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WO2006025817A1 - Procédé de redéfinition de la conception de systèmes de production microbienne - Google Patents

Procédé de redéfinition de la conception de systèmes de production microbienne Download PDF

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Publication number
WO2006025817A1
WO2006025817A1 PCT/US2004/027614 US2004027614W WO2006025817A1 WO 2006025817 A1 WO2006025817 A1 WO 2006025817A1 US 2004027614 W US2004027614 W US 2004027614W WO 2006025817 A1 WO2006025817 A1 WO 2006025817A1
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Prior art keywords
reactions
production
computer
functionalities
identifying
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Inventor
Costas D. Maranas
Anthony P. Burgard
Priti Pharkya
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Penn State Research Foundation
University of Pennsylvania Penn
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Penn State Research Foundation
University of Pennsylvania Penn
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Priority to CA002578028A priority Critical patent/CA2578028A1/fr
Priority to JP2007529791A priority patent/JP2008510485A/ja
Priority to EP04782168A priority patent/EP1782061A4/fr
Priority to PCT/US2004/027614 priority patent/WO2006025817A1/fr
Priority to AU2004322970A priority patent/AU2004322970B2/en
Publication of WO2006025817A1 publication Critical patent/WO2006025817A1/fr
Anticipated expiration legal-status Critical
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding

Definitions

  • TITLE METHOD FOR REDESIGN OF MICROBIAL PRODUCTION SYSTEMS
  • the present invention relates to a computational framework that guides pathway modifications, through reaction additions and deletions.
  • Another object, feature, or advantage of the present invention is to provide a method that enables the evaluation of multiple substrate choices.
  • Yet another object, feature, or advantage of the present invention is to provide a method for computationally suggesting the manner in which to achieve bioengineering objectives, including increased production objectives.
  • a further object, feature or advantage of the present invention is to determine
  • Yet another object, feature or advantage of the present invention is to provide an optimized method for computationally achieving a bioengineering objective that is flexible and robust.
  • a still further object, feature, or advantage of the present invention is to provide a method for computationally achieving a bioengineering objective that can take into account not only central metabolic pathways, but also other pathways such as amino acid biosynthetic and degradation pathways.
  • Yet another object, feature, or advantage of the present invention is to provide a method for computationally achieving a bioengineering objective that that can take into
  • the present invention provides hierarchical computational framework, which is
  • OptStrain is aimed at guiding pathways modifications, through
  • These compounds may range from electrons or hydrogen in bio-fuel cell and
  • OptStrain provides a useful tool to aid microbial strain design and, more importantly, it establishes an integrated framework to accommodate future modeling developments.
  • the OptStrain process incorporates the OptKnock process which has been previously described in U.S. Patent Application Serial No. 10/616,659, filed July 9, U.S. Patent Application Serial No. 60/395,763, filed July 10, 2002, U.S. Patent Application Serial No. 60/417,511, filed October, 9, 2002, and U.S. Patent Application Serial No. 60/444,933, filed February 3, 2003, all of which have been previously incorporated by
  • the OptKnock process provides for the systematic development of engineered microbial strains for optimizing the production of chemical or biochemicals which is an overarching challenge in biotechnology.
  • the advent of genome-scale models of metabolism has laid the foundation for the development of computational procedures for suggesting genetic manipulations that lead to overproduction. This is accomplished by ensuring that a drain towards growth resources (i.e., carbon, redox potential, and energy) is accompanied, due to stoichiometry, by the production of a desired production.
  • the computation framework identifies multiple gene deletion combinations that maximally couple a postulated cellular objective (e.g., biomass formation) with
  • OptKnock can also incorporate strategies that not only include central metabolic function
  • the present invention is both robust and flexible in order to address the complexity associated with genome-scale networks.
  • FIG. 1 is a pictorial representation of the OptStrain procedure.
  • Step 1 involves the curation of database(s) of reactions to compile the Universal database which comprises of only elementally balanced reactions.
  • Step 2 identifies a path enabling the desired biotransformation from a substrate (e.g., glucose, methanol, xylose) to product (e.g., hydrogen, vanillin) without any consideration for the origin of reactions.
  • a substrate e.g., glucose, methanol, xylose
  • product e.g., hydrogen, vanillin
  • Step 3 minimizes the reliance on non-native reactions while Step 4 incorporates the non-native functionalities into the microbial host's stoichiometric model and applies the OptKnock procedure to identify and eliminate competing reactions with the targeted product.
  • the (X)' s pinpoint the deleted reactions.
  • Figure 2 is a graph indicating maximum hydrogen yield on a weight basis for different substrates.
  • Figure 3 is a graph illustrating hydrogen production envelopes as a function of the biomass production rate of the wild-type E. coli network under aerobic and anaerobic
  • Point A denotes the required theoretical hydrogen production rate at
  • Points B and C identify the theoretical hydrogen production rates at maximum growth for the two mutant networks respectively after fixing the corresponding carbon dioxide transport rates at the values suggested by OptKnock.
  • Figure 4 is a graph illustrating hydrogen formation limits of the wild-type (solid) and mutant (dotted) Clostridium acetobutylicum metabolic network for a basis glucose uptake rate of 1 mmol/gDW/hr.
  • Line AB denotes different alternate maximum biomass yield solutions that are available to the wild-type network.
  • Point C pinpoints the hydrogen yield of the mutant network at maximum growth. This can be contrasted with the reported
  • Figure 5 is a graph illustrating vanillin production envelope of the augmented E. coli metabolic network for a basis 10 mmol/gDW/hr uptake rate of glucose.
  • Points A, B and C denote the maximum growth points associated with the one, two and four reaction deletion mutant networks, respectively.
  • the mutant networks require significant vanillin yields to achieve high levels of biomass production. Note that an anaerobic mode of growth is suggested in all cases.
  • Figure 6 depicts the bilevel optimization structure of Optknock.
  • bioengineering objective e.g., chemical production
  • restricting access to key reactions available to the optimization of the inner problem e.g., chemical production
  • the present invention provides for methods and systems for guiding pathway modifications, through reaction additions and deletions. Preferably the methods are computer implemented or computer assisted or otherwise automated.
  • the term "computer” as used herein should be construed broadly to include, but not to be limited to, any number
  • the present invention provides a hierarchical optimization-based framework
  • OptStrain to identify stoichiometrically-balanced pathways to be generated upon recombination of non-native functionalities into a host organism to confer the desired
  • Candidate metabolic pathways are identified from an ever-expanding array of thousands (currently 5,734) of reactions pooled together from different stoichiometric
  • a gene set that encodes for all the enzymes needed to catalyze the identified non-native functionalities can then be constructed accounting for isozymes and multi-subunit enzymes. Subsequently, gene deletions are identified (Burgard et al. , 2003; Pharkya et al. , 2003) in the augmented host networks to improve product yields by removing competing functionalities which decouple biochemical production and growth objectives.
  • the breadth and scope of OptStrain is demonstrated by addressing in detail two different product molecules (i.e., hydrogen and vanillin) which lie at the two extremes in terms of product-molecule size.
  • the first challenge addressed is to develop a systematic computational framework to identify which functionalities to add to the organism-specific metabolic network (e.g., E. coli (Reed et al, 2003; Edwards & Palsson, 2000), S. cerevisiae (Forster et al, 2003), C acetobutylicum (Desai et al, 1999; Papoutsakis, 1984), etc.) to enable the desired
  • Step 1 Automated downloading and curation of the reactions in our Universal database to ensure stoichiometric balance
  • Step 2 Calculation of the maximum theoretical yield of the product given a substrate choice without restrictions on the reaction origin (i.e., native or non-native);
  • Step 3 Identification of a stoichiometrically-balanced pathway(s) that minimizes the number of non-native functionalities in the examined production host given the maximum
  • Step 4 Incorporation of the identified non-native biotransformations into the stoichiometric models, if available, of the examined microbial production hosts.
  • the OptKnock framework is next applied (Burgard et al, 2003; Pharkya et ai, 2003) on these augmented models to suggest gene deletions that ensure the production of the desired
  • trans-2-Enoyl-CoA represented by C25H39N7 ⁇ i 7 P3S(CH2) n ) or unspecified alkyl groups R in their chemical formulae are removed from the downloaded sets. This step enables the automated downloading of functionalities present in genomic databases and the subsequent
  • the second step is geared towards determining the maximum theoretical yield of the target
  • bioengineering objective relates to maximizing production
  • present invention contemplates that other bioengineering
  • objectives can be used. In such instances, instead of determining or selecting a maximum yield, a separate and appropriate objective or constraint can be used.
  • the binary variable yj assumes a value of one if reaction./ is active and a value of zero if it is inactive. This constraint will be imposed on only reactions associated with genes heterologous to the specified production host.
  • the parameters vf" n and V j max are calculated by minimizing and maximizing every reaction flux v y - subject to the stoichiometry of the metabolic network (Burgard & Mamas, 2001). This leads to a Mixed Integer Linear Programming (MILP) model for finding the minimum number of genes to be added into the host organism network while meeting the yield target for the desired product.
  • MILP Mixed Integer Linear Programming
  • Formate is catabolized into hydrogen and carbon dioxide through formate hydrogen lyase.
  • ferredoxin hydrogenase (E.C.# 1.12.7.2) is the key associated reaction.
  • OptStrain (Step 3) we verified that no non-native reactions were required for hydrogen production (Papoutsakis & Meyer, 1985) in Clostridium acetobultylicum with glucose as a substrate.
  • OptKnock suggested the deletion of the acetate-forming and butyrate-transport reactions.
  • Methylobacterium extorquens AMI a facultative methylotroph capable of surviving solely on methanol as a carbon and energy source.
  • the organism has been well-studied (Anthony, 1982; Chistoserdova et al., 2004;
  • Methenyltetrahydromethanopterin + H 2 The need for an additional reaction is expected because the central metabolic pathways in the methylotroph, as abstracted in (Van Dien & Lidstrom, 2002), do not include any reactions that convert protons into hydrogen such as the hydrogenases found in E. coli and the anaerobes of the Clostridia species. Therefore, it is not surprising that, to
  • Vanillin is an important flavor and aroma molecule. The low yields of vanilla from
  • Step 4 the genome- scale model of E. coli metabolism, augmented with the three functionalities identified above, is integrated into the OptKnock framework to determine the set(s) of reactions
  • the first deletion strategy identified by OptStrain suggests removing acetaldehyde dehydrogenase (E.C.# 1.2.1.10) to prevent the conversion of acetyl-CoA into ethanol.
  • Vanillin production in this network at the maximum biomass production rate of 0.205 hr '1 , is 3.9 mmol/gDW/hr or 0.33 g/g glucose based on the assumed uptake rate of glucose.
  • flux is redirected through the vanillin precursor metabolites,
  • glucose-6-phosphate isomerase (E.C.# 5.3.1.9) essentially blocking
  • reaction deletion mutant network without imposing a high penalty on the growth rate.
  • fructose 6-phosphate aldolase acetate kinase (E.C.# 2.7.2.1), pyruvate kinase (E.C.# 2.7.1.40), the PTS transport mechanism, and fructose 6-phosphate aldolase.
  • the first three deletions prevent leakage of flux from PEP and redirect it instead to vanillin synthesis.
  • the elimination of fructose 6- phosphate aldolase prevents the direct conversion of F6P into GAP and dihydroxyacetone
  • DHA dihydroxyacetone phosphate
  • 6-phosphate aldolase prevents the utilization of both F6P and PEP which are required for vanillin synthesis. Furthermore, a surprising network flux redistribution involves the
  • Figure 5 compares the vanillin production envelopes, obtained by maximizing and
  • the OptStrain framework of the present invention is aimed at systematically reshaping whole genome-scale metabolic networks of microbial systems for the
  • computers can be used, and any number of types of software or programming languages
  • the representations of the networks can be stored
  • the first optimization task involves determining the maximum yield of
  • set ⁇ R of substrates is formulated as:
  • MFi is the molecular weight of metabolite i, v, is the molar flux of reaction./, and S u
  • Constraint (2) scales the results for a total substrate uptake flux of one gram.
  • V j can either be irreversible (i.e., v, > 0) or reversible in which case they can assume
  • Step 3 of OptStrain the minimum number of non-native reactions needed to meet the identified maximum yield from Step 2 is found.
  • MILP Mixed Integer Linear Programming
  • the set M non-native comprises of the non-native reactions for the examined host and is a
  • Constraints (1) and (2) are identical to those in the product yield
  • Constraint (3) ensures that the product yield meets the maximum theoretical yield, Yield target , calculated in step 2.
  • the binary variables y j in constraints (4) and (5) serve as switches to turn reactions on or off. A value of zero for y j forces the corresponding flux y,- to be zero, while a value of one enables it to take on nonzero values.
  • the parameters vf" n and vf"" can either assume very low and very high values, respectively, or they can be calculated by minimizing and maximizing every reaction flux
  • V j subject to constraints (1-3). Alternative pathways that satisfy both optimality criteria of maximum yield and minimum non-native reactions are obtained by the iterative solution of the MIL?
  • Integer cut constraints exclude from consideration all sets of reactions previously
  • Step 4 of OptStrain identifies which reactions to eliminate from the network augmented with the non-native functionalities, using the OptKnock framework developed
  • the envelope of allowable targeted product yields versus biomass yields is constructed by solving a series of linear optimization problems which maximize and then, minimize biochemical production .for various levels of biomass formation rates available to the network. More details on the optimization formulation can be found in (Pharkya et ah, 2003). All the optimization problems were solved in the order of minutes to hours using CPLEX 7.0 (http://www.ilog.com/products/cplex/) accessed via the GAMS (Brooke et al., 1998)
  • the present invention includes a computational framework termed OptKnock for suggesting gene deletion strategies leading to the
  • a preferred embodiment of this invention describes a computational framework, termed OptKnock, for suggesting gene deletions strategies that could lead to chemical
  • the present invention contemplates any number of cellular objectives, including but not limited to maximizing a growth rate, maximizing ATP production, minimizing' metabolic adjustment, minimizing nutrient uptake, minimizing redox production, minimizing a Euclidean norm, and combinations of these and other cellular objectives.
  • the substrate uptake flux i.e., glucose
  • OptKnock is used to identify any gene deletions as the sole mechanism for chemical overproduction.
  • the lack of any regulatory or kinetic information in the model is a simplification that may in some cases suggest unrealistic flux distributions.
  • the incorporation of regulatory information will not only enhance the quality of the suggested gene deletions by more appropriately resolving flux allocation, but also allow us to suggest regulatory modifications along with gene deletions as mechanisms for strain improvement.
  • alternate modeling approaches e.g., cybernetic (Kompala et al., 1984;
  • OptKnock provides useful suggestions for
  • v glc up!ake is the basis glucose uptake scenario
  • v alp _ main is the non-growth
  • the vector v includes both internal and transport reactions.
  • the forward i.e.,
  • biomass formation is expressed as g biomass produced/gDWhr or 1/hr.
  • ⁇ j is the dual variable associated with any other restrictions on its corresponding flux v j in the Primal.
  • the optimization step could opt for or against the
  • phosphotransferase system phosphotransferase system, glucokinase, or both mechanisms for the uptake of glucose.
  • Table I summarizes three of the identified gene knockout strategies for succinate overproduction (i.e., mutants A, B, and C). The results for mutant A suggested that the
  • Mutant A redirects flux toward lactate at the maximum biomass yield by blocking acetate and ethanol production. This result is consistent with previous work demonstrating that an adh, pta mutant E. coli strain could grow anaerobically on glucose by producing lactate (Gupta & Clark, 1989).
  • Mutant B provides an alternate strategy involving the removal of an initial glycolysis reaction along with the acetate production mechanism. This results in a lactate yield of 90% of its theoretical limit at the maximum biomass yield. It is also noted that the network could avoid producing lactate while maximizing biomass formation. This is due to the fact that OptKnock does not explicitly account for the "worst-case" alternate solution. It should be
  • mutant C exhibited a tighter coupling between lactate and biomass production.
  • the gene addition framework identified a straightforward three-reaction pathway involving the conversion of glycerol-3-P to glycerol by glycerol phosphatase, followed by the conversion of glycerol to 1,3 propanediol by glycerol dehydratase and 1,3-propanediol oxidoreductase. These reactions were then added to the E. coli stoichiometric model and the OptKnock procedure was subsequently applied. OptKnock revealed that there was neither a single nor a double deletion mutant j with coupled PDO and biomass production. However, one triple and multiple quadruple knockout strategies that can couple PDO production with biomass production was
  • phosphate isomerase activity was recently reported to produce glycerol, a key precursor to
  • DHAP dihydroxyacetone-phosphate
  • phosphate isomerase reaction prevents any interconversion between DHAP and GAP.
  • a fourth knockout is predicted to retain the coupling between biomass formation and chemical production. This knockout prevents the "leaking" of flux through a complex pathway involving 15 reactions that together convert ribose-5-phosphate (R5P) to acetate and GAP, thereby decoupling growth from chemical production.
  • R5P ribose-5-phosphate
  • the Entner-Doudoroff pathway (either phosphogluconate dehydratase or 2- keto-3-deoxy-6-phosphogluconate aldolase), four respiration reactions (i.e., NADH dehydrogenase I, NADH dehydrogenase ⁇ , glycerol-3-phosphate dehydrogenase, and the succinate dehydrogenase complex), and an initial glycolyis step (i.e., phosphoglucose isomerase) are disrupted.
  • This strategy resulted in a 3-HPA yield that, assuming the maximum biomass yield, is 69% higher than the previously identified mutants utilizing the glycerol conversion route.
  • MOMA metabolic adjustment

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Abstract

L'invention se rapporte à un procédé assisté par ordinateur d’identification des fonctionnalités à ajouter à un réseau métabolique spécifique à un organisme pour permettre une bio- transformation souhaitée chez un hôte comprenant l’accès à des réactions depuis une base de données universelle en vue de fournir un équilibre stœchiométrique, l'identification d'au moins une voie stœchiométriquement équilibrée basée au moins partiellement sur les réactions et un substrat pour minimiser un certain nombre de fonctionnalités non natives chez l'hôte de production, et l'incorporation d'au moins une voie stœchiométriquement équilibrée dans l'hôte pour fournir la bio- transformation souhaitée. Une représentation du réseau métabolique ainsi modifié peut être mémorisée.
PCT/US2004/027614 2004-08-26 2004-08-26 Procédé de redéfinition de la conception de systèmes de production microbienne Ceased WO2006025817A1 (fr)

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CA002578028A CA2578028A1 (fr) 2004-08-26 2004-08-26 Procede de redefinition de la conception de systemes de production microbienne
JP2007529791A JP2008510485A (ja) 2004-08-26 2004-08-26 微生物産生系の再設計法
EP04782168A EP1782061A4 (fr) 2004-08-26 2004-08-26 Procédé de redéfinition de la conception de systèmes de production microbienne
PCT/US2004/027614 WO2006025817A1 (fr) 2004-08-26 2004-08-26 Procédé de redéfinition de la conception de systèmes de production microbienne
AU2004322970A AU2004322970B2 (en) 2004-08-26 2004-08-26 Method for redesign of microbial production systems

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EP2566969A4 (fr) * 2010-05-05 2014-01-15 Genomatica Inc Micro-organismes et procédés pour la biosynthèse de butadiène

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JP2014150747A (ja) * 2013-02-06 2014-08-25 Sekisui Chem Co Ltd 変異微生物、並びに、コハク酸の生産方法

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2566969A4 (fr) * 2010-05-05 2014-01-15 Genomatica Inc Micro-organismes et procédés pour la biosynthèse de butadiène
US10487343B2 (en) 2010-05-05 2019-11-26 Genomatica, Inc. Microorganisms and methods for the biosynthesis of butadiene

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AU2004322970B2 (en) 2010-04-01
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EP1782061A4 (fr) 2009-08-12
CA2578028A1 (fr) 2006-03-09

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