyoxylate bypass. The inactivation of these genes in SBS550MG increased the succinate yield from glucose to about 1.6 mol/mol with an av. anaerobic productivity rate of 10 mM/h(∼0.64 mM/h-OD600). This strain is capable of fermenting high concns. of glucose in less than 24 h. Addnl. derepression of the glyxoylate pathway by inactivation of arcA, leading to a strain designated as SBS660MG, did not significantly increase the succinate yield and it decreased glucose consumption by 80%. It was also obsd. that an adhE, ldhA and ack-pta mutant designated as SBS990MG, was able to achieve a high succinate yield similar to SBS550MG when expressing a Bacillus subtilis NADH-insensitive citrate synthase from a plasmid.
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    Satanowski, A.; Dronsella, B.; Noor, E.; Vögeli, B.; He, H.; Wichmann, P.; Erb, T. J.; Lindner, S. N.; Bar-Even, A. Awakening a latent carbon fixation cycle in Escherichia coli. Nat. Commun. 2020, 11, 5812,  DOI: 10.1038/s41467-020-19564-5
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    Awakening a latent carbon fixation cycle in Escherichia coli
    Satanowski, Ari; Dronsella, Beau; Noor, Elad; Voegeli, Bastian; He, Hai; Wichmann, Philipp; Erb, Tobias J.; Lindner, Steffen N.; Bar-Even, Arren
    Nature Communications (2020), 11 (1), 5812CODEN: NCAOBW; ISSN:2041-1723. (Nature Research)
    Abstr.: Carbon fixation is one of the most important biochem. processes. Most natural carbon fixation pathways are thought to have emerged from enzymes that originally performed other metabolic tasks. Can we recreate the emergence of a carbon fixation pathway in a heterotrophic host by recruiting only endogenous enzymes. In this study, we address this question by systematically analyzing possible carbon fixation pathways composed only of Escherichia coli native enzymes. We identify the GED (Gnd-Entner-Doudoroff) cycle as the simplest pathway that can operate with high thermodn. driving force. This autocatalytic route is based on reductive carboxylation of ribulose 5-phosphate (Ru5P) by 6-phosphogluconate dehydrogenase (Gnd), followed by reactions of the Entner-Doudoroff pathway, gluconeogenesis, and the pentose phosphate pathway. We demonstrate the in vivo feasibility of this new-to-nature pathway by constructing E. coli gene deletion strains whose growth on pentose sugars depends on the GED shunt, a linear variant of the GED cycle which does not require the regeneration of Ru5P. Several metabolic adaptations, most importantly the increased prodn. of NADPH, assist in establishing sufficiently high flux to sustain this growth. Our study exemplifies a trajectory for the emergence of carbon fixation in a heterotrophic organism and demonstrates a synthetic pathway of biotechnol. interest.
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    Kim, Y.; Ingram, L. O.; Shanmugam, K. T. Dihydrolipoamide dehydrogenase mutation alters the NADH sensitivity of pyruvate dehydrogenase complex of Escherichia coli K-12. J. Bacteriol. 2008, 190, 3851– 3858,  DOI: 10.1128/jb.00104-08
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    Dihydrolipoamide dehydrogenase mutation alters the NADH sensitivity of pyruvate dehydrogenase complex of Escherichia coli K-12
    Kim, Youngnyun; Ingram, L. O.; Shanmugam, K. T.
    Journal of Bacteriology (2008), 190 (11), 3851-3858CODEN: JOBAAY; ISSN:0021-9193. (American Society for Microbiology)
    Under anaerobic growth conditions, an active pyruvate dehydrogenase (PDH) is expected to create a redox imbalance in wild-type Escherichia coli due to increased prodn. of NADH (>2 NADH mols./glucose mol.) that could lead to growth inhibition. However, the addnl. NADH produced by PDH can be used for conversion of acetyl CoA into reduced fermn. products, like alcs., during metabolic engineering of the bacterium. E. coli mutants that produced ethanol as the main fermn. product were recently isolated as derivs. of an ldhA pflB double mutant. In all six mutants tested, the mutation was in the lpd gene encoding dihydrolipoamide dehydrogenase (LPD), a component of PDH. Three of the LPD mutants carried an H322Y mutation (lpd102), while the other mutants carried an E354K mutation (lpd101). Genetic and physiol. anal. revealed that the mutation in either allele supported anaerobic growth and homoethanol fermn. in an ldhA pflB double mutant. Enzyme kinetic studies revealed that the LPD(E354K) enzyme was significantly less sensitive to NADH inhibition than the native LPD. This reduced NADH sensitivity of the mutated LPD was translated into lower sensitivity of the appropriate PDH complex to NADH inhibition. The mutated forms of the PDH had a 10-fold-higher Ki for NADH than the native PDH. The lower sensitivity of PDH to NADH inhibition apparently increased PDH activity in anaerobic E. coli cultures and created the new ethanologenic fermn. pathway in this bacterium. Analogous mutations in the LPD of other bacteria may also significantly influence the growth and physiol. of the organisms in a similar fashion.
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    Trichez, D.; Auriol, C.; Baylac, A.; Irague, R.; Dressaire, C.; Carnicer-Heras, M.; Heux, S.; François, J. M.; Walther, T. Engineering of Escherichia coli for Krebs cycle-dependent production of malic acid. Microb. Cell Factories 2018, 17, 113,  DOI: 10.1186/s12934-018-0959-y
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    Engineering of Escherichia coli for Krebs cycle-dependent production of malic acid
    Trichez, Debora; Auriol, Clement; Baylac, Audrey; Irague, Romain; Dressaire, Clementine; Carnicer-Heras, Marc; Heux, Stephanie; Francois, Jean Marie; Walther, Thomas
    Microbial Cell Factories (2018), 17 (), 113/1-113/12CODEN: MCFICT; ISSN:1475-2859. (BioMed Central Ltd.)
    However, as malate can be a precursor for specialty chems., such as 2,4-dihydroxybutyric acid, that require addnl. cofactors NADP(H) and ATP, we set out to reengineer Escherichia coli for Krebs cycle-dependent prodn. of malic acid that can satisfy these requirements. Results: We found that significant malate prodn. required at least simultaneous deletion of all malic enzymes and dehydrogenases, and concomitant expression of a malate-insensitive PEP carboxylase. Metabolic flux anal. using 13C-labeled glucose indicated that malate-producing strains had a very high flux over the glyoxylate shunt with almost no flux passing through the isocitrate dehydrogenase reaction. The highest malate yield of 0.82 mol/mol was obtained with E. coli Δmdh Δmqo ΔmaeAB ΔiclR ΔarcA which expressed malate-insensitive PEP carboxylase PpcK620S and NADH-insensitive citrate synthase GltAR164L. We also showed that inactivation of the dicarboxylic acid transporter DcuA strongly reduced malate prodn. arguing for a pivotal role of this permease in malate export. Conclusions: Since more NAD(P)H and ATP cofactors are generated in the Krebs cycle-dependent malate prodn. when compared to pathways which depend on the function of anaplerotic PEP carboxylase or PEP carboxykinase enzymes, the engineered strain developed in this study can serve as a platform to increase biosynthesis of malate-derived metabolites such as 2,4-dihydroxybutyric acid.
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    Machado, D.; Soons, Z.; Patil, K. R.; Ferreira, E. C.; Rocha, I. Random sampling of elementary flux modes in large-scale metabolic networks. Bioinformatics 2012, 28, i515– i521,  DOI: 10.1093/bioinformatics/bts401
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    Random sampling of elementary flux modes in large-scale metabolic networks
    Machado, Daniel; Soons, Zita; Patil, Kiran Raosaheb; Ferreira, Eugenio C.; Rocha, Isabel
    Bioinformatics (2012), 28 (18), i515-i521CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
    Motivation: The description of a metabolic network in terms of elementary (flux) modes (EMs) provides an important framework for metabolic pathway anal. However, their application to large networks has been hampered by the combinatorial explosion in the no. of modes. In this work, we develop a method for generating random samples of EMs without computing the whole set. Results: Our algorithm is an adaptation of the canonical basis approach, where we add an addnl. filtering step which, at each iteration, selects a random subset of the new combinations of modes. In order to obtain an unbiased sample, all candidates are assigned the same probability of getting selected. This approach avoids the exponential growth of the no. of modes during computation, thus generating a random sample of the complete set of EMs within reasonable time. We generated samples of different sizes for a metabolic network of Escherichia coli, and obsd. that they preserve several properties of the full EM set. It is also shown that EM sampling can be used for rational strain design. A well distributed sample, that is representative of the complete set of EMs, should be suitable to most EM-based methods for anal. and optimization of metabolic networks.
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    Tan, Z.; Zhu, X.; Chen, J.; Li, Q.; Zhang, X. Activating phosphoenolpyruvate carboxylase and phosphoenolpyruvate carboxykinase in combination for improvement of succinate production. Appl. Environ. Microbiol. 2013, 79, 4838– 4844,  DOI: 10.1128/aem.00826-13
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    Activating phosphoenolpyruvate carboxylase and phosphoenolpyruvate carboxykinase in combination for improvement of succinate production
    Tan, Zaigao; Zhu, Xinna; Chen, Jing; Li, Qingyan; Zhang, Xueli
    Applied and Environmental Microbiology (2013), 79 (16), 4838-4844CODEN: AEMIDF; ISSN:1098-5336. (American Society for Microbiology)
    Phosphoenolpyruvate (PEP) carboxylation is an important step in the prodn. of succinate by Escherichia coli. Two enzymes, PEP carboxylase (PPC) and PEP carboxykinase (PCK), are responsible for PEP carboxylation. PPC has high substrate affinity and catalytic velocity but wastes the high energy of PEP. PCK has low substrate affinity and catalytic velocity but can conserve the high energy of PEP for ATP formation. In this work, the expression of both the ppc and pck genes was modulated, with multiple regulatory parts of different strengths, in order to investigate the relationship between PPC or PCK activity and succinate prodn. There was a pos. correlation between PCK activity and succinate prodn. In contrast, there was a pos. correlation between PPC activity and succinate prodn. only when PPC activity was within a certain range; excessive PPC activity decreased the rates of both cell growth and succinate formation. These two enzymes were also activated in combination in order to recruit the advantages of each for the improvement of succinate prodn. It was demonstrated that PPC and PCK had a synergistic effect in improving succinate prodn.
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    Baba, T.; Ara, T.; Hasegawa, M.; Takai, Y.; Okumura, Y.; Baba, M.; Datsenko, K. A.; Tomita, M.; Wanner, B. L.; Mori, H. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol. Syst. Biol. 2006, 2, 2006.0008,  DOI: 10.1038/msb4100050
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    Peng, L.; Arauzo-Bravo, M. J.; Shimizu, K. Metabolic flux analysis for a ppc mutant Escherichia coli based on 13C-labelling experiments together with enzyme activity assays and intracellular metabolite measurements. FEMS Microbiol. Lett. 2004, 235, 17– 23,  DOI: 10.1111/j.1574-6968.2004.tb09562.x
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    Metabolic flux analysis for a ppc mutant Escherichia coli based on 13C-labelling experiments together with enzyme activity assays and intracellular metabolite measurements
    Peng, Lifeng; Arauzo-Bravo, Marcos J.; Shimizu, Kazuyuki
    FEMS Microbiology Letters (2004), 235 (1), 17-23CODEN: FMLED7; ISSN:0378-1097. (Elsevier Science B.V.)
    The physiol. and central metab. of a ppc mutant Escherichia coli were investigated based on the metabolic flux distribution obtained by 13C-labeling expts. using gas chromatog.-mass spectrometry (GC-MS) and 2-dimensional NMR (2D NMR) strategies together with enzyme activity assays and intracellular metabolite concn. measurements. Compared to the wild type, its ppc mutant excreted little acetate and produced less carbon dioxide at the expense of a slower growth rate and a lower glucose uptake rate. Consequently, an improvement of the biomass yield on glucose was obsd. in the ppc mutant. Enzyme activity measurements revealed that isocitrate lyase activity increased by more than 3-fold in the ppc mutant. Some TCA cycle enzymes such as citrate synthase, aconitase and malate dehydrogenase were also upregulated, but enzymes of glycolysis and the pentose phosphate pathway were down-regulated. The intracellular intermediates in the glycolysis and the pentose phosphate pathway, therefore, accumulated, while acetyl CoA and oxaloacetate concns. decreased in the ppc mutant. The intracellular metabolic flux anal. uncovered that deletion of ppc resulted in the appearance of the glyoxylate shunt, with 18.9% of the carbon flux being channeled via the glyoxylate shunt. However, the flux of the pentose phosphate pathway significantly decreased in the ppc mutant.
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    Kim, S.-W.; Keasling, J. D. Metabolic engineering of the nonmevalonate isopentenyl diphosphate synthesis pathway in Escherichia coli enhances lycopene production. Biotechnol. Bioeng. 2001, 72, 408– 415,  DOI: 10.1002/1097-0290(20000220)72:4<408::aid-bit1003>3.0.co;2-h
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    Metabolic engineering of the nonmevalonate isopentenyl diphosphate synthesis pathway in Escherichia coli enhances lycopene production
    Kim, Seon-Won; Keasling, J. D.
    Biotechnology and Bioengineering (2001), 72 (4), 408-415CODEN: BIBIAU; ISSN:0006-3592. (John Wiley & Sons, Inc.)
    Isopentenyl diphosphate (IPP) is the common, five-carbon building block in the biosynthesis of all carotenoids. IPP in Escherichia coli is synthesized through the nonmevalonate pathway, which has not been completely elucidated. The first reaction of IPP biosynthesis in E. coli is the formation of 1-deoxy-D-xylulose-5-phosphate (DXP), catalyzed by DXP synthase and encoded by dxs. The second reaction in the pathway is the redn. of DXP to 2-C-methyl-D-erythritol-4-phosphate, catalyzed by DXP reductoisomerase and encoded by dxr. To det. if one or more of the reactions in the nonmevalonate pathway controlled flux to IPP, dxs and dxr were placed on several expression vectors under the control of three different promoters and transformed into three E. coli strains (DH5α, XL1-Blue, and JM101) that had been engineered to produce lycopene. Lycopene prodn. was improved significantly in strains transformed with the dxs expression vectors. When the dxs gene was expressed from the arabinose-inducible araBAD promoter (PBAD) on a medium-copy plasmid, lycopene prodn. was twofold higher than when dxs was expressed from the IPTG-inducible trc and lac promoters (Ptrc and Plac' resp.) on medium-copy and high-copy plasmids. Given the low final densities of cells expressing dxs from IPTG-inducible promoters, the low lycopene prodn. was probably due to the metabolic burden of plasmid maintenance and an excessive drain of central metabolic intermediates. At arabinose concns. between 0 and 1.33 mM, cells expressing both dxs and dxr from PBAD on a medium-copy plasmid produced 1.4-2.0 times more lycopene than cells expressing dxs only. However, at higher arabinose concns. lycopene prodn. in cells expressing both dxs and dxr was lower than in cells expressing dxs only. A comparison of the three E. coli strains transformed with the arabinose-inducible dxs on a medium-copy plasmid revealed that lycopene prodn. was highest in XL1-Blue.
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    Alper, H.; Jin, Y.-S.; Moxley, J. F.; Stephanopoulos, G. Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli. Metab. Eng. 2005, 7, 155– 164,  DOI: 10.1016/j.ymben.2004.12.003
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    Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli
    Alper, Hal; Jin, Yong-Su; Moxley, J. F.; Stephanopoulos, G.
    Metabolic Engineering (2005), 7 (3), 155-164CODEN: MEENFM; ISSN:1096-7176. (Elsevier)
    The identification of genetic targets that are effective in bringing about a desired phenotype change is still an open problem. While random gene knockouts have yielded improved strains in certain cases, it is also important to seek the guidance of cell-wide stoichiometric constraints in identifying promising gene knockout targets. To investigate these issues, we undertook a genome-wide stoichiometric flux balance anal. as an aid in discovering putative genes impacting network properties and cellular phenotype. Specifically, we calcd. metabolic fluxes such as to optimize growth and then scanned the genome for single and multiple gene knockouts that yield improved product yield while maintaining acceptable overall growth rate. For the particular case of lycopene biosynthesis in Escherichia coli, we identified such targets that we subsequently tested exptl. by constructing the corresponding single, double and triple gene knockouts. While such strains are suggested (by the stoichiometric calcns.) to increase precursor availability, this beneficial effect may be further impacted by kinetic and regulatory effects not captured by the stoichiometric model. For the case of lycopene biosynthesis, the so identified knockout targets yielded a triple knockout construct that exhibited a nearly 40% increase over an engineered, high producing parental strain.
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    Wang, J.; Niyompanich, S.; Tai, Y.-S.; Wang, J.; Bai, W.; Mahida, P.; Gao, T.; Zhang, K. Engineering of a highly efficient Escherichia coli strain for mevalonate fermentation through chromosomal integration. Appl. Environ. Microbiol. 2016, 82, 7176– 7184,  DOI: 10.1128/aem.02178-16
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    Engineering of a highly efficient Escherichia coli strain for mevalonate fermentation through chromosomal integration
    Wang, Jilong; Niyompanich, Suthamat; Tai, Yi-Shu; Wang, Jingyu; Bai, Wenqin; Mahida, Prithviraj; Gao, Tuo; Zhang, Kechun
    Applied and Environmental Microbiology (2016), 82 (24), 7176-7184CODEN: AEMIDF; ISSN:1098-5336. (American Society for Microbiology)
    Chromosomal integration of heterologous metabolic pathways is optimal for industrially relevant fermn., as plasmid-based fermn. causes extra metabolic burden and genetic instabilities. In this work, chromosomal integration was adapted for the prodn. of mevalonate, which can be readily converted into β-methyl-δ-valerolactone, a monomer for the prodn. of mech. tunable polyesters. The mevalonate pathway, driven by a constitutive promoter, was integrated into the chromosome of Escherichia coli to replace the native fermn. gene adhE or ldhA. The engineered strains (CMEV-1 and CMEV-2) did not require inducer or antibiotic and showed slightly higher maximal productivities (0.38 to ∼0.43 g/L/h) and yields (67.8 to ∼71.4% of the max. theor. yield) than those of the plasmid-based fermn. Since the glycolysis pathway is the first module for mevalonate synthesis, atpFH deletion was employed to improve the glycolytic rate and the prodn. rate of mevalonate. Shake flask fermn. results showed that the deletion of atpFH in CMEV-1 resulted in a 2.1- fold increase in the max. productivity. Furthermore, enhancement of the downstream pathway by integrating two copies of the mevalonate pathway genes into the chromosome further improved the mevalonate yield. Finally, our fed-batch fermn. showed that, with deletion of the atpFH and sucA genes and integration of two copies of the mevalonate pathway genes into the chromosome, the engineered strain CMEV-7 exhibited both high maximal productivity (∼1.01 g/L/h) and high yield (86.1% of the max. theor. yield, 30 g/L mevalonate from 61 g/L glucose after 48 h in a shake flask).
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    Chatterjee, R.; Millard, C. S.; Champion, K.; Clark, D. P.; Donnelly, M. I. Mutation of the ptsG gene results in increased production of succinate in fermentation of glucose by Escherichia coli. Appl. Environ. Microbiol. 2001, 67, 148– 154,  DOI: 10.1128/aem.67.1.148-154.2001
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    Mutation of the ptsG gene results in increased production of succinate in fermentation of glucose by Escherichia coli
    Chatterjee, Ranjini; Millard, Cynthia Sanville; Champion, Kathleen; Clark, David P.; Donnelly, Mark I.
    Applied and Environmental Microbiology (2001), 67 (1), 148-154CODEN: AEMIDF; ISSN:0099-2240. (American Society for Microbiology)
    Escherichia coli NZN111 is blocked in the ability to grow fermentatively on glucose but gave rise spontaneously to a mutant that had this ability. The mutant carries out a balanced fermn. of glucose to give approx. 1 mol of succinate, 0.5 mol of acetate, and 0.5 mol of ethanol per mol of glucose. The causative mutation was mapped to the ptsG gene, which encodes the membrane-bound, glucose-specific permease of the phosphotransferase system, protein EIICBglc. Replacement of the chromosomal ptsG gene with an insertionally inactivated form also restored growth on glucose and resulted in the same distribution of fermn. products. The physiol. characteristics of the spontaneous and null mutants were consistent with loss of function of the ptsG gene product; the mutants possessed greatly reduced glucose phosphotransferase activity and lacked normal glucose repression. Introduction of the null mutant into strains not blocked in the ability to ferment glucose also increased succinate prodn. in those strains. This phenomenon was widespread, occurring in different lineages of E. coli, including E. coli B.
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    Lyngstadaas, A.; Sprenger, G. A.; Boye, E. Impaired growth of an Escherichia coli rpe mutant lacking ribulose-5-phosphate epimerase activity. Biochim. Biophys. Acta Gen. Subj. 1998, 1381, 319– 330,  DOI: 10.1016/s0304-4165(98)00046-4
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    Impaired growth of an Escherichia coli rpe mutant lacking ribulose-5-phosphate epimerase activity
    Lyngstadaas, Anita; Sprenger, Georg A.; Boye, Erik
    Biochimica et Biophysica Acta, General Subjects (1998), 1381 (3), 319-330CODEN: BBGSB3; ISSN:0304-4165. (Elsevier B.V.)
    The authors present evidence that ribulose-5-phosphate epimerase, a central metabolic enzyme acting in the non-oxidative branch of the pentose-phosphate pathway, is encoded by a gene in the dam contg. operon of Escherichia coli. Enzymic assays confirm that this gene encodes ribulose-5-phosphate epimerase activity. Disruption of the gene (rpe) causes loss of enzymic activity and renders the rpe mutant unable to utilize single pentose sugars, indicating that rpe supplies the only ribulose-5-phosphate epimerase activity in E. coli. Growth of the rpe mutant is impaired in complex LB medium and severely impaired in minimal medium contg. glycolytic carbon sources or gluconate. Enrichment with casamino acids abolishes or strongly relieves growth suppression in minimal medium. Aspartate counteracts the impaired growth in glycolytic carbon sources but not in gluconate. It is suggested that the absence of the Rpe enzyme causes changes in the pentose-phosphate levels which alter the regulation of (a) metabolic enzyme(s) and thereby cause growth suppression and that the severity of growth suppression is related to the internal concn. of pentose-phosphates. Target enzymes for neg. regulation may be located in the early parts of the Embden-Meyerhof-Parnas pathway and of the Entner-Doudoroff pathway and/or of carbohydrate transport systems feeding sugars into these sections of central metabolic pathways.
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    Lewis, N. E.; Hixson, K. K.; Conrad, T. M.; Lerman, J. A.; Charusanti, P.; Polpitiya, A. D.; Adkins, J. N.; Schramm, G.; Purvine, S. O.; Lopez-Ferrer, D.; Weitz, K. K.; Eils, R.; König, R.; Smith, R. D.; Palsson, B. Ø. Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol. Syst. Biol. 2010, 6, 390,  DOI: 10.1038/msb.2010.47
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    Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models
    Lewis Nathan E; Hixson Kim K; Conrad Tom M; Lerman Joshua A; Charusanti Pep; Polpitiya Ashoka D; Adkins Joshua N; Schramm Gunnar; Purvine Samuel O; Lopez-Ferrer Daniel; Weitz Karl K; Eils Roland; Konig Rainer; Smith Richard D; Palsson Bernhard O
    Molecular systems biology (2010), 6 (), 390 ISSN:.
    After hundreds of generations of adaptive evolution at exponential growth, Escherichia coli grows as predicted using flux balance analysis (FBA) on genome-scale metabolic models (GEMs). However, it is not known whether the predicted pathway usage in FBA solutions is consistent with gene and protein expression in the wild-type and evolved strains. Here, we report that >98% of active reactions from FBA optimal growth solutions are supported by transcriptomic and proteomic data. Moreover, when E. coli adapts to growth rate selective pressure, the evolved strains upregulate genes within the optimal growth predictions, and downregulate genes outside of the optimal growth solutions. In addition, bottlenecks from dosage limitations of computationally predicted essential genes are overcome in the evolved strains. We also identify regulatory processes that may contribute to the development of the optimal growth phenotype in the evolved strains, such as the downregulation of known regulons and stringent response suppression. Thus, differential gene and protein expression from wild-type and adaptively evolved strains supports observed growth phenotype changes, and is consistent with GEM-computed optimal growth states.
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    Machado, D.; Herrgard, M. J.; Rocha, I. Stoichiometric representation of gene-protein-reaction associations leverages constraint-based analysis from reaction to gene-Level phenotype prediction. PLoS Comput. Biol. 2016, 12, e1005140  DOI: 10.1371/journal.pcbi.1005140
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    Stoichiometric representation of gene-protein-reaction associations leverages constraint-based analysis from reaction to gene-level phenotype prediction
    Machado, Daniel; Herrgard, Markus J.; Rocha, Isabel
    PLoS Computational Biology (2016), 12 (10), e1005140/1-e1005140/24CODEN: PCBLBG; ISSN:1553-7358. (Public Library of Science)
    Genome-scale metabolic reconstructions are currently available for hundreds of organisms. Constraint-based modeling enables the anal. of the phenotypic landscape of these organisms, predicting the response to genetic and environmental perturbations. However, since constraint-based models can only describe the metabolic phenotype at the reaction level, understanding the mechanistic link between genotype and phenotype is still hampered by the complexity of gene-protein-reaction assocns. We implement a model transformation that enables constraint-based methods to be applied at the gene level by explicitly accounting for the individual fluxes of enzymes (and subunits) encoded by each gene. We show how this can be applied to different kinds of constraint-based anal.: flux distribution prediction, gene essentiality anal., random flux sampling, elementary mode anal., transcriptomics data integration, and rational strain design. In each case we demonstrate how this approach can lead to improved phenotype predictions and a deeper understanding of the genotype-to-phenotype link. In particular, we show that a large fraction of reaction-based designs obtained by current strain design methods are not actually feasible, and show how our approach allows using the same methods to obtain feasible gene-based designs. We also show, by extensive comparison with exptl. 13C-flux data, how simple reformulations of different simulation methods with gene-wise objective functions result in improved prediction accuracy. The model transformation proposed in this work enables existing constraint-based methods to be used at the gene level without modification. This automatically leverages phenotype anal. from reaction to gene level, improving the biol. insight that can be obtained from genome-scale models.
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  • Abstract

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    Figure 1

    Figure 1. Toy metabolic network (A) and flux distributions of the wild type and mutant (B). Symbols in (A) are as follows: S, carbon source; X, biomass; P, product; Mi (i = 1, 2, 3), metabolite name; Ri (i = 1,..., 5), reaction name. Each axis in (B) represents the absolute flux for a reaction.

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    Figure 2

    Figure 2. Reactions identified as up/down-regulation targets by OptDesign for succinate overproduction. Abbreviations of reaction names are borrowed from the iML1515 model definitions. Up-regulation and down-regulation reactions are in green and blue ovals, respectively. These reactions have been classified into different subsystems represented by orange rectangles.

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    Figure 3

    Figure 3. Design strategies identified by OptDesign for biochemical production in E. coli. Reaction names and their arrow symbols in the same color mean that they must be manipulated in mutant strains. Reaction names colored only (i.e., red, green, or blue) mean that they are alternative manipulations. Dashed arrows represent a merge of multiple conversion steps to metabolites. Design strategies are summarized in boxes above the simplified metabolic maps. Abbreviations of metabolite names are as follows: g6p, glucose-6-phosphate; f6p, d-fructose 6-phosphate; g3p, glyceraldehyde-3-phosphate; 13dpg, 3-phospho-D-glyceroyl phosphate; 3gp, 3-phospho-d-glycerate; 6pgc, 6-phospho-d-gluconate; ru5p-D, d-ribulose 5-phosphate; r5p, alpha-d-ribose 5-phosphate; xu5p-D, d-xylulose 5-phosphate; dhap, dihydroxyacetone phosphate; mthgxl, methylglyoxal; pep, phosphoenolpyruvate; pyr, pyruvate; lac-D: d-lactate; dxyl5p, 1-deoxy-d-xylulose 5-phosphate; ipdp, isopentenyl diphosphate; frdp, farnesyl diphosphate; ggdp, geranylgeranyl diphosphate; phyto, all-trans-phytoene; ppi, diphosphate; pi, phosphate; gly, glycine; mlthf, 5,10-methylenetetrahydrofolate; flxso, flavodoxin semi oxidized; flxr, flavodoxin reduced; accoa, acetyl-CoA; cit, citrate; icit, isocitrate; akg, 2-oxoglutarate; succ, succinate; fum, fumarate; mal-L, l-malate; oaa, oxaloacetate; hom-L, l-homoserine; thr-L, l-threonine; dhor-S, (S)-dihydroorotate; orot, orotate; malcoa, malonyl-CoA; cma, coumaric acid; cmcoa, coumaroyl-CoA; chal, naringenine chalcone; fad, flavin adenine dinucleotide oxidized; fadh2, flavin adenine dinucleotide reduced. Abbreviations of reaction names are referred to the iML1515 model definitions.

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    Figure 4

    Figure 4. Production envelopes of different growth-coupled design strategies consisting of no more than five manipulations for lycopene. The production envelope illustrates the minimum and maximum production rates a production strain can achieve at different growth rates compared to the wild type. The solid-blue production envelope is for the design strategy using the minimal regulation set: ALCD19 (knockout), TKT2 (knockout), DXPS (overexpressed), PItex (overexpressed), and TPI (underexpressed). The dashed red production envelope is for the design strategy using the maximal regulation set: FUM (knockout), R1PK (knockout), ADK3 (overexpressed), PItex (overexpressed), and ADK1 (underexpressed). Reaction names are consistent with the genome-scale metabolic network model of E. coli iML1515.

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    Figure 5

    Figure 5. Influence of δ and minimum growth on succinate production.

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    Figure 6

    Figure 6. Comparison of production envelopes obtained by OptDesign with and without a reference flux vector for three target products. The reference flux vector for the wild type was computed using parsimonious FBA (pFBA), which minimizes the sum of squared fluxes in the network. (54)

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    Figure 7

    Figure 7. Comparison of different strain design tools without reference flux vectors for succinate overproduction. The intervention targets were identified by using the default genome-scale metabolic network model of E. coli iML1515. (32) A 100% theoretical succinate yield was used in OptForce, and the regulation parameter C in OptReg was set to 0.5. Reaction names are consistent with the iML1515 model.

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  • References

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    This article references 55 other publications.

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      Metabolic Engineering (2011), 13 (5), 578-587CODEN: MEENFM; ISSN:1096-7176. (Elsevier B. V.)
      Malonyl-CoA is an important precursor metabolite for the biosynthesis of polyketides, flavonoids and biofuels. However, malonyl-CoA naturally synthesized in microorganisms is consumed for the prodn. of fatty acids and phospholipids leaving only a small amt. available for the prodn. of other metabolic targets in recombinant biosynthesis. Here we present an integrated computational and exptl. approach aimed at improving the intracellular availability of malonyl-CoA in Escherichia coli. We used a customized version of the recently developed OptForce methodol. to predict a minimal set of genetic interventions that guarantee a prespecified yield of malonyl-CoA in E. coli strain BL21 Star. In order to validate the model predictions, we have successfully constructed an E. coli recombinant strain that exhibits a 4-fold increase in the levels of intracellular malonyl-CoA compared to the wild type strain. Furthermore, we demonstrate the potential of this E. coli strain for the prodn. of plant-specific secondary metabolites naringenin (474 mg/L) with the highest yield ever achieved in a lab-scale fermn. process. Combined effect of the genetic interventions was found to be synergistic based on a developed anal. method that correlates genetic modification to cell phenotype, specifically the identified knockout targets (ΔfumC and ΔsucC) and overexpression targets (ACC, PGK, GAPD and PDH) can cooperatively force carbon flux towards malonyl-CoA. The presented strategy can also be readily expanded for the prodn. of other malonyl-CoA-derived compds. like polyketides and biofuels.
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      Metabolic Engineering (2018), 46 (), 1-12CODEN: MEENFM; ISSN:1096-7176. (Elsevier B.V.)
      Biol.-derived hydrocarbons are considered to have great potential as next-generation biofuels owing to the similarity of their chem. properties to contemporary diesel and jet fuels. However, the low yield of these hydrocarbons in biotechnol. prodn. is a major obstacle for commercialization. Several genetic and process engineering approaches have been adopted to increase the yield of hydrocarbon, but a model driven approach has not been implemented so far. Here, we applied a constraint-based metabolic modeling approach in which a variable demand for alkane biosynthesis was imposed, and co-varying reactions were considered as potential targets for further engineering of an E. coli strain already expressing cyanobacterial enzymes towards higher chain alkane prodn. The reactions that co-varied with the imposed alkane prodn. were found to be mainly assocd. with the pentose phosphate pathway (PPP) and the lower half of glycolysis. An optimal modeling soln. was achieved by imposing increased flux through the reaction catalyzed by glucose-6-phosphate dehydrogenase (zwf) and iteratively removing 7 reactions from the network, leading to an alkane yield of 94.2% of the theor. max. conversion detd. by in silico anal. at a given biomass rate. To validate the in silico findings, we first performed pathway optimization of the cyanobacterial enzymes in E. coli via different dosages of genes, promoting substrate channelling through protein fusion and inducing substantial equiv. protein expression, which led to a 36-fold increase in alka(e)ne prodn. from 2.8 mg/L to 102 mg/L. Further, engineering of E. coli based on in silico findings, including biomass constraint, led to an increase in the alka(e)ne titer to 425 mg/L (major components being 249 mg/L pentadecane and 160 mg/L heptadecene), a 148.6-fold improvement over the initial strain, resp.; with a yield of 34.2% of the theor. max. The impact of model-assisted engineering was also tested for the prodn. of long chain fatty alc., another com. important mol. sharing the same pathway while differing only at the terminal reaction, and a titer of 1506 mg/L was achieved with a yield of 86.4% of the theor. max. Moreover, the model assisted engineered strains had produced 2.54 g/L and 12.5 g/L of long chain alkane and fatty alc., resp., in the bioreactor under fed-batch cultivation condition. Our study demonstrated successful implementation of a combined in silico modeling approach along with the pathway and process optimization in achieving the highest reported titers of long chain hydrocarbons in E. coli.
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      ACS Synthetic Biology (2021), 10 (12), 3237-3250CODEN: ASBCD6; ISSN:2161-5063. (American Chemical Society)
      A review. Corynebacterium glutamicum is an important workhorse in industrial white biotechnol. It has been widely applied in the producing processes of amino acids, fuels, and diverse value-added chems. With the continuous disclosure of genetic regulation mechanisms, various strategies and technologies of synthetic biol. were used to design and construct C. glutamicum cells for biomanufg. and bioremediation. This study mainly aimed to summarize the design and construction strategies of C. glutamicum-engineered strains, which were based on genomic modification, synthetic biol. device-assisted metabolic flux optimization, and directed evolution-based engineering. Then, taking two important bioproducts (N-acetylglucosamine and hyaluronic acid) as examples, the applications of C. glutamicum cell factories were introduced. Finally, we discussed the current challenges and future development trends of C. glutamicum-engineered strain construction.
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      Maia Paulo; Rocha Miguel; Rocha Isabel
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      Shifting from chemical to biotechnological processes is one of the cornerstones of 21st century industry. The production of a great range of chemicals via biotechnological means is a key challenge on the way toward a bio-based economy. However, this shift is occurring at a pace slower than initially expected. The development of efficient cell factories that allow for competitive production yields is of paramount importance for this leap to happen. Constraint-based models of metabolism, together with in silico strain design algorithms, promise to reveal insights into the best genetic design strategies, a step further toward achieving that goal. In this work, a thorough analysis of the main in silico constraint-based strain design strategies and algorithms is presented, their application in real-world case studies is analyzed, and a path for the future is discussed.
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      ePathBrick: A Synthetic Biology Platform for Engineering Metabolic Pathways in E. coli
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      Harnessing cell factories for producing biofuel and pharmaceutical mols. has stimulated efforts to develop novel synthetic biol. tools customized for modular pathway engineering and optimization. Here we report the development of a set of vectors compatible with BioBrick stds. and its application in metabolic engineering. The engineered ePathBrick vectors comprise four compatible restriction enzyme sites allocated on strategic positions so that different regulatory control signals can be reused and manipulation of expression cassette can be streamlined. Specifically, these vectors allow for fine-tuning gene expression by integrating multiple transcriptional activation or repression signals into the operator region. At the same time, ePathBrick vectors support the modular assembly of pathway components and combinatorial generation of pathway diversities with three distinct configurations. We also demonstrated the functionality of a seven-gene pathway (∼9 Kb) assembled on one single ePathBrick vector. The ePathBrick vectors presented here provide a versatile platform for rapid design and optimization of metabolic pathways in E. coli.
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      Burgard, A. P.; Pharkya, P.; Maranas, C. D. Optknock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng. 2003, 84, 647– 657,  DOI: 10.1002/bit.10803
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      OptKnock: A bilevel programming framework for identifying gene knockout strategies for microbial strain optimization
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      The advent of genome-scale models of metab. has laid the foundation for the development of computational procedures for suggesting genetic manipulations that lead to overprodn. In this work, the computational OptKnock framework is introduced for suggesting gene deletion strategies leading to the overprodn. of chems. or biochems. in E. coli. This is accomplished by ensuring that a drain towards growth resources (i.e., carbon, redox potential, and energy) must be accompanied, due to stoichiometry, by the prodn. of a desired product. Computational results for gene deletions for succinate, lactate, and 1,3-propanediol (PDO) prodn. are in good agreement with mutant strains published in the literature. While some of the suggested deletion strategies are straightforward and involve eliminating competing reaction pathways, many others suggest complex and nonintuitive mechanisms of compensating for the removed functionalities. Finally, the OptKnock procedure, by coupling biomass formation with chem. prodn., hints at a growth selection/adaptation system for indirectly evolving overproducing mutants.
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      Pharkya, P.; Maranas, C. D. An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems. Metab. Eng. 2006, 8, 1– 13,  DOI: 10.1016/j.ymben.2005.08.003
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      An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems
      Pharkya, Priti; Maranas, Costas D.
      Metabolic Engineering (2006), 8 (1), 1-13CODEN: MEENFM; ISSN:1096-7176. (Elsevier)
      We introduce a computational framework termed OptReg that dets. the optimal reaction activations/inhibitions and eliminations for targeted biochem. prodn. A reaction is deemed up- or downregulated if it is constrained to assume flux values significantly above or below its steady-state before the genetic manipulations. The developed framework is demonstrated by studying the overprodn. of ethanol in Escherichia coli. Computational results reveal the existence of synergism between reaction deletions and modulations implying that the simultaneous application of both types of genetic manipulations yields the most promising results. For example, the downregulation of phosphoglucomutase in conjunction with the deletion of oxygen uptake and pyruvate formate lyase yields 99.8% of the max. theor. ethanol yield. Conceptually, the proposed strategies redirect both the carbon flux as well as the cofactors to enhance ethanol prodn. in the network. The OptReg framework is a versatile tool for strain design which allows for a broad array of genetic manipulations.
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      King, Z. A.; Feist, A. M. Optimal cofactor swapping can increase the theoretical yield for chemical production in Escherichia coli and Saccharomyces cerevisiae. Metab. Eng. 2014, 24, 117– 128,  DOI: 10.1016/j.ymben.2014.05.009
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      Optimal cofactor swapping can increase the theoretical yield for chemical production in Escherichia coli and Saccharomyces cerevisiae
      King, Zachary A.; Feist, Adam M.
      Metabolic Engineering (2014), 24 (), 117-128CODEN: MEENFM; ISSN:1096-7176. (Elsevier B. V.)
      Maintaining cofactor balance is a crit. function in microorganisms, but often the native cofactor balance does not match the needs of an engineered metabolic flux state. Here, an optimization procedure is utilized to identify optimal cofactor-specificity "swaps" for oxidoreductase enzymes utilizing NAD(H) or NADP(H) in the genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae. The theor. yields of all native carbon-contg. mols. are considered, as well as theor. yields of twelve heterologous prodn. pathways in E. coli. Swapping the cofactor specificity of central metabolic enzymes (esp. GAPD and ALCD2x) is shown to increase NADPH prodn. and increase theor. yields for native products in E. coli and yeast-including L-aspartate, L-lysine, L-isoleucine, L-proline, L-serine, and putrescine-and non-native products in E. coli-including 1,3-propanediol, 3-hydroxybutyrate, 3-hydroxypropanoate, 3-hydroxyvalerate, and styrene.
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      Pharkya, P.; Burgard, A. P.; Maranas, C. D. OptStrain: a computational framework for redesign of microbial production systems. Genome Res. 2004, 14, 2367– 2376,  DOI: 10.1101/gr.2872004
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      Optstrain: A computational framework for redesign of microbial production systems
      Pharkya, Priti; Burgard, Anthony P.; Maranas, Costas D.
      Genome Research (2004), 14 (11), 2367-2376CODEN: GEREFS; ISSN:1088-9051. (Cold Spring Harbor Laboratory Press)
      This paper introduces the hierarchical computational framework OptStrain aimed at guiding pathway modifications, through reaction addns. and deletions, of microbial networks for the overprodn. of targeted compds. These compds. may range from electrons or hydrogen in biofuel cell and environmental applications to complex drug precursor mols. A comprehensive database of biotransformations, referred to as the Universal database (with >5700 reactions), is compiled and regularly updated by downloading and curating reactions from multiple biopathway database sources. Combinatorial optimization is then used to elucidate the set(s) of non-native functionalities, extd. from this Universal database, to add to the examd. prodn. host for enabling the desired product formation. Subsequently, competing functionalities that divert flux away from the targeted product are identified and removed to ensure higher product yields coupled with growth. This work represents an advancement over earlier efforts by establishing an integrated computational framework capable of constructing stoichiometrically balanced pathways, imposing max. product yield requirements, pinpointing the optimal substrate(s), and evaluating different microbial hosts. The range and utility of OptStrain are demonstrated by addressing two very different product mols. The hydrogen case study pinpoints reaction elimination strategies for improving hydrogen yields using two different substrates for three sep. prodn. hosts. In contrast, the vanillin study primarily showcases which non-native pathways need to be added into Escherichia coli. In summary, OptStrain provides a useful tool to aid microbial strain design and, more importantly, it establishes an integrated framework to accommodate future modeling developments.
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    13. 13
      Pratapa, A.; Balachandran, S.; Raman, K. Fast-SL: An efficient algorithm to identify synthetic lethal sets in metabolic networks. Bioinformatics 2015, 31, 3299– 3305,  DOI: 10.1093/bioinformatics/btv352
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      13
      Fast-SL: an efficient algorithm to identify synthetic lethal sets in metabolic networks
      Pratapa, Aditya; Balachandran, Shankar; Raman, Karthik
      Bioinformatics (2015), 31 (20), 3299-3305CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
      Motivation: Synthetic lethal sets are sets of reactions/genes where only the simultaneous removal of all reactions/genes in the set abolishes growth of an organism. Previous approaches to identify synthetic lethal genes in genome-scale metabolic networks have built on the framework of flux balance anal. (FBA), extending it either to exhaustively analyze all possible combinations of genes or formulate the problem as a bi-level mixed integer linear programming (MILP) problem. We here propose an algorithm, Fast-SL, which surmounts the computational complexity of previous approaches by iteratively reducing the search space for synthetic lethals, resulting in a substantial redn. in running time, even for higher order synthetic lethals. Results: We performed synthetic reaction and gene lethality anal., using Fast-SL, for genomescale metabolic networks of Escherichia coli, Salmonella enterica Typhimurium and Mycobacterium tuberculosis. Fast-SL also rigorously identifies synthetic lethal gene deletions, uncovering synthetic lethal triplets that were not reported previously. We confirm that the triple lethal gene sets obtained for the three organisms have a precise match with the results obtained through exhaustive enumeration of lethals performed on a computer cluster. We also parallelized our algorithm, enabling the identification of synthetic lethal gene quadruplets for all three organisms in under 6 h. Overall, Fast-SL enables an efficient enumeration of higher order synthetic lethals in metabolic networks, which may help uncover previously unknown genetic interactions and combinatorial drug targets.
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    14. 14
      Lun, D. S.; Rockwell, G.; Guido, N. J.; Baym, M.; Kelner, J. A.; Berger, B.; Galagan, J. E.; Church, G. M. Large-scale identification of genetic design strategies using local search. Mol. Syst. Biol. 2009, 5, 296,  DOI: 10.1038/msb.2009.57
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      14
      Large-scale identification of genetic design strategies using local search
      Lun Desmond S; Rockwell Graham; Guido Nicholas J; Baym Michael; Kelner Jonathan A; Berger Bonnie; Galagan James E; Church George M
      Molecular systems biology (2009), 5 (), 296 ISSN:.
      In the past decade, computational methods have been shown to be well suited to unraveling the complex web of metabolic reactions in biological systems. Methods based on flux-balance analysis (FBA) and bi-level optimization have been used to great effect in aiding metabolic engineering. These methods predict the result of genetic manipulations and allow for the best set of manipulations to be found computationally. Bi-level FBA is, however, limited in applicability because the required computational time and resources scale poorly as the size of the metabolic system and the number of genetic manipulations increase. To overcome these limitations, we have developed Genetic Design through Local Search (GDLS), a scalable, heuristic, algorithmic method that employs an approach based on local search with multiple search paths, which results in effective, low-complexity search of the space of genetic manipulations. Thus, GDLS is able to find genetic designs with greater in silico production of desired metabolites than can feasibly be found using a globally optimal search and performs favorably in comparison with heuristic searches based on evolutionary algorithms and simulated annealing.
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    15. 15
      Egen, D.; Lun, D. S. Truncated branch and bound achieves efficient constraint-based genetic design. Bioinformatics 2012, 28, 1619– 1623,  DOI: 10.1093/bioinformatics/bts255
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      15
      Truncated branch and bound achieves efficient constraint-based genetic design
      Egen, Dennis; Lun, Desmond S.
      Bioinformatics (2012), 28 (12), 1619-1623CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
      Motivation: Computer-aided genetic design is a promising approach to a core problem of metabolic engineering-that of identifying genetic manipulation strategies that result in engineered strains with favorable product accumulation. This approach has proved to be effective for organisms including Escherichia coli and Saccharomyces cerevisiae, allowing for rapid, rational design of engineered strains. Finding optimal genetic manipulation strategies, however, is a complex computational problem in which running time grows exponentially with the no. of manipulations (i.e. knockouts, knock-ins or regulation changes) in the strategy. Thus, computer-aided gene identification has to date been limited in the complexity or optimality of the strategies it finds or in the size and level of detail of the metabolic networks under consideration. Results: Here, we present an efficient computational soln. to the gene identification problem. Our approach significantly outperforms previous approaches-in seconds or minutes, we find strategies that previously required running times of days or more. Availability and implementation: GDBB is implemented using MATLAB and is freely available for non-profit use at http://crab.rutgers.edu/∼dslun/gdbb. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.
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    16. 16
      Rocha, I.; Maia, P.; Evangelista, P.; Vilaça, P.; Soares, S.; Pinto, J. P.; Nielsen, J.; Patil, K. R.; Ferreira, E. C.; Rocha, M. OptFlux: an open-source software platform for in silico metabolic engineering. BMC Syst. Biol. 2010, 4, 45,  DOI: 10.1186/1752-0509-4-45
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      16
      OptFlux: an open-source software platform for in silico metabolic engineering
      Rocha Isabel; Maia Paulo; Evangelista Pedro; Vilaca Paulo; Soares Simao; Pinto Jose P; Nielsen Jens; Patil Kiran R; Ferreira Eugenio C; Rocha Miguel
      BMC systems biology (2010), 4 (), 45 ISSN:.
      BACKGROUND: Over the last few years a number of methods have been proposed for the phenotype simulation of microorganisms under different environmental and genetic conditions. These have been used as the basis to support the discovery of successful genetic modifications of the microbial metabolism to address industrial goals. However, the use of these methods has been restricted to bioinformaticians or other expert researchers. The main aim of this work is, therefore, to provide a user-friendly computational tool for Metabolic Engineering applications. RESULTS: OptFlux is an open-source and modular software aimed at being the reference computational application in the field. It is the first tool to incorporate strain optimization tasks, i.e., the identification of Metabolic Engineering targets, using Evolutionary Algorithms/Simulated Annealing metaheuristics or the previously proposed OptKnock algorithm. It also allows the use of stoichiometric metabolic models for (i) phenotype simulation of both wild-type and mutant organisms, using the methods of Flux Balance Analysis, Minimization of Metabolic Adjustment or Regulatory on/off Minimization of Metabolic flux changes, (ii) Metabolic Flux Analysis, computing the admissible flux space given a set of measured fluxes, and (iii) pathway analysis through the calculation of Elementary Flux Modes. OptFlux also contemplates several methods for model simplification and other pre-processing operations aimed at reducing the search space for optimization algorithms. The software supports importing/exporting to several flat file formats and it is compatible with the SBML standard. OptFlux has a visualization module that allows the analysis of the model structure that is compatible with the layout information of Cell Designer, allowing the superimposition of simulation results with the model graph. CONCLUSIONS: The OptFlux software is freely available, together with documentation and other resources, thus bridging the gap from research in strain optimization algorithms and the final users. It is a valuable platform for researchers in the field that have available a number of useful tools. Its open-source nature invites contributions by all those interested in making their methods available for the community. Given its plug-in based architecture it can be extended with new functionalities. Currently, several plug-ins are being developed, including network topology analysis tools and the integration with Boolean network based regulatory models.
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    17. 17
      Choon, Y. W.; Mohamad, M. S.; Deris, S.; Chong, C. K.; Omatu, S.; Corchado, J. M. Gene knockout identification using an extension of bees hill flux balance analysis. BioMed Res. Int. 2015, 2015, 124537,  DOI: 10.1155/2015/124537
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      17
      Gene knockout identification using an extension of Bees Hill Flux Balance Analysis
      Choon Yee Wen; Mohamad Mohd Saberi; Deris Safaai; Chong Chuii Khim; Omatu Sigeru; Corchado Juan Manuel
      BioMed research international (2015), 2015 (), 124537 ISSN:.
      Microbial strain optimisation for the overproduction of a desired phenotype has been a popular topic in recent years. Gene knockout is a genetic engineering technique that can modify the metabolism of microbial cells to obtain desirable phenotypes. Optimisation algorithms have been developed to identify the effects of gene knockout. However, the complexities of metabolic networks have made the process of identifying the effects of genetic modification on desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to a combinatorial problem in obtaining optimal gene knockout. The computational time increases exponentially as the size of the problem increases. This work reports an extension of Bees Hill Flux Balance Analysis (BHFBA) to identify optimal gene knockouts to maximise the production yield of desired phenotypes while sustaining the growth rate. This proposed method functions by integrating OptKnock into BHFBA for validating the results automatically. The results show that the extension of BHFBA is suitable, reliable, and applicable in predicting gene knockout. Through several experiments conducted on Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as model organisms, extension of BHFBA has shown better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes.
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    18. 18
      Sandberg, T. E.; Lloyd, C. J.; Palsson, B. O.; Feist, A. M. Laboratory evolution to alternating substrate environments yields distinct phenotypic and genetic adaptive strategies. Appl. Environ. Microbiol. 2017, 83, e00410– e00417,  DOI: 10.1128/AEM.00410-17
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      18
      Laboratory evolution to alternating substrate environments yields distinct phenotypic and genetic adaptive strategies
      Sandberg, Troy E.; Lloyd, Colton J.; Palsson, Bernhard O.; Feist, Adam M.
      Applied and Environmental Microbiology (2017), 83 (13), e00410-17/1-e00410-17/15CODEN: AEMIDF; ISSN:1098-5336. (American Society for Microbiology)
      Adaptive lab. evolution (ALE) expts. are often designed to maintain a static culturing environment to minimize confounding variables that could influence the adaptive process, but dynamic nutrient conditions occur frequently in natural and bioprocessing settings. To study the nature of carbon substrate fitness tradeoffs, we evolved batch cultures of Escherichia coli via serial propagation into tubes alternating between glucose and either xylose, glycerol, or acetate. Genome sequencing of evolved cultures revealed several genetic changes preferentially selected for under dynamic conditions and different adaptation strategies depending on the substrates being switched between; in some environments, a persistent "generalist" strain developed, while in another, two "specialist" subpopulations arose that alternated dominance. Diauxic lag phenotype varied across the generalists and specialists, in one case being completely abolished, while gene expression data distinguished the transcriptional strategies implemented by strains in pursuit of growth optimality. Genome-scale metabolic modeling techniques were then used to help explain the inherent substrate differences giving rise to the obsd. distinct adaptive strategies. This study gives insight into the population dynamics of adaptation in an alternating environment and into the underlying metabolic and genetic mechanisms. Furthermore, ALE-generated optimized strains have phenotypes with potential industrial bioprocessing applications.
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    19. 19
      Alter, T. B.; Ebert, B. E. Determination of growth-coupling strategies and their underlying principles. BMC Bioinf. 2019, 20, 447,  DOI: 10.1186/s12859-019-2946-7
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      19
      Determination of growth-coupling strategies and their underlying principles
      Alter Tobias B; Ebert Birgitta E; Ebert Birgitta E
      BMC bioinformatics (2019), 20 (1), 447 ISSN:.
      BACKGROUND: Metabolic coupling of product synthesis and microbial growth is a prominent approach for maximizing production performance. Growth-coupling (GC) also helps stabilizing target production and allows the selection of superior production strains by adaptive laboratory evolution. To support the implementation of growth-coupling strain designs, we seek to identify biologically relevant, metabolic principles that enforce strong growth-coupling on the basis of reaction knockouts. RESULTS: We adapted an established bilevel programming framework to maximize the minimally guaranteed production rate at a fixed, medium growth rate. Using this revised formulation, we identified various GC intervention strategies for metabolites of the central carbon metabolism, which were examined for GC generating principles under diverse conditions. Curtailing the metabolism to render product formation an essential carbon drain was identified as one major strategy generating strong coupling of metabolic activity and target synthesis. Impeding the balancing of cofactors and protons in the absence of target production was the underlying principle of all other strategies and further increased the GC strength of the aforementioned strategies. CONCLUSION: Maximizing the minimally guaranteed production rate at a medium growth rate is an attractive principle for the identification of strain designs that couple growth to target metabolite production. Moreover, it allows for controlling the inevitable compromise between growth coupling strength and the retaining of microbial viability. With regard to the corresponding metabolic principles, generating a dependency between the supply of global metabolic cofactors and product synthesis appears to be advantageous in enforcing strong GC for any metabolite. Deriving such strategies manually, is a hard task, due to which we suggest incorporating computational metabolic network analyses in metabolic engineering projects seeking to determine GC strain designs.
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    20. 20
      Jensen, K.; Broeken, V.; Hansen, A. S. L.; Sonnenschein, N.; Herrgård, M. J. OptCouple: Joint simulation of gene knockouts, insertions and medium modifications for prediction of growth-coupled strain designs. Metab. Eng. Commun. 2019, 8, e00087  DOI: 10.1016/j.mec.2019.e00087
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      Pusa, T.; Wannagat, M.; Sagot, M.-F. Metabolic Games. Front. Appl. Math. Stat. 2019, 5, 18,  DOI: 10.3389/fams.2019.00018
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      Jiang, S.; Wang, Y.; Kaiser, M.; Krasnogor, N. NIHBA: a network interdiction approach for metabolic engineering design. Bioinformatics 2020, 36, 3482– 3492,  DOI: 10.1093/bioinformatics/btaa163
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      22
      NIHBA: a network interdiction approach for metabolic engineering design
      Jiang, Shouyong; Wang, Yong; Kaiser, Marcus; Krasnogor, Natalio
      Bioinformatics (2020), 36 (11), 3482-3492CODEN: BOINFP; ISSN:1367-4811. (Oxford University Press)
      Motivation: Flux balance anal. (FBA) based bilevel optimization has been a great success in redesigning metabolic networks for biochem. overprodn. To date, many computational approaches have been developed to solve the resulting bilevel optimization problems. However, most of them are of limited use due to biased optimality principle, poor scalability with the size of metabolic networks, potential numeric issues or low quantity of design solns. in a single run. Results: Here, we have employed a network interdiction model free of growth optimality assumptions, a special case of bilevel optimization, for computational strain design and have developed a hybrid Benders algorithm (HBA) that deals with complicating binary variables in the model, thereby achieving high efficiency without numeric issues in search of best design strategies. More importantly, HBA can list solns. that meet users' prodn. requirements during the search, making it possible to obtain numerous design strategies at a small runtime overhead (typically ~ 1 h, e.g. studied in this article).
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      Apaolaza, I.; Valcarcel, L. V.; Planes, F. J. gMCS: Fast computation of genetic minimal cut sets in large networks. Bioinformatics 2018, 35, 535– 537,  DOI: 10.1093/bioinformatics/bty656
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      von Kamp, A.; Klamt, S. Enumeration of smallest intervention strategies in genome-scale metabolic networks. PLoS Comput. Biol. 2014, 10, e1003378  DOI: 10.1371/journal.pcbi.1003378
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      Enumeration of smallest intervention strategies in genome-scale metabolic networks
      von Kamp, Axel; Klamt, Steffen
      PLoS Computational Biology (2014), 10 (1), e1003378/1-e1003378/13, 13 pp.CODEN: PCBLBG; ISSN:1553-7358. (Public Library of Science)
      One ultimate goal of metabolic network modeling is the rational redesign of biochem. networks to optimize the prodn. of certain compds. by cellular systems. Although several constraint-based optimization techniques have been developed for this purpose, methods for systematic enumeration of intervention strategies in genome-scale metabolic networks are still lacking. In principle, Minimal Cut Sets (MCSs; inclusion-minimal combinations of reaction or gene deletions that lead to the fulfilment of a given intervention goal) provide an exhaustive enumeration approach. However, their disadvantage is the combinatorial explosion in larger networks and the requirement to compute first the elementary modes (EMs) which itself is impractical in genome-scale networks. We present MCSEnumerator, a new method for effective enumeration of the smallest MCSs (with fewest interventions) in genome-scale metabolic network models. For this we combine two approaches, namely (i) the mapping of MCSs to EMs in a dual network, and (ii) a modified algorithm by which shortest EMs can be effectively detd. in large networks. In this way, we can identify the smallest MCSs by calcg. the shortest EMs in the dual network. Realistic application examples demonstrate that our algorithm is able to list thousands of the most efficient intervention strategies in genome-scale networks for various intervention problems. For instance, for the first time we could enumerate all synthetic lethals in E.coli with combinations of up to 5 reactions. We also applied the new algorithm exemplarily to compute strain designs for growth-coupled synthesis of different products (ethanol, fumarate, serine) by E.coli. We found numerous new engineering strategies partially requiring less knockouts and guaranteeing higher product yields (even without the assumption of optimal growth) than reported previously. The strength of the presented approach is that smallest intervention strategies can be quickly calcd. and screened with neither network size nor the no. of required interventions posing major challenges.
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      Harder, B.-J.; Bettenbrock, K.; Klamt, S. Model-based metabolic engineering enables high yield itaconic acid production by Escherichia coli. Metab. Eng. 2016, 38, 29– 37,  DOI: 10.1016/j.ymben.2016.05.008
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      25
      Model-based metabolic engineering enables high yield itaconic acid production by Escherichia coli
      Harder, Bjoern-Johannes; Bettenbrock, Katja; Klamt, Steffen
      Metabolic Engineering (2016), 38 (), 29-37CODEN: MEENFM; ISSN:1096-7176. (Elsevier B. V.)
      Itaconic acid is a high potential platform chem. which is currently industrially produced by Aspergillus terreus. Heterologous prodn. of itaconic acid with Escherichia coli could help to overcome limitations of A. terreus regarding slow growth and high sensitivity to oxygen supply. However, the performance achieved so far with E. coli strains is still low. We introduced a plasmid (pCadCS) carrying genes for itaconic acid prodn. into E. coli and applied a model-based approach to construct a high yield prodn. strain. Based on the concept of minimal cut sets, we identified intervention strategies that guarantee high itaconic acid yield while still allowing growth. One cut set was selected and the corresponding genes were iteratively knocked-out. As a conceptual novelty, we pursued an adaptive approach allowing changes in the model and initially calcd. intervention strategy if a genetic modification induces changes in byproduct formation. Using this approach, we iteratively implemented five interventions leading to high yield itaconic acid prodn. in minimal medium with glucose as substrate supplemented with small amts. of glutamic acid. The derived E. coli strain (ita23: MG1655 ΔaceA ΔsucCD ΔpykA ΔpykF Δpta ΔPicd::cam_BBa_J23115 pCadCS) synthesized 2.27 g/l itaconic acid with an excellent yield of 0.77 mol/(mol glucose). In a fed-batch cultivation, this strain produced 32 g/l itaconic acid with an overall yield of 0.68 mol/(mol. glucose) and a peak productivity of 0.45 g/l/h. These values are by far the highest that have ever been achieved for heterologous itaconic acid prodn. and indicate that realistic applications come into reach.
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      Ranganathan, S.; Suthers, P. F.; Maranas, C. D. OptForce: An optimization procedure for identifying all genetic manipulations leading to targeted overproductions. PLoS Comput. Biol. 2010, 6, e1000744  DOI: 10.1371/journal.pcbi.1000744
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      Otero-Muras, I.; Carbonell, P. Automated engineering of synthetic metabolic pathways for efficient association to reaction rules. biomanufacturing. Metab. Eng. 2021, 63, 61– 80,  DOI: 10.1016/j.ymben.2020.11.012
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      27
      Automated engineering of synthetic metabolic pathways for efficient biomanufacturing
      Otero-Muras, Irene; Carbonell, Pablo
      Metabolic Engineering (2021), 63 (), 61-80CODEN: MEENFM; ISSN:1096-7176. (Elsevier B.V.)
      A review. Metabolic engineering involves the engineering and optimization of processes from single-cell to fermn. in order to increase prodn. of valuable chems. for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial prodn. Metabolic engineering is nowadays on track towards a truly manufg. technol., with reduced times from conception to prodn. enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising prodn. routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an exptl. design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufg. process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomols. in an affordable, inclusive and sustainable bioeconomy.
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    28. 28
      Shen, F.; Sun, R.; Yao, J.; Li, J.; Liu, Q.; Price, N. D.; Liu, C.; Wang, Z. OptRAM: In-silico strain design via integrative regulatory-metabolic network modeling. PLoS Comput. Biol. 2019, 15, e1006835  DOI: 10.1371/journal.pcbi.1006835
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      28
      OptRAM: in-silico strain design via integrative regulatory-metabolic network modeling
      Shen, Fangzhou; Sun, Renliang; Yao, Jie; Li, Jian; Liu, Qian; Price, Nathan D.; Liu, Chenguang; Wang, Zhuo
      PLoS Computational Biology (2019), 15 (3), e1006835/1-e1006835/25CODEN: PCBLBG; ISSN:1553-7358. (Public Library of Science)
      The ultimate goal of metabolic engineering is to produce desired compds. on an industrial scale in a cost effective manner. To address challenges in metabolic engineering, computational strain optimization algorithms based on genome-scale metabolic models have increasingly been used to aid in overproducing products of interest. However, most of these strain optimization algorithms utilize a metabolic network alone, with few approaches providing strategies that also include transcriptional regulation. Moreover previous integrated approaches generally require a pre-existing regulatory network. In this study, we developed a novel strain design algorithm, named OptRAM (Optimization of Regulatory And Metabolic Networks), which can identify combinatorial optimization strategies including overexpression, knockdown or knockout of both metabolic genes and transcription factors. OptRAM is based on our previous IDREAM integrated network framework, which makes it able to deduce a regulatory network from data. OptRAM uses simulated annealing with a novel objective function, which can ensure a favorable coupling between desired chem. and cell growth. The other advance we propose is a systematic evaluation metric of multiple solns., by considering the essential genes, flux variation, and engineering manipulation cost. We applied OptRAM to generate strain designs for succinate, 2,3-butanediol, and ethanol overprodn. in yeast, which predicted high min. predicted target prodn. rate compared with other methods and previous literature values. Moreover, most of the genes and TFs proposed to be altered by OptRAM in these scenarios have been validated by modification of the exact genes or the target genes regulated by the TFs, for overprodn. of these desired compds. by in vivo expts. cataloged in the LASER database. Particularly, we successfully validated the predicted strain optimization strategy for ethanol prodn. by fermn. expt. In conclusion, OptRAM can provide a useful approach that leverages an integrated transcriptional regulatory network and metabolic network to guide metabolic engineering applications.
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    29. 29
      Schuetz, R.; Zamboni, N.; Zampieri, M.; Heinemann, M.; Sauer, U. Multidimensional optimality of microbial metabolism. Science 2012, 336, 601– 604,  DOI: 10.1126/science.1216882
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      29
      Multidimensional optimality of microbial metabolism
      Schuetz, Robert; Zamboni, Nicola; Zampieri, Mattia; Heinemann, Matthias; Sauer, Uwe
      Science (Washington, DC, United States) (2012), 336 (6081), 601-604CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
      Although the network topol. of metab. is well known, understanding the principles that govern the distribution of fluxes through metab. lags behind. Exptl., these fluxes can be measured by 13C-flux anal., and there has been a long-standing interest in understanding this functional network operation from an evolutionary perspective. On the basis of 13C-detd. fluxes from nine bacteria and multi-objective optimization theory, the authors show that metab. operates close to the Pareto-optimal surface of a three-dimensional space defined by competing objectives. Consistent with flux data from evolved Escherichia coli, they propose that flux states evolve under the trade-off between two principles: optimality under one given condition and minimal adjustment between conditions. These principles form the forces by which evolution shapes metabolic fluxes in microorganisms' environmental context.
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    30. 30
      Tepper, N.; Shlomi, T. Predicting metabolic engineering knockout strategies for chemical production: accounting for competing pathways. Bioinformatics 2010, 26, 536– 543,  DOI: 10.1093/bioinformatics/btp704
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      Predicting metabolic engineering knockout strategies for chemical production: accounting for competing pathways
      Tepper, Naama; Shlomi, Tomer
      Bioinformatics (2010), 26 (4), 536-543CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
      Motivation: Computational modeling in metabolic engineering involves the prediction of genetic manipulations that would lead to optimized microbial strains, maximizing the prodn. rate of chems. of interest. Various computational methods are based on constraint-based modeling, which enables to anticipate the effect of genetic manipulations on cellular metab. considering a genome-scale metabolic network. However, current methods do not account for the presence of competing pathways in a metabolic network that may diverge metabolic flux away from producing a required chem., resulting in lower (or even zero) chem. prodn. rates in reality-making these methods somewhat over optimistic. Results: In this article, we describe a novel constraint-based method called RobustKnock that predicts gene deletion strategies that lead to the over-prodn. of chems. of interest, by accounting for the presence of competing pathways in the network. We describe results of applying RobustKnock to Escherichia coli's metabolic network towards the prodn. of various chems., demonstrating its ability to provide more robust predictions than those obtained via current state-of-the-art methods.
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      Feist, A. M.; Zielinski, D. C.; Orth, J. D.; Schellenberger, J.; Herrgard, M. J.; Palsson, B. Ø. Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli. Metab. Eng. 2010, 12, 173– 186,  DOI: 10.1016/j.ymben.2009.10.003
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      Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli
      Feist, Adam M.; Zielinski, Daniel C.; Orth, Jeffrey D.; Schellenberger, Jan; Herrgard, Markus J.; Palsson, Bernhard O.
      Metabolic Engineering (2010), 12 (3), 173-186CODEN: MEENFM; ISSN:1096-7176. (Elsevier B. V.)
      Integrated approaches utilizing in silico analyses will be necessary to successfully advance the field of metabolic engineering. Here, we present an integrated approach through a systematic model-driven evaluation of the prodn. potential for the bacterial prodn. organism Escherichia coli to produce multiple native products from different representative feedstocks through coupling metabolite prodn. to growth rate. Designs were examd. for 11 unique central metab. and amino acid targets from three different substrates under aerobic and anaerobic conditions. Optimal strain designs were reported for designs which possess max. yield, substrate-specific productivity, and strength of growth-coupling for up to 10 reaction eliminations (knockouts). In total, growth-coupled designs could be identified for 36 out of the total 54 conditions tested, corresponding to eight out of the 11 targets. There were 17 different substrate/target pairs for which over 80% of the theor. max. potential could be achieved. The developed method introduces a new concept of objective function tilting for strain design. This study provides specific metabolic interventions (strain designs) for prodn. strains that can be exptl. implemented, characterizes the potential for E. coli to produce native compds., and outlines a strain design pipeline that can be utilized to design prodn. strains for addnl. organisms.
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      Monk, J. M.; Lloyd, C. J.; Brunk, E.; Mih, N.; Sastry, A.; King, Z.; Takeuchi, R.; Nomura, W.; Zhang, Z.; Mori, H.; Feist, A. M.; Palsson, B. O. iML1515, a knowledgebase that computes Escherichia coli traits. Nat. Biotechnol. 2017, 35, 904– 908,  DOI: 10.1038/nbt.3956
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      iML1515, a knowledgebase that computes Escherichia coli traits
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      Nature Biotechnology (2017), 35 (10), 904-908CODEN: NABIF9; ISSN:1087-0156. (Nature Research)
      There is no expanded citation for this reference.
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      Heirendt, L. Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3. 0. Nat. Protoc. 2019, 14, 639– 702,  DOI: 10.1038/s41596-018-0098-2
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      Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0
      Heirendt, Laurent; Arreckx, Sylvain; Pfau, Thomas; Mendoza, Sebastian N.; Richelle, Anne; Heinken, Almut; Haraldsdottir, Hulda S.; Wachowiak, Jacek; Keating, Sarah M.; Vlasov, Vanja; Magnusdottir, Stefania; Ng, Chiam Yu; Preciat, German; Zagare, Alise; Chan, Siu H. J.; Aurich, Maike K.; Clancy, Catherine M.; Modamio, Jennifer; Sauls, John T.; Noronha, Alberto; Bordbar, Aarash; Cousins, Benjamin; El Assal, Diana C.; Valcarcel, Luis V.; Apaolaza, Inigo; Ghaderi, Susan; Ahookhosh, Masoud; Ben Guebila, Marouen; Kostromins, Andrejs; Sompairac, Nicolas; Le, Hoai M.; Ma, Ding; Sun, Yuekai; Wang, Lin; Yurkovich, James T.; Oliveira, Miguel A. P.; Vuong, Phan T.; El Assal, Lemmer P.; Kuperstein, Inna; Zinovyev, Andrei; Hinton, H. Scott; Bryant, William A.; Aragon Artacho, Francisco J.; Planes, Francisco J.; Stalidzans, Egils; Maass, Alejandro; Vempala, Santosh; Hucka, Michael; Saunders, Michael A.; Maranas, Costas D.; Lewis, Nathan E.; Sauter, Thomas; Palsson, Bernhard Oe.; Thiele, Ines; Fleming, Ronan M. T.
      Nature Protocols (2019), 14 (3), 639-702CODEN: NPARDW; ISSN:1750-2799. (Nature Research)
      Constraint-based reconstruction and anal. (COBRA) provides a mol. mechanistic framework for integrative anal. of exptl. mol. systems biol. data and quant. prediction of physicochem. and biochem. feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biol., biomedicine, and biotechnol. because its functions can be flexibly combined to implement tailored COBRA protocols for any biochem. network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topol. anal., strain and exptl. design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochem. data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, resp. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.
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      Fowler, Z. L.; Gikandi, W. W.; Koffas, M. A. G. Increased malonyl coenzyme A biosynthesis by tuning the Escherichia coli metabolic network and its application to flavanone production. Appl. Environ. Microbiol. 2009, 75, 5831– 5839,  DOI: 10.1128/aem.00270-09
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      Increased malonyl coenzyme A biosynthesis by tuning the Escherichia coli metabolic network and its application to flavanone production
      Fowler, Zachary L.; Gikandi, William W.; Koffas, Mattheos A. G.
      Applied and Environmental Microbiology (2009), 75 (18), 5831-5839CODEN: AEMIDF; ISSN:0099-2240. (American Society for Microbiology)
      Identification of genetic targets able to bring about changes to the metabolite profiles of microorganisms continues to be a challenging task. We have independently developed a cipher of evolutionary design (CiED) to identify genetic perturbations, such as gene deletions and other network modifications, that result in optimal phenotypes for the prodn. of end products, such as recombinant natural products. Coupled to an evolutionary search, our method demonstrates the utility of a purely stoichiometric network to predict improved Escherichia coli genotypes that more effectively channel carbon flux toward malonyl CoA (CoA) and other cofactors in an effort to generate recombinant strains with enhanced flavonoid prodn. capacity. The engineered E. coli strains were constructed first by the targeted deletion of native genes predicted by CiED and then second by incorporating selected overexpressions, including those of genes required for the coexpression of the plant-derived flavanones, acetate assimilation, acetyl-CoA carboxylase, and the biosynthesis of CoA. As a result, the specific flavanone prodn. from our optimally engineered strains was increased by over 660% for naringenin (15 to 100 mg/L/optical d. unit [OD]) and by over 420% for eriodictyol (13 to 55 mg/L/OD).
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      Thakker, C.; Martínez, I.; San, K.-Y.; Bennett, G. N. Succinate production in Escherichia coli. Biotechnol. J. 2012, 7, 213– 224,  DOI: 10.1002/biot.201100061
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      Succinate production in Escherichia coli
      Thakker, Chandresh; Martinez, Irene; San, Ka-Yiu; Bennett, George N.
      Biotechnology Journal (2012), 7 (2), 213-224CODEN: BJIOAM; ISSN:1860-6768. (Wiley-VCH Verlag GmbH & Co. KGaA)
      A review. Succinate has been recognized as an important platform chem. that can be produced from biomass. While a no. of organisms are capable of succinate prodn. naturally, this review focuses on the engineering of Escherichia coli for the prodn. of four-carbon dicarboxylic acid. Important features of a succinate prodn. system are to achieve an optimal balance of reducing equiv. generated by consumption of the feedstock, while maximizing the amt. of carbon channeled into the product. Aerobic and anaerobic prodn. strains have been developed and applied to prodn. from glucose and other abundant carbon sources. Metabolic engineering methods and strain evolution have been used and supplemented by the recent application of systems biol. and in silico modeling tools to construct optimal prodn. strains. The metabolic capacity of the prodn. strain, the requirement for efficient recovery of succinate, and the reliability of the performance under scaleup are important in the overall process. The costs of the overall biorefinery-compatible process will det. the economic commercialization of succinate and its impact in larger chem. markets.
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      Jantama, K.; Haupt, M. J.; Svoronos, S. A.; Zhang, X.; Moore, J. C.; Shanmugam, K. T.; Ingram, L. O. Combining metabolic engineering and metabolic evolution to develop nonrecombinant strains of Escherichia coli C that produce succinate and malate. Biotechnol. Bioeng. 2008, 99, 1140– 1153,  DOI: 10.1002/bit.21694
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      Combining metabolic engineering and metabolic evolution to develop nonrecombinant strains of Escherichia coli C that produce succinate and malate
      Jantama, Kaemwich; Haupt, M. J.; Svoronos, Spyros A.; Zhang, Xueli; Moore, J. C.; Shanmugam, K. T.; Ingram, L. O.
      Biotechnology and Bioengineering (2008), 99 (5), 1140-1153CODEN: BIBIAU; ISSN:0006-3592. (John Wiley & Sons, Inc.)
      Derivs. of Escherichia coli C were engineered to produce primarily succinate or malate in mineral salts media using simple fermns. (anaerobic stirred batch with pH control) without the addn. of plasmids or foreign genes. This was done by a combination of gene deletions (genetic engineering) and metabolic evolution with over 2,000 generations of growth-based selection. After deletion of the central anaerobic fermn. genes (ldhA, adhE, ackA), the pathway for malate and succinate prodn. remained as the primary route for the regeneration of NAD+. Under anaerobic conditions, ATP prodn. for growth was obligately coupled to malate dehydrogenase and fumarate reductase by the requirement for NADH oxidn. Selecting strains for improved growth co-selected increased prodn. of these dicarboxylic acids. Addnl. deletions were introduced as further improvements (focA, pflB, poxB, mgsA). The best succinate biocatalysts, strains KJ060(ldhA, adhE, ackA, focA, pflB) and KJ073(ldhA, adhE, ackA, focA, pflB, mgsA, poxB), produce 622-733 mM of succinate with molar yields of 1.2-1.6 per mol of metabolized glucose. The best malate biocatalyst, strain KJ071(ldhA, adhE, ackA, focA, pflB, mgsA), produced 516 mM malate with molar yields of 1.4 per mol of glucose metabolized.
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      Zhang, X.; Jantama, K.; Moore, J. C.; Jarboe, L. R.; Shanmugam, K. T.; Ingram, L. O. Metabolic evolution of energy-conserving pathways for succinate production in Escherichia coli. Proc. Natl. Acad. Sci. 2009, 106, 20180– 20185,  DOI: 10.1073/pnas.0905396106
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      Metabolic evolution of energy-conserving pathways for succinate production in Escherichia coli
      Zhang, Xueli; Jantama, Kaemwich; Moore, Jonathan C.; Jarboe, Laura R.; Shanmugam, Keelnatham T.; Ingram, Lonnie O.
      Proceedings of the National Academy of Sciences of the United States of America (2009), 106 (48), 20180-20185, S20180/1-S20180/7CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
      During metabolic evolution to improve succinate prodn. in Escherichia coli strains, significant changes in cellular metab. were acquired that increased energy efficiency in two respects. The energy- conserving phosphoenolpyruvate (PEP) carboxykinase (pck), which normally functions in the reverse direction (gluconeogenesis; glucose repressed) during the oxidative metab. of org. acids, evolved to become the major carboxylation pathway for succinate prodn. Both PCK enzyme activity and gene expression levels increased significantly in two stages because of several mutations during the metabolic evolution process. High-level expression of this enzyme- dominated CO2 fixation and increased ATP yield (1 ATP per oxaloacetate). In addn., the native PEP-dependent phosphotransferase system for glucose uptake was inactivated by a mutation in ptsl. This glucose transport function was replaced by increased expression of the GalP permease (galP) and glucokinase (glk). Results of deleting individual transport genes confirmed that GalP served as the dominant glucose transporter in evolved strains. Using this alternative transport system would increase the pool of PEP available for redox balance. This change would also increase energy efficiency by eliminating the need to produce addnl. PEP from pyruvate, a reaction that requires two ATP equiv. Together, these changes converted the wild-type E. coli fermn. pathway for succinate into a functional equiv. of the native pathway that nature evolved in succinate-producing rumen bacteria.
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      Sánchez, A. M.; Bennett, G. N.; San, K.-Y. Efficient succinic acid production from glucose through overexpression of pyruvate carboxylase in an Escherichia coli alcohol dehydrogenase and lactate dehydrogenase mutant. Biotechnol. Prog. 2005, 21, 358– 365,  DOI: 10.1021/bp049676e
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      Efficient Succinic Acid Production from Glucose through Overexpression of Pyruvate Carboxylase in an Escherichia coli Alcohol Dehydrogenase and Lactate Dehydrogenase Mutant
      Sanchez, Ailen M.; Bennett, George N.; San, Ka-Yiu
      Biotechnology Progress (2005), 21 (2), 358-365CODEN: BIPRET; ISSN:8756-7938. (American Chemical Society)
      An adhE, ldhA double mutant Escherichia coli strain, SBS110MG, has been constructed to produce succinic acid in the presence of heterologous pyruvate carboxylase (PYC). The strategic design aims at diverting max. quantities of NADH for succinate synthesis by inactivation of NADH competing pathways to increase succinate yield and productivity. Addnl. an operational PFL enzyme allows formation of acetyl-CoA for biosynthesis and formate as a potential source of reducing equiv. Furthermore, PYC diverts pyruvate toward OAA to favor succinate generation. SBS110MG harboring plasmid pHL413, which encodes the heterologous pyruvate carboxylase from Lactococcus lactis, produced 15.6 g/L (132 mM) of succinate from 18.7 g/L (104 mM) of glucose after 24 h of culture in an atm. of CO2 yielding 1.3 mol of succinate per mol of glucose. This molar yield exceeded the max. theor. yield of succinate that can be achieved from glucose (1 mol/mol) under anaerobic conditions in terms of NADH balance. The current work further explores the importance of the presence of formate as a source of reducing equiv. in SBS110MG(pHL413). Inactivation of the native formate dehydrogenase pathway (FDH) in this strain significantly reduced succinate yield, suggesting that reducing power was lost in the form of formate. Addnl. we investigated the effect of ptsG inactivation in SBS110MG(pHL413) to evaluate the possibility of a further increase in succinate yield. Elimination of the ptsG system increased the succinate yield to 1.4 mol/mol at the expense of a redn. in glucose consumption of 33%. In the presence of PYC and an efficient conversion of glucose to products, the ptsG mutation is not indispensable since PEP converted to pyruvate as a result of glucose phosphorylation by the glucose specific PTS permease EIICBglu can be rediverted toward OAA favoring succinate prodn.
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      Jantama, K.; Zhang, X.; Moore, J. C.; Shanmugam, K. T.; Svoronos, S. A.; Ingram, L. O. Eliminating side products and increasing succinate yields in engineered strains of Escherichia coli C. Biotechnol. Bioeng. 2008, 101, 881– 893,  DOI: 10.1002/bit.22005
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      Eliminating side products and increasing succinate yields in engineered strains of Escherichia coli C
      Jantama, Kaemwich; Zhang, Xueli; Moore, J. C.; Shanmugam, K. T.; Svoronos, S. A.; Ingram, L. O.
      Biotechnology and Bioengineering (2008), 101 (5), 881-893CODEN: BIBIAU; ISSN:0006-3592. (John Wiley & Sons, Inc.)
      Derivs. of Escherichia coli C were previously described for succinate prodn. by combining the deletion of genes that disrupt fermn. pathways for alternative products (ldhA::FRT, adhE::FRT, ackA::FRT, focA-pflB::FRT, mgsA, poxB) with growth-based selection for increased ATP prodn. The resulting strain, KJ073, produced 1.2 mol of succinate per mol glucose in mineral salts medium with acetate, malate, and pyruvate as significant co-products. KJ073 has been further improved by removing residual recombinase sites (FRT sites) from the chromosomal regions of gene deletion to create a strain devoid of foreign DNA, strain KJ091 (ΔldhA ΔadhE ΔackA ΔfocA-pflB ΔmgsA ΔpoxB). KJ091 was further engineered for improvements in succinate prodn. Deletion of the threonine decarboxylase (tdcD; acetate kinase homolog) and 2-ketobutyrate formate-lyase (tdcE; pyruvate formatelyase homolog) reduced the acetate level by 50% and increased succinate yield (1.3 mol mol-1 glucose) by almost 10% as compared to KJ091 and KJ073. Deletion of two genes involved in oxaloacetate metab., aspartate aminotransferase (aspC) and the NAD+-linked malic enzyme (sfcA) (KJ122) significantly increased succinate yield (1.5 mol mol-1 glucose), succinate titer (700 mM), and av. volumetric productivity (0.9 g L-1 h-1). Residual pyruvate and acetate were substantially reduced by further deletion of pta encoding phosphotransacetylase to produce KJ134 (ΔldhA ΔadhE ΔfocA-pflB ΔmgsA ΔpoxB ΔtdcDE ΔcitF ΔaspC ΔsfcA Δpta-ackA). Strains KJ122 and KJ134 produced near theor. yields of succinate during simple, anaerobic, batch fermns. using mineral salts medium. Both may be useful as biocatalysts for the com. prodn. of succinate.
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      Sánchez, A. M.; Bennett, G. N.; San, K.-Y. Novel pathway engineering design of the anaerobic central metabolic pathway in Escherichia coli to increase succinate yield and productivity. Metab. Eng. 2005, 7, 229– 239,  DOI: 10.1016/j.ymben.2005.03.001
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      Novel pathway engineering design of the anaerobic central metabolic pathway in Escherichia coli to increase succinate yield and productivity
      Sanchez, Ailen M.; Bennett, George N.; San, Ka-Yiu
      Metabolic Engineering (2005), 7 (3), 229-239CODEN: MEENFM; ISSN:1096-7176. (Elsevier)
      A novel in vivo method of producing succinate has been developed. A genetically engineered Escherichia coli strain has been constructed to meet the NADH requirement and carbon demand to produce high quantities and yield of succinate by strategically implementing metabolic pathway alterations. Currently, the max. theor. succinate yield under strictly anaerobic conditions through the fermentative succinate biosynthesis pathway is limited to one mole per mol of glucose due to NADH limitation. The implemented strategic design involves the construction of a dual succinate synthesis route, which diverts required quantities of NADH through the traditional fermentative pathway and maximizes the carbon converted to succinate by balancing the carbon flux through the fermentative pathway and the glyoxylate pathway (which has less NADH requirement). The synthesis of succinate uses a combination of the two pathways to balance the NADH. Consequently, exptl. results indicated that these combined pathways gave the most efficient conversion of glucose to succinate with the highest yield using only 1.25 mol of NADH per mol of succinate in contrast to the sole fermentative pathway, which uses 2 mol of NADH per mol of succinate. A recombinant E. coli strain, SBS550MG, was created by deactivating adhE, ldhA and ack-pta from the central metabolic pathway and by activating the glyoxylate pathway through the inactivation of iclR, which encodes a transcriptional repressor protein of the glyoxylate bypass. The inactivation of these genes in SBS550MG increased the succinate yield from glucose to about 1.6 mol/mol with an av. anaerobic productivity rate of 10 mM/h(∼0.64 mM/h-OD600). This strain is capable of fermenting high concns. of glucose in less than 24 h. Addnl. derepression of the glyxoylate pathway by inactivation of arcA, leading to a strain designated as SBS660MG, did not significantly increase the succinate yield and it decreased glucose consumption by 80%. It was also obsd. that an adhE, ldhA and ack-pta mutant designated as SBS990MG, was able to achieve a high succinate yield similar to SBS550MG when expressing a Bacillus subtilis NADH-insensitive citrate synthase from a plasmid.
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      Satanowski, A.; Dronsella, B.; Noor, E.; Vögeli, B.; He, H.; Wichmann, P.; Erb, T. J.; Lindner, S. N.; Bar-Even, A. Awakening a latent carbon fixation cycle in Escherichia coli. Nat. Commun. 2020, 11, 5812,  DOI: 10.1038/s41467-020-19564-5
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      Awakening a latent carbon fixation cycle in Escherichia coli
      Satanowski, Ari; Dronsella, Beau; Noor, Elad; Voegeli, Bastian; He, Hai; Wichmann, Philipp; Erb, Tobias J.; Lindner, Steffen N.; Bar-Even, Arren
      Nature Communications (2020), 11 (1), 5812CODEN: NCAOBW; ISSN:2041-1723. (Nature Research)
      Abstr.: Carbon fixation is one of the most important biochem. processes. Most natural carbon fixation pathways are thought to have emerged from enzymes that originally performed other metabolic tasks. Can we recreate the emergence of a carbon fixation pathway in a heterotrophic host by recruiting only endogenous enzymes. In this study, we address this question by systematically analyzing possible carbon fixation pathways composed only of Escherichia coli native enzymes. We identify the GED (Gnd-Entner-Doudoroff) cycle as the simplest pathway that can operate with high thermodn. driving force. This autocatalytic route is based on reductive carboxylation of ribulose 5-phosphate (Ru5P) by 6-phosphogluconate dehydrogenase (Gnd), followed by reactions of the Entner-Doudoroff pathway, gluconeogenesis, and the pentose phosphate pathway. We demonstrate the in vivo feasibility of this new-to-nature pathway by constructing E. coli gene deletion strains whose growth on pentose sugars depends on the GED shunt, a linear variant of the GED cycle which does not require the regeneration of Ru5P. Several metabolic adaptations, most importantly the increased prodn. of NADPH, assist in establishing sufficiently high flux to sustain this growth. Our study exemplifies a trajectory for the emergence of carbon fixation in a heterotrophic organism and demonstrates a synthetic pathway of biotechnol. interest.
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      Kim, Y.; Ingram, L. O.; Shanmugam, K. T. Dihydrolipoamide dehydrogenase mutation alters the NADH sensitivity of pyruvate dehydrogenase complex of Escherichia coli K-12. J. Bacteriol. 2008, 190, 3851– 3858,  DOI: 10.1128/jb.00104-08
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      Dihydrolipoamide dehydrogenase mutation alters the NADH sensitivity of pyruvate dehydrogenase complex of Escherichia coli K-12
      Kim, Youngnyun; Ingram, L. O.; Shanmugam, K. T.
      Journal of Bacteriology (2008), 190 (11), 3851-3858CODEN: JOBAAY; ISSN:0021-9193. (American Society for Microbiology)
      Under anaerobic growth conditions, an active pyruvate dehydrogenase (PDH) is expected to create a redox imbalance in wild-type Escherichia coli due to increased prodn. of NADH (>2 NADH mols./glucose mol.) that could lead to growth inhibition. However, the addnl. NADH produced by PDH can be used for conversion of acetyl CoA into reduced fermn. products, like alcs., during metabolic engineering of the bacterium. E. coli mutants that produced ethanol as the main fermn. product were recently isolated as derivs. of an ldhA pflB double mutant. In all six mutants tested, the mutation was in the lpd gene encoding dihydrolipoamide dehydrogenase (LPD), a component of PDH. Three of the LPD mutants carried an H322Y mutation (lpd102), while the other mutants carried an E354K mutation (lpd101). Genetic and physiol. anal. revealed that the mutation in either allele supported anaerobic growth and homoethanol fermn. in an ldhA pflB double mutant. Enzyme kinetic studies revealed that the LPD(E354K) enzyme was significantly less sensitive to NADH inhibition than the native LPD. This reduced NADH sensitivity of the mutated LPD was translated into lower sensitivity of the appropriate PDH complex to NADH inhibition. The mutated forms of the PDH had a 10-fold-higher Ki for NADH than the native PDH. The lower sensitivity of PDH to NADH inhibition apparently increased PDH activity in anaerobic E. coli cultures and created the new ethanologenic fermn. pathway in this bacterium. Analogous mutations in the LPD of other bacteria may also significantly influence the growth and physiol. of the organisms in a similar fashion.
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    44. 44
      Trichez, D.; Auriol, C.; Baylac, A.; Irague, R.; Dressaire, C.; Carnicer-Heras, M.; Heux, S.; François, J. M.; Walther, T. Engineering of Escherichia coli for Krebs cycle-dependent production of malic acid. Microb. Cell Factories 2018, 17, 113,  DOI: 10.1186/s12934-018-0959-y
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      44
      Engineering of Escherichia coli for Krebs cycle-dependent production of malic acid
      Trichez, Debora; Auriol, Clement; Baylac, Audrey; Irague, Romain; Dressaire, Clementine; Carnicer-Heras, Marc; Heux, Stephanie; Francois, Jean Marie; Walther, Thomas
      Microbial Cell Factories (2018), 17 (), 113/1-113/12CODEN: MCFICT; ISSN:1475-2859. (BioMed Central Ltd.)
      However, as malate can be a precursor for specialty chems., such as 2,4-dihydroxybutyric acid, that require addnl. cofactors NADP(H) and ATP, we set out to reengineer Escherichia coli for Krebs cycle-dependent prodn. of malic acid that can satisfy these requirements. Results: We found that significant malate prodn. required at least simultaneous deletion of all malic enzymes and dehydrogenases, and concomitant expression of a malate-insensitive PEP carboxylase. Metabolic flux anal. using 13C-labeled glucose indicated that malate-producing strains had a very high flux over the glyoxylate shunt with almost no flux passing through the isocitrate dehydrogenase reaction. The highest malate yield of 0.82 mol/mol was obtained with E. coli Δmdh Δmqo ΔmaeAB ΔiclR ΔarcA which expressed malate-insensitive PEP carboxylase PpcK620S and NADH-insensitive citrate synthase GltAR164L. We also showed that inactivation of the dicarboxylic acid transporter DcuA strongly reduced malate prodn. arguing for a pivotal role of this permease in malate export. Conclusions: Since more NAD(P)H and ATP cofactors are generated in the Krebs cycle-dependent malate prodn. when compared to pathways which depend on the function of anaplerotic PEP carboxylase or PEP carboxykinase enzymes, the engineered strain developed in this study can serve as a platform to increase biosynthesis of malate-derived metabolites such as 2,4-dihydroxybutyric acid.
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    45. 45
      Machado, D.; Soons, Z.; Patil, K. R.; Ferreira, E. C.; Rocha, I. Random sampling of elementary flux modes in large-scale metabolic networks. Bioinformatics 2012, 28, i515– i521,  DOI: 10.1093/bioinformatics/bts401
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      Random sampling of elementary flux modes in large-scale metabolic networks
      Machado, Daniel; Soons, Zita; Patil, Kiran Raosaheb; Ferreira, Eugenio C.; Rocha, Isabel
      Bioinformatics (2012), 28 (18), i515-i521CODEN: BOINFP; ISSN:1367-4803. (Oxford University Press)
      Motivation: The description of a metabolic network in terms of elementary (flux) modes (EMs) provides an important framework for metabolic pathway anal. However, their application to large networks has been hampered by the combinatorial explosion in the no. of modes. In this work, we develop a method for generating random samples of EMs without computing the whole set. Results: Our algorithm is an adaptation of the canonical basis approach, where we add an addnl. filtering step which, at each iteration, selects a random subset of the new combinations of modes. In order to obtain an unbiased sample, all candidates are assigned the same probability of getting selected. This approach avoids the exponential growth of the no. of modes during computation, thus generating a random sample of the complete set of EMs within reasonable time. We generated samples of different sizes for a metabolic network of Escherichia coli, and obsd. that they preserve several properties of the full EM set. It is also shown that EM sampling can be used for rational strain design. A well distributed sample, that is representative of the complete set of EMs, should be suitable to most EM-based methods for anal. and optimization of metabolic networks.
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      Tan, Z.; Zhu, X.; Chen, J.; Li, Q.; Zhang, X. Activating phosphoenolpyruvate carboxylase and phosphoenolpyruvate carboxykinase in combination for improvement of succinate production. Appl. Environ. Microbiol. 2013, 79, 4838– 4844,  DOI: 10.1128/aem.00826-13
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      Activating phosphoenolpyruvate carboxylase and phosphoenolpyruvate carboxykinase in combination for improvement of succinate production
      Tan, Zaigao; Zhu, Xinna; Chen, Jing; Li, Qingyan; Zhang, Xueli
      Applied and Environmental Microbiology (2013), 79 (16), 4838-4844CODEN: AEMIDF; ISSN:1098-5336. (American Society for Microbiology)
      Phosphoenolpyruvate (PEP) carboxylation is an important step in the prodn. of succinate by Escherichia coli. Two enzymes, PEP carboxylase (PPC) and PEP carboxykinase (PCK), are responsible for PEP carboxylation. PPC has high substrate affinity and catalytic velocity but wastes the high energy of PEP. PCK has low substrate affinity and catalytic velocity but can conserve the high energy of PEP for ATP formation. In this work, the expression of both the ppc and pck genes was modulated, with multiple regulatory parts of different strengths, in order to investigate the relationship between PPC or PCK activity and succinate prodn. There was a pos. correlation between PCK activity and succinate prodn. In contrast, there was a pos. correlation between PPC activity and succinate prodn. only when PPC activity was within a certain range; excessive PPC activity decreased the rates of both cell growth and succinate formation. These two enzymes were also activated in combination in order to recruit the advantages of each for the improvement of succinate prodn. It was demonstrated that PPC and PCK had a synergistic effect in improving succinate prodn.
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      Baba, T.; Ara, T.; Hasegawa, M.; Takai, Y.; Okumura, Y.; Baba, M.; Datsenko, K. A.; Tomita, M.; Wanner, B. L.; Mori, H. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol. Syst. Biol. 2006, 2, 2006.0008,  DOI: 10.1038/msb4100050
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      Peng, L.; Arauzo-Bravo, M. J.; Shimizu, K. Metabolic flux analysis for a ppc mutant Escherichia coli based on 13C-labelling experiments together with enzyme activity assays and intracellular metabolite measurements. FEMS Microbiol. Lett. 2004, 235, 17– 23,  DOI: 10.1111/j.1574-6968.2004.tb09562.x
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      Metabolic flux analysis for a ppc mutant Escherichia coli based on 13C-labelling experiments together with enzyme activity assays and intracellular metabolite measurements
      Peng, Lifeng; Arauzo-Bravo, Marcos J.; Shimizu, Kazuyuki
      FEMS Microbiology Letters (2004), 235 (1), 17-23CODEN: FMLED7; ISSN:0378-1097. (Elsevier Science B.V.)
      The physiol. and central metab. of a ppc mutant Escherichia coli were investigated based on the metabolic flux distribution obtained by 13C-labeling expts. using gas chromatog.-mass spectrometry (GC-MS) and 2-dimensional NMR (2D NMR) strategies together with enzyme activity assays and intracellular metabolite concn. measurements. Compared to the wild type, its ppc mutant excreted little acetate and produced less carbon dioxide at the expense of a slower growth rate and a lower glucose uptake rate. Consequently, an improvement of the biomass yield on glucose was obsd. in the ppc mutant. Enzyme activity measurements revealed that isocitrate lyase activity increased by more than 3-fold in the ppc mutant. Some TCA cycle enzymes such as citrate synthase, aconitase and malate dehydrogenase were also upregulated, but enzymes of glycolysis and the pentose phosphate pathway were down-regulated. The intracellular intermediates in the glycolysis and the pentose phosphate pathway, therefore, accumulated, while acetyl CoA and oxaloacetate concns. decreased in the ppc mutant. The intracellular metabolic flux anal. uncovered that deletion of ppc resulted in the appearance of the glyoxylate shunt, with 18.9% of the carbon flux being channeled via the glyoxylate shunt. However, the flux of the pentose phosphate pathway significantly decreased in the ppc mutant.
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      Kim, S.-W.; Keasling, J. D. Metabolic engineering of the nonmevalonate isopentenyl diphosphate synthesis pathway in Escherichia coli enhances lycopene production. Biotechnol. Bioeng. 2001, 72, 408– 415,  DOI: 10.1002/1097-0290(20000220)72:4<408::aid-bit1003>3.0.co;2-h
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      Metabolic engineering of the nonmevalonate isopentenyl diphosphate synthesis pathway in Escherichia coli enhances lycopene production
      Kim, Seon-Won; Keasling, J. D.
      Biotechnology and Bioengineering (2001), 72 (4), 408-415CODEN: BIBIAU; ISSN:0006-3592. (John Wiley & Sons, Inc.)
      Isopentenyl diphosphate (IPP) is the common, five-carbon building block in the biosynthesis of all carotenoids. IPP in Escherichia coli is synthesized through the nonmevalonate pathway, which has not been completely elucidated. The first reaction of IPP biosynthesis in E. coli is the formation of 1-deoxy-D-xylulose-5-phosphate (DXP), catalyzed by DXP synthase and encoded by dxs. The second reaction in the pathway is the redn. of DXP to 2-C-methyl-D-erythritol-4-phosphate, catalyzed by DXP reductoisomerase and encoded by dxr. To det. if one or more of the reactions in the nonmevalonate pathway controlled flux to IPP, dxs and dxr were placed on several expression vectors under the control of three different promoters and transformed into three E. coli strains (DH5α, XL1-Blue, and JM101) that had been engineered to produce lycopene. Lycopene prodn. was improved significantly in strains transformed with the dxs expression vectors. When the dxs gene was expressed from the arabinose-inducible araBAD promoter (PBAD) on a medium-copy plasmid, lycopene prodn. was twofold higher than when dxs was expressed from the IPTG-inducible trc and lac promoters (Ptrc and Plac' resp.) on medium-copy and high-copy plasmids. Given the low final densities of cells expressing dxs from IPTG-inducible promoters, the low lycopene prodn. was probably due to the metabolic burden of plasmid maintenance and an excessive drain of central metabolic intermediates. At arabinose concns. between 0 and 1.33 mM, cells expressing both dxs and dxr from PBAD on a medium-copy plasmid produced 1.4-2.0 times more lycopene than cells expressing dxs only. However, at higher arabinose concns. lycopene prodn. in cells expressing both dxs and dxr was lower than in cells expressing dxs only. A comparison of the three E. coli strains transformed with the arabinose-inducible dxs on a medium-copy plasmid revealed that lycopene prodn. was highest in XL1-Blue.
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      Alper, H.; Jin, Y.-S.; Moxley, J. F.; Stephanopoulos, G. Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli. Metab. Eng. 2005, 7, 155– 164,  DOI: 10.1016/j.ymben.2004.12.003
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      Identifying gene targets for the metabolic engineering of lycopene biosynthesis in Escherichia coli
      Alper, Hal; Jin, Yong-Su; Moxley, J. F.; Stephanopoulos, G.
      Metabolic Engineering (2005), 7 (3), 155-164CODEN: MEENFM; ISSN:1096-7176. (Elsevier)
      The identification of genetic targets that are effective in bringing about a desired phenotype change is still an open problem. While random gene knockouts have yielded improved strains in certain cases, it is also important to seek the guidance of cell-wide stoichiometric constraints in identifying promising gene knockout targets. To investigate these issues, we undertook a genome-wide stoichiometric flux balance anal. as an aid in discovering putative genes impacting network properties and cellular phenotype. Specifically, we calcd. metabolic fluxes such as to optimize growth and then scanned the genome for single and multiple gene knockouts that yield improved product yield while maintaining acceptable overall growth rate. For the particular case of lycopene biosynthesis in Escherichia coli, we identified such targets that we subsequently tested exptl. by constructing the corresponding single, double and triple gene knockouts. While such strains are suggested (by the stoichiometric calcns.) to increase precursor availability, this beneficial effect may be further impacted by kinetic and regulatory effects not captured by the stoichiometric model. For the case of lycopene biosynthesis, the so identified knockout targets yielded a triple knockout construct that exhibited a nearly 40% increase over an engineered, high producing parental strain.
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      Wang, J.; Niyompanich, S.; Tai, Y.-S.; Wang, J.; Bai, W.; Mahida, P.; Gao, T.; Zhang, K. Engineering of a highly efficient Escherichia coli strain for mevalonate fermentation through chromosomal integration. Appl. Environ. Microbiol. 2016, 82, 7176– 7184,  DOI: 10.1128/aem.02178-16
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      Engineering of a highly efficient Escherichia coli strain for mevalonate fermentation through chromosomal integration
      Wang, Jilong; Niyompanich, Suthamat; Tai, Yi-Shu; Wang, Jingyu; Bai, Wenqin; Mahida, Prithviraj; Gao, Tuo; Zhang, Kechun
      Applied and Environmental Microbiology (2016), 82 (24), 7176-7184CODEN: AEMIDF; ISSN:1098-5336. (American Society for Microbiology)
      Chromosomal integration of heterologous metabolic pathways is optimal for industrially relevant fermn., as plasmid-based fermn. causes extra metabolic burden and genetic instabilities. In this work, chromosomal integration was adapted for the prodn. of mevalonate, which can be readily converted into β-methyl-δ-valerolactone, a monomer for the prodn. of mech. tunable polyesters. The mevalonate pathway, driven by a constitutive promoter, was integrated into the chromosome of Escherichia coli to replace the native fermn. gene adhE or ldhA. The engineered strains (CMEV-1 and CMEV-2) did not require inducer or antibiotic and showed slightly higher maximal productivities (0.38 to ∼0.43 g/L/h) and yields (67.8 to ∼71.4% of the max. theor. yield) than those of the plasmid-based fermn. Since the glycolysis pathway is the first module for mevalonate synthesis, atpFH deletion was employed to improve the glycolytic rate and the prodn. rate of mevalonate. Shake flask fermn. results showed that the deletion of atpFH in CMEV-1 resulted in a 2.1- fold increase in the max. productivity. Furthermore, enhancement of the downstream pathway by integrating two copies of the mevalonate pathway genes into the chromosome further improved the mevalonate yield. Finally, our fed-batch fermn. showed that, with deletion of the atpFH and sucA genes and integration of two copies of the mevalonate pathway genes into the chromosome, the engineered strain CMEV-7 exhibited both high maximal productivity (∼1.01 g/L/h) and high yield (86.1% of the max. theor. yield, 30 g/L mevalonate from 61 g/L glucose after 48 h in a shake flask).
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    52. 52
      Chatterjee, R.; Millard, C. S.; Champion, K.; Clark, D. P.; Donnelly, M. I. Mutation of the ptsG gene results in increased production of succinate in fermentation of glucose by Escherichia coli. Appl. Environ. Microbiol. 2001, 67, 148– 154,  DOI: 10.1128/aem.67.1.148-154.2001
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      Mutation of the ptsG gene results in increased production of succinate in fermentation of glucose by Escherichia coli
      Chatterjee, Ranjini; Millard, Cynthia Sanville; Champion, Kathleen; Clark, David P.; Donnelly, Mark I.
      Applied and Environmental Microbiology (2001), 67 (1), 148-154CODEN: AEMIDF; ISSN:0099-2240. (American Society for Microbiology)
      Escherichia coli NZN111 is blocked in the ability to grow fermentatively on glucose but gave rise spontaneously to a mutant that had this ability. The mutant carries out a balanced fermn. of glucose to give approx. 1 mol of succinate, 0.5 mol of acetate, and 0.5 mol of ethanol per mol of glucose. The causative mutation was mapped to the ptsG gene, which encodes the membrane-bound, glucose-specific permease of the phosphotransferase system, protein EIICBglc. Replacement of the chromosomal ptsG gene with an insertionally inactivated form also restored growth on glucose and resulted in the same distribution of fermn. products. The physiol. characteristics of the spontaneous and null mutants were consistent with loss of function of the ptsG gene product; the mutants possessed greatly reduced glucose phosphotransferase activity and lacked normal glucose repression. Introduction of the null mutant into strains not blocked in the ability to ferment glucose also increased succinate prodn. in those strains. This phenomenon was widespread, occurring in different lineages of E. coli, including E. coli B.
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    53. 53
      Lyngstadaas, A.; Sprenger, G. A.; Boye, E. Impaired growth of an Escherichia coli rpe mutant lacking ribulose-5-phosphate epimerase activity. Biochim. Biophys. Acta Gen. Subj. 1998, 1381, 319– 330,  DOI: 10.1016/s0304-4165(98)00046-4
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      Impaired growth of an Escherichia coli rpe mutant lacking ribulose-5-phosphate epimerase activity
      Lyngstadaas, Anita; Sprenger, Georg A.; Boye, Erik
      Biochimica et Biophysica Acta, General Subjects (1998), 1381 (3), 319-330CODEN: BBGSB3; ISSN:0304-4165. (Elsevier B.V.)
      The authors present evidence that ribulose-5-phosphate epimerase, a central metabolic enzyme acting in the non-oxidative branch of the pentose-phosphate pathway, is encoded by a gene in the dam contg. operon of Escherichia coli. Enzymic assays confirm that this gene encodes ribulose-5-phosphate epimerase activity. Disruption of the gene (rpe) causes loss of enzymic activity and renders the rpe mutant unable to utilize single pentose sugars, indicating that rpe supplies the only ribulose-5-phosphate epimerase activity in E. coli. Growth of the rpe mutant is impaired in complex LB medium and severely impaired in minimal medium contg. glycolytic carbon sources or gluconate. Enrichment with casamino acids abolishes or strongly relieves growth suppression in minimal medium. Aspartate counteracts the impaired growth in glycolytic carbon sources but not in gluconate. It is suggested that the absence of the Rpe enzyme causes changes in the pentose-phosphate levels which alter the regulation of (a) metabolic enzyme(s) and thereby cause growth suppression and that the severity of growth suppression is related to the internal concn. of pentose-phosphates. Target enzymes for neg. regulation may be located in the early parts of the Embden-Meyerhof-Parnas pathway and of the Entner-Doudoroff pathway and/or of carbohydrate transport systems feeding sugars into these sections of central metabolic pathways.
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    54. 54
      Lewis, N. E.; Hixson, K. K.; Conrad, T. M.; Lerman, J. A.; Charusanti, P.; Polpitiya, A. D.; Adkins, J. N.; Schramm, G.; Purvine, S. O.; Lopez-Ferrer, D.; Weitz, K. K.; Eils, R.; König, R.; Smith, R. D.; Palsson, B. Ø. Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol. Syst. Biol. 2010, 6, 390,  DOI: 10.1038/msb.2010.47
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      Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models
      Lewis Nathan E; Hixson Kim K; Conrad Tom M; Lerman Joshua A; Charusanti Pep; Polpitiya Ashoka D; Adkins Joshua N; Schramm Gunnar; Purvine Samuel O; Lopez-Ferrer Daniel; Weitz Karl K; Eils Roland; Konig Rainer; Smith Richard D; Palsson Bernhard O
      Molecular systems biology (2010), 6 (), 390 ISSN:.
      After hundreds of generations of adaptive evolution at exponential growth, Escherichia coli grows as predicted using flux balance analysis (FBA) on genome-scale metabolic models (GEMs). However, it is not known whether the predicted pathway usage in FBA solutions is consistent with gene and protein expression in the wild-type and evolved strains. Here, we report that >98% of active reactions from FBA optimal growth solutions are supported by transcriptomic and proteomic data. Moreover, when E. coli adapts to growth rate selective pressure, the evolved strains upregulate genes within the optimal growth predictions, and downregulate genes outside of the optimal growth solutions. In addition, bottlenecks from dosage limitations of computationally predicted essential genes are overcome in the evolved strains. We also identify regulatory processes that may contribute to the development of the optimal growth phenotype in the evolved strains, such as the downregulation of known regulons and stringent response suppression. Thus, differential gene and protein expression from wild-type and adaptively evolved strains supports observed growth phenotype changes, and is consistent with GEM-computed optimal growth states.
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      Machado, D.; Herrgard, M. J.; Rocha, I. Stoichiometric representation of gene-protein-reaction associations leverages constraint-based analysis from reaction to gene-Level phenotype prediction. PLoS Comput. Biol. 2016, 12, e1005140  DOI: 10.1371/journal.pcbi.1005140
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      Stoichiometric representation of gene-protein-reaction associations leverages constraint-based analysis from reaction to gene-level phenotype prediction
      Machado, Daniel; Herrgard, Markus J.; Rocha, Isabel
      PLoS Computational Biology (2016), 12 (10), e1005140/1-e1005140/24CODEN: PCBLBG; ISSN:1553-7358. (Public Library of Science)
      Genome-scale metabolic reconstructions are currently available for hundreds of organisms. Constraint-based modeling enables the anal. of the phenotypic landscape of these organisms, predicting the response to genetic and environmental perturbations. However, since constraint-based models can only describe the metabolic phenotype at the reaction level, understanding the mechanistic link between genotype and phenotype is still hampered by the complexity of gene-protein-reaction assocns. We implement a model transformation that enables constraint-based methods to be applied at the gene level by explicitly accounting for the individual fluxes of enzymes (and subunits) encoded by each gene. We show how this can be applied to different kinds of constraint-based anal.: flux distribution prediction, gene essentiality anal., random flux sampling, elementary mode anal., transcriptomics data integration, and rational strain design. In each case we demonstrate how this approach can lead to improved phenotype predictions and a deeper understanding of the genotype-to-phenotype link. In particular, we show that a large fraction of reaction-based designs obtained by current strain design methods are not actually feasible, and show how our approach allows using the same methods to obtain feasible gene-based designs. We also show, by extensive comparison with exptl. 13C-flux data, how simple reformulations of different simulation methods with gene-wise objective functions result in improved prediction accuracy. The model transformation proposed in this work enables existing constraint-based methods to be used at the gene level without modification. This automatically leverages phenotype anal. from reaction to gene level, improving the biol. insight that can be obtained from genome-scale models.
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    • Bilevel problem reformulation, lycopene and naringenin biosynthetic pathway, model reduction, and impact of OptDesign parameters on biochemical production (PDF)

    • Comparison between in silico predictions and in vivo manipulations for nine compounds; knockout and regulation candidates for succinate, lycopene, and naringenin; impact of reference flux vectors (XLSX)


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